diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index bf5c6500b0f6eb2fcbeb15546072e85619a5336b..9b7cabb11312cb24781cabdeaf40622ee495df82 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -13,6 +13,7 @@ doc:
     tags:
         - docker
     only:
+        - master
         - develop
     script:
         - export LC_ALL=$(locale -a | grep en_US)
@@ -24,24 +25,25 @@ doc:
         - cd ../..
         - cp -r ./docs/build public
     artifacts:
-         paths:           
-           - public
+        paths:
+            - public
 
-
-# TODO: Replace the task doc by the following task pages when making the
 # project public
-#
-#pages:
-#    image: registry.gitlab.lis-lab.fr:5005/baptiste.bauvin/multiview-machine-learning-omis/ubuntu:18.04
-#    tags:
-#        - docker
-#    only:
-#        - master
-#    script:
-#        - export LC_ALL=$(locale -a | grep en_US)
-#        - export LANG=$(locale -a | grep en_US)
-#        - python3 setup.py build_sphinx
-#        - cp -r build/sphinx/html public
-#    artifacts:
-#        paths:
-#            - public
+pages:
+    image: registry.gitlab.lis-lab.fr:5005/baptiste.bauvin/multiview-machine-learning-omis/ubuntu:18.04
+    tags:
+        - docker
+    only:
+        - master
+    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
+        - cd docs/source
+        - sphinx-build -b html . ../build
+        - cd ../..
+        - cp -r ./docs/build public
+    artifacts:
+       paths:
+            - public
diff --git a/.travis.yml b/.travis.yml
deleted file mode 100644
index c5a1aac72c387a4a8c9d41ff414940dcf125098f..0000000000000000000000000000000000000000
--- a/.travis.yml
+++ /dev/null
@@ -1,23 +0,0 @@
-language: python
-python:
-- 2.7
-- 3.5
-addons:
-  apt:
-    packages:
-    - libblas-dev
-    - liblapack-dev
-    - gfortran
-install:
-- pip install -U pip pip-tools
-- pip install numpy scipy scikit-learn==0.19 matplotlib joblib argparse h5py
-- cd .. && git clone https://github.com/aldro61/pyscm.git && cd pyscm/ && python setup.py install && cd ../multiview-machine-learning-omis
-- pip install -e .
-
-script:
-- python -m unittest discover
-
-#notifications:
-#  email:
-#    on_success: change
-#on_failure: change
\ No newline at end of file
diff --git a/README.md b/README.md
index 16478c4519f501c615709644774796774403eaae..a22d3c29d31fa4779a51e7ed7ed17fbf383218a2 100644
--- a/README.md
+++ b/README.md
@@ -25,18 +25,18 @@ And the following python modules :
 * [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.
+* [plotly](https://plot.ly/) - Used to generate interactive HTML visuals,
+* [tabulate](https://pypi.org/project/tabulate/) - Used to generated the confusion matrix.
 
-They are all tested in  `multiview-machine-mearning-omis/multiview_platform/MonoMutliViewClassifiers/Versions.py` which is automatically checked each time you run the `execute` script
 
 ### Installing
 
-Once you cloned the project from this repository, you just have to use :  
+Once you cloned the project from the [gitlab repository](https://gitlab.lis-lab.fr/baptiste.bauvin/multiview-machine-learning-omis/), you just have to use :  
 
 ```
 pip install -e .
 ```
-In the `multiview_machine-learning-omis` directory.
+In the `multiview_machine-learning-omis` directory to install SuMMIT and its dependencies.
 
 ### Running on simulated data
 
@@ -45,16 +45,16 @@ In order to run it you'll need to try on **simulated** data with the command
 from multiview_platform.execute import execute
 execute()
 ```
-This will run the first example. For more information about the examples, see the documentation 
+This will run the first example. For more information about the examples, see the [documentation](http://baptiste.bauvin.pages.lis-lab.fr/multiview-machine-learning-omis/) 
 Results will be stored in the results directory of the installation path : 
 `path/to/install/multiview-machine-learning-omis/multiview_platform/examples/results`.
-The documentations proposes a detailed interpretation of the results. 
+The documentation proposes a detailed interpretation of the results. 
 
 ### 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 `multiview-machine-learning-omis/config_files/config.yml` is documented and it is highly recommended
- to read it carefully before playing around with the parameters.   
+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
@@ -62,23 +62,24 @@ 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. 
+For further information about classifier-specific arguments, see the [documentation](http://baptiste.bauvin.pages.lis-lab.fr/multiview-machine-learning-omis/). 
  
 
 ### Dataset compatibility
 
-In order to start a benchmark on your dataset, you need to format it so the script can use it. 
-You can have either a directory containing `.csv` files or a HDF5 file. 
+In order to start a benchmark on your own dataset, you need to format it so SuMMIT can use it. 
 
-##### If you have multiple `.csv` files, you must organize them as : 
+[comment]: <> (You can have either a directory containing `.csv` files or a HDF5 file.) 
+
+[comment]: <> (#### If you have multiple `.csv` files, you must organize them as : 
 * `top_directory/database_name-labels.csv`
 * `top_directory/database_name-labels-names.csv`
-* `top_directory/Views/view_name.csv` or `top_directory/Views/view_name-s.csv` if the view is sparse
+* `top_directory/Views/view_name.csv` or `top_directory/Views/view_name-s.csv` if the view is sparse)
 
-With `top_directory` being the last directory in the `pathF` argument
+[comment]: <> (With `top_directory` being the last directory in the `pathF` argument)
  
 ##### If you already have an HDF5 dataset file it must be formatted as : 
-One dataset for each view called `ViewX` with `X` being the view index with 2 attribures : 
+One dataset for each view called `ViewI` with `I` being the view index with 2 attribures : 
 * `attrs["name"]` a string for the name of the view
 * `attrs["sparse"]` a boolean specifying whether the view is sparse or not
 * `attrs["ranges"]` a `np.array` containing the ranges of each attribute in the view (for ex. : for a pixel the range will be 255, for a real attribute in [-1,1], the range will be 2).
@@ -93,35 +94,25 @@ One group for the additional data called `Metadata` containing at least 3 attrib
 * `attrs["nbClass"]` an int counting the total number of different labels in the dataset
 * `attrs["datasetLength"]` an int counting the total number of examples in the dataset
 
+The `format_dataset.py` file is documented and can be used to format a multiview dataset in a SuMMIT-compatible HDF5 file.
 
 ### Running on your dataset 
 
-In order to run the script on your dataset you need to use : 
-```
-cd multiview-machine-learning-omis/multiview_platform
-python execute.py -log --name <your_dataset_name> --type <.cvs_or_.hdf5> --pathF <path_to_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.
  
-You may configure the `--CL_statsiter`, `--CL_split`, `--CL_nbFolds`, `--CL_GS_iter` arguments to start a meaningful benchmark
+It is highly recommended to follow the documentation's [tutorials](http://baptiste.bauvin.pages.lis-lab.fr/multiview-machine-learning-omis/tutorials/index.html) to learn the use of each parameter. 
  
 
-## Running the tests
-
-**/!\ still in development, test sucess is not meaningful ATM /!\\**
-
-In order to run it you'll need to try on simulated data with the command
-```
-cd multiview-machine-learning-omis/
-python -m unittest discover
-```
-
 ## Author
 
 * **Baptiste BAUVIN**
 
 ### Contributors
 
-* **Mazid Osseni**
-* **Alexandre Drouin**
-* **Nikolas Huelsmann**
+* **Dominique Benielli**
diff --git a/config_files/config.yml b/config_files/config.yml
index b0f051bd79e70cc643d99b5eac8f0ab4293be7d9..3643419bd75fe149d106a5749b5e37fd190f2616 100644
--- a/config_files/config.yml
+++ b/config_files/config.yml
@@ -56,9 +56,13 @@ metrics: ["accuracy_score", "f1_score"]
 # The metric that will be used in the hyper-parameter optimization process
 metric_princ: "f1_score"
 # The type of hyper-parameter optimization method
-hps_type: "randomized_search"
-# The number of iteration in the hyper-parameter optimization process
-hps_iter: 2
+hps_type: "Random"
+# The arguments of the hyper-parameter optimization method
+hps_args:
+  # The number of iteration of the optimization process
+  n_iter: 4
+  # If True, for multiview algoriithm, will use n_iter*n_views iterations to optimize.
+  equivalent_draws: True
 
 
 # The following arguments are classifier-specific, and are documented in each
diff --git a/config_files/config_test.yml b/config_files/config_test.yml
index ac44a417ed854d4072fee8913c681493ebeed117..b795df0662e298a6e36e71ee01b4723ada02b85b 100644
--- a/config_files/config_test.yml
+++ b/config_files/config_test.yml
@@ -8,7 +8,7 @@ pathf: "/home/baptiste/Documents/Datasets/Digits/"
 nice: 0
 random_state: 42
 nb_cores: 1
-full: False
+full: True
 debug: True
 add_noise: False
 noise_std: 0.0
@@ -17,76 +17,65 @@ track_tracebacks: False
 
 # All the classification-realted configuration options
 multiclass_method: "oneVersusOne"
-split: 0.49
+split: 0.25
 nb_folds: 2
-nb_class: 2
+nb_class:
 classes:
-type: ["multiview"]
+type: ["monoview", "multiview"]
 algos_monoview: ["decision_tree" ]
-algos_multiview: ["weighted_linear_early_fusion",]
-stats_iter: 2
-metrics: ["accuracy_score", "f1_score"]
+algos_multiview: ["weighted_linear_early_fusion","weighted_linear_late_fusion"]
+stats_iter: 3
+metrics:
+  accuracy_score: {}
+  f1_score: {}
 metric_princ: "accuracy_score"
-hps_type: "None"
+hps_type: "Random"
 hps_args:
-  n_iter: 4
+  n_iter: 10
   equivalent_draws: False
 
-  decision_tree:
-    max_depth: [1,2,3,4,5]
-
-
-  weighted_linear_early_fusion:
-    view_weights: [null]
-    monoview_classifier: ["decision_tree"]
-    monoview_classifier__max_depth: [1,2]
-
 
+weighted_linear_early_fusion:
+  view_weights: null
+  monoview_classifier_name: "decision_tree"
+  monoview_classifier_config:
+    decision_tree:
+      max_depth: 12
+      criterion: "gini"
+      splitter: "best"
+weighted_linear_late_fusion:
+  weights: null
+  classifiers_names: "decision_tree"
+  classifier_configs:
+    decision_tree:
+      max_depth: 3
+      criterion: "gini"
+      splitter: "best"
+decision_tree:
+  max_depth: 3
 
 
 
 ######################################
 ## The Monoview Classifier arguments #
 ######################################
-<<<<<<< HEAD
-
 mumbo:
   base_estimator__criterion: 'gini'
-  base_estimator__max_depth: 4
+  base_estimator__max_depth: 3
   base_estimator__random_state: None
   base_estimator__splitter: 'best'
   best_view_mode: 'edge'
-  n_estimators: 50
-=======
-weighted_linear_early_fusion:
-  monoview_classifier_config:
-    init: CustomDecisionTree
-    init__ccp_alpha: 0.0
-    init__class_weight: null
-    init__criterion: gini
-    init__max_depth: 1
-    init__max_features: null
-    init__max_leaf_nodes: null
-    init__min_impurity_decrease: 0.0
-    init__min_impurity_split: null
-    init__min_samples_leaf: 1
-    init__min_samples_split: 2
-    init__min_weight_fraction_leaf: 0.0
-    init__presort: deprecated
-    init__random_state: null
-    init__splitter: best
-    loss: exponential
-    max_depth: 5
-    n_estimators: 412
-  monoview_classifier_name: gradient_boosting
-  view_weights:
-  - 0.16666666666666669
-  - 0.16666666666666669
-  - 0.16666666666666669
-  - 0.16666666666666669
-  - 0.16666666666666669
-  - 0.16666666666666669
->>>>>>> develop
+  base_estimator: 'decision_tree'
+  n_estimators: 10
+
+mucombo:
+  base_estimator__criterion: 'gini'
+  base_estimator__max_depth: 3
+  base_estimator__random_state: None
+  base_estimator__splitter: 'best'
+  best_view_mode: 'edge'
+  base_estimator: 'decision_tree'
+  n_estimators: 10
 #
 #random_forest:
 #  n_estimators: [25]
@@ -116,10 +105,6 @@ weighted_linear_early_fusion:
 #  n_iterations: [50]
 #  n_stumps: [1]
 #
-#decision_tree:
-#  max_depth: [2]
-#  criterion: ["gini"]
-#  splitter: ["best"]
 #
 #decision_tree_pregen:
 #  max_depth: [10]
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diff --git a/docs/build/_sources/index.rst.txt b/docs/build/_sources/index.rst.txt
index d60e4a57b8a4f24cf839f717562d1f43e70fff89..b30d339a1f8c5b7b7447bf6e76bf4634888d6ed9 100644
--- a/docs/build/_sources/index.rst.txt
+++ b/docs/build/_sources/index.rst.txt
@@ -3,15 +3,15 @@ sphinx-quickstart on Mon Jan 29 17:13:09 2018.
 You can adapt this file completely to your liking, but it should at least
 contain the root `toctree` directive.
 
-Welcome to MultiviewPlatform's documentation!
+Welcome to SuMMIT's documentation!
 =============================================
 
-This package is used as an easy-to-use platform to estimate different mono- and multi-view classifiers' performance on a multiview dataset.
+This package ha been designed as an easy-to-use platform to estimate different mono- and multi-view classifiers' performances on a multiview dataset.
 
 The main advantage of the platform is that it allows to add and remove a classifier without modifying its core code (the procedure is described thoroughly in this documentation).
 
 .. toctree::
-   :maxdepth: 3
+   :maxdepth: 1
    :caption: Contents:
 
    readme_link
diff --git a/docs/build/_sources/tutorials/example1.rst.txt b/docs/build/_sources/tutorials/example1.rst.txt
index 023bb37df8d11000deaf1f82ca4a62a53bf3238b..07d53f7851ed3c99a5f278913f4e240ad924772d 100644
--- a/docs/build/_sources/tutorials/example1.rst.txt
+++ b/docs/build/_sources/tutorials/example1.rst.txt
@@ -10,7 +10,7 @@ Context
 This platform aims at running multiple state-of-the-art classifiers on a multiview dataset in a classification context.
 It has been developed in order to get a baseline on common algorithms for any classification task.
 
-Adding a new classifier (monoview and/or multiview) as been made as simple as possible in order for users to be able to
+Adding a new classifier (monoview and/or multiview) to the benchmark as been made as simple as possible in order for users to be able to
 customize the set of classifiers and test their performances in a controlled environment.
 
 
@@ -39,26 +39,31 @@ The config file that will be used in this example is located in ``multiview-mach
 
 We will decrypt the main arguments :
 
-+ The first part of the arguments are the basic ones :
++ The first part regroups the basics :
 
     - ``log: True`` allows to print the log in the terminal,
     - ``name: ["plausible"]`` uses the plausible simulated dataset,
-    - ``random_state: 42`` fixes the random state of this benchmark, it is useful for reproductibility,
-    - ``full: True`` the benchmark will used the full dataset,
-    - ``res_dir: "examples/results/example_1/"`` the results will be saved in ``multiview-machine-learning-omis/multiview_platform/examples/results/example_1``
+    - ``random_state: 42`` fixes the seed of the random state for this benchmark, it is useful for reproductibility,
+    - ``full: True`` means the benchmark will use the full dataset,
+    - ``res_dir: "examples/results/example_1/"`` saves the results in ``multiview-machine-learning-omis/multiview_platform/examples/results/example_1``
 
 + Then the classification-related arguments :
 
-    - ``split: 0.8`` means that 80% of the dataset will be used to test the different classifiers and 20% to train them
-    - ``type: ["monoview", "multiview"]`` allows for monoview and multiview algorithms to be used in the benchmark
+    - ``split: 0.8`` means that 80% of the dataset will be used to test the different classifiers and 20% to train them,
+    - ``type: ["monoview", "multiview"]`` allows for monoview and multiview algorithms to be used in the benchmark,
     - ``algos_monoview: ["all"]`` runs on all the available monoview algorithms (same for ``algos_muliview``)
-    - ``metrics: ["accuracy_score", "f1_score"]`` means that the benchmark will evaluate the performance of each algortihms on these two metrics.
+    - The metrics configuration ::
 
-+ Then, the two following categories are algorithm-related and contain all the default values for the hyper-parameters.
+                        metrics:
+                          accuracy_score:{}
+                          f1_score:
+                            average:"binary"
+
+      means that the benchmark will evaluate the performance of each algorithms on accuracy, and f1-score with a binary average.
 
 **Start the benchmark**
 
-During the whole benchmark, the log file will be printed in the terminal. To start the benchmark run :
+During the whole benchmark, the log file will be printed in the terminal. To start the benchmark, run :
 
 .. code-block:: python
 
@@ -69,7 +74,7 @@ The execution should take less than five minutes. We will first analyze the resu
 
 **Understanding the results**
 
-The result structure can be startling at first, but as the platform provides a lot of information, it has to be organized.
+The result structure can be startling at first, but, as the platform provides a lot of information, it has to be organized.
 
 The results are stored in ``multiview_platform/examples/results/example_1/``. Here, you will find a directory with the name of the database used for the benchmark, here : ``plausible/``
 Then, a directory with the amount of noise in the experiments, we didn't add any, so ``n_0/`` finally, a directory with
@@ -81,65 +86,70 @@ From here the result directory has the structure that follows  :
 .. code-block:: bash
 
     | started_1560_12_25-15_42
-    | ├── No-vs-Yes
-    | | ├── adaboost
-    | | |   ├── ViewNumber0
-    | | |   |   ├── 1560_12_25-15_42-*-summary.txt
-    | | |   |   ├── <other classifier dependant files>
-    | | |   ├── ViewNumber1
-    | | |   |   ├── 1560_12_25-15_42-*-summary.txt
-    | | |   |   ├── <other classifier dependant files>
-    | | |   ├── ViewNumber2
-    | | |   |   ├── 1560_12_25-15_42-*-summary.txt
-    | | |   |   ├── <other classifier dependant files>
-    | | ├── decision_tree
-    | | |   ├── ViewNumber0
-    | | |   |  ├── <summary & classifier dependant files>
-    | | |   ├── ViewNumber1
-    | | |   |  ├── <summary & classifier dependant files>
-    | | |   ├── ViewNumber2
-    | | |   |  ├── <summary & classifier dependant files>
-    | | ├── [..
-    | | ├── ..]
-    | | ├── weighted_linear_late_fusion
-    | | |   ├── <summary & classifier dependant files>
-    | | ├── [..
-    | | ├── ..]
-    | | ├── train_labels.csv
-    | │ └── train_indices.csv
+    | ├── adaboost
+    | |   ├── ViewNumber0
+    | |   |   ├── *-summary.txt
+    | |   |   ├── <other classifier dependant files>
+    | |   ├── ViewNumber1
+    | |   |   ├── *-summary.txt
+    | |   |   ├── <other classifier dependant files>
+    | |   ├── ViewNumber2
+    | |   |   ├── *-summary.txt
+    | |   |   ├── <other classifier dependant files>
+    | ├── decision_tree
+    | |   ├── ViewNumber0
+    | |   |  ├── <summary & classifier dependant files>
+    | |   ├── ViewNumber1
+    | |   |  ├── <summary & classifier dependant files>
+    | |   ├── ViewNumber2
+    | |   |  ├── <summary & classifier dependant files>
+    | ├── [..
+    | ├── ..]
+    | ├── weighted_linear_late_fusion
+    | |   ├── <summary & classifier dependant files>
+    | ├── [..
+    | ├── ..]
+    | ├── train_labels.csv
+    | └── train_indices.csv
     | ├── 1560_12_25-15_42-*-LOG.log
     | ├── config_file.yml
-    | ├── 1560_12_25-15_42-*-accuracy_score.png
-    | ├── 1560_12_25-15_42-*-accuracy_score.csv
-    | ├── 1560_12_25-15_42-*-f1_score.png
-    | ├── 1560_12_25-15_42-*-f1_score.csv
-    | ├── 1560_12_25-15_42-*-error_analysis_2D.png
-    | ├── 1560_12_25-15_42-*-error_analysis_2D.html
-    | ├── 1560_12_25-15_42-*-error_analysis_bar.png
-    | ├── 1560_12_25-15_42-*-ViewNumber0-feature_importance.html
-    | ├── 1560_12_25-15_42-*-ViewNumber0-feature_importance_dataframe.csv
-    | ├── 1560_12_25-15_42-*-ViewNumber1-feature_importance.html
-    | ├── 1560_12_25-15_42-*-ViewNumber1-feature_importance_dataframe.csv
-    | ├── 1560_12_25-15_42-*-ViewNumber2-feature_importance.html
-    | ├── 1560_12_25-15_42-*-ViewNumber2-feature_importance_dataframe.csv
-    | ├── 1560_12_25-15_42-*-bar_plot_data.csv
-    | ├── 1560_12_25-15_42-*-2D_plot_data.csv
+    | ├── *-accuracy_score*.png
+    | ├── *-accuracy_score*.html
+    | ├── *-accuracy_score*-class.html
+    | ├── *-accuracy_score*.csv
+    | ├── *-f1_score.png
+    | ├── *-f1_score.html
+    | ├── *-f1_score-class.html
+    | ├── *-f1_score.csv
+    | ├── *-error_analysis_2D.png
+    | ├── *-error_analysis_2D.html
+    | ├── *-error_analysis_bar.png
+    | ├── *-error_analysis_bar.html
+    | ├── feature_importances
+    | | ├── *-ViewNumber0-feature_importance.html
+    | | ├── *-ViewNumber0-feature_importance_dataframe.csv
+    | | ├── *-ViewNumber1-feature_importance.html
+    | | ├── *-ViewNumber1-feature_importance_dataframe.csv
+    | | ├── *-ViewNumber2-feature_importance.html
+    | | ├── *-ViewNumber2-feature_importance_dataframe.csv
+    | ├── *-bar_plot_data.csv
+    | ├── *-2D_plot_data.csv
     | └── random_state.pickle
 
 
-The structure can seem complex, but it priovides a lot of information, from the most general to the most precise.
+The structure can seem complex, but it provides a lot of information, from the most general to the most precise.
 
 Let's comment each file :
 
-``1560_12_25-15_42-*-accuracy_score.png`` and ``1560_12_25-15_42-*-accuracy_score.csv``
+``*-accuracy_score*.html``, ``*-accuracy_score*.png`` and ``*-accuracy_score*.csv``
 <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
 
 These files contain the scores of each classifier for the accuracy metric, ordered with le best ones on the right and
-the worst ones on the left, as an image or as as csv matrix.
+the worst ones on the left, as an interactive html page, an image or a csv matrix. The star after ``accuracy_score*`` means that it is the principal metric.
 The image version is as follows :
 
-.. figure:: ./images/accuracy.png
-    :scale: 25
+.. raw:: html
+    :file: ./images/accuracy_score.html
 
     This is a bar plot showing the score on the training set (light gray), and testing set (dark gray). For each
     monoview classifier, on each view and or each multiview classifier, the scores are printed over the name, under each bar.
@@ -147,15 +157,15 @@ The image version is as follows :
 
 The csv file is a matrix with the score on train stored in the first row and the score on test stored in the second one. Each classifier is presented in a row. It is loadable with pandas.
 
-Similar files have been generated for the f1 metric (``1560_12_25-15_42-*-f1_score.png`` and ``1560_12_25-15_42-*-f1_score.csv``).
+Similar files have been generated for the f1 metric.
 
 
-``1560_12_25-15_42-*-error_analysis_2D.png`` and ``1560_12_25-15_42-*-error_analysis_2D.html``
+``*-error_analysis_2D.png`` and ``*-error_analysis_2D.html``
 <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
 
 In these files, one can visualize the success or failure of each classifier on each example.
 
-Below, ``1560_12_25-15_42-*-error_analysis_2D.html`` is displayed.
+Below, ``*-error_analysis_2D.html`` is displayed.
 
 It is the representation of a matrix, where the rows are the examples, and the columns are the classifiers.
 
@@ -185,10 +195,10 @@ It is an understandable behaviour as the Plausible dataset's view are generated
 Morever, as confirmed by the accuracy graph, four monoview classifiers classified all the example to the same class,
 and then, display a black half-column.
 
-The data used to generate those matrices is available in ``1560_12_25-15_42-*-2D_plot_data.csv``
+The data used to generate those matrices is available in ``*-2D_plot_data.csv``
 
-``1560_12_25-15_42-*-error_analysis_bar.png``
-<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
+``*-error_analysis_bar.png`` and ``*-error_analysis_bar.html``
+<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
 
 This file is a different way to visualize the same information as the two previous ones. Indeed, it is a bar plot,
 with a bar for each example, counting the number of classifiers that failed to classify this particular example.
@@ -198,7 +208,7 @@ with a bar for each example, counting the number of classifiers that failed to c
 
     The bar plot showing for each example how many classifiers failed on it.
 
-The data used to generate this graph is available in ``1560_12_25-15_42-*-bar_plot_data.csv``
+The data used to generate this graph is available in ``*-bar_plot_data.csv``
 
 ``config_file.yml``
 <<<<<<<<<<<<<<<<<<<
@@ -218,10 +228,10 @@ The log file
 Classifier-dependant files
 <<<<<<<<<<<<<<<<<<<<<<<<<<
 
-For each classifier, at least one file is generated, called ``1560_12_25-15_42-*-summary.txt``.
+For each classifier, at least one file is generated, called ``*-summary.txt``.
 
 .. include:: ./images/summary.txt
     :literal:
 
-This regroups the useful information on the classifiers configuration and it's performance. An interpretation section is
+This regroups the useful information on the classifier's configuration and it's performance. An interpretation section is
 available for classifiers that present some interpretation-related information (as feature importance).
\ No newline at end of file
diff --git a/docs/build/_sources/tutorials/example2.rst.txt b/docs/build/_sources/tutorials/example2.rst.txt
index 16f8381363b9ef182ea3d1fe68f6dfe16e1966c2..b12a318e969a94ab9bc268807f939843c3abd9f8 100644
--- a/docs/build/_sources/tutorials/example2.rst.txt
+++ b/docs/build/_sources/tutorials/example2.rst.txt
@@ -2,66 +2,103 @@
 Example 2 : Understanding the hyper-parameter optimization
 ==========================================================
 
-Intuitive explanation on hyper-parameters
+Hyper-parameters intuition
 -----------------------------------------
 
-Hyper-parameters are parameters of a classifier (monoview or multiview) that are task-dependant and have a huge part in the performance of the algorithm for a given task.
+Hyper-parameters are parameters of a classifier (monoview or multiview) that are
+task-dependant and have a huge part in the performance of the algorithm for a given task.
 
-The simplest example is the decision tree. One of it's hyper-parameter is the depth of the tree. The deeper the tree is,
-the most it will fit on the learning data. However a tree too deep will most likely overfit and won't have any value on
+The simplest example is the decision tree. One of it's hyper-parameter is the
+depth of the tree. The deeper the tree is,
+the most it will fit on the learning data. However, a tree too deep will most
+likely overfit and won't have any relevance on
 unseen testing data.
 
-This platform proposes a randomized search for optimizing hyperparamter on the given task. In this example,
-we first will analyze how it works and then how to use it.
+This platform proposes a randomized search and a grid search to optimize
+hyper-parameters. In this example, we first will analyze the theory and
+then how to use it.
 
 
 Understanding train/test split
 ------------------------------
 
-In order to provide robust results, this platform splits the dataset in a training set, tha will be used by the
-classifier to optimize their hyper-parameter and learn a relevant model, and a testing set that will take no part in
-the learning process and serve as unseen data to estimate each model's generalization capacity.
+In order to provide robust results, this platform splits the dataset in a
+training set, that will be used by the classifier to optimize their
+hyper-parameter and learn a relevant model, and a testing set that will take
+no part in the learning process and serve as unseen data to estimate each
+model's generalization capacity.
 
-This split is controlled by the config file's argument ``split:``. It uses a float to pass the ratio between the size of the testing set and the training set  :
-:math:`\text{split} = \frac{\text{test size}}{\text{train size}}`. In order to be as fare as possible, this split is made by keeping the ratio btween each class in the training set and in the testing set.
+This split ratio is controlled by the config file's argument ``split:``. It uses a float to pass the ratio between the size of the testing set and the training set  :
+:math:`\text{split} = \frac{\text{test size}}{\text{dataset size}}`. In order to be as fair as possible, this split is made by keeping the ratio between each class in the training set and in the testing set.
 
 So if a dataset has 100 examples with 60% of them in class A, and 40% of them in class B, using ``split: 0.2``
 will generate a training set with 48 examples of class A and 32 examples of class B and a testing set
 with 12 examples of class A and 8 examples of class B.
 
-Ths process uses sklearn's StratifiedShuffleSplit_ to split the dataset at random while being reproductilbe thanks to the random state.
+Ths process uses sklearn's StratifiedShuffleSplit_ to split the dataset at random while being reproductible thanks to the random_state.
 
 .. _StratifiedShuffleSplit: https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.StratifiedShuffleSplit.html
 
 Understanding hyper-parameter optimization
 ------------------------------------------
 
-As hyper-paramters are task dependant, there are two ways in the platform to set their value :
+As hyper-parameters are task dependant, there are three ways in the platform to set their value :
+
+- If you know the value (or a set of values), specify them at the end of the config file for each algorithm you want to test, and use ``hps_type: None`` in the classifiaction section of the config file. This will bypass the optimization process to run the algorithm on the specified values.
+- If you have several possible values in mind, specify them in the config file and use ``hps_type: 'Grid'`` to run a grid search on the possible values.
+- If you have no ideas on the values, the platform proposes a random search for hyper-parameter optimization.
+
+Grid search
+<<<<<<<<<<<
+
+The grid search is useful when one has several possible sets of hyper-parameters to test, as it is faster thant random-search but requires a relevant prior on the classification task.
+
+In order to use grid search in SuMMIT, one has to specify ``hps_type: "Grid"`` in the config file and provide the values for each parameter of each algorithm in ``hps_args:``.
+For example, let us suppose that one wants to run a decision tree but wants to try several depth values (1,5,10), then one has to specify in the config file :
+
+.. code-block:: yaml
+
+    hps_type: "Grid"
+    hps_args:
+      decision_tree:
+        max_depth: [1,5,10]
+
+For more complex classifiers this process can be quite long, but it allows a shorter computational time.
 
-- If you know the value (or a set of values), specify them at the end of the config file for each algorithm you want to test, and use ``hps_type: None`` in the classifiaction section of the config file. This will set the Hyper Parameter Search to None, and bypass the optimization process to run the algorithm on the specified values.
-- If you don't know the value, the platform proposes a random search for hyper-parameter optimization.
 
 Random search
 <<<<<<<<<<<<<
 
-The random search is one of the most efficient while fairest method to optimize hyper-parameter.
-Thus, for each algorithm in the platform, each of its hyper-paramter is provided with distribution of possible values,
+The random search is one of the most efficient while fairest method to optimize hyper-parameter without any prior knowledge on the dataset.
+Thus, for each algorithm in the platform, each of its hyper-parameter is provided with a distribution of possible values,
 (for example, the decision tree's max depth parameter is provided with a uniform distribution between 1 and 300).
 The random search method will randomly select hyper-parameters within this distribution and evaluate the performance of
 the classifier with this configuration. It will repeat that process with different randomly selected sets of
 hyper-parameter and keep the best configuration performance-wise.
 
-In the config file, to enable random search, set the ``hps_type:`` line to ``hps_type: "randomized_search"`` and to
-control the number of draws, use the ``hps_iter:`` line.
+In the config file, to enable random search, set the ``hps_type:`` line to ``hps_type: "Random"``.
+The the randomized search can be configured with two arguments :
+
+.. code-block:: yaml
+
+    hps_type: "Random"
+    hps_args:
+      n_iter: 5
+      equivalent_draws: True
+
+The ``n_iter`` parameter controls the number of random draws for each classifier
+and if ``equivalent_draws`` is set to ``True``, then the multiview classifiers
+will be allowed :math:`\text{n\_iter} \times \text{n\_views}` iterations,
+to compensate the fact that they have to solve a musch more complex problem than the monoview ones.
 
 K-folds cross-validation
 <<<<<<<<<<<<<<<<<<<<<<<<
 
-During the process of optimizing the hyper-parameter, the random serach has to estimate the perofmance of the classifier.
+During the process of optimizing the hyper-parameters, the random search has to estimate the performance of each classifier.
 
-In order to do so, the platform uses k-folds cross-validation. This method consists in splitting the training set in
-:math:`k` equal sub-sets, training the classifier (with the randomly chose hyper-parameters) on :math:`k-1` subsets an
-testing it on the last one, evaluating it's predictive performance.
+To do so, the platform uses k-folds cross-validation. This method consists in splitting the training set in
+:math:`k` equal sub-sets, training the classifier (with the hyper-parameters to evaluate) on :math:`k-1` subsets an
+testing it on the last one, evaluating it's predictive performance on unseen data.
 
 This learning-and-testing process is repeated :math:`k` times and the estimated performance is the mean of the
 performance on each testing set.
@@ -90,7 +127,7 @@ This example will focus only on some lines of the configuration file :
 
 - ``split:``, controlling the ration of size between the testing set and the training set,
 - ``hps_type:``, controlling the type of hyper-parameter search,
-- ``hps_iter:``, controlling the number of random draws during the hyper-parameter search,
+- ``hps_args:``, controlling the parameters of the hyper-parameters search method,
 - ``nb_folds:``, controlling the number of folds in the cross-validation process.
 
 Example 2.1 : No hyper-parameter optimization, impact of split size
@@ -107,17 +144,17 @@ three lines in the configuration file are useful :
 
 - ``type:`` in which one has to specify which type of algorithms are needed, here we used  ``type: ["monoview","multiview"]``,
 - ``algos_monoview:`` in which one specifies the names of the monoview algorithms to run, here we used : ``algos_monoview: ["decision_tree", "adaboost", ]``
-- ``algos_multiview:`` is the same but with multiview algorithms, here we used : `` algos_multiview: ["majority_voting_fusion", ]``
+- ``algos_multiview:`` is the same but with multiview algorithms, here we used : ``algos_multiview: ["majority_voting_fusion", ]``
 
-In order for the platofrm to understand the names, the user has to give the name of the python module in which the classifier is implemented in the platform.
+In order for the platform to understand the names, the user has to give the name of the python module in which the classifier is implemented in the platform.
 
 In the config file, the default values for adaboost's hyper-parameters are :
 
 .. code-block:: yaml
 
     adaboost:
-        n_estimators: [50]
-        base_estimator: ["DecisionTreeClassifier"]
+      n_estimators: 50
+      base_estimator: "DecisionTreeClassifier"
 
 (see `adaboost's sklearn's page <https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.AdaBoostClassifier.html#sklearn.ensemble.AdaBoostClassifier>`_ for more information)
 
@@ -126,9 +163,9 @@ For decision_tree :
 .. code-block:: yaml
 
     decision_tree:
-        max_depth: [10]
-        criterion: ["gini"]
-        splitter: ["best"]
+      max_depth: 10
+      criterion: "gini"
+      splitter: "best"
 
 (`sklearn's decision tree <https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html>`_)
 
@@ -137,12 +174,12 @@ And for the late fusion majority vote :
 .. code-block:: yaml
 
     majority_voting_fusion:
-        classifier_names: [["decision_tree", "decision_tree", "decision_tree", ]]
+        classifier_names: ["decision_tree", "decision_tree", "decision_tree", ]
         classifier_configs:
             decision_tree:
-                max_depth: [1]
-                criterion: ["gini"]
-                splitter: ["best"]
+                max_depth: 1
+                criterion: "gini"
+                splitter: "best"
 
 (It will build a vote with one decision tree on each view, with the specified configuration for the decision trees)
 
@@ -182,10 +219,10 @@ TODO COMMENT
 
 **Conclusion**
 
-THe impact of split ratio : dataset related.
+The impact of split ratio : dataset related.
 
-Example 2.2 : Usage of hyper-parameter optimization :
-<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
+Example 2.2 : Usage of randomized hyper-parameter optimization :
+<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
 
 In the previous example, we have seen that the split ratio has an impact on the computational time.
 But the most time-consuming task is optimizing the hyper parameters.
@@ -196,7 +233,8 @@ However, most of the time, they are unknown to the user, and then have to be opt
 In this example, we will use the hyper-parameter optimization method implemented in the platform, to do so we will use three lines of the config file :
 
 - ``hps_type:``, controlling the type of hyper-parameter search,
-- ``hps_iter:``, controlling the number of random draws during the hyper-parameter search,
+- ``n_iter:``, controlling the number of random draws during the hyper-parameter search,
+- ``equivalent_draws``, controlling the number fo draws for multiview algorithms,
 - ``nb_folds:``, controlling the number of folds in the cross-validation process.
 
 So if you run ``example 2.2.1`` with :
@@ -206,16 +244,12 @@ So if you run ``example 2.2.1`` with :
    >>> from multiview_platform.execute import execute
    >>> execute("example2.2.1")
 
-The ``hps_type`` argument is set to ``"randomised_search"``, which is at the moment the only hyper-parameter optimization method implemented in the platform.
-The ``hps_iter`` argument is set to ``5``,
+The ``hps_type`` argument is set to ``"Random"``, which is at the moment the only hyper-parameter optimization method implemented in the platform.
+The ``n_iter`` argument is set to ``5``,
+The ``equivalent_draws`` argument  is set to ``True``,
 The ``nb_folds`` argument is set o ``5``.
 
-**WARNING : The "csv-table" directive's ":file:" and ":url:" options represent a potential security holes. They can be disabled with the "file_insertion_enabled" runtime setting.**
-
-.. csv-table::
-    :file: ./images/result_default_hp_high_train.csv
-
-Here, we used ``split: 0.2`` and the results are far better than with the preset of hyper paramters, as the classifiers are able to fit the task.
+Here, we used ``split: 0.2`` and the results are far better than with the preset of hyper parameters, as the classifiers are able to fit the task.
 
 
 The computing time should be longer than the previous examples. Let's see the pseudo code of the benchmark, while using the hyper-parameter optimization::
@@ -241,32 +275,61 @@ The computing time should be longer than the previous examples. Let's see the ps
         └
         learn on the whole training set
 
-The instructions inside the brackets are the one that the HP optimization adds. So for the monoview algorithms,
+The instructions inside the brackets are the one that the hyper-parameter
+optimization adds. So for the monoview algorithms,
 the computational impact of the HPO is bigger than for the multiview algorithms.
 
-The choice made here is to allow the same amount of draws for each HPO. However, as many of the multiview algorithms
+The choice made here is to allow the same amount of draws for each HPO. However,
+as many of the multiview algorithms
 are more complex and have bigger HP spaces, allowing them more draws, can be a defendable idea.
 
-However, for most of the tasks, using the HPO is a necessity to be able to get the most of each classifier in terms
-of performance.
+However, for most of the tasks, using the HPO is a necessity to be able to
+get the most of each classifier in terms of performance.
 
-The HPO is a matter of tradeoff between precision and computational demand. For most algorithms the more draws you
-allow, the closer to ideal the outputted HP will be, however, many draws mean much longer computational time.
+The HPO is a matter of trade-off between precision and computational demand.
+For most algorithms the more draws you allow, the closer to ideal the outputted
+HP will be, however, many draws mean much longer computational time.
 
-Similarly, the number of folds has a great importance in estimating the performance of a specific Hp combination,
-and the more folds the but more folds take also more time, as one has to train more times and on bigger parts of the
+Similarly, the number of folds has a great importance in estimating the
+performance of a specific HP combination, and the more folds the but more folds
+take also more time, as one has to train more times and on bigger parts of the
 dataset.
 
-The figure below represents the duration of the execution on a personal computer with different fold/draws settings :
+The figure below represents the duration of the execution on a personal computer
+with different fold/draws settings :
 
 .. raw:: html
     :file: ./images/durations.html
 
-The duration is in seconds, and we used 2,5,10,15,20 as values for ``nb_folds`` and 2,5,10,20,30,50,100 for ``hps_iter`` with two monoview classifiers and one multiview classifier on simulated data.
+The duration is in seconds, and we used 2,5,10,15,20 as values for ``nb_folds``
+and 2,5,10,20,30,50,100 for ``n_iter`` with two monoview classifiers and one
+multiview classifier on simulated data.
 
 .. note::
-    In order to compensate the fact that the multiview classifiers have more complex problems to solve, it is possible to use ``"randomized_search-equiv"`` as the HPS optimization method to allow
-    ``hps_iter`` draws for the monoview classifiers and ``hps_iter * nb_view`` draws for the ones that are multiview.
+    The hyper-parameter optimization process generates a report for each
+    classifier, providing each set of parameters and its cross-validation score,
+    to be able to extract the relevant parameters for a future benchmark on the
+    same dataset.
 
 
+Example 2.3 : Usage of grid search :
+<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
+
+In SuMMIT, it is possible to use a grid search if one has several possible
+hyper-parameter values in mind to test.
+
+In order to set up the grid search one has to provide in the ``hps_args:``
+argument the names, parameters and values to test. Let us say we want to try
+several depths for a decision tree, and several ``C`` values for a
+linear `SVM <ttps://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html>`_:
+
+.. code-block:: yaml
+
+    hps_type: "Grid"
+    hps_args:
+      decision_tree:
+        max_depth: [1,2,3,4,5]
+      svm_linear:
+        C: [0.1,0.2,0.3,0.4,0.5]
 
+TODO : a more complex example
diff --git a/docs/build/analyzeresult.html b/docs/build/analyzeresult.html
index 706d0209a86fe4fbaafd8a8f07b81b5857d40421..4d317bf2c108dff81c2837e760a7a9021075facc 100644
--- a/docs/build/analyzeresult.html
+++ b/docs/build/analyzeresult.html
@@ -25,9 +25,6 @@
         <li class="right" style="margin-right: 10px">
           <a href="genindex.html" title="General Index"
              accesskey="I">index</a></li>
-        <li class="right" >
-          <a href="py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
         <li class="nav-item nav-item-0"><a href="index.html">MultiviewPlatform 0 documentation</a> &#187;</li> 
       </ul>
     </div>  
@@ -37,440 +34,8 @@
         <div class="bodywrapper">
           <div class="body" role="main">
             
-  <div class="section" id="module-multiview_platform.mono_multi_view_classifiers.result_analysis">
-<span id="result-analysis-module"></span><h1>Result analysis module<a class="headerlink" href="#module-multiview_platform.mono_multi_view_classifiers.result_analysis" title="Permalink to this headline">¶</a></h1>
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.result_analysis.analyzeMulticlass">
-<code class="descname">analyzeMulticlass</code><span class="sig-paren">(</span><em>results</em>, <em>stats_iter</em>, <em>benchmark_argument_dictionaries</em>, <em>nb_examples</em>, <em>nb_labels</em>, <em>multiclass_labels</em>, <em>metrics</em>, <em>classification_indices</em>, <em>directories</em>, <em>example_ids</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.result_analysis.analyzeMulticlass" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to transform one versus one results in multiclass results and to publish it</p>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.result_analysis.analyze_biclass">
-<code class="descname">analyze_biclass</code><span class="sig-paren">(</span><em>results</em>, <em>benchmark_argument_dictionaries</em>, <em>stats_iter</em>, <em>metrics</em>, <em>example_ids</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.result_analysis.analyze_biclass" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to extract and format the results of the different biclass experimentations performed.</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>results</strong> (<em>list</em>) – The result list returned by the bencmark execution function. For each executed benchmark, contains
-a flag &amp; a result element.
-The flag is a way to identify to which benchmark the results belong, formatted this way :
-<cite>flag = iter_index, [classifierPositive, classifierNegative]</cite> with
-- <cite>iter_index</cite> the index of the statistical iteration
-- <cite>[classifierPositive, classifierNegative]</cite> the indices of the labels considered positive and negative
-by the classifier (mainly useful for one versus one multiclass classification).</li>
-<li><strong>benchmark_argument_dictionaries</strong> (<em>list of dicts</em>) – The list of all the arguments passed to the benchmark executing functions.</li>
-<li><strong>statsIter</strong> (<em>int</em>) – The number of statistical iterations.</li>
-<li><strong>metrics</strong> (<em>list of lists</em>) – THe list containing the metrics and their configuration.</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>biclassResults</strong> – The list contains a dictionary for each statistical iteration. This dictionary contains a dictionary for each
-label combination, regrouping the scores for each metrics and the information useful to plot errors on examples.</p>
-</td>
-</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">list of dicts of dicts</p>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.result_analysis.analyze_iter_multiclass">
-<code class="descname">analyze_iter_multiclass</code><span class="sig-paren">(</span><em>multiclass_results</em>, <em>directory</em>, <em>stats_iter</em>, <em>metrics</em>, <em>data_base_name</em>, <em>nb_examples</em>, <em>example_ids</em>, <em>multiclass_labels</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.result_analysis.analyze_iter_multiclass" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to mean the multiclass results on the iterations executed with different random states</p>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.result_analysis.analyzebiclass_iter">
-<code class="descname">analyzebiclass_iter</code><span class="sig-paren">(</span><em>biclass_results</em>, <em>stats_iter</em>, <em>directory</em>, <em>labels_dictionary</em>, <em>data_base_name</em>, <em>example_ids</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.result_analysis.analyzebiclass_iter" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to format the results in order to plot the mean results on the iterations</p>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.result_analysis.autolabel">
-<code class="descname">autolabel</code><span class="sig-paren">(</span><em>rects</em>, <em>ax</em>, <em>set=1</em>, <em>std=None</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.result_analysis.autolabel" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to print the score below the bars.</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>rects</strong> (<em>pyplot bar object</em>) – THe bars.</li>
-<li><strong>ax</strong> (<em>pyplot ax object</em>) – The ax.</li>
-<li><strong>set</strong> (<em>integer</em>) – 1 means the test scores, anything else means the train score</li>
-<li><strong>std</strong> (<em>None</em><em> or </em><em>array</em>) – The standard deviations in the case of statsIter results.</li>
-</ul>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.result_analysis.format_previous_results">
-<code class="descname">format_previous_results</code><span class="sig-paren">(</span><em>biclass_results</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.result_analysis.format_previous_results" title="Permalink to this definition">¶</a></dt>
-<dd><p>Formats each statistical iteration’s result into a mean/std analysis for
-the metrics and adds the errors of each statistical iteration.</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>biclass_results</strong> (<em>The raw results</em><em>, </em><em>for each statistical iteration i contains</em>) – <ul class="simple">
-<li>biclass_results[i][“metrics_scores”] is a dictionary with a pd.dataframe
-for each metrics</li>
-<li>biclass_results[i][“example_errors”], a dicaitonary with a np.array</li>
-</ul>
-<p>for each classifier.</p>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><ul class="simple">
-<li><strong>metrics_analysis</strong> (<em>The mean and std dataframes for each metrics</em>)</li>
-<li><strong>error_analysis</strong> (<em>A dictionary containing the added errors</em>) – arrays for each classifier</li>
-</ul>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.result_analysis.gen_error_data">
-<code class="descname">gen_error_data</code><span class="sig-paren">(</span><em>example_errors</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.result_analysis.gen_error_data" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to format the error data in order to plot it efficiently. The data is saves in a <cite>.csv</cite> file.</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>example_errors</strong> (<em>dict of dicts of np.arrays</em>) – A dictionary conatining all the useful data. Organized as :
-<cite>example_errors[&lt;classifier_name&gt;][“error_on_examples”]</cite> is a np.array of ints with a
-- 1 if the classifier <cite>&lt;classifier_name&gt;</cite> classifier well the example,
-- 0 if it fail to classify the example,
-- -100 if it did not classify the example (multiclass one versus one).</li>
-<li><strong>base_file_name</strong> (<em>list of str</em>) – The name of the file in which the figure will be saved (“2D_plot_data.csv” and “bar_plot_data.csv” will
-be added at the end).</li>
-<li><strong>nbCopies</strong> (<em>int</em><em>, </em><em>optinal</em><em>, </em><em>default: 2</em>) – The number of times the data is copied (classifier wise) in order for the figure to be more readable.</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last"><ul class="simple">
-<li><strong>nbClassifiers</strong> (<em>int</em>) – Number of different classifiers.</li>
-<li><strong>nbExamples</strong> (<em>int</em>) – NUmber of examples.</li>
-<li><strong>nbCopies</strong> (<em>int</em>) – The number of times the data is copied (classifier wise) in order for the figure to be more readable.</li>
-<li><strong>classifiers_names</strong> (<em>list of strs</em>) – The names fo the classifiers.</li>
-<li><strong>data</strong> (np.array of shape <cite>(nbClassifiers, nbExamples)</cite>) – A matrix with zeros where the classifier failed to classifiy the example, ones where it classified it well
-and -100 if the example was not classified.</li>
-<li><strong>error_on_examples</strong> (np.array of shape <cite>(nbExamples,)</cite>) – An array counting how many classifiers failed to classifiy each examples.</li>
-</ul>
-</p>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.result_analysis.gen_metrics_scores_multiclass">
-<code class="descname">gen_metrics_scores_multiclass</code><span class="sig-paren">(</span><em>results</em>, <em>true_labels</em>, <em>metrics_list</em>, <em>arguments_dictionaries</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.result_analysis.gen_metrics_scores_multiclass" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to add all the metrics scores to the multiclass result structure  for each clf and each iteration</p>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.result_analysis.get_arguments">
-<code class="descname">get_arguments</code><span class="sig-paren">(</span><em>benchmark_argument_dictionaries</em>, <em>flag</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.result_analysis.get_arguments" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to get the arguments passed to the benchmark executing function corresponding to the flag of a
-biclass experimentation.</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>flag</strong> (<em>list</em>) – The needed experimentation’s flag.</li>
-<li><strong>benchmark_argument_dictionaries</strong> (<em>list of dicts</em>) – The list of all the arguments passed to the benchmark executing functions.</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>benchmarkArgumentDictionary</strong> – All the arguments passed to the benchmark executing function for the needed experimentation.</p>
-</td>
-</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">dict</p>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.result_analysis.get_error_on_labels_multiclass">
-<code class="descname">get_error_on_labels_multiclass</code><span class="sig-paren">(</span><em>multiclass_results</em>, <em>multiclass_labels</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.result_analysis.get_error_on_labels_multiclass" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to add all the arrays showing on which example there is an error for each clf and each iteration</p>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.result_analysis.get_example_errors_biclass">
-<code class="descname">get_example_errors_biclass</code><span class="sig-paren">(</span><em>groud_truth</em>, <em>results</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.result_analysis.get_example_errors_biclass" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to get for each classifier and each example whether the classifier has misclassified the example or not.</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>ground_truth</strong> (<em>numpy array of 0</em><em>, </em><em>1 and -100</em><em> (</em><em>if multiclass</em><em>)</em>) – The array with the real labels of the examples</li>
-<li><strong>results</strong> (<em>list of MonoviewResult and MultiviewResults objects</em>) – A list containing all the resluts for all the mono- &amp; multi-view experimentations.</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>example_errors</strong> – For each classifier, has an entry with a <cite>np.array</cite> over the examples, with a 1 if the examples was
-well-classified, a 0 if not and if it’s multiclass classification, a -100 if the examples was not seen during
-the one versus one classification.</p>
-</td>
-</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">dict of np.array</p>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.result_analysis.get_feature_importances">
-<code class="descname">get_feature_importances</code><span class="sig-paren">(</span><em>result</em>, <em>feature_names=None</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.result_analysis.get_feature_importances" title="Permalink to this definition">¶</a></dt>
-<dd><p>Extracts the feature importance from the monoview results and stores them in a dictionnary :
-feature_importance[view_name] is a pandas.DataFrame of size n_feature*n_clf
-containing a score of importance for each feature.</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>result</strong> (<em>list of results</em>) – </td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><strong>feature_importances</strong> – The dictionary containing all the feature importance for each view as pandas DataFrames</td>
-</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body">dict of pd.DataFrame</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.result_analysis.get_fig_size">
-<code class="descname">get_fig_size</code><span class="sig-paren">(</span><em>nb_results</em>, <em>min_size=15</em>, <em>multiplier=1.0</em>, <em>bar_width=0.35</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.result_analysis.get_fig_size" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to get the image size to save the figure and the bar width, depending on the number of scores to plot.</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>nb_results</strong> (<em>int</em>) – The number of couple of bar to plot.</li>
-<li><strong>min_size</strong> (<em>int</em>) – The minimum size of the image, if there are few classifiers to plot.</li>
-<li><strong>multiplier</strong> (<em>float</em>) – The ratio between the image size and the number of classifiers.</li>
-<li><strong>bar_width</strong> (<em>float</em>) – The width of the bars in the figure. Mainly here to centralize bar_width.</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last"><ul class="simple">
-<li><strong>fig_kwargs</strong> (<em>dict of arguments</em>) – The argument restraining the size of the figure, usable directly in the <cite>subplots</cite> function of
-<cite>matplotlib.pyplot</cite>.</li>
-<li><strong>bar_width</strong> (<em>float</em>) – The width of the bars in the figure. Mainly here to centralize bar_width.</li>
-</ul>
-</p>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.result_analysis.get_metrics_scores_biclass">
-<code class="descname">get_metrics_scores_biclass</code><span class="sig-paren">(</span><em>metrics</em>, <em>results</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.result_analysis.get_metrics_scores_biclass" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to extract metrics scores in case of biclass classification</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>metrics</strong> (<em>list of lists</em>) – The metrics names with configuration metrics[i][0] = name of metric i</li>
-<li><strong>results</strong> (<em>list of MonoviewResult and MultiviewResults objects</em>) – A list containing all the results for all the monoview experimentations.</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>metricsScores</strong> – Regroups all the scores for each metrics for each classifier and for the train and test sets.
-organized as :
--<cite>metricScores[metric_name][“classifiers_names”]</cite> is a list of all the classifiers available for this metric,
--<cite>metricScores[metric_name][“train_scores”]</cite> is a list of all the available classifiers scores on the train set,
--<cite>metricScores[metric_name][“test_scores”]</cite> is a list of all the available classifiers scores on the test set.</p>
-</td>
-</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">dict of dict of list</p>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.result_analysis.get_results">
-<code class="descname">get_results</code><span class="sig-paren">(</span><em>results</em>, <em>stats_iter</em>, <em>nb_multiclass</em>, <em>benchmark_argument_dictionaries</em>, <em>multiclass_labels</em>, <em>metrics</em>, <em>classification_indices</em>, <em>directories</em>, <em>directory</em>, <em>labels_dictionary</em>, <em>nb_examples</em>, <em>nb_labels</em>, <em>example_ids</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.result_analysis.get_results" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to analyze the results of the previous benchmarks</p>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.result_analysis.iterCmap">
-<code class="descname">iterCmap</code><span class="sig-paren">(</span><em>statsIter</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.result_analysis.iterCmap" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to generate a colormap that will have a tick for each iteration : the whiter the better.</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>statsIter</strong> (<em>int</em>) – The number of statistical iterations.</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><ul class="simple">
-<li><strong>cmap</strong> (<em>matplotlib.colors.ListedColorMap object</em>) – The colormap.</li>
-<li><strong>norm</strong> (<em>matplotlib.colors.BoundaryNorm object</em>) – The bounds for the colormap.</li>
-</ul>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.result_analysis.plot_2d">
-<code class="descname">plot_2d</code><span class="sig-paren">(</span><em>data</em>, <em>classifiers_names</em>, <em>nbClassifiers</em>, <em>nbExamples</em>, <em>file_name</em>, <em>minSize=10</em>, <em>labels=None</em>, <em>width_denominator=2.0</em>, <em>height_denominator=20.0</em>, <em>stats_iter=1</em>, <em>use_plotly=True</em>, <em>example_ids=None</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.result_analysis.plot_2d" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to generate a 2D plot of the errors.</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>data</strong> (np.array of shape <cite>(nbClassifiers, nbExamples)</cite>) – A matrix with zeros where the classifier failed to classifiy the example, ones where it classified it well
-and -100 if the example was not classified.</li>
-<li><strong>classifiers_names</strong> (<em>list of str</em>) – The names of the classifiers.</li>
-<li><strong>nbClassifiers</strong> (<em>int</em>) – The number of classifiers.</li>
-<li><strong>nbExamples</strong> (<em>int</em>) – The number of examples.</li>
-<li><strong>nbCopies</strong> (<em>int</em>) – The number of times the data is copied (classifier wise) in order for the figure to be more readable</li>
-<li><strong>file_name</strong> (<em>str</em>) – The name of the file in which the figure will be saved (“error_analysis_2D.png” will be added at the end)</li>
-<li><strong>minSize</strong> (<em>int</em><em>, </em><em>optinal</em><em>, </em><em>default: 10</em>) – The minimum width and height of the figure.</li>
-<li><strong>width_denominator</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default: 1.0</em>) – To obtain the image width, the number of classifiers will be divided by this number.</li>
-<li><strong>height_denominator</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default: 1.0</em>) – To obtain the image width, the number of examples will be divided by this number.</li>
-<li><strong>stats_iter</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default: 1</em>) – The number of statistical iterations realized.</li>
-</ul>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.result_analysis.plot_errors_bar">
-<code class="descname">plot_errors_bar</code><span class="sig-paren">(</span><em>error_on_examples</em>, <em>nbClassifiers</em>, <em>nbExamples</em>, <em>fileName</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.result_analysis.plot_errors_bar" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to generate a barplot of the muber of classifiers that failed to classify each examples</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>error_on_examples</strong> (np.array of shape <cite>(nbExamples,)</cite>) – An array counting how many classifiers failed to classifiy each examples.</li>
-<li><strong>classifiers_names</strong> (<em>list of str</em>) – The names of the classifiers.</li>
-<li><strong>nbClassifiers</strong> (<em>int</em>) – The number of classifiers.</li>
-<li><strong>nbExamples</strong> (<em>int</em>) – The number of examples.</li>
-<li><strong>fileName</strong> (<em>str</em>) – The name of the file in which the figure will be saved (“error_analysis_2D.png” will be added at the end)</li>
-</ul>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.result_analysis.plot_metric_scores">
-<code class="descname">plot_metric_scores</code><span class="sig-paren">(</span><em>train_scores</em>, <em>test_scores</em>, <em>names</em>, <em>nb_results</em>, <em>metric_name</em>, <em>file_name</em>, <em>tag=''</em>, <em>train_STDs=None</em>, <em>test_STDs=None</em>, <em>use_plotly=True</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.result_analysis.plot_metric_scores" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to plot and save the score barplot for a specific metric.</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>train_scores</strong> (<em>list</em><em> or </em><em>np.array of floats</em>) – The scores of each classifier on the training set.</li>
-<li><strong>test_scores</strong> (<em>list</em><em> or </em><em>np.array of floats</em>) – The scores of each classifier on the testing set.</li>
-<li><strong>names</strong> (<em>list</em><em> or </em><em>np.array of strs</em>) – The names of all the classifiers.</li>
-<li><strong>nb_results</strong> (<em>int</em>) – The number of classifiers to plot.</li>
-<li><strong>metric_name</strong> (<em>str</em>) – The plotted metric’s name</li>
-<li><strong>file_name</strong> (<em>str</em>) – The name of the file where the figure will be saved.</li>
-<li><strong>tag</strong> (<em>str</em>) – Some text to personalize the title, must start with a whitespace.</li>
-<li><strong>train_STDs</strong> (<em>np.array of floats</em><em> or </em><em>None</em>) – The array containing the standard deviations for the averaged scores on the training set.</li>
-<li><strong>test_STDs</strong> (<em>np.array of floats</em><em> or </em><em>None</em>) – The array containing the standard deviations for the averaged scores on the testing set.</li>
-</ul>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.result_analysis.publishMetricsGraphs">
-<code class="descname">publishMetricsGraphs</code><span class="sig-paren">(</span><em>metrics_scores</em>, <em>directory</em>, <em>database_name</em>, <em>labels_names</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.result_analysis.publishMetricsGraphs" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to sort the results (names and both scores) in descending test score order.</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>metrics_scores</strong> (<em>dict of dicts of lists</em><em> or </em><em>np.arrays</em>) – Keys : The names of the metrics.
-Values : The scores and names of each classifier .</li>
-<li><strong>directory</strong> (<em>str</em>) – The path to the directory where the figures will be saved.</li>
-<li><strong>database_name</strong> (<em>str</em>) – The name of the database on which the experiments where conducted.</li>
-<li><strong>labels_names</strong> (<em>list of strs</em>) – The name corresponding to each numerical label.</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"></p>
-</td>
-</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">results</p>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.result_analysis.sort_by_test_score">
-<code class="descname">sort_by_test_score</code><span class="sig-paren">(</span><em>train_scores</em>, <em>test_scores</em>, <em>names</em>, <em>train_STDs=None</em>, <em>test_STDs=None</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.result_analysis.sort_by_test_score" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to sort the results (names and both scores) in descending test score order.</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>train_scores</strong> (<em>np.array of floats</em>) – The scores of each classifier on the training set.</li>
-<li><strong>test_scores</strong> (<em>np.array of floats</em>) – The scores of each classifier on the testing set.</li>
-<li><strong>names</strong> (<em>np.array of strs</em>) – The names of all the classifiers.</li>
-<li><strong>train_STDs</strong> (<em>np.array of floats</em><em> or </em><em>None</em>) – The array containing the standard deviations for the averaged scores on the training set.</li>
-<li><strong>test_STDs</strong> (<em>np.array of floats</em><em> or </em><em>None</em>) – The array containing the standard deviations for the averaged scores on the testing set.</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last"><ul class="simple">
-<li><strong>sorted_names</strong> (<em>np.array of strs</em>) – The names of all the classifiers, sorted in descending test score order.</li>
-<li><strong>sorted_train_scores</strong> (<em>np.array of floats</em>) – The scores of each classifier on the training set, sorted in descending test score order.</li>
-<li><strong>sorted_test_scores</strong> (<em>np.array of floats</em>) – The scores of each classifier on the testing set, sorted in descending test score order.</li>
-<li><strong>sorted_train_STDs</strong> (<em>np.array of floats or None</em>) – The array containing the standard deviations for the averaged scores on the training set,
-sorted in descending test score order.</li>
-<li><strong>sorted_test_STDs</strong> (<em>np.array of floats or None</em>) – The array containing the standard deviations for the averaged scores on the testing set,
-sorted in descending test score order.</li>
-</ul>
-</p>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
+  <div class="section" id="result-analysis-module">
+<h1>Result analysis module<a class="headerlink" href="#result-analysis-module" title="Permalink to this headline">¶</a></h1>
 </div>
 
 
@@ -508,9 +73,6 @@ sorted in descending test score order.</li>
         <li class="right" style="margin-right: 10px">
           <a href="genindex.html" title="General Index"
              >index</a></li>
-        <li class="right" >
-          <a href="py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
         <li class="nav-item nav-item-0"><a href="index.html">MultiviewPlatform 0 documentation</a> &#187;</li> 
       </ul>
     </div>
diff --git a/docs/build/api.html b/docs/build/api.html
index fcbcdee9daddaf7421338aa1bbcb34f618a82e57..ca3b06b922b43c38f7d77971e88b5a066b158ce9 100644
--- a/docs/build/api.html
+++ b/docs/build/api.html
@@ -25,9 +25,6 @@
         <li class="right" style="margin-right: 10px">
           <a href="genindex.html" title="General Index"
              accesskey="I">index</a></li>
-        <li class="right" >
-          <a href="py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
         <li class="nav-item nav-item-0"><a href="index.html">MultiviewPlatform 0 documentation</a> &#187;</li> 
       </ul>
     </div>  
@@ -83,9 +80,6 @@
         <li class="right" style="margin-right: 10px">
           <a href="genindex.html" title="General Index"
              >index</a></li>
-        <li class="right" >
-          <a href="py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
         <li class="nav-item nav-item-0"><a href="index.html">MultiviewPlatform 0 documentation</a> &#187;</li> 
       </ul>
     </div>
diff --git a/docs/build/execution.html b/docs/build/execution.html
index aaf6dcd9d3c133d8162956c342bd36506f22827f..d2fd72acc9b1ba89720374765637ee14fec9b48c 100644
--- a/docs/build/execution.html
+++ b/docs/build/execution.html
@@ -25,9 +25,6 @@
         <li class="right" style="margin-right: 10px">
           <a href="genindex.html" title="General Index"
              accesskey="I">index</a></li>
-        <li class="right" >
-          <a href="py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
         <li class="nav-item nav-item-0"><a href="index.html">MultiviewPlatform 0 documentation</a> &#187;</li> 
       </ul>
     </div>  
@@ -37,9 +34,8 @@
         <div class="bodywrapper">
           <div class="body" role="main">
             
-  <div class="section" id="module-multiview_platform.execute">
-<span id="welcome-to-the-exection-documentation"></span><h1>Welcome to the exection documentation<a class="headerlink" href="#module-multiview_platform.execute" title="Permalink to this headline">¶</a></h1>
-<p>This is the execution module, used to execute the code</p>
+  <div class="section" id="welcome-to-the-exection-documentation">
+<h1>Welcome to the exection documentation<a class="headerlink" href="#welcome-to-the-exection-documentation" title="Permalink to this headline">¶</a></h1>
 </div>
 
 
@@ -77,9 +73,6 @@
         <li class="right" style="margin-right: 10px">
           <a href="genindex.html" title="General Index"
              >index</a></li>
-        <li class="right" >
-          <a href="py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
         <li class="nav-item nav-item-0"><a href="index.html">MultiviewPlatform 0 documentation</a> &#187;</li> 
       </ul>
     </div>
diff --git a/docs/build/genindex.html b/docs/build/genindex.html
index e4ced6ffbb110cacff02372809087791799133f4..7283695dfad87992702e58b3bf0bb5080ce968f0 100644
--- a/docs/build/genindex.html
+++ b/docs/build/genindex.html
@@ -26,9 +26,6 @@
         <li class="right" style="margin-right: 10px">
           <a href="#" title="General Index"
              accesskey="I">index</a></li>
-        <li class="right" >
-          <a href="py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
         <li class="nav-item nav-item-0"><a href="index.html">MultiviewPlatform 0 documentation</a> &#187;</li> 
       </ul>
     </div>  
@@ -42,796 +39,8 @@
 <h1 id="index">Index</h1>
 
 <div class="genindex-jumpbox">
- <a href="#A"><strong>A</strong></a>
- | <a href="#B"><strong>B</strong></a>
- | <a href="#C"><strong>C</strong></a>
- | <a href="#D"><strong>D</strong></a>
- | <a href="#E"><strong>E</strong></a>
- | <a href="#F"><strong>F</strong></a>
- | <a href="#G"><strong>G</strong></a>
- | <a href="#I"><strong>I</strong></a>
- | <a href="#M"><strong>M</strong></a>
- | <a href="#N"><strong>N</strong></a>
- | <a href="#P"><strong>P</strong></a>
- | <a href="#R"><strong>R</strong></a>
- | <a href="#S"><strong>S</strong></a>
- | <a href="#T"><strong>T</strong></a>
- | <a href="#U"><strong>U</strong></a>
- | <a href="#V"><strong>V</strong></a>
  
 </div>
-<h2 id="A">A</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
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-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset.add_gaussian_noise">add_gaussian_noise() (Dataset method)</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.html#multiview_platform.mono_multi_view_classifiers.result_analysis.add_new_labels_combination">add_new_labels_combination() (in module multiview_platform.mono_multi_view_classifiers.result_analysis)</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.html#multiview_platform.mono_multi_view_classifiers.result_analysis.add_new_metric">add_new_metric() (in module multiview_platform.mono_multi_view_classifiers.result_analysis)</a>
-</li>
-      <li><a href="analyzeresult.html#multiview_platform.mono_multi_view_classifiers.result_analysis.analyze_biclass">analyze_biclass() (in module multiview_platform.mono_multi_view_classifiers.result_analysis)</a>, <a href="references/multiview_platform.mono_multi_view_classifiers.html#multiview_platform.mono_multi_view_classifiers.result_analysis.analyze_biclass">[1]</a>
-</li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="analyzeresult.html#multiview_platform.mono_multi_view_classifiers.result_analysis.analyze_iter_multiclass">analyze_iter_multiclass() (in module multiview_platform.mono_multi_view_classifiers.result_analysis)</a>, <a href="references/multiview_platform.mono_multi_view_classifiers.html#multiview_platform.mono_multi_view_classifiers.result_analysis.analyze_iter_multiclass">[1]</a>
-</li>
-      <li><a href="analyzeresult.html#multiview_platform.mono_multi_view_classifiers.result_analysis.analyzebiclass_iter">analyzebiclass_iter() (in module multiview_platform.mono_multi_view_classifiers.result_analysis)</a>, <a href="references/multiview_platform.mono_multi_view_classifiers.html#multiview_platform.mono_multi_view_classifiers.result_analysis.analyzebiclass_iter">[1]</a>
-</li>
-      <li><a href="analyzeresult.html#multiview_platform.mono_multi_view_classifiers.result_analysis.analyzeMulticlass">analyzeMulticlass() (in module multiview_platform.mono_multi_view_classifiers.result_analysis)</a>, <a href="references/multiview_platform.mono_multi_view_classifiers.html#multiview_platform.mono_multi_view_classifiers.result_analysis.analyzeMulticlass">[1]</a>
-</li>
-      <li><a href="references/monomulti/exec_classif.html#multiview_platform.mono_multi_view_classifiers.exec_classif.arange_metrics">arange_metrics() (in module multiview_platform.mono_multi_view_classifiers.exec_classif)</a>, <a href="references/multiview_platform.mono_multi_view_classifiers.html#multiview_platform.mono_multi_view_classifiers.exec_classif.arange_metrics">[1]</a>
-</li>
-      <li><a href="analyzeresult.html#multiview_platform.mono_multi_view_classifiers.result_analysis.autolabel">autolabel() (in module multiview_platform.mono_multi_view_classifiers.result_analysis)</a>, <a href="references/multiview_platform.mono_multi_view_classifiers.html#multiview_platform.mono_multi_view_classifiers.result_analysis.autolabel">[1]</a>
-</li>
-  </ul></td>
-</tr></table>
-
-<h2 id="B">B</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="references/monomulti/exec_classif.html#multiview_platform.mono_multi_view_classifiers.exec_classif.benchmark_init">benchmark_init() (in module multiview_platform.mono_multi_view_classifiers.exec_classif)</a>, <a href="references/multiview_platform.mono_multi_view_classifiers.html#multiview_platform.mono_multi_view_classifiers.exec_classif.benchmark_init">[1]</a>
-</li>
-  </ul></td>
-</tr></table>
-
-<h2 id="C">C</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset.check_selected_label_names">check_selected_label_names() (Dataset method)</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.hyper_parameter_search.compute_possible_combinations">compute_possible_combinations() (in module multiview_platform.mono_multi_view_classifiers.utils.hyper_parameter_search)</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.make_file_config.ConfigurationMaker">ConfigurationMaker (class in multiview_platform.mono_multi_view_classifiers.utils.make_file_config)</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.dataset.confirm">confirm() (in module multiview_platform.mono_multi_view_classifiers.utils.dataset)</a>
-</li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.dataset.copy_hdf5">copy_hdf5() (in module multiview_platform.mono_multi_view_classifiers.utils.dataset)</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset.copy_view">copy_view() (Dataset method)</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.hyper_parameter_search.CustomRandint">CustomRandint (class in multiview_platform.mono_multi_view_classifiers.utils.hyper_parameter_search)</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.hyper_parameter_search.CustomUniform">CustomUniform (class in multiview_platform.mono_multi_view_classifiers.utils.hyper_parameter_search)</a>
-</li>
-  </ul></td>
-</tr></table>
-
-<h2 id="D">D</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset">Dataset (class in multiview_platform.mono_multi_view_classifiers.utils.dataset)</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset.dataset">dataset (Dataset attribute)</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.get_multiview_db.DatasetError">DatasetError</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.dataset.datasets_already_exist">datasets_already_exist() (in module multiview_platform.mono_multi_view_classifiers.utils.dataset)</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.dataset.delete_HDF5">delete_HDF5() (in module multiview_platform.mono_multi_view_classifiers.utils.dataset)</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.multiview_classifiers.difficulty_fusion.html#multiview_platform.mono_multi_view_classifiers.multiview_classifiers.difficulty_fusion.DifficultyFusion">DifficultyFusion (class in multiview_platform.mono_multi_view_classifiers.multiview_classifiers.difficulty_fusion)</a>
-</li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.multiview_classifiers.disagree_fusion.html#multiview_platform.mono_multi_view_classifiers.multiview_classifiers.disagree_fusion.DisagreeFusion">DisagreeFusion (class in multiview_platform.mono_multi_view_classifiers.multiview_classifiers.disagree_fusion)</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.multiview_classifiers.difficulty_fusion.html#multiview_platform.mono_multi_view_classifiers.multiview_classifiers.difficulty_fusion.DifficultyFusion.diversity_measure">diversity_measure() (DifficultyFusion method)</a>
-
-      <ul>
-        <li><a href="references/multiview_platform.mono_multi_view_classifiers.multiview_classifiers.disagree_fusion.html#multiview_platform.mono_multi_view_classifiers.multiview_classifiers.disagree_fusion.DisagreeFusion.diversity_measure">(DisagreeFusion method)</a>
-</li>
-        <li><a href="references/multiview_platform.mono_multi_view_classifiers.multiview_classifiers.double_fault_fusion.html#multiview_platform.mono_multi_view_classifiers.multiview_classifiers.double_fault_fusion.DoubleFaultFusion.diversity_measure">(DoubleFaultFusion method)</a>
-</li>
-        <li><a href="references/multiview_platform.mono_multi_view_classifiers.multiview_classifiers.entropy_fusion.html#multiview_platform.mono_multi_view_classifiers.multiview_classifiers.entropy_fusion.EntropyFusion.diversity_measure">(EntropyFusion method)</a>
-</li>
-      </ul></li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.multiview_classifiers.double_fault_fusion.html#multiview_platform.mono_multi_view_classifiers.multiview_classifiers.double_fault_fusion.DoubleFaultFusion">DoubleFaultFusion (class in multiview_platform.mono_multi_view_classifiers.multiview_classifiers.double_fault_fusion)</a>
-</li>
-  </ul></td>
-</tr></table>
-
-<h2 id="E">E</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.multiview_classifiers.entropy_fusion.html#multiview_platform.mono_multi_view_classifiers.multiview_classifiers.entropy_fusion.EntropyFusion">EntropyFusion (class in multiview_platform.mono_multi_view_classifiers.multiview_classifiers.entropy_fusion)</a>
-</li>
-      <li><a href="references/monomulti/exec_classif.html#multiview_platform.mono_multi_view_classifiers.exec_classif.exec_benchmark">exec_benchmark() (in module multiview_platform.mono_multi_view_classifiers.exec_classif)</a>, <a href="references/multiview_platform.mono_multi_view_classifiers.html#multiview_platform.mono_multi_view_classifiers.exec_classif.exec_benchmark">[1]</a>
-</li>
-      <li><a href="references/monomulti/exec_classif.html#multiview_platform.mono_multi_view_classifiers.exec_classif.exec_classif">exec_classif() (in module multiview_platform.mono_multi_view_classifiers.exec_classif)</a>, <a href="references/multiview_platform.mono_multi_view_classifiers.html#multiview_platform.mono_multi_view_classifiers.exec_classif.exec_classif">[1]</a>
-</li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.html#multiview_platform.mono_multi_view_classifiers.exec_classif.exec_one_benchmark_mono_core">exec_one_benchmark_mono_core() (in module multiview_platform.mono_multi_view_classifiers.exec_classif)</a>
-</li>
-      <li><a href="references/multiview_platform.html#multiview_platform.execute.execute">execute() (in module multiview_platform.execute)</a>
-</li>
-      <li><a href="references/monomulti/exec_classif.html#multiview_platform.mono_multi_view_classifiers.exec_classif.extract_dict">extract_dict() (in module multiview_platform.mono_multi_view_classifiers.exec_classif)</a>, <a href="references/multiview_platform.mono_multi_view_classifiers.html#multiview_platform.mono_multi_view_classifiers.exec_classif.extract_dict">[1]</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.dataset.extract_subset">extract_subset() (in module multiview_platform.mono_multi_view_classifiers.utils.dataset)</a>
-</li>
-  </ul></td>
-</tr></table>
-
-<h2 id="F">F</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="references/multiview_platform.tests.test_utils.html#multiview_platform.tests.test_utils.test_execution.FakeArg">FakeArg (class in multiview_platform.tests.test_utils.test_execution)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.html#multiview_platform.tests.test_ExecClassif.fakeBenchmarkExec">fakeBenchmarkExec() (in module multiview_platform.tests.test_ExecClassif)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.html#multiview_platform.tests.test_ExecClassif.fakeBenchmarkExec_monocore">fakeBenchmarkExec_monocore() (in module multiview_platform.tests.test_ExecClassif)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.html#multiview_platform.tests.test_ExecClassif.fakeBenchmarkExec_mutlicore">fakeBenchmarkExec_mutlicore() (in module multiview_platform.tests.test_ExecClassif)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.html#multiview_platform.tests.test_ExecClassif.fakeDelete">fakeDelete() (in module multiview_platform.tests.test_ExecClassif)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.html#multiview_platform.tests.test_ExecClassif.fakeExecMono">fakeExecMono() (in module multiview_platform.tests.test_ExecClassif)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.html#multiview_platform.tests.test_ExecClassif.fakeExecMulti">fakeExecMulti() (in module multiview_platform.tests.test_ExecClassif)</a>
-</li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="references/multiview_platform.tests.html#multiview_platform.tests.test_ExecClassif.fakegetResults">fakegetResults() (in module multiview_platform.tests.test_ExecClassif)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.html#multiview_platform.tests.test_ExecClassif.fakeInitMulti">fakeInitMulti() (in module multiview_platform.tests.test_ExecClassif)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.html#multiview_platform.tests.test_ExecClassif.FakeKfold">FakeKfold (class in multiview_platform.tests.test_ExecClassif)</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset.filter">filter() (Dataset method)</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.execution.find_dataset_names">find_dataset_names() (in module multiview_platform.mono_multi_view_classifiers.utils.execution)</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.hyper_parameter_search.MultiviewCompatibleRandomizedSearchCV.fit">fit() (MultiviewCompatibleRandomizedSearchCV method)</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.hyper_parameter_search.MultiviewCompatibleRandomizedSearchCV.fit_multiview">fit_multiview() (MultiviewCompatibleRandomizedSearchCV method)</a>
-</li>
-      <li><a href="analyzeresult.html#multiview_platform.mono_multi_view_classifiers.result_analysis.format_previous_results">format_previous_results() (in module multiview_platform.mono_multi_view_classifiers.result_analysis)</a>, <a href="references/multiview_platform.mono_multi_view_classifiers.html#multiview_platform.mono_multi_view_classifiers.result_analysis.format_previous_results">[1]</a>
-</li>
-  </ul></td>
-</tr></table>
-
-<h2 id="G">G</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.execution.gen_argument_dictionaries">gen_argument_dictionaries() (in module multiview_platform.mono_multi_view_classifiers.utils.execution)</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.html#multiview_platform.mono_multi_view_classifiers.result_analysis.gen_classifiers_dict">gen_classifiers_dict() (in module multiview_platform.mono_multi_view_classifiers.result_analysis)</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.execution.gen_direcorties_names">gen_direcorties_names() (in module multiview_platform.mono_multi_view_classifiers.utils.execution)</a>
-</li>
-      <li><a href="analyzeresult.html#multiview_platform.mono_multi_view_classifiers.result_analysis.gen_error_data">gen_error_data() (in module multiview_platform.mono_multi_view_classifiers.result_analysis)</a>, <a href="references/multiview_platform.mono_multi_view_classifiers.html#multiview_platform.mono_multi_view_classifiers.result_analysis.gen_error_data">[1]</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.html#multiview_platform.mono_multi_view_classifiers.result_analysis.gen_error_data_glob">gen_error_data_glob() (in module multiview_platform.mono_multi_view_classifiers.result_analysis)</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.hyper_parameter_search.gen_heat_maps">gen_heat_maps() (in module multiview_platform.mono_multi_view_classifiers.utils.hyper_parameter_search)</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.execution.gen_k_folds">gen_k_folds() (in module multiview_platform.mono_multi_view_classifiers.utils.execution)</a>
-</li>
-      <li><a href="analyzeresult.html#multiview_platform.mono_multi_view_classifiers.result_analysis.gen_metrics_scores_multiclass">gen_metrics_scores_multiclass() (in module multiview_platform.mono_multi_view_classifiers.result_analysis)</a>, <a href="references/multiview_platform.mono_multi_view_classifiers.html#multiview_platform.mono_multi_view_classifiers.result_analysis.gen_metrics_scores_multiclass">[1]</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.multiclass.gen_multiclass_labels">gen_multiclass_labels() (in module multiview_platform.mono_multi_view_classifiers.utils.multiclass)</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.multiclass.gen_multiclass_monoview_decision">gen_multiclass_monoview_decision() (in module multiview_platform.mono_multi_view_classifiers.utils.multiclass)</a>
-</li>
-      <li><a href="references/monomulti/exec_classif.html#multiview_platform.mono_multi_view_classifiers.exec_classif.gen_multiple_args_dictionnaries">gen_multiple_args_dictionnaries() (in module multiview_platform.mono_multi_view_classifiers.exec_classif)</a>, <a href="references/multiview_platform.mono_multi_view_classifiers.html#multiview_platform.mono_multi_view_classifiers.exec_classif.gen_multiple_args_dictionnaries">[1]</a>
-</li>
-      <li><a href="references/monomulti/exec_classif.html#multiview_platform.mono_multi_view_classifiers.exec_classif.gen_multiple_kwargs_combinations">gen_multiple_kwargs_combinations() (in module multiview_platform.mono_multi_view_classifiers.exec_classif)</a>, <a href="references/multiview_platform.mono_multi_view_classifiers.html#multiview_platform.mono_multi_view_classifiers.exec_classif.gen_multiple_kwargs_combinations">[1]</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.html#multiview_platform.mono_multi_view_classifiers.exec_classif.gen_single_monoview_arg_dictionary">gen_single_monoview_arg_dictionary() (in module multiview_platform.mono_multi_view_classifiers.exec_classif)</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.html#multiview_platform.mono_multi_view_classifiers.exec_classif.gen_single_multiview_arg_dictionary">gen_single_multiview_arg_dictionary() (in module multiview_platform.mono_multi_view_classifiers.exec_classif)</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.execution.gen_splits">gen_splits() (in module multiview_platform.mono_multi_view_classifiers.utils.execution)</a>
-</li>
-      <li><a href="analyzeresult.html#multiview_platform.mono_multi_view_classifiers.result_analysis.get_arguments">get_arguments() (in module multiview_platform.mono_multi_view_classifiers.result_analysis)</a>, <a href="references/multiview_platform.mono_multi_view_classifiers.html#multiview_platform.mono_multi_view_classifiers.result_analysis.get_arguments">[1]</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.get_multiview_db.get_classic_db_csv">get_classic_db_csv() (in module multiview_platform.mono_multi_view_classifiers.utils.get_multiview_db)</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.get_multiview_db.get_classic_db_hdf5">get_classic_db_hdf5() (in module multiview_platform.mono_multi_view_classifiers.utils.get_multiview_db)</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.execution.get_database_function">get_database_function() (in module multiview_platform.mono_multi_view_classifiers.utils.execution)</a>
-</li>
-      <li><a href="analyzeresult.html#multiview_platform.mono_multi_view_classifiers.result_analysis.get_error_on_labels_multiclass">get_error_on_labels_multiclass() (in module multiview_platform.mono_multi_view_classifiers.result_analysis)</a>, <a href="references/multiview_platform.mono_multi_view_classifiers.html#multiview_platform.mono_multi_view_classifiers.result_analysis.get_error_on_labels_multiclass">[1]</a>
-</li>
-      <li><a href="analyzeresult.html#multiview_platform.mono_multi_view_classifiers.result_analysis.get_example_errors_biclass">get_example_errors_biclass() (in module multiview_platform.mono_multi_view_classifiers.result_analysis)</a>, <a href="references/multiview_platform.mono_multi_view_classifiers.html#multiview_platform.mono_multi_view_classifiers.result_analysis.get_example_errors_biclass">[1]</a>
-</li>
-      <li><a href="analyzeresult.html#multiview_platform.mono_multi_view_classifiers.result_analysis.get_feature_importances">get_feature_importances() (in module multiview_platform.mono_multi_view_classifiers.result_analysis)</a>, <a href="references/multiview_platform.mono_multi_view_classifiers.html#multiview_platform.mono_multi_view_classifiers.result_analysis.get_feature_importances">[1]</a>
-</li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="analyzeresult.html#multiview_platform.mono_multi_view_classifiers.result_analysis.get_fig_size">get_fig_size() (in module multiview_platform.mono_multi_view_classifiers.result_analysis)</a>, <a href="references/multiview_platform.mono_multi_view_classifiers.html#multiview_platform.mono_multi_view_classifiers.result_analysis.get_fig_size">[1]</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset.get_label_names">get_label_names() (Dataset method)</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset.get_labels">get_labels() (Dataset method)</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.multiview_result_analysis.get_metrics_scores">get_metrics_scores() (in module multiview_platform.mono_multi_view_classifiers.utils.multiview_result_analysis)</a>
-</li>
-      <li><a href="analyzeresult.html#multiview_platform.mono_multi_view_classifiers.result_analysis.get_metrics_scores_biclass">get_metrics_scores_biclass() (in module multiview_platform.mono_multi_view_classifiers.result_analysis)</a>, <a href="references/multiview_platform.mono_multi_view_classifiers.html#multiview_platform.mono_multi_view_classifiers.result_analysis.get_metrics_scores_biclass">[1]</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset.get_name">get_name() (Dataset method)</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset.get_nb_class">get_nb_class() (Dataset method)</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset.get_nb_examples">get_nb_examples() (Dataset method)</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.hyper_parameter_search.CustomRandint.get_nb_possibilities">get_nb_possibilities() (CustomRandint method)</a>
-</li>
-      <li><a href="references/monomulti/exec_classif.html#multiview_platform.mono_multi_view_classifiers.exec_classif.get_path_dict">get_path_dict() (in module multiview_platform.mono_multi_view_classifiers.exec_classif)</a>, <a href="references/multiview_platform.mono_multi_view_classifiers.html#multiview_platform.mono_multi_view_classifiers.exec_classif.get_path_dict">[1]</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.get_multiview_db.get_plausible_db_hdf5">get_plausible_db_hdf5() (in module multiview_platform.mono_multi_view_classifiers.utils.get_multiview_db)</a>
-</li>
-      <li><a href="analyzeresult.html#multiview_platform.mono_multi_view_classifiers.result_analysis.get_results">get_results() (in module multiview_platform.mono_multi_view_classifiers.result_analysis)</a>, <a href="references/multiview_platform.mono_multi_view_classifiers.html#multiview_platform.mono_multi_view_classifiers.result_analysis.get_results">[1]</a>
-</li>
-      <li><a href="references/monomulti/metrics.html#multiview_platform.mono_multi_view_classifiers.metrics.framework.get_scorer">get_scorer() (in module multiview_platform.mono_multi_view_classifiers.metrics.framework)</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset.get_shape">get_shape() (Dataset method)</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.hyper_parameter_search.get_test_folds_preds">get_test_folds_preds() (in module multiview_platform.mono_multi_view_classifiers.utils.hyper_parameter_search)</a>
-
-      <ul>
-        <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.hyper_parameter_search.MultiviewCompatibleRandomizedSearchCV.get_test_folds_preds">(MultiviewCompatibleRandomizedSearchCV method)</a>
-</li>
-      </ul></li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.configuration.get_the_args">get_the_args() (in module multiview_platform.mono_multi_view_classifiers.utils.configuration)</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.multiview_result_analysis.get_total_metric_scores">get_total_metric_scores() (in module multiview_platform.mono_multi_view_classifiers.utils.multiview_result_analysis)</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset.get_v">get_v() (Dataset method)</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset.get_view_dict">get_view_dict() (Dataset method)</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset.get_view_name">get_view_name() (Dataset method)</a>
-</li>
-      <li><a href="references/monomulti/metrics.html#multiview_platform.mono_multi_view_classifiers.metrics.framework.getConfig">getConfig() (in module multiview_platform.mono_multi_view_classifiers.metrics.framework)</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.hyper_parameter_search.grid_search">grid_search() (in module multiview_platform.mono_multi_view_classifiers.utils.hyper_parameter_search)</a>
-</li>
-  </ul></td>
-</tr></table>
-
-<h2 id="I">I</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.html#multiview_platform.mono_multi_view_classifiers.exec_classif.init_argument_dictionaries">init_argument_dictionaries() (in module multiview_platform.mono_multi_view_classifiers.exec_classif)</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset.init_attrs">init_attrs() (Dataset method)</a>
-</li>
-      <li><a href="references/monomulti/exec_classif.html#multiview_platform.mono_multi_view_classifiers.exec_classif.init_benchmark">init_benchmark() (in module multiview_platform.mono_multi_view_classifiers.exec_classif)</a>, <a href="references/multiview_platform.mono_multi_view_classifiers.html#multiview_platform.mono_multi_view_classifiers.exec_classif.init_benchmark">[1]</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset.init_example_indces">init_example_indces() (Dataset method)</a>
-</li>
-      <li><a href="references/monomulti/exec_classif.html#multiview_platform.mono_multi_view_classifiers.exec_classif.init_kwargs">init_kwargs() (in module multiview_platform.mono_multi_view_classifiers.exec_classif)</a>, <a href="references/multiview_platform.mono_multi_view_classifiers.html#multiview_platform.mono_multi_view_classifiers.exec_classif.init_kwargs">[1]</a>
-</li>
-      <li><a href="references/monomulti/exec_classif.html#multiview_platform.mono_multi_view_classifiers.exec_classif.init_kwargs_func">init_kwargs_func() (in module multiview_platform.mono_multi_view_classifiers.exec_classif)</a>, <a href="references/multiview_platform.mono_multi_view_classifiers.html#multiview_platform.mono_multi_view_classifiers.exec_classif.init_kwargs_func">[1]</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.execution.init_log_file">init_log_file() (in module multiview_platform.mono_multi_view_classifiers.utils.execution)</a>
-</li>
-      <li><a href="references/monomulti/exec_classif.html#multiview_platform.mono_multi_view_classifiers.exec_classif.init_monoview_exps">init_monoview_exps() (in module multiview_platform.mono_multi_view_classifiers.exec_classif)</a>, <a href="references/multiview_platform.mono_multi_view_classifiers.html#multiview_platform.mono_multi_view_classifiers.exec_classif.init_monoview_exps">[1]</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.dataset.init_multiple_datasets">init_multiple_datasets() (in module multiview_platform.mono_multi_view_classifiers.utils.dataset)</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.html#multiview_platform.mono_multi_view_classifiers.exec_classif.init_multiview_exps">init_multiview_exps() (in module multiview_platform.mono_multi_view_classifiers.exec_classif)</a>
-</li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.html#multiview_platform.mono_multi_view_classifiers.result_analysis.init_plot">init_plot() (in module multiview_platform.mono_multi_view_classifiers.result_analysis)</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.execution.init_random_state">init_random_state() (in module multiview_platform.mono_multi_view_classifiers.utils.execution)</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.execution.init_stats_iter_random_states">init_stats_iter_random_states() (in module multiview_platform.mono_multi_view_classifiers.utils.execution)</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset.init_view_names">init_view_names() (Dataset method)</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.execution.init_views">init_views() (in module multiview_platform.mono_multi_view_classifiers.utils.execution)</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.dataset.input_">input_() (in module multiview_platform.mono_multi_view_classifiers.utils.dataset)</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.multiclass.is_biclass">is_biclass() (in module multiview_platform.mono_multi_view_classifiers.utils.multiclass)</a>
-</li>
-      <li><a href="references/monomulti/exec_classif.html#multiview_platform.mono_multi_view_classifiers.exec_classif.is_dict_in">is_dict_in() (in module multiview_platform.mono_multi_view_classifiers.exec_classif)</a>, <a href="references/multiview_platform.mono_multi_view_classifiers.html#multiview_platform.mono_multi_view_classifiers.exec_classif.is_dict_in">[1]</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.dataset.is_just_number">is_just_number() (in module multiview_platform.mono_multi_view_classifiers.utils.dataset)</a>
-</li>
-      <li><a href="analyzeresult.html#multiview_platform.mono_multi_view_classifiers.result_analysis.iterCmap">iterCmap() (in module multiview_platform.mono_multi_view_classifiers.result_analysis)</a>, <a href="references/multiview_platform.mono_multi_view_classifiers.html#multiview_platform.mono_multi_view_classifiers.result_analysis.iterCmap">[1]</a>
-</li>
-  </ul></td>
-</tr></table>
-
-<h2 id="M">M</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.get_multiview_db.make_me_noisy">make_me_noisy() (in module multiview_platform.mono_multi_view_classifiers.utils.get_multiview_db)</a>
-</li>
-      <li><a href="references/monomulti/exec_classif.html#multiview_platform.mono_multi_view_classifiers.exec_classif.multiple_args">multiple_args() (in module multiview_platform.mono_multi_view_classifiers.exec_classif)</a>, <a href="references/multiview_platform.mono_multi_view_classifiers.html#multiview_platform.mono_multi_view_classifiers.exec_classif.multiple_args">[1]</a>
-</li>
-      <li><a href="references/multiview_platform.tests.html#multiview_platform.tests.test_ResultAnalysis.Test_get_metrics_scores_biclass.multiple_monoview_classifiers">multiple_monoview_classifiers() (Test_get_metrics_scores_biclass method)</a>
-</li>
-      <li><a href="references/multiview_platform.html#module-multiview_platform">multiview_platform (module)</a>
-</li>
-      <li><a href="execution.html#module-multiview_platform.execute">multiview_platform.execute (module)</a>, <a href="references/multiview_platform.html#module-multiview_platform.execute">[1]</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.html#module-multiview_platform.mono_multi_view_classifiers">multiview_platform.mono_multi_view_classifiers (module)</a>
-</li>
-      <li><a href="references/monomulti/exec_classif.html#module-multiview_platform.mono_multi_view_classifiers.exec_classif">multiview_platform.mono_multi_view_classifiers.exec_classif (module)</a>, <a href="references/multiview_platform.mono_multi_view_classifiers.html#module-multiview_platform.mono_multi_view_classifiers.exec_classif">[1]</a>
-</li>
-      <li><a href="references/monomulti/metrics.html#module-multiview_platform.mono_multi_view_classifiers.metrics.framework">multiview_platform.mono_multi_view_classifiers.metrics.framework (module)</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.multiview_classifiers.html#module-multiview_platform.mono_multi_view_classifiers.multiview_classifiers">multiview_platform.mono_multi_view_classifiers.multiview_classifiers (module)</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.multiview_classifiers.difficulty_fusion.html#module-multiview_platform.mono_multi_view_classifiers.multiview_classifiers.difficulty_fusion">multiview_platform.mono_multi_view_classifiers.multiview_classifiers.difficulty_fusion (module)</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.multiview_classifiers.disagree_fusion.html#module-multiview_platform.mono_multi_view_classifiers.multiview_classifiers.disagree_fusion">multiview_platform.mono_multi_view_classifiers.multiview_classifiers.disagree_fusion (module)</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.multiview_classifiers.double_fault_fusion.html#module-multiview_platform.mono_multi_view_classifiers.multiview_classifiers.double_fault_fusion">multiview_platform.mono_multi_view_classifiers.multiview_classifiers.double_fault_fusion (module)</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.multiview_classifiers.entropy_fusion.html#module-multiview_platform.mono_multi_view_classifiers.multiview_classifiers.entropy_fusion">multiview_platform.mono_multi_view_classifiers.multiview_classifiers.entropy_fusion (module)</a>
-</li>
-      <li><a href="analyzeresult.html#module-multiview_platform.mono_multi_view_classifiers.result_analysis">multiview_platform.mono_multi_view_classifiers.result_analysis (module)</a>, <a href="references/multiview_platform.mono_multi_view_classifiers.html#module-multiview_platform.mono_multi_view_classifiers.result_analysis">[1]</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#module-multiview_platform.mono_multi_view_classifiers.utils">multiview_platform.mono_multi_view_classifiers.utils (module)</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#module-multiview_platform.mono_multi_view_classifiers.utils.configuration">multiview_platform.mono_multi_view_classifiers.utils.configuration (module)</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#module-multiview_platform.mono_multi_view_classifiers.utils.dataset">multiview_platform.mono_multi_view_classifiers.utils.dataset (module)</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#module-multiview_platform.mono_multi_view_classifiers.utils.execution">multiview_platform.mono_multi_view_classifiers.utils.execution (module)</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#module-multiview_platform.mono_multi_view_classifiers.utils.get_multiview_db">multiview_platform.mono_multi_view_classifiers.utils.get_multiview_db (module)</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#module-multiview_platform.mono_multi_view_classifiers.utils.hyper_parameter_search">multiview_platform.mono_multi_view_classifiers.utils.hyper_parameter_search (module)</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#module-multiview_platform.mono_multi_view_classifiers.utils.make_file_config">multiview_platform.mono_multi_view_classifiers.utils.make_file_config (module)</a>
-</li>
-      <li><a href="references/monomulti/utils/multiclass.html#module-multiview_platform.mono_multi_view_classifiers.utils.multiclass">multiview_platform.mono_multi_view_classifiers.utils.multiclass (module)</a>, <a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#module-multiview_platform.mono_multi_view_classifiers.utils.multiclass">[1]</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#module-multiview_platform.mono_multi_view_classifiers.utils.multiview_result_analysis">multiview_platform.mono_multi_view_classifiers.utils.multiview_result_analysis (module)</a>
-</li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#module-multiview_platform.mono_multi_view_classifiers.utils.transformations">multiview_platform.mono_multi_view_classifiers.utils.transformations (module)</a>
-</li>
-      <li><a href="references/multiview_platform.html#module-multiview_platform.tests">multiview_platform.tests (module)</a>, <a href="references/multiview_platform.tests.html#module-multiview_platform.tests">[1]</a>
-</li>
-      <li><a href="references/multiview_platform.tests.html#module-multiview_platform.tests.test_ExecClassif">multiview_platform.tests.test_ExecClassif (module)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.test_metrics.html#module-multiview_platform.tests.test_metrics">multiview_platform.tests.test_metrics (module)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.test_metrics.html#module-multiview_platform.tests.test_metrics.test_accuracy_score">multiview_platform.tests.test_metrics.test_accuracy_score (module)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.test_mono_view.html#module-multiview_platform.tests.test_mono_view">multiview_platform.tests.test_mono_view (module)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.test_mono_view.html#module-multiview_platform.tests.test_mono_view.test_ExecClassifMonoView">multiview_platform.tests.test_mono_view.test_ExecClassifMonoView (module)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.test_mono_view.html#module-multiview_platform.tests.test_mono_view.test_MonoviewUtils">multiview_platform.tests.test_mono_view.test_MonoviewUtils (module)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.test_monoview_classifiers.html#module-multiview_platform.tests.test_monoview_classifiers">multiview_platform.tests.test_monoview_classifiers (module)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.test_monoview_classifiers.html#module-multiview_platform.tests.test_monoview_classifiers.test_adaboost">multiview_platform.tests.test_monoview_classifiers.test_adaboost (module)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.test_monoview_classifiers.html#module-multiview_platform.tests.test_monoview_classifiers.test_compatibility">multiview_platform.tests.test_monoview_classifiers.test_compatibility (module)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.test_multiview_classifiers.html#module-multiview_platform.tests.test_multiview_classifiers">multiview_platform.tests.test_multiview_classifiers (module)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.test_multiview_classifiers.html#module-multiview_platform.tests.test_multiview_classifiers.test_diversity_utils">multiview_platform.tests.test_multiview_classifiers.test_diversity_utils (module)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.test_multiview_classifiers.Test_PseudoCQMeasure.html#module-multiview_platform.tests.test_multiview_classifiers.Test_PseudoCQMeasure">multiview_platform.tests.test_multiview_classifiers.Test_PseudoCQMeasure (module)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.test_multiview_classifiers.Test_PseudoCQMeasure.html#module-multiview_platform.tests.test_multiview_classifiers.Test_PseudoCQMeasure.test_PseudoCQFusionModule">multiview_platform.tests.test_multiview_classifiers.Test_PseudoCQMeasure.test_PseudoCQFusionModule (module)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.html#module-multiview_platform.tests.test_ResultAnalysis">multiview_platform.tests.test_ResultAnalysis (module)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.test_utils.html#module-multiview_platform.tests.test_utils">multiview_platform.tests.test_utils (module)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.test_utils.html#module-multiview_platform.tests.test_utils.test_configuration">multiview_platform.tests.test_utils.test_configuration (module)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.test_utils.html#module-multiview_platform.tests.test_utils.test_execution">multiview_platform.tests.test_utils.test_execution (module)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.test_utils.html#module-multiview_platform.tests.test_utils.test_GetMultiviewDB">multiview_platform.tests.test_utils.test_GetMultiviewDB (module)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.test_utils.html#module-multiview_platform.tests.test_utils.test_multiclass">multiview_platform.tests.test_utils.test_multiclass (module)</a>
-</li>
-      <li><a href="references/multiview_platform.html#module-multiview_platform.versions">multiview_platform.versions (module)</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.hyper_parameter_search.MultiviewCompatibleRandomizedSearchCV">MultiviewCompatibleRandomizedSearchCV (class in multiview_platform.mono_multi_view_classifiers.utils.hyper_parameter_search)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.html#multiview_platform.tests.test_ResultAnalysis.Test_get_metrics_scores_biclass.mutiview_result">mutiview_result() (Test_get_metrics_scores_biclass method)</a>
-</li>
-  </ul></td>
-</tr></table>
-
-<h2 id="N">N</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset.nb_view">nb_view (Dataset attribute)</a>
-</li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.html#multiview_platform.mono_multi_view_classifiers.result_analysis.numpy_mean_and_std">numpy_mean_and_std() (in module multiview_platform.mono_multi_view_classifiers.result_analysis)</a>
-</li>
-  </ul></td>
-</tr></table>
-
-<h2 id="P">P</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.execution.parse_the_args">parse_the_args() (in module multiview_platform.mono_multi_view_classifiers.utils.execution)</a>
-</li>
-      <li><a href="analyzeresult.html#multiview_platform.mono_multi_view_classifiers.result_analysis.plot_2d">plot_2d() (in module multiview_platform.mono_multi_view_classifiers.result_analysis)</a>, <a href="references/multiview_platform.mono_multi_view_classifiers.html#multiview_platform.mono_multi_view_classifiers.result_analysis.plot_2d">[1]</a>
-</li>
-      <li><a href="analyzeresult.html#multiview_platform.mono_multi_view_classifiers.result_analysis.plot_errors_bar">plot_errors_bar() (in module multiview_platform.mono_multi_view_classifiers.result_analysis)</a>, <a href="references/multiview_platform.mono_multi_view_classifiers.html#multiview_platform.mono_multi_view_classifiers.result_analysis.plot_errors_bar">[1]</a>
-</li>
-      <li><a href="analyzeresult.html#multiview_platform.mono_multi_view_classifiers.result_analysis.plot_metric_scores">plot_metric_scores() (in module multiview_platform.mono_multi_view_classifiers.result_analysis)</a>, <a href="references/multiview_platform.mono_multi_view_classifiers.html#multiview_platform.mono_multi_view_classifiers.result_analysis.plot_metric_scores">[1]</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.html#multiview_platform.mono_multi_view_classifiers.result_analysis.plot_results_noise">plot_results_noise() (in module multiview_platform.mono_multi_view_classifiers.result_analysis)</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.multiview_result_analysis.print_metric_score">print_metric_score() (in module multiview_platform.mono_multi_view_classifiers.utils.multiview_result_analysis)</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.html#multiview_platform.mono_multi_view_classifiers.result_analysis.publish_feature_importances">publish_feature_importances() (in module multiview_platform.mono_multi_view_classifiers.result_analysis)</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.html#multiview_platform.mono_multi_view_classifiers.result_analysis.publish_iter_biclass_example_errors">publish_iter_biclass_example_errors() (in module multiview_platform.mono_multi_view_classifiers.result_analysis)</a>
-</li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.html#multiview_platform.mono_multi_view_classifiers.result_analysis.publish_iter_biclass_metrics_scores">publish_iter_biclass_metrics_scores() (in module multiview_platform.mono_multi_view_classifiers.result_analysis)</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.html#multiview_platform.mono_multi_view_classifiers.result_analysis.publish_iter_multiclass_example_errors">publish_iter_multiclass_example_errors() (in module multiview_platform.mono_multi_view_classifiers.result_analysis)</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.html#multiview_platform.mono_multi_view_classifiers.result_analysis.publish_iter_multiclass_metrics_scores">publish_iter_multiclass_metrics_scores() (in module multiview_platform.mono_multi_view_classifiers.result_analysis)</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.html#multiview_platform.mono_multi_view_classifiers.result_analysis.publish_tracebacks">publish_tracebacks() (in module multiview_platform.mono_multi_view_classifiers.result_analysis)</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.html#multiview_platform.mono_multi_view_classifiers.result_analysis.publishExampleErrors">publishExampleErrors() (in module multiview_platform.mono_multi_view_classifiers.result_analysis)</a>
-</li>
-      <li><a href="analyzeresult.html#multiview_platform.mono_multi_view_classifiers.result_analysis.publishMetricsGraphs">publishMetricsGraphs() (in module multiview_platform.mono_multi_view_classifiers.result_analysis)</a>, <a href="references/multiview_platform.mono_multi_view_classifiers.html#multiview_platform.mono_multi_view_classifiers.result_analysis.publishMetricsGraphs">[1]</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.html#multiview_platform.mono_multi_view_classifiers.result_analysis.publishMulticlassExmapleErrors">publishMulticlassExmapleErrors() (in module multiview_platform.mono_multi_view_classifiers.result_analysis)</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.html#multiview_platform.mono_multi_view_classifiers.result_analysis.publishMulticlassScores">publishMulticlassScores() (in module multiview_platform.mono_multi_view_classifiers.result_analysis)</a>
-</li>
-  </ul></td>
-</tr></table>
-
-<h2 id="R">R</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.hyper_parameter_search.randomized_search">randomized_search() (in module multiview_platform.mono_multi_view_classifiers.utils.hyper_parameter_search)</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset.rm">rm() (Dataset method)</a>
-</li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.hyper_parameter_search.CustomRandint.rvs">rvs() (CustomRandint method)</a>
-
-      <ul>
-        <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.hyper_parameter_search.CustomUniform.rvs">(CustomUniform method)</a>
-</li>
-      </ul></li>
-  </ul></td>
-</tr></table>
-
-<h2 id="S">S</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.configuration.save_config">save_config() (in module multiview_platform.mono_multi_view_classifiers.utils.configuration)</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.html#multiview_platform.mono_multi_view_classifiers.result_analysis.save_dict_to_text">save_dict_to_text() (in module multiview_platform.mono_multi_view_classifiers.result_analysis)</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.html#multiview_platform.mono_multi_view_classifiers.result_analysis.save_failed">save_failed() (in module multiview_platform.mono_multi_view_classifiers.result_analysis)</a>
-</li>
-      <li><a href="references/monomulti/metrics.html#multiview_platform.mono_multi_view_classifiers.metrics.framework.score">score() (in module multiview_platform.mono_multi_view_classifiers.metrics.framework)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.test_metrics.html#multiview_platform.tests.test_metrics.test_accuracy_score.Test_accuracy_score.score_test">score_test() (Test_accuracy_score method)</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.hyper_parameter_search.search_best_settings">search_best_settings() (in module multiview_platform.mono_multi_view_classifiers.utils.hyper_parameter_search)</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset.select_labels">select_labels() (Dataset method)</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset.select_views_and_labels">select_views_and_labels() (Dataset method)</a>
-</li>
-      <li><a href="references/monomulti/exec_classif.html#multiview_platform.mono_multi_view_classifiers.exec_classif.set_element">set_element() (in module multiview_platform.mono_multi_view_classifiers.exec_classif)</a>, <a href="references/multiview_platform.mono_multi_view_classifiers.html#multiview_platform.mono_multi_view_classifiers.exec_classif.set_element">[1]</a>
-</li>
-      <li><a href="references/multiview_platform.tests.test_utils.html#multiview_platform.tests.test_utils.test_execution.Test_genKFolds.setUp">setUp() (Test_genKFolds method)</a>
-
-      <ul>
-        <li><a href="references/multiview_platform.tests.test_utils.html#multiview_platform.tests.test_utils.test_execution.Test_genSplits.setUp">(Test_genSplits method)</a>
-</li>
-        <li><a href="references/multiview_platform.tests.html#multiview_platform.tests.test_ResultAnalysis.Test_get_arguments.setUp">(Test_get_arguments method)</a>
-</li>
-        <li><a href="references/multiview_platform.tests.test_utils.html#multiview_platform.tests.test_utils.test_GetMultiviewDB.Test_get_classic_db_csv.setUp">(Test_get_classic_db_csv method)</a>
-</li>
-        <li><a href="references/multiview_platform.tests.test_utils.html#multiview_platform.tests.test_utils.test_GetMultiviewDB.Test_get_classic_db_hdf5.setUp">(Test_get_classic_db_hdf5 method)</a>
-</li>
-        <li><a href="references/multiview_platform.tests.test_utils.html#multiview_platform.tests.test_utils.test_execution.Test_initRandomState.setUp">(Test_initRandomState method)</a>
-</li>
-        <li><a href="references/multiview_platform.tests.test_utils.html#multiview_platform.tests.test_utils.test_execution.Test_parseTheArgs.setUp">(Test_parseTheArgs method)</a>
-</li>
-      </ul></li>
-      <li><a href="references/multiview_platform.tests.html#multiview_platform.tests.test_ExecClassif.Test_execBenchmark.setUpClass">setUpClass() (multiview_platform.tests.test_ExecClassif.Test_execBenchmark class method)</a>
-
-      <ul>
-        <li><a href="references/multiview_platform.tests.test_metrics.html#multiview_platform.tests.test_metrics.test_accuracy_score.Test_accuracy_score.setUpClass">(Test_accuracy_score method)</a>
-</li>
-        <li><a href="references/multiview_platform.tests.html#multiview_platform.tests.test_ExecClassif.Test_InitArgumentDictionaries.setUpClass">(multiview_platform.tests.test_ExecClassif.Test_InitArgumentDictionaries class method)</a>
-</li>
-        <li><a href="references/multiview_platform.tests.html#multiview_platform.tests.test_ExecClassif.Test_get_path_dict.setUpClass">(multiview_platform.tests.test_ExecClassif.Test_get_path_dict class method)</a>
-</li>
-        <li><a href="references/multiview_platform.tests.html#multiview_platform.tests.test_ExecClassif.Test_set_element.setUpClass">(multiview_platform.tests.test_ExecClassif.Test_set_element class method)</a>
-</li>
-        <li><a href="references/multiview_platform.tests.test_mono_view.html#multiview_platform.tests.test_mono_view.test_ExecClassifMonoView.Test_getHPs.setUpClass">(multiview_platform.tests.test_mono_view.test_ExecClassifMonoView.Test_getHPs class method)</a>
-</li>
-        <li><a href="references/multiview_platform.tests.test_mono_view.html#multiview_platform.tests.test_mono_view.test_ExecClassifMonoView.Test_initConstants.setUpClass">(multiview_platform.tests.test_mono_view.test_ExecClassifMonoView.Test_initConstants class method)</a>
-</li>
-        <li><a href="references/multiview_platform.tests.test_mono_view.html#multiview_platform.tests.test_mono_view.test_ExecClassifMonoView.Test_initTrainTest.setUpClass">(multiview_platform.tests.test_mono_view.test_ExecClassifMonoView.Test_initTrainTest class method)</a>
-</li>
-        <li><a href="references/multiview_platform.tests.test_mono_view.html#multiview_platform.tests.test_mono_view.test_MonoviewUtils.Test_genTestFoldsPreds.setUpClass">(multiview_platform.tests.test_mono_view.test_MonoviewUtils.Test_genTestFoldsPreds class method)</a>
-</li>
-        <li><a href="references/multiview_platform.tests.test_utils.html#multiview_platform.tests.test_utils.test_GetMultiviewDB.Test_get_plausible_db_hdf5.setUpClass">(multiview_platform.tests.test_utils.test_GetMultiviewDB.Test_get_plausible_db_hdf5 class method)</a>
-</li>
-        <li><a href="references/multiview_platform.tests.test_utils.html#multiview_platform.tests.test_utils.test_configuration.Test_get_the_args.setUpClass">(multiview_platform.tests.test_utils.test_configuration.Test_get_the_args class method)</a>
-</li>
-        <li><a href="references/multiview_platform.tests.test_utils.html#multiview_platform.tests.test_utils.test_execution.Test_genArgumentDictionaries.setUpClass">(multiview_platform.tests.test_utils.test_execution.Test_genArgumentDictionaries class method)</a>
-</li>
-        <li><a href="references/multiview_platform.tests.test_utils.html#multiview_platform.tests.test_utils.test_execution.Test_genDirecortiesNames.setUpClass">(multiview_platform.tests.test_utils.test_execution.Test_genDirecortiesNames class method)</a>
-</li>
-        <li><a href="references/multiview_platform.tests.test_utils.html#multiview_platform.tests.test_utils.test_execution.Test_getDatabaseFunction.setUpClass">(multiview_platform.tests.test_utils.test_execution.Test_getDatabaseFunction class method)</a>
-</li>
-        <li><a href="references/multiview_platform.tests.test_utils.html#multiview_platform.tests.test_utils.test_execution.Test_initStatsIterRandomStates.setUpClass">(multiview_platform.tests.test_utils.test_execution.Test_initStatsIterRandomStates class method)</a>
-</li>
-        <li><a href="references/multiview_platform.tests.test_utils.html#multiview_platform.tests.test_utils.test_multiclass.Test_genMulticlassLabels.setUpClass">(multiview_platform.tests.test_utils.test_multiclass.Test_genMulticlassLabels class method)</a>
-</li>
-      </ul></li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.transformations.sign_labels">sign_labels() (in module multiview_platform.mono_multi_view_classifiers.utils.transformations)</a>
-</li>
-      <li><a href="analyzeresult.html#multiview_platform.mono_multi_view_classifiers.result_analysis.sort_by_test_score">sort_by_test_score() (in module multiview_platform.mono_multi_view_classifiers.result_analysis)</a>, <a href="references/multiview_platform.mono_multi_view_classifiers.html#multiview_platform.mono_multi_view_classifiers.result_analysis.sort_by_test_score">[1]</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.hyper_parameter_search.spear_mint">spear_mint() (in module multiview_platform.mono_multi_view_classifiers.utils.hyper_parameter_search)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.html#multiview_platform.tests.test_ExecClassif.FakeKfold.split">split() (FakeKfold method)</a>
-</li>
-  </ul></td>
-</tr></table>
-
-<h2 id="T">T</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="references/multiview_platform.tests.test_utils.html#multiview_platform.tests.test_utils.test_GetMultiviewDB.Test_get_classic_db_csv.tearDown">tearDown() (multiview_platform.tests.test_utils.test_GetMultiviewDB.Test_get_classic_db_csv class method)</a>
-
-      <ul>
-        <li><a href="references/multiview_platform.tests.test_utils.html#multiview_platform.tests.test_utils.test_GetMultiviewDB.Test_get_classic_db_hdf5.tearDown">(Test_get_classic_db_hdf5 method)</a>
-</li>
-        <li><a href="references/multiview_platform.tests.test_utils.html#multiview_platform.tests.test_utils.test_execution.Test_initRandomState.tearDown">(Test_initRandomState method)</a>
-</li>
-      </ul></li>
-      <li><a href="references/multiview_platform.tests.html#multiview_platform.tests.test_ExecClassif.Test_execBenchmark.tearDownClass">tearDownClass() (multiview_platform.tests.test_ExecClassif.Test_execBenchmark class method)</a>
-
-      <ul>
-        <li><a href="references/multiview_platform.tests.html#multiview_platform.tests.test_ExecClassif.Test_get_path_dict.tearDownClass">(multiview_platform.tests.test_ExecClassif.Test_get_path_dict class method)</a>
-</li>
-        <li><a href="references/multiview_platform.tests.html#multiview_platform.tests.test_ExecClassif.Test_set_element.tearDownClass">(multiview_platform.tests.test_ExecClassif.Test_set_element class method)</a>
-</li>
-        <li><a href="references/multiview_platform.tests.test_mono_view.html#multiview_platform.tests.test_mono_view.test_ExecClassifMonoView.Test_getHPs.tearDownClass">(multiview_platform.tests.test_mono_view.test_ExecClassifMonoView.Test_getHPs class method)</a>
-</li>
-        <li><a href="references/multiview_platform.tests.test_mono_view.html#multiview_platform.tests.test_mono_view.test_ExecClassifMonoView.Test_initConstants.tearDownClass">(multiview_platform.tests.test_mono_view.test_ExecClassifMonoView.Test_initConstants class method)</a>
-</li>
-        <li><a href="references/multiview_platform.tests.test_utils.html#multiview_platform.tests.test_utils.test_GetMultiviewDB.Test_get_plausible_db_hdf5.tearDownClass">(multiview_platform.tests.test_utils.test_GetMultiviewDB.Test_get_plausible_db_hdf5 class method)</a>
-</li>
-        <li><a href="references/multiview_platform.tests.test_utils.html#multiview_platform.tests.test_utils.test_configuration.Test_get_the_args.tearDownClass">(multiview_platform.tests.test_utils.test_configuration.Test_get_the_args class method)</a>
-</li>
-      </ul></li>
-      <li><a href="references/multiview_platform.tests.test_metrics.html#multiview_platform.tests.test_metrics.test_accuracy_score.Test_accuracy_score">Test_accuracy_score (class in multiview_platform.tests.test_metrics.test_accuracy_score)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.test_utils.html#multiview_platform.tests.test_utils.test_GetMultiviewDB.Test_get_classic_db_hdf5.test_all_views_asked">test_all_views_asked() (Test_get_classic_db_hdf5 method)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.test_utils.html#multiview_platform.tests.test_utils.test_configuration.Test_get_the_args.test_arguments">test_arguments() (Test_get_the_args method)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.test_utils.html#multiview_platform.tests.test_utils.test_GetMultiviewDB.Test_get_classic_db_hdf5.test_asked_the_whole_dataset">test_asked_the_whole_dataset() (Test_get_classic_db_hdf5 method)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.html#multiview_platform.tests.test_ResultAnalysis.Test_get_arguments.test_benchmark_wanted">test_benchmark_wanted() (Test_get_arguments method)</a>
-
-      <ul>
-        <li><a href="references/multiview_platform.tests.html#multiview_platform.tests.test_ExecClassif.Test_initBenchmark.test_benchmark_wanted">(Test_initBenchmark method)</a>
-</li>
-      </ul></li>
-      <li><a href="references/multiview_platform.tests.test_utils.html#multiview_platform.tests.test_utils.test_configuration.Test_get_the_args.test_dict_format">test_dict_format() (Test_get_the_args method)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.test_utils.html#multiview_platform.tests.test_utils.test_execution.Test_parseTheArgs.test_empty_args">test_empty_args() (Test_parseTheArgs method)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.html#multiview_platform.tests.test_ExecClassif.Test_execBenchmark">Test_execBenchmark (class in multiview_platform.tests.test_ExecClassif)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.test_utils.html#multiview_platform.tests.test_utils.test_configuration.Test_get_the_args.test_file_loading">test_file_loading() (Test_get_the_args method)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.html#multiview_platform.tests.test_ResultAnalysis.Test_format_previous_results">Test_format_previous_results (class in multiview_platform.tests.test_ResultAnalysis)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.html#multiview_platform.tests.test_ResultAnalysis.Test_gen_error_data">Test_gen_error_data (class in multiview_platform.tests.test_ResultAnalysis)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.html#multiview_platform.tests.test_ResultAnalysis.Test_gen_error_data_glob">Test_gen_error_data_glob (class in multiview_platform.tests.test_ResultAnalysis)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.test_utils.html#multiview_platform.tests.test_utils.test_execution.Test_genArgumentDictionaries">Test_genArgumentDictionaries (class in multiview_platform.tests.test_utils.test_execution)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.test_utils.html#multiview_platform.tests.test_utils.test_execution.Test_genDirecortiesNames">Test_genDirecortiesNames (class in multiview_platform.tests.test_utils.test_execution)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.test_utils.html#multiview_platform.tests.test_utils.test_execution.Test_genKFolds">Test_genKFolds (class in multiview_platform.tests.test_utils.test_execution)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.test_utils.html#multiview_platform.tests.test_utils.test_execution.Test_genKFolds.test_genKFolds_iter">test_genKFolds_iter() (Test_genKFolds method)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.test_utils.html#multiview_platform.tests.test_utils.test_multiclass.Test_genMulticlassLabels">Test_genMulticlassLabels (class in multiview_platform.tests.test_utils.test_multiclass)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.test_utils.html#multiview_platform.tests.test_utils.test_execution.Test_genSplits">Test_genSplits (class in multiview_platform.tests.test_utils.test_execution)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.test_utils.html#multiview_platform.tests.test_utils.test_execution.Test_genSplits.test_genSplits_no_iter">test_genSplits_no_iter() (Test_genSplits method)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.test_mono_view.html#multiview_platform.tests.test_mono_view.test_MonoviewUtils.Test_genTestFoldsPreds">Test_genTestFoldsPreds (class in multiview_platform.tests.test_mono_view.test_MonoviewUtils)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.html#multiview_platform.tests.test_ResultAnalysis.Test_get_arguments">Test_get_arguments (class in multiview_platform.tests.test_ResultAnalysis)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.test_utils.html#multiview_platform.tests.test_utils.test_GetMultiviewDB.Test_get_classic_db_csv">Test_get_classic_db_csv (class in multiview_platform.tests.test_utils.test_GetMultiviewDB)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.test_utils.html#multiview_platform.tests.test_utils.test_GetMultiviewDB.Test_get_classic_db_hdf5">Test_get_classic_db_hdf5 (class in multiview_platform.tests.test_utils.test_GetMultiviewDB)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.html#multiview_platform.tests.test_ResultAnalysis.Test_get_example_errors_biclass">Test_get_example_errors_biclass (class in multiview_platform.tests.test_ResultAnalysis)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.html#multiview_platform.tests.test_ResultAnalysis.Test_get_metrics_scores_biclass">Test_get_metrics_scores_biclass (class in multiview_platform.tests.test_ResultAnalysis)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.html#multiview_platform.tests.test_ExecClassif.Test_get_path_dict">Test_get_path_dict (class in multiview_platform.tests.test_ExecClassif)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.test_utils.html#multiview_platform.tests.test_utils.test_GetMultiviewDB.Test_get_plausible_db_hdf5">Test_get_plausible_db_hdf5 (class in multiview_platform.tests.test_utils.test_GetMultiviewDB)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.test_utils.html#multiview_platform.tests.test_utils.test_configuration.Test_get_the_args">Test_get_the_args (class in multiview_platform.tests.test_utils.test_configuration)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.test_utils.html#multiview_platform.tests.test_utils.test_execution.Test_getDatabaseFunction">Test_getDatabaseFunction (class in multiview_platform.tests.test_utils.test_execution)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.test_mono_view.html#multiview_platform.tests.test_mono_view.test_ExecClassifMonoView.Test_getHPs">Test_getHPs (class in multiview_platform.tests.test_mono_view.test_ExecClassifMonoView)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.test_utils.html#multiview_platform.tests.test_utils.test_execution.Test_getDatabaseFunction.test_hdf5">test_hdf5() (Test_getDatabaseFunction method)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.html#multiview_platform.tests.test_ExecClassif.Test_InitArgumentDictionaries.test_init_argument_dictionaries_monoview">test_init_argument_dictionaries_monoview() (Test_InitArgumentDictionaries method)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.html#multiview_platform.tests.test_ExecClassif.Test_InitArgumentDictionaries.test_init_argument_dictionaries_multiview">test_init_argument_dictionaries_multiview() (Test_InitArgumentDictionaries method)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.html#multiview_platform.tests.test_ExecClassif.Test_InitArgumentDictionaries.test_init_argument_dictionaries_multiview_complex">test_init_argument_dictionaries_multiview_complex() (Test_InitArgumentDictionaries method)</a>
-</li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="references/multiview_platform.tests.html#multiview_platform.tests.test_ExecClassif.Test_InitArgumentDictionaries.test_init_argument_dictionaries_multiview_multiple">test_init_argument_dictionaries_multiview_multiple() (Test_InitArgumentDictionaries method)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.html#multiview_platform.tests.test_ExecClassif.Test_InitArgumentDictionaries.test_init_argument_dictionaries_multiview_multiple_complex">test_init_argument_dictionaries_multiview_multiple_complex() (Test_InitArgumentDictionaries method)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.html#multiview_platform.tests.test_ResultAnalysis.Test_init_plot">Test_init_plot (class in multiview_platform.tests.test_ResultAnalysis)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.html#multiview_platform.tests.test_ExecClassif.Test_InitArgumentDictionaries">Test_InitArgumentDictionaries (class in multiview_platform.tests.test_ExecClassif)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.html#multiview_platform.tests.test_ExecClassif.Test_initBenchmark">Test_initBenchmark (class in multiview_platform.tests.test_ExecClassif)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.test_mono_view.html#multiview_platform.tests.test_mono_view.test_ExecClassifMonoView.Test_initConstants">Test_initConstants (class in multiview_platform.tests.test_mono_view.test_ExecClassifMonoView)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.html#multiview_platform.tests.test_ExecClassif.Test_initKWARGS">Test_initKWARGS (class in multiview_platform.tests.test_ExecClassif)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.html#multiview_platform.tests.test_ExecClassif.Test_initKWARGS.test_initKWARGSFunc_no_monoview">test_initKWARGSFunc_no_monoview() (Test_initKWARGS method)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.test_utils.html#multiview_platform.tests.test_utils.test_execution.Test_initRandomState">Test_initRandomState (class in multiview_platform.tests.test_utils.test_execution)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.test_utils.html#multiview_platform.tests.test_utils.test_execution.Test_initStatsIterRandomStates">Test_initStatsIterRandomStates (class in multiview_platform.tests.test_utils.test_execution)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.test_mono_view.html#multiview_platform.tests.test_mono_view.test_ExecClassifMonoView.Test_initTrainTest">Test_initTrainTest (class in multiview_platform.tests.test_mono_view.test_ExecClassifMonoView)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.html#multiview_platform.tests.test_ExecClassif.Test_execBenchmark.test_multiclass_and_iter">test_multiclass_and_iter() (Test_execBenchmark method)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.html#multiview_platform.tests.test_ExecClassif.Test_execBenchmark.test_multiclass_no_iter">test_multiclass_no_iter() (Test_execBenchmark method)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.test_utils.html#multiview_platform.tests.test_utils.test_execution.Test_initStatsIterRandomStates.test_multiple_iter">test_multiple_iter() (Test_initStatsIterRandomStates method)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.html#multiview_platform.tests.test_ExecClassif.Test_execBenchmark.test_no_iter_biclass_multicore">test_no_iter_biclass_multicore() (Test_execBenchmark method)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.test_utils.html#multiview_platform.tests.test_utils.test_execution.Test_initStatsIterRandomStates.test_one_statiter">test_one_statiter() (Test_initStatsIterRandomStates method)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.test_utils.html#multiview_platform.tests.test_utils.test_multiclass.Test_genMulticlassLabels.test_one_versus_one">test_one_versus_one() (Test_genMulticlassLabels method)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.test_utils.html#multiview_platform.tests.test_utils.test_execution.Test_genDirecortiesNames.test_ovo_no_iter">test_ovo_no_iter() (Test_genDirecortiesNames method)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.test_utils.html#multiview_platform.tests.test_utils.test_execution.Test_parseTheArgs">Test_parseTheArgs (class in multiview_platform.tests.test_utils.test_execution)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.test_utils.html#multiview_platform.tests.test_utils.test_execution.Test_getDatabaseFunction.test_plausible_hdf5">test_plausible_hdf5() (Test_getDatabaseFunction method)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.test_utils.html#multiview_platform.tests.test_utils.test_execution.Test_initRandomState.test_random_state_42">test_random_state_42() (Test_initRandomState method)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.test_utils.html#multiview_platform.tests.test_utils.test_execution.Test_initRandomState.test_random_state_pickle">test_random_state_pickle() (Test_initRandomState method)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.html#multiview_platform.tests.test_ExecClassif.Test_set_element">Test_set_element (class in multiview_platform.tests.test_ExecClassif)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.html#multiview_platform.tests.test_ExecClassif.Test_execBenchmark.test_simple">test_simple() (Test_execBenchmark method)</a>
-
-      <ul>
-        <li><a href="references/multiview_platform.tests.html#multiview_platform.tests.test_ResultAnalysis.Test_format_previous_results.test_simple">(Test_format_previous_results method)</a>
-</li>
-        <li><a href="references/multiview_platform.tests.test_utils.html#multiview_platform.tests.test_utils.test_execution.Test_genSplits.test_simple">(Test_genSplits method)</a>
-</li>
-        <li><a href="references/multiview_platform.tests.test_mono_view.html#multiview_platform.tests.test_mono_view.test_MonoviewUtils.Test_genTestFoldsPreds.test_simple">(Test_genTestFoldsPreds method)</a>
-</li>
-        <li><a href="references/multiview_platform.tests.html#multiview_platform.tests.test_ResultAnalysis.Test_gen_error_data.test_simple">(Test_gen_error_data method)</a>
-</li>
-        <li><a href="references/multiview_platform.tests.html#multiview_platform.tests.test_ResultAnalysis.Test_gen_error_data_glob.test_simple">(Test_gen_error_data_glob method)</a>
-</li>
-        <li><a href="references/multiview_platform.tests.test_utils.html#multiview_platform.tests.test_utils.test_execution.Test_getDatabaseFunction.test_simple">(Test_getDatabaseFunction method)</a>
-</li>
-        <li><a href="references/multiview_platform.tests.test_mono_view.html#multiview_platform.tests.test_mono_view.test_ExecClassifMonoView.Test_getHPs.test_simple">(Test_getHPs method)</a>
-</li>
-        <li><a href="references/multiview_platform.tests.test_utils.html#multiview_platform.tests.test_utils.test_GetMultiviewDB.Test_get_classic_db_csv.test_simple">(Test_get_classic_db_csv method)</a>
-</li>
-        <li><a href="references/multiview_platform.tests.test_utils.html#multiview_platform.tests.test_utils.test_GetMultiviewDB.Test_get_classic_db_hdf5.test_simple">(Test_get_classic_db_hdf5 method)</a>
-</li>
-        <li><a href="references/multiview_platform.tests.html#multiview_platform.tests.test_ResultAnalysis.Test_get_example_errors_biclass.test_simple">(Test_get_example_errors_biclass method)</a>
-</li>
-        <li><a href="references/multiview_platform.tests.html#multiview_platform.tests.test_ResultAnalysis.Test_get_metrics_scores_biclass.test_simple">(Test_get_metrics_scores_biclass method)</a>
-</li>
-        <li><a href="references/multiview_platform.tests.html#multiview_platform.tests.test_ExecClassif.Test_get_path_dict.test_simple">(Test_get_path_dict method)</a>
-</li>
-        <li><a href="references/multiview_platform.tests.test_utils.html#multiview_platform.tests.test_utils.test_GetMultiviewDB.Test_get_plausible_db_hdf5.test_simple">(Test_get_plausible_db_hdf5 method)</a>
-</li>
-        <li><a href="references/multiview_platform.tests.test_mono_view.html#multiview_platform.tests.test_mono_view.test_ExecClassifMonoView.Test_initConstants.test_simple">(Test_initConstants method)</a>
-</li>
-        <li><a href="references/multiview_platform.tests.test_mono_view.html#multiview_platform.tests.test_mono_view.test_ExecClassifMonoView.Test_initTrainTest.test_simple">(Test_initTrainTest method)</a>
-</li>
-        <li><a href="references/multiview_platform.tests.html#multiview_platform.tests.test_ResultAnalysis.Test_init_plot.test_simple">(Test_init_plot method)</a>
-</li>
-        <li><a href="references/multiview_platform.tests.html#multiview_platform.tests.test_ExecClassif.Test_set_element.test_simple">(Test_set_element method)</a>
-</li>
-      </ul></li>
-      <li><a href="references/multiview_platform.tests.test_utils.html#multiview_platform.tests.test_utils.test_execution.Test_genDirecortiesNames.test_simple_ovo">test_simple_ovo() (Test_genDirecortiesNames method)</a>
-</li>
-      <li><a href="references/multiview_platform.tests.test_utils.html#multiview_platform.tests.test_utils.test_GetMultiviewDB.Test_get_plausible_db_hdf5.test_two_class">test_two_class() (Test_get_plausible_db_hdf5 method)</a>
-</li>
-      <li><a href="references/multiview_platform.html#multiview_platform.versions.test_versions">test_versions() (in module multiview_platform.versions)</a>
-</li>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset.to_numpy_array">to_numpy_array() (Dataset method)</a>
-</li>
-  </ul></td>
-</tr></table>
-
-<h2 id="U">U</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.transformations.unsign_labels">unsign_labels() (in module multiview_platform.mono_multi_view_classifiers.utils.transformations)</a>
-</li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset.update_hdf5_dataset">update_hdf5_dataset() (Dataset method)</a>
-</li>
-  </ul></td>
-</tr></table>
-
-<h2 id="V">V</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="references/multiview_platform.mono_multi_view_classifiers.utils.html#multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset.view_dict">view_dict (Dataset attribute)</a>
-</li>
-  </ul></td>
-</tr></table>
-
 
 
           </div>
@@ -861,9 +70,6 @@
         <li class="right" style="margin-right: 10px">
           <a href="#" title="General Index"
              >index</a></li>
-        <li class="right" >
-          <a href="py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
         <li class="nav-item nav-item-0"><a href="index.html">MultiviewPlatform 0 documentation</a> &#187;</li> 
       </ul>
     </div>
diff --git a/docs/build/index.html b/docs/build/index.html
index 01f09ebad87717b64e746989ef3e1c37cbcc90b6..775d0767c3fdb16d6ab7608f168a40c1fb5535b2 100644
--- a/docs/build/index.html
+++ b/docs/build/index.html
@@ -6,7 +6,7 @@
   <head>
     <meta http-equiv="X-UA-Compatible" content="IE=Edge" />
     <meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
-    <title>Welcome to MultiviewPlatform’s documentation! &#8212; MultiviewPlatform 0 documentation</title>
+    <title>Welcome to SuMMIT’s documentation! &#8212; MultiviewPlatform 0 documentation</title>
     <link rel="stylesheet" href="_static/classic.css" type="text/css" />
     <link rel="stylesheet" href="_static/pygments.css" type="text/css" />
     
@@ -26,9 +26,6 @@
         <li class="right" style="margin-right: 10px">
           <a href="genindex.html" title="General Index"
              accesskey="I">index</a></li>
-        <li class="right" >
-          <a href="py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
         <li class="right" >
           <a href="readme_link.html" title="Readme"
              accesskey="N">next</a> |</li>
@@ -45,81 +42,17 @@
 sphinx-quickstart on Mon Jan 29 17:13:09 2018.
 You can adapt this file completely to your liking, but it should at least
 contain the root <cite>toctree</cite> directive.</p>
-<div class="section" id="welcome-to-multiviewplatform-s-documentation">
-<h1>Welcome to MultiviewPlatform’s documentation!<a class="headerlink" href="#welcome-to-multiviewplatform-s-documentation" title="Permalink to this headline">¶</a></h1>
-<p>This package is used as an easy-to-use platform to estimate different mono- and multi-view classifiers’ performance on a multiview dataset.</p>
+<div class="section" id="welcome-to-summit-s-documentation">
+<h1>Welcome to SuMMIT’s documentation!<a class="headerlink" href="#welcome-to-summit-s-documentation" title="Permalink to this headline">¶</a></h1>
+<p>This package ha been designed as an easy-to-use platform to estimate different mono- and multi-view classifiers’ performances on a multiview dataset.</p>
 <p>The main advantage of the platform is that it allows to add and remove a classifier without modifying its core code (the procedure is described thoroughly in this documentation).</p>
 <div class="toctree-wrapper compound">
 <p class="caption"><span class="caption-text">Contents:</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="readme_link.html">Readme</a></li>
-<li class="toctree-l1"><a class="reference internal" href="readme_link.html#mono-and-multi-view-classification-benchmark">Mono- and Multi-view classification benchmark</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="readme_link.html#getting-started">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="readme_link.html#prerequisites">Prerequisites</a></li>
-<li class="toctree-l3"><a class="reference internal" href="readme_link.html#installing">Installing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="readme_link.html#running-on-simulated-data">Running on simulated data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="readme_link.html#discovering-the-arguments">Discovering the arguments</a></li>
-<li class="toctree-l3"><a class="reference internal" href="readme_link.html#dataset-compatibility">Dataset compatibility</a></li>
-<li class="toctree-l3"><a class="reference internal" href="readme_link.html#running-on-your-dataset">Running on your dataset</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="readme_link.html#running-the-tests">Running the tests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="readme_link.html#author">Author</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="readme_link.html#contributors">Contributors</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="tutorials/index.html">Multiview Platform Tutorials</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="tutorials/installation.html">Install Multiview Platform</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/installation.html#launching-the-setup-tool">Launching the setup tool</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="tutorials/example1.html">Example 1 : First steps with Multiview Platform</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/example1.html#context">Context</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/example1.html#introduction-to-this-tutorial">Introduction to this tutorial</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/example1.html#getting-started">Getting started</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="tutorials/example2.html">Example 2 : Understanding the hyper-parameter optimization</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/example2.html#intuitive-explanation-on-hyper-parameters">Intuitive explanation on hyper-parameters</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/example2.html#understanding-train-test-split">Understanding train/test split</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/example2.html#understanding-hyper-parameter-optimization">Understanding hyper-parameter optimization</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/example2.html#hands-on-experience">Hands-on experience</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="tutorials/example3.html">Example 3 : Understanding the statistical iterations</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/example3.html#context">Context</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/example3.html#how-to-use-it">How to use it</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="tutorials/example4.html">Taking control : Use your own dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/example4.html#the-bare-necessities">The bare necessities</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/example4.html#the-dataset-structure">The dataset structure</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/example4.html#hdf5-conversion">HDF5 conversion</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/example4.html#adding-additional-information-on-the-examples">Adding additional information on the examples</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="tutorials/example5.html">Taking control : Use your own algorithms</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/example5.html#simple-task-adding-a-monoview-classifier">Simple task : Adding a monoview classifier</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/example5.html#more-complex-task-adding-a-multiview-classifier">More complex task : Adding a multiview classifier</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="references/multiview_platform.html">multiview_platform references</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="references/multiview_platform.html#subpackages">Subpackages</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="references/multiview_platform.mono_multi_view_classifiers.html">multiview_platform.mono_multi_view_classifiers package</a></li>
-<li class="toctree-l3"><a class="reference internal" href="references/multiview_platform.tests.html">multiview_platform.tests package</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="references/multiview_platform.html#submodules">Submodules</a></li>
-<li class="toctree-l2"><a class="reference internal" href="references/multiview_platform.html#module-multiview_platform.execute">multiview_platform.execute module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="references/multiview_platform.html#module-multiview_platform.tests">multiview_platform.tests module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="references/multiview_platform.html#module-multiview_platform.versions">multiview_platform.versions module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="references/multiview_platform.html#module-multiview_platform">Module contents</a></li>
-</ul>
-</li>
+<li class="toctree-l1"><a class="reference internal" href="readme_link.html#mono-and-multi-view-classification-benchmark">Mono- and Multi-view classification benchmark</a></li>
+<li class="toctree-l1"><a class="reference internal" href="tutorials/index.html">Multiview Platform Tutorials</a></li>
+<li class="toctree-l1"><a class="reference internal" href="references/multiview_platform.html">multiview_platform references</a></li>
 </ul>
 </div>
 </div>
@@ -140,7 +73,7 @@ contain the root <cite>toctree</cite> directive.</p>
         <div class="sphinxsidebarwrapper">
   <h3><a href="#">Table of Contents</a></h3>
   <ul>
-<li><a class="reference internal" href="#">Welcome to MultiviewPlatform’s documentation!</a></li>
+<li><a class="reference internal" href="#">Welcome to SuMMIT’s documentation!</a></li>
 <li><a class="reference internal" href="#indices-and-tables">Indices and tables</a></li>
 </ul>
 
@@ -176,9 +109,6 @@ contain the root <cite>toctree</cite> directive.</p>
         <li class="right" style="margin-right: 10px">
           <a href="genindex.html" title="General Index"
              >index</a></li>
-        <li class="right" >
-          <a href="py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
         <li class="right" >
           <a href="readme_link.html" title="Readme"
              >next</a> |</li>
diff --git a/docs/build/modules.html b/docs/build/modules.html
index fb7fbdde9929f687dde346367ce6a09ebb51aa95..65812ded1a062566d61d4bff99130abf7d91f66d 100644
--- a/docs/build/modules.html
+++ b/docs/build/modules.html
@@ -25,9 +25,6 @@
         <li class="right" style="margin-right: 10px">
           <a href="genindex.html" title="General Index"
              accesskey="I">index</a></li>
-        <li class="right" >
-          <a href="py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
         <li class="nav-item nav-item-0"><a href="index.html">MultiviewPlatform 0 documentation</a> &#187;</li> 
       </ul>
     </div>  
@@ -40,35 +37,6 @@
   <div class="section" id="multiview-platform">
 <h1>multiview_platform<a class="headerlink" href="#multiview-platform" title="Permalink to this headline">¶</a></h1>
 <div class="toctree-wrapper compound">
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="multiview_platform.html">multiview_platform package</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="multiview_platform.html#subpackages">Subpackages</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.html">multiview_platform.mono_multi_view_classifiers package</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.html#subpackages">Subpackages</a></li>
-<li class="toctree-l4"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.html#submodules">Submodules</a></li>
-<li class="toctree-l4"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.html#module-multiview_platform.mono_multi_view_classifiers.exec_classif">multiview_platform.mono_multi_view_classifiers.exec_classif module</a></li>
-<li class="toctree-l4"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.html#module-multiview_platform.mono_multi_view_classifiers.result_analysis">multiview_platform.mono_multi_view_classifiers.result_analysis module</a></li>
-<li class="toctree-l4"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.html#module-multiview_platform.mono_multi_view_classifiers">Module contents</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="multiview_platform.tests.html">multiview_platform.tests package</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="multiview_platform.tests.html#subpackages">Subpackages</a></li>
-<li class="toctree-l4"><a class="reference internal" href="multiview_platform.tests.html#submodules">Submodules</a></li>
-<li class="toctree-l4"><a class="reference internal" href="multiview_platform.tests.html#module-multiview_platform.tests.test_ExecClassif">multiview_platform.tests.test_ExecClassif module</a></li>
-<li class="toctree-l4"><a class="reference internal" href="multiview_platform.tests.html#module-multiview_platform.tests.test_ResultAnalysis">multiview_platform.tests.test_ResultAnalysis module</a></li>
-<li class="toctree-l4"><a class="reference internal" href="multiview_platform.tests.html#module-multiview_platform.tests">Module contents</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="multiview_platform.html#submodules">Submodules</a></li>
-<li class="toctree-l2"><a class="reference internal" href="multiview_platform.html#module-multiview_platform.execute">multiview_platform.execute module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="multiview_platform.html#module-multiview_platform.tests">multiview_platform.tests module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="multiview_platform.html#module-multiview_platform.versions">multiview_platform.versions module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="multiview_platform.html#module-multiview_platform">Module contents</a></li>
-</ul>
-</li>
-</ul>
 </div>
 </div>
 
@@ -107,9 +75,6 @@
         <li class="right" style="margin-right: 10px">
           <a href="genindex.html" title="General Index"
              >index</a></li>
-        <li class="right" >
-          <a href="py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
         <li class="nav-item nav-item-0"><a href="index.html">MultiviewPlatform 0 documentation</a> &#187;</li> 
       </ul>
     </div>
diff --git a/docs/build/objects.inv b/docs/build/objects.inv
index 894f065d75819f2f0ffe9edf44c7b56a0c9aa19a..e4649ac7b24c3d6b2bb32c14d6b8001d16b2c833 100644
Binary files a/docs/build/objects.inv and b/docs/build/objects.inv differ
diff --git a/docs/build/readme_link.html b/docs/build/readme_link.html
index 4938e4587d54ee01536d4b7cc743432745d830da..678949cecc60ee6babcb14255cd3bd80e59754a1 100644
--- a/docs/build/readme_link.html
+++ b/docs/build/readme_link.html
@@ -19,7 +19,7 @@
     <link rel="index" title="Index" href="genindex.html" />
     <link rel="search" title="Search" href="search.html" />
     <link rel="next" title="Multiview Platform Tutorials" href="tutorials/index.html" />
-    <link rel="prev" title="Welcome to MultiviewPlatform’s documentation!" href="index.html" /> 
+    <link rel="prev" title="Welcome to SuMMIT’s documentation!" href="index.html" /> 
   </head><body>
     <div class="related" role="navigation" aria-label="related navigation">
       <h3>Navigation</h3>
@@ -27,14 +27,11 @@
         <li class="right" style="margin-right: 10px">
           <a href="genindex.html" title="General Index"
              accesskey="I">index</a></li>
-        <li class="right" >
-          <a href="py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
         <li class="right" >
           <a href="tutorials/index.html" title="Multiview Platform Tutorials"
              accesskey="N">next</a> |</li>
         <li class="right" >
-          <a href="index.html" title="Welcome to MultiviewPlatform’s documentation!"
+          <a href="index.html" title="Welcome to SuMMIT’s documentation!"
              accesskey="P">previous</a> |</li>
         <li class="nav-item nav-item-0"><a href="index.html">MultiviewPlatform 0 documentation</a> &#187;</li> 
       </ul>
@@ -48,7 +45,7 @@
   <div class="section" id="readme">
 <h1>Readme<a class="headerlink" href="#readme" title="Permalink to this headline">¶</a></h1>
 <a class="reference external image-reference" href="http://www.gnu.org/licenses/gpl-3.0"><img alt="License: GPL v3" src="https://img.shields.io/badge/License-GPL%20v3-blue.svg" /></a>
-<a class="reference external image-reference" href="https://gitlab.lis-lab.fr/baptiste.bauvin/multiview-machine-learning-omis/badges/develop/build.svg"><img alt="Build Status" src="https://gitlab.lis-lab.fr/baptiste.bauvin/multiview-machine-learning-omis/badges/develop/build.svg" /></a>
+<a class="reference external image-reference" href="https://gitlab.lis-lab.fr/baptiste.bauvin/multiview-machine-learning-omis/badges/develop/pipeline.svg"><img alt="Build Status" src="https://gitlab.lis-lab.fr/baptiste.bauvin/multiview-machine-learning-omis/badges/develop/pipeline.svg" /></a>
 </div>
 <div class="section" id="mono-and-multi-view-classification-benchmark">
 <h1>Mono- and Multi-view classification benchmark<a class="headerlink" href="#mono-and-multi-view-classification-benchmark" title="Permalink to this headline">¶</a></h1>
@@ -74,17 +71,17 @@
 <li><a class="reference external" href="https://pypi.org/project/m2r/">m2r</a> - Used to generate documentation from the readme,</li>
 <li><a class="reference external" href="https://pypi.org/project/docutils/">docutils</a> - Used to generate documentation,</li>
 <li><a class="reference external" href="https://pypi.org/project/PyYAML/">pyyaml</a> - Used to read the config files,</li>
-<li><a class="reference external" href="https://plot.ly/">plotly</a> - Used to generate interactive HTML visuals.</li>
+<li><a class="reference external" href="https://plot.ly/">plotly</a> - Used to generate interactive HTML visuals,</li>
+<li><a class="reference external" href="https://pypi.org/project/tabulate/">tabulate</a> - Used to generated the confusion matrix.</li>
 </ul>
-<p>They are all tested in  <code class="docutils literal notranslate"><span class="pre">multiview-machine-mearning-omis/multiview_platform/MonoMutliViewClassifiers/Versions.py</span></code> which is automatically checked each time you run the <code class="docutils literal notranslate"><span class="pre">execute</span></code> script</p>
 </div>
 <div class="section" id="installing">
 <h3>Installing<a class="headerlink" href="#installing" title="Permalink to this headline">¶</a></h3>
-<p>Once you cloned the project from this repository, you just have to use :</p>
+<p>Once you cloned the project from the <a class="reference external" href="https://gitlab.lis-lab.fr/baptiste.bauvin/multiview-machine-learning-omis/">gitlab repository</a>, you just have to use :</p>
 <div class="highlight-guess notranslate"><div class="highlight"><pre><span></span>pip install -e .
 </pre></div>
 </div>
-<p>In the <code class="docutils literal notranslate"><span class="pre">multiview_machine-learning-omis</span></code> directory.</p>
+<p>In the <code class="docutils literal notranslate"><span class="pre">multiview_machine-learning-omis</span></code> directory to install SuMMIT and its dependencies.</p>
 </div>
 <div class="section" id="running-on-simulated-data">
 <h3>Running on simulated data<a class="headerlink" href="#running-on-simulated-data" title="Permalink to this headline">¶</a></h3>
@@ -93,40 +90,35 @@
 <span class="n">execute</span><span class="p">()</span>
 </pre></div>
 </div>
-<p>This will run the first example. For more information about the examples, see the documentation
+<p>This will run the first example. For more information about the examples, see the <a class="reference external" href="http://baptiste.bauvin.pages.lis-lab.fr/multiview-machine-learning-omis/">documentation</a>
 Results will be stored in the results directory of the installation path :
 <code class="docutils literal notranslate"><span class="pre">path/to/install/multiview-machine-learning-omis/multiview_platform/examples/results</span></code>.
-The documentations proposes a detailed interpretation of the results.</p>
+The documentation proposes a detailed interpretation of the results.</p>
 </div>
 <div class="section" id="discovering-the-arguments">
 <h3>Discovering the arguments<a class="headerlink" href="#discovering-the-arguments" title="Permalink to this headline">¶</a></h3>
 <p>All the arguments of the platform are stored in a YAML config file. Some config files are given as examples.
-The file stored in <code class="docutils literal notranslate"><span class="pre">multiview-machine-learning-omis/config_files/config.yml</span></code> is documented and it is highly recommended</p>
-<blockquote>
-<div>to read it carefully before playing around with the parameters.</div></blockquote>
+The file stored in <code class="docutils literal notranslate"><span class="pre">multiview-machine-learning-omis/config_files/config.yml</span></code> is documented and it is highly recommended
+to read it carefully before playing around with the parameters.</p>
 <p>You can create your own configuration file. In order to run the platform with it, run :</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">multiview_platform.execute</span> <span class="kn">import</span> <span class="n">execute</span>
 <span class="n">execute</span><span class="p">(</span><span class="n">config_path</span><span class="o">=</span><span class="s2">&quot;/absolute/path/to/your/config/file&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
-<p>For further information about classifier-specific arguments, see the documentation.</p>
+<p>For further information about classifier-specific arguments, see the <a class="reference external" href="http://baptiste.bauvin.pages.lis-lab.fr/multiview-machine-learning-omis/">documentation</a>.</p>
 </div>
 <div class="section" id="dataset-compatibility">
 <h3>Dataset compatibility<a class="headerlink" href="#dataset-compatibility" title="Permalink to this headline">¶</a></h3>
-<p>In order to start a benchmark on your dataset, you need to format it so the script can use it.
-You can have either a directory containing <code class="docutils literal notranslate"><span class="pre">.csv</span></code> files or a HDF5 file.</p>
-<div class="section" id="if-you-have-multiple-csv-files-you-must-organize-them-as">
-<h4>If you have multiple <code class="docutils literal notranslate"><span class="pre">.csv</span></code> files, you must organize them as :<a class="headerlink" href="#if-you-have-multiple-csv-files-you-must-organize-them-as" title="Permalink to this headline">¶</a></h4>
+<p>In order to start a benchmark on your own dataset, you need to format it so SuMMIT can use it.</p>
+<p><span class="raw-html-m2r"><a href="<" title="With `top_directory</span> being the last directory in the <cite>pathF</cite> argument”&gt;comment&lt;/a&gt;`: &lt;&gt; (#### If you have multiple <code class="docutils literal notranslate"><span class="pre">.csv</span></code> files, you must organize them as :</p>
 <ul class="simple">
 <li><code class="docutils literal notranslate"><span class="pre">top_directory/database_name-labels.csv</span></code></li>
 <li><code class="docutils literal notranslate"><span class="pre">top_directory/database_name-labels-names.csv</span></code></li>
-<li><code class="docutils literal notranslate"><span class="pre">top_directory/Views/view_name.csv</span></code> or <code class="docutils literal notranslate"><span class="pre">top_directory/Views/view_name-s.csv</span></code> if the view is sparse</li>
+<li><code class="docutils literal notranslate"><span class="pre">top_directory/Views/view_name.csv</span></code> or <code class="docutils literal notranslate"><span class="pre">top_directory/Views/view_name-s.csv</span></code> if the view is sparse)</li>
 </ul>
-<p>With <code class="docutils literal notranslate"><span class="pre">top_directory</span></code> being the last directory in the <code class="docutils literal notranslate"><span class="pre">pathF</span></code> argument</p>
-</div>
 <div class="section" id="if-you-already-have-an-hdf5-dataset-file-it-must-be-formatted-as">
 <h4>If you already have an HDF5 dataset file it must be formatted as :<a class="headerlink" href="#if-you-already-have-an-hdf5-dataset-file-it-must-be-formatted-as" title="Permalink to this headline">¶</a></h4>
-<p>One dataset for each view called <code class="docutils literal notranslate"><span class="pre">ViewX</span></code> with <code class="docutils literal notranslate"><span class="pre">X</span></code> being the view index with 2 attribures :</p>
+<p>One dataset for each view called <code class="docutils literal notranslate"><span class="pre">ViewI</span></code> with <code class="docutils literal notranslate"><span class="pre">I</span></code> being the view index with 2 attribures :</p>
 <ul class="simple">
 <li><code class="docutils literal notranslate"><span class="pre">attrs[&quot;name&quot;]</span></code> a string for the name of the view</li>
 <li><code class="docutils literal notranslate"><span class="pre">attrs[&quot;sparse&quot;]</span></code> a boolean specifying whether the view is sparse or not</li>
@@ -143,26 +135,19 @@ You can have either a directory containing <code class="docutils literal notrans
 <li><code class="docutils literal notranslate"><span class="pre">attrs[&quot;nbClass&quot;]</span></code> an int counting the total number of different labels in the dataset</li>
 <li><code class="docutils literal notranslate"><span class="pre">attrs[&quot;datasetLength&quot;]</span></code> an int counting the total number of examples in the dataset</li>
 </ul>
+<p>The <code class="docutils literal notranslate"><span class="pre">format_dataset.py</span></code> file is documented and can be used to format a multiview dataset in a SuMMIT-compatible HDF5 file.</p>
 </div>
 </div>
 <div class="section" id="running-on-your-dataset">
 <h3>Running on your dataset<a class="headerlink" href="#running-on-your-dataset" title="Permalink to this headline">¶</a></h3>
-<p>In order to run the script on your dataset you need to use :</p>
-<div class="highlight-guess notranslate"><div class="highlight"><pre><span></span>cd multiview-machine-learning-omis/multiview_platform
-python execute.py -log --name &lt;your_dataset_name&gt; --type &lt;.cvs_or_.hdf5&gt; --pathF &lt;path_to_your_dataset&gt;
+<p>Once you have formatted your dataset, to run SuMMIT on it you need to modify the config file as</p>
+<div class="highlight-yaml notranslate"><div class="highlight"><pre><span></span>name: [&quot;your_file_name&quot;]
+*
+pathf: &quot;path/to/your/dataset&quot;
 </pre></div>
 </div>
 <p>This will run a full benchmark on your dataset using all available views and labels.</p>
-<p>You may configure the <code class="docutils literal notranslate"><span class="pre">--CL_statsiter</span></code>, <code class="docutils literal notranslate"><span class="pre">--CL_split</span></code>, <code class="docutils literal notranslate"><span class="pre">--CL_nbFolds</span></code>, <code class="docutils literal notranslate"><span class="pre">--CL_GS_iter</span></code> arguments to start a meaningful benchmark</p>
-</div>
-</div>
-<div class="section" id="running-the-tests">
-<h2>Running the tests<a class="headerlink" href="#running-the-tests" title="Permalink to this headline">¶</a></h2>
-<p><a href="#id1"><span class="problematic" id="id2">**</span></a>/!still in development, test sucess is not meaningful ATM /!**</p>
-<p>In order to run it you’ll need to try on simulated data with the command</p>
-<div class="highlight-guess notranslate"><div class="highlight"><pre><span></span>cd multiview-machine-learning-omis/
-python -m unittest discover
-</pre></div>
+<p>It is highly recommended to follow the documentation’s <a class="reference external" href="http://baptiste.bauvin.pages.lis-lab.fr/multiview-machine-learning-omis/tutorials/index.html">tutorials</a> to learn the use of each parameter.</p>
 </div>
 </div>
 <div class="section" id="author">
@@ -173,9 +158,7 @@ python -m unittest discover
 <div class="section" id="contributors">
 <h3>Contributors<a class="headerlink" href="#contributors" title="Permalink to this headline">¶</a></h3>
 <ul class="simple">
-<li><strong>Mazid Osseni</strong></li>
-<li><strong>Alexandre Drouin</strong></li>
-<li><strong>Nikolas Huelsmann</strong></li>
+<li><strong>Dominique Benielli</strong></li>
 </ul>
 </div>
 </div>
@@ -197,14 +180,12 @@ python -m unittest discover
 <li><a class="reference internal" href="#running-on-simulated-data">Running on simulated data</a></li>
 <li><a class="reference internal" href="#discovering-the-arguments">Discovering the arguments</a></li>
 <li><a class="reference internal" href="#dataset-compatibility">Dataset compatibility</a><ul>
-<li><a class="reference internal" href="#if-you-have-multiple-csv-files-you-must-organize-them-as">If you have multiple <code class="docutils literal notranslate"><span class="pre">.csv</span></code> files, you must organize them as :</a></li>
 <li><a class="reference internal" href="#if-you-already-have-an-hdf5-dataset-file-it-must-be-formatted-as">If you already have an HDF5 dataset file it must be formatted as :</a></li>
 </ul>
 </li>
 <li><a class="reference internal" href="#running-on-your-dataset">Running on your dataset</a></li>
 </ul>
 </li>
-<li><a class="reference internal" href="#running-the-tests">Running the tests</a></li>
 <li><a class="reference internal" href="#author">Author</a><ul>
 <li><a class="reference internal" href="#contributors">Contributors</a></li>
 </ul>
@@ -215,7 +196,7 @@ python -m unittest discover
 
   <h4>Previous topic</h4>
   <p class="topless"><a href="index.html"
-                        title="previous chapter">Welcome to MultiviewPlatform’s documentation!</a></p>
+                        title="previous chapter">Welcome to SuMMIT’s documentation!</a></p>
   <h4>Next topic</h4>
   <p class="topless"><a href="tutorials/index.html"
                         title="next chapter">Multiview Platform Tutorials</a></p>
@@ -248,14 +229,11 @@ python -m unittest discover
         <li class="right" style="margin-right: 10px">
           <a href="genindex.html" title="General Index"
              >index</a></li>
-        <li class="right" >
-          <a href="py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
         <li class="right" >
           <a href="tutorials/index.html" title="Multiview Platform Tutorials"
              >next</a> |</li>
         <li class="right" >
-          <a href="index.html" title="Welcome to MultiviewPlatform’s documentation!"
+          <a href="index.html" title="Welcome to SuMMIT’s documentation!"
              >previous</a> |</li>
         <li class="nav-item nav-item-0"><a href="index.html">MultiviewPlatform 0 documentation</a> &#187;</li> 
       </ul>
diff --git a/docs/build/references/monomulti/exec_classif.html b/docs/build/references/monomulti/exec_classif.html
index 499b0c231b8dbd0668d8bf74771eda34daab2b78..a3e0f33ee070f3e389e1db0ae79f23b0126b46db 100644
--- a/docs/build/references/monomulti/exec_classif.html
+++ b/docs/build/references/monomulti/exec_classif.html
@@ -25,9 +25,6 @@
         <li class="right" style="margin-right: 10px">
           <a href="../../genindex.html" title="General Index"
              accesskey="I">index</a></li>
-        <li class="right" >
-          <a href="../../py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
         <li class="nav-item nav-item-0"><a href="../../index.html">MultiviewPlatform 0 documentation</a> &#187;</li> 
       </ul>
     </div>  
@@ -37,316 +34,8 @@
         <div class="bodywrapper">
           <div class="body" role="main">
             
-  <div class="section" id="module-multiview_platform.mono_multi_view_classifiers.exec_classif">
-<span id="classification-execution-module"></span><h1>Classification execution module<a class="headerlink" href="#module-multiview_platform.mono_multi_view_classifiers.exec_classif" title="Permalink to this headline">¶</a></h1>
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.exec_classif.arange_metrics">
-<code class="descname">arange_metrics</code><span class="sig-paren">(</span><em>metrics</em>, <em>metric_princ</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.exec_classif.arange_metrics" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to get the metrics list in the right order so that
-the first one is the principal metric specified in args</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>metrics</strong> (<em>list of lists</em>) – The metrics that will be used in the benchmark</li>
-<li><strong>metric_princ</strong> (<em>str</em>) – The name of the metric that need to be used for the hyper-parameter
-optimization process</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>metrics</strong> – The metrics list, but arranged  so the first one is the principal one.</p>
-</td>
-</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">list of lists</p>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.exec_classif.benchmark_init">
-<code class="descname">benchmark_init</code><span class="sig-paren">(</span><em>directory</em>, <em>classification_indices</em>, <em>labels</em>, <em>labels_dictionary</em>, <em>k_folds</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.exec_classif.benchmark_init" title="Permalink to this definition">¶</a></dt>
-<dd><p>Initializes the benchmark, by saving the indices of the train
-examples and the cross validation folds.</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>directory</strong> (<em>str</em>) – The benchmark’s result directory</li>
-<li><strong>classification_indices</strong> (<em>numpy array</em>) – The indices of the examples, splitted for the train/test split</li>
-<li><strong>labels</strong> (<em>numpy array</em>) – The labels of the dataset</li>
-<li><strong>labels_dictionary</strong> (<em>dict</em>) – The dictionary with labels as keys and their names as values</li>
-<li><strong>k_folds</strong> (<em>sklearn.model_selection.Folds object</em>) – The folds for the cross validation process</li>
-</ul>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.exec_classif.exec_benchmark">
-<code class="descname">exec_benchmark</code><span class="sig-paren">(</span><em>nb_cores</em>, <em>stats_iter</em>, <em>nb_multiclass</em>, <em>benchmark_arguments_dictionaries</em>, <em>classification_indices</em>, <em>directories</em>, <em>directory</em>, <em>multi_class_labels</em>, <em>metrics</em>, <em>labels_dictionary</em>, <em>nb_labels</em>, <em>dataset_var</em>, <em>exec_one_benchmark_mono_core=&lt;function exec_one_benchmark_mono_core&gt;</em>, <em>get_results=&lt;function get_results&gt;</em>, <em>delete=&lt;function delete_HDF5&gt;</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.exec_classif.exec_benchmark" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to execute the needed benchmark(s) on multicore or mono-core functions.</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>nb_cores</strong> (<em>int</em>) – Number of threads that the benchmarks can use.</li>
-<li><strong>stats_iter</strong> (<em>int</em>) – Number of statistical iterations that have to be done.</li>
-<li><strong>benchmark_arguments_dictionaries</strong> (<em>list of dictionaries</em>) – All the needed arguments for the benchmarks.</li>
-<li><strong>classification_indices</strong> (<em>list of lists of numpy.ndarray</em>) – For each statistical iteration a couple of numpy.ndarrays is stored with the indices for the training set and
-the ones of the testing set.</li>
-<li><strong>directories</strong> (<em>list of strings</em>) – List of the paths to the result directories for each statistical iteration.</li>
-<li><strong>directory</strong> (<em>string</em>) – Path to the main results directory.</li>
-<li><strong>multi_class_labels</strong> (<em>ist of lists of numpy.ndarray</em>) – For each label couple, for each statistical iteration a triplet of numpy.ndarrays is stored with the
-indices for the biclass training set, the ones for the biclass testing set and the ones for the
-multiclass testing set.</li>
-<li><strong>metrics</strong> (<em>list of lists</em>) – metrics that will be used to evaluate the algorithms performance.</li>
-<li><strong>labels_dictionary</strong> (<em>dictionary</em>) – Dictionary mapping labels indices to labels names.</li>
-<li><strong>nb_labels</strong> (<em>int</em>) – Total number of different labels in the dataset.</li>
-<li><strong>dataset_var</strong> (<em>HDF5 dataset file</em>) – The full dataset that wil be used by the benchmark.</li>
-<li><strong>classifiers_names</strong> (<em>list of strings</em>) – List of the benchmarks’s monoview classifiers names.</li>
-<li><strong>rest_of_the_args</strong> – Just used for testing purposes</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>results</strong> – The results of the benchmark.</p>
-</td>
-</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">list of lists</p>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.exec_classif.exec_classif">
-<code class="descname">exec_classif</code><span class="sig-paren">(</span><em>arguments</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.exec_classif.exec_classif" title="Permalink to this definition">¶</a></dt>
-<dd><p>Runs the benchmark with the given arguments</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>arguments</strong> – </td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><ul class="simple">
-<li><em>&gt;&gt;&gt; exec_classif([–config_path, /path/to/config/files/])</em></li>
-<li><em>&gt;&gt;&gt;</em></li>
-</ul>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.exec_classif.extract_dict">
-<code class="descname">extract_dict</code><span class="sig-paren">(</span><em>classifier_config</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.exec_classif.extract_dict" title="Permalink to this definition">¶</a></dt>
-<dd><p>Reverse function of get_path_dict</p>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.exec_classif.gen_multiple_args_dictionnaries">
-<code class="descname">gen_multiple_args_dictionnaries</code><span class="sig-paren">(</span><em>nb_class</em>, <em>kwargs_init</em>, <em>classifier</em>, <em>view_name=None</em>, <em>view_index=None</em>, <em>views_dictionary=None</em>, <em>framework='monoview'</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.exec_classif.gen_multiple_args_dictionnaries" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used in the case of mutliple arguments asked in the config file.
-Will combine the arguments to explore all the possibilities.</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>nb_class</strong> (<em>int</em><em>,</em>) – The number of classes in the dataset</li>
-<li><strong>kwargs_init</strong> (<em>dict</em>) – The arguments given in the config file</li>
-<li><strong>classifier</strong> (<em>str</em>) – The name of the classifier for which multiple arguments have been asked</li>
-<li><strong>view_name</strong> (<em>str</em>) – The name of the view in consideration.</li>
-<li><strong>view_index</strong> (<em>int</em>) – The index of the view in consideration</li>
-<li><strong>views_dictionary</strong> (<em>dict</em>) – The dictionary of all the views indices and their names</li>
-<li><strong>framework</strong> (<em>str</em>) – Either monoview or multiview</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>args_dictionaries</strong> – The list of all the possible combination of asked arguments</p>
-</td>
-</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">list</p>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.exec_classif.gen_multiple_kwargs_combinations">
-<code class="descname">gen_multiple_kwargs_combinations</code><span class="sig-paren">(</span><em>cl_kwrags</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.exec_classif.gen_multiple_kwargs_combinations" title="Permalink to this definition">¶</a></dt>
-<dd><p>Generates all the possible combination of the asked args</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>cl_kwrags</strong> (<em>dict</em>) – The arguments, with one at least having multiple values</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><ul class="simple">
-<li><strong>kwargs_combination</strong> (<em>list</em>) – The list of all the combinations of arguments</li>
-<li><strong>reduced_kwargs_combination</strong> (<em>list</em>) – The reduced names and values of the arguments will be used in the naming
-process of the different classifiers</li>
-</ul>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.exec_classif.get_path_dict">
-<code class="descname">get_path_dict</code><span class="sig-paren">(</span><em>multiview_classifier_args</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.exec_classif.get_path_dict" title="Permalink to this definition">¶</a></dt>
-<dd><p>This function is used to generate a dictionary with each key being
-the path to the value.
-If given {“key1”:{“key1_1”:value1}, “key2”:value2}, it will return
-{“key1.key1_1”:value1, “key2”:value2}</p>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.exec_classif.init_benchmark">
-<code class="descname">init_benchmark</code><span class="sig-paren">(</span><em>cl_type</em>, <em>monoview_algos</em>, <em>multiview_algos</em>, <em>args</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.exec_classif.init_benchmark" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to create a list of all the algorithm packages names used for the benchmark.</p>
-<p>First this function will check if the benchmark need mono- or/and multiview
-algorithms and adds to the right
-dictionary the asked algorithms. If none is asked by the user, all will be added.</p>
-<p>If the keyword <cite>“Benchmark”</cite> is used, all mono- and multiview algorithms will be added.</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>cl_type</strong> (<em>List of string</em>) – List of types of needed benchmark</li>
-<li><strong>multiview_algos</strong> (<em>List of strings</em>) – List of multiview algorithms needed for the benchmark</li>
-<li><strong>monoview_algos</strong> (<em>Listof strings</em>) – List of monoview algorithms needed for the benchmark</li>
-<li><strong>args</strong> (<em>ParsedArgumentParser args</em>) – All the input args (used to tune the algorithms)</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>benchmark</strong> – Dictionary resuming which mono- and multiview algorithms which will be used in the benchmark.</p>
-</td>
-</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">Dictionary of dictionaries</p>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.exec_classif.init_kwargs">
-<code class="descname">init_kwargs</code><span class="sig-paren">(</span><em>args</em>, <em>classifiers_names</em>, <em>framework='monoview'</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.exec_classif.init_kwargs" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to init kwargs thanks to a function in each monoview classifier package.</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>args</strong> (<em>parsed args objects</em>) – All the args passed by the user.</li>
-<li><strong>classifiers_names</strong> (<em>list of strings</em>) – List of the benchmarks’s monoview classifiers names.</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><p><strong>kwargs</strong> – Dictionary resuming all the specific arguments for the benchmark, one dictionary for each classifier.</p>
-<p>For example, for Adaboost, the KWARGS will be <cite>{“n_estimators”:&lt;value&gt;, “base_estimator”:&lt;value&gt;}</cite></p>
-</p>
-</td>
-</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">Dictionary</p>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.exec_classif.init_kwargs_func">
-<code class="descname">init_kwargs_func</code><span class="sig-paren">(</span><em>args</em>, <em>benchmark</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.exec_classif.init_kwargs_func" title="Permalink to this definition">¶</a></dt>
-<dd><p>Dispached the kwargs initialization to monoview and multiview and creates
-the kwargs variable</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>args</strong> (<em>parsed args objects</em>) – All the args passed by the user.</li>
-<li><strong>benchmark</strong> (<em>dict</em>) – The name of the mono- and mutli-view classifiers to run in the benchmark</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>kwargs</strong> – The arguments for each mono- and multiview algorithms</p>
-</td>
-</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">dict</p>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.exec_classif.init_monoview_exps">
-<code class="descname">init_monoview_exps</code><span class="sig-paren">(</span><em>classifier_names</em>, <em>views_dictionary</em>, <em>nb_class</em>, <em>kwargs_init</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.exec_classif.init_monoview_exps" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to add each monoview exeperience args to the list of monoview experiences args.</p>
-<p>First this function will check if the benchmark need mono- or/and multiview algorithms and adds to the right
-dictionary the asked algorithms. If none is asked by the user, all will be added.</p>
-<p>If the keyword <cite>“Benchmark”</cite> is used, all mono- and multiview algorithms will be added.</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>classifier_names</strong> (<em>dictionary</em>) – All types of monoview and multiview experiments that have to be benchmarked</li>
-<li><strong>argument_dictionaries</strong> (<em>dictionary</em>) – Maps monoview and multiview experiments arguments.</li>
-<li><strong>views_dictionary</strong> (<em>dictionary</em>) – Maps the view names to their index in the HDF5 dataset</li>
-<li><strong>nb_class</strong> (<em>integer</em>) – Number of different labels in the classification</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>benchmark</strong> – Dictionary resuming which mono- and multiview algorithms which will be used in the benchmark.</p>
-</td>
-</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">Dictionary of dictionaries</p>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.exec_classif.is_dict_in">
-<code class="descname">is_dict_in</code><span class="sig-paren">(</span><em>dictionary</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.exec_classif.is_dict_in" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns True if any of the dictionary value is a dictionary itself.</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>dictionary</strong> – </td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.exec_classif.multiple_args">
-<code class="descname">multiple_args</code><span class="sig-paren">(</span><em>classifier_configuration</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.exec_classif.multiple_args" title="Permalink to this definition">¶</a></dt>
-<dd><p>Checks if multiple values were provided for at least one arg</p>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.exec_classif.set_element">
-<code class="descname">set_element</code><span class="sig-paren">(</span><em>dictionary</em>, <em>path</em>, <em>value</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.exec_classif.set_element" title="Permalink to this definition">¶</a></dt>
-<dd><p>Set value in dictionary at the location indicated by path</p>
-</dd></dl>
-
+  <div class="section" id="classification-execution-module">
+<h1>Classification execution module<a class="headerlink" href="#classification-execution-module" title="Permalink to this headline">¶</a></h1>
 </div>
 
 
@@ -384,9 +73,6 @@ dictionary the asked algorithms. If none is asked by the user, all will be added
         <li class="right" style="margin-right: 10px">
           <a href="../../genindex.html" title="General Index"
              >index</a></li>
-        <li class="right" >
-          <a href="../../py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
         <li class="nav-item nav-item-0"><a href="../../index.html">MultiviewPlatform 0 documentation</a> &#187;</li> 
       </ul>
     </div>
diff --git a/docs/build/references/monomulti/metrics.html b/docs/build/references/monomulti/metrics.html
index 2686befa36325c845d819da2399e521c176f07cd..03b989ad92322a069913ee4b1c28cbe2b4fee734 100644
--- a/docs/build/references/monomulti/metrics.html
+++ b/docs/build/references/monomulti/metrics.html
@@ -25,9 +25,6 @@
         <li class="right" style="margin-right: 10px">
           <a href="../../genindex.html" title="General Index"
              accesskey="I">index</a></li>
-        <li class="right" >
-          <a href="../../py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
         <li class="nav-item nav-item-0"><a href="../../index.html">MultiviewPlatform 0 documentation</a> &#187;</li> 
       </ul>
     </div>  
@@ -37,78 +34,8 @@
         <div class="bodywrapper">
           <div class="body" role="main">
             
-  <div class="section" id="module-multiview_platform.mono_multi_view_classifiers.metrics.framework">
-<span id="metrics-framework"></span><h1>Metrics framework<a class="headerlink" href="#module-multiview_platform.mono_multi_view_classifiers.metrics.framework" title="Permalink to this headline">¶</a></h1>
-<p>In ths file, we explain how to add a metric to the platform.</p>
-<p>In order to do that, on needs to add a file with the following functions
-which are mandatory for the metric to work with the platform.</p>
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.metrics.framework.getConfig">
-<code class="descname">getConfig</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.metrics.framework.getConfig" title="Permalink to this definition">¶</a></dt>
-<dd><p>Get the metric’s configuration as a string.</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>kwargs</strong> (<em>dict</em>) – The arguments stored in this dictionary must be keyed by string of
-integers as “0”, .., etc and decrypted in the function. These arguments
-are a configuration of the metric.</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><strong>configString</strong> – The string describing the metric’s configuration.</td>
-</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body">string</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.metrics.framework.get_scorer">
-<code class="descname">get_scorer</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.metrics.framework.get_scorer" title="Permalink to this definition">¶</a></dt>
-<dd><p>Get the metric’s scorer as in the sklearn.metrics package.</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>kwargs</strong> (<em>dict</em>) – The arguments stored in this dictionary must be keyed by string of
-integers as “0”, .., etc and decrypted in the function. These arguments
-are a configuration of the metric.</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><strong>scorer</strong> – Callable object that returns a scalar score; greater is better. (cf sklearn.metrics.make_scorer)</td>
-</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body">object</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.metrics.framework.score">
-<code class="descname">score</code><span class="sig-paren">(</span><em>y_true</em>, <em>y_pred</em>, <em>multiclass=False</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.metrics.framework.score" title="Permalink to this definition">¶</a></dt>
-<dd><p>Get the metric’s score from the ground truth (<code class="docutils literal notranslate"><span class="pre">y_true</span></code>) and predictions (<code class="docutils literal notranslate"><span class="pre">y_pred</span></code>).</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>y_true</strong> (<em>array-like</em><em>, </em><em>shape =</em><em> (</em><em>n_samples</em><em>,</em><em>)</em>) – Target values (class labels).</li>
-<li><strong>y_pred</strong> (<em>array-like</em><em>, </em><em>shape =</em><em> (</em><em>n_samples</em><em>,</em><em>)</em>) – Predicted target values (class labels).</li>
-<li><strong>multiclass</strong> (<em>boolean</em><em> (</em><em>default=False</em><em>)</em>) – Parameter specifying whether the target values are multiclass or not.</li>
-<li><strong>kwargs</strong> (<em>dict</em>) – The arguments stored in this dictionary must be keyed by string of
-integers as “0”, .., etc and decrypted in the function</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>score</strong> – Returns the score of the prediction.</p>
-</td>
-</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">float</p>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
+  <div class="section" id="metrics-framework">
+<h1>Metrics framework<a class="headerlink" href="#metrics-framework" title="Permalink to this headline">¶</a></h1>
 </div>
 
 
@@ -146,9 +73,6 @@ integers as “0”, .., etc and decrypted in the function</li>
         <li class="right" style="margin-right: 10px">
           <a href="../../genindex.html" title="General Index"
              >index</a></li>
-        <li class="right" >
-          <a href="../../py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
         <li class="nav-item nav-item-0"><a href="../../index.html">MultiviewPlatform 0 documentation</a> &#187;</li> 
       </ul>
     </div>
diff --git a/docs/build/references/monomulti/multiview_classifiers/classifiers.html b/docs/build/references/monomulti/multiview_classifiers/classifiers.html
index d195e0dff67e4d208e0f16e6b3c0d05836269cf5..346a6de72673059e7a86a4e5b367b52f0824034f 100644
--- a/docs/build/references/monomulti/multiview_classifiers/classifiers.html
+++ b/docs/build/references/monomulti/multiview_classifiers/classifiers.html
@@ -25,9 +25,6 @@
         <li class="right" style="margin-right: 10px">
           <a href="../../../genindex.html" title="General Index"
              accesskey="I">index</a></li>
-        <li class="right" >
-          <a href="../../../py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
         <li class="nav-item nav-item-0"><a href="../../../index.html">MultiviewPlatform 0 documentation</a> &#187;</li> 
       </ul>
     </div>  
@@ -76,9 +73,6 @@
         <li class="right" style="margin-right: 10px">
           <a href="../../../genindex.html" title="General Index"
              >index</a></li>
-        <li class="right" >
-          <a href="../../../py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
         <li class="nav-item nav-item-0"><a href="../../../index.html">MultiviewPlatform 0 documentation</a> &#187;</li> 
       </ul>
     </div>
diff --git a/docs/build/references/monomulti/multiview_classifiers/diversity_fusion.html b/docs/build/references/monomulti/multiview_classifiers/diversity_fusion.html
index b05e46c182e93b1e5169a190084d56e99f9eba80..8f6eb1ce5cecd5b4062ddbf1db81a4c0182a337a 100644
--- a/docs/build/references/monomulti/multiview_classifiers/diversity_fusion.html
+++ b/docs/build/references/monomulti/multiview_classifiers/diversity_fusion.html
@@ -25,9 +25,6 @@
         <li class="right" style="margin-right: 10px">
           <a href="../../../genindex.html" title="General Index"
              accesskey="I">index</a></li>
-        <li class="right" >
-          <a href="../../../py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
         <li class="nav-item nav-item-0"><a href="../../../index.html">MultiviewPlatform 0 documentation</a> &#187;</li> 
       </ul>
     </div>  
@@ -76,9 +73,6 @@
         <li class="right" style="margin-right: 10px">
           <a href="../../../genindex.html" title="General Index"
              >index</a></li>
-        <li class="right" >
-          <a href="../../../py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
         <li class="nav-item nav-item-0"><a href="../../../index.html">MultiviewPlatform 0 documentation</a> &#187;</li> 
       </ul>
     </div>
diff --git a/docs/build/references/monomulti/utils/execution.html b/docs/build/references/monomulti/utils/execution.html
index 4b2361799efbbd43218bd9ace19b0e5e2918a66a..120e748f50863c9149e36548c2fe0e60c7e6d988 100644
--- a/docs/build/references/monomulti/utils/execution.html
+++ b/docs/build/references/monomulti/utils/execution.html
@@ -25,9 +25,6 @@
         <li class="right" style="margin-right: 10px">
           <a href="../../../genindex.html" title="General Index"
              accesskey="I">index</a></li>
-        <li class="right" >
-          <a href="../../../py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
         <li class="nav-item nav-item-0"><a href="../../../index.html">MultiviewPlatform 0 documentation</a> &#187;</li> 
       </ul>
     </div>  
@@ -93,9 +90,6 @@
         <li class="right" style="margin-right: 10px">
           <a href="../../../genindex.html" title="General Index"
              >index</a></li>
-        <li class="right" >
-          <a href="../../../py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
         <li class="nav-item nav-item-0"><a href="../../../index.html">MultiviewPlatform 0 documentation</a> &#187;</li> 
       </ul>
     </div>
diff --git a/docs/build/references/monomulti/utils/multiclass.html b/docs/build/references/monomulti/utils/multiclass.html
index 4a7107ed89d6a38bf67dd7928bd1a544c741d126..aeab85cedc8d0d82feae1398bb5d92b1cd9e1798 100644
--- a/docs/build/references/monomulti/utils/multiclass.html
+++ b/docs/build/references/monomulti/utils/multiclass.html
@@ -25,9 +25,6 @@
         <li class="right" style="margin-right: 10px">
           <a href="../../../genindex.html" title="General Index"
              accesskey="I">index</a></li>
-        <li class="right" >
-          <a href="../../../py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
         <li class="nav-item nav-item-0"><a href="../../../index.html">MultiviewPlatform 0 documentation</a> &#187;</li> 
       </ul>
     </div>  
@@ -37,8 +34,8 @@
         <div class="bodywrapper">
           <div class="body" role="main">
             
-  <div class="section" id="module-multiview_platform.mono_multi_view_classifiers.utils.multiclass">
-<span id="utils-multiclass-module"></span><h1>Utils Multiclass module<a class="headerlink" href="#module-multiview_platform.mono_multi_view_classifiers.utils.multiclass" title="Permalink to this headline">¶</a></h1>
+  <div class="section" id="utils-multiclass-module">
+<h1>Utils Multiclass module<a class="headerlink" href="#utils-multiclass-module" title="Permalink to this headline">¶</a></h1>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />
@@ -93,9 +90,6 @@
         <li class="right" style="margin-right: 10px">
           <a href="../../../genindex.html" title="General Index"
              >index</a></li>
-        <li class="right" >
-          <a href="../../../py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
         <li class="nav-item nav-item-0"><a href="../../../index.html">MultiviewPlatform 0 documentation</a> &#187;</li> 
       </ul>
     </div>
diff --git a/docs/build/references/monomultidoc.html b/docs/build/references/monomultidoc.html
index 2cac5483860b77a65c180d9b06800f41f31345b1..76fd65f3581e5b5426b121185ca93dd38c996da0 100644
--- a/docs/build/references/monomultidoc.html
+++ b/docs/build/references/monomultidoc.html
@@ -25,9 +25,6 @@
         <li class="right" style="margin-right: 10px">
           <a href="../genindex.html" title="General Index"
              accesskey="I">index</a></li>
-        <li class="right" >
-          <a href="../py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
         <li class="nav-item nav-item-0"><a href="../index.html">MultiviewPlatform 0 documentation</a> &#187;</li> 
       </ul>
     </div>  
@@ -86,9 +83,6 @@
         <li class="right" style="margin-right: 10px">
           <a href="../genindex.html" title="General Index"
              >index</a></li>
-        <li class="right" >
-          <a href="../py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
         <li class="nav-item nav-item-0"><a href="../index.html">MultiviewPlatform 0 documentation</a> &#187;</li> 
       </ul>
     </div>
diff --git a/docs/build/references/multiview_platform.html b/docs/build/references/multiview_platform.html
index 6d89ec329903cc4aadbc9fc8f72de6cd40b78d6a..fa3171ee102bf2e80c4a2b77acb22e88052026ba 100644
--- a/docs/build/references/multiview_platform.html
+++ b/docs/build/references/multiview_platform.html
@@ -27,9 +27,6 @@
         <li class="right" style="margin-right: 10px">
           <a href="../genindex.html" title="General Index"
              accesskey="I">index</a></li>
-        <li class="right" >
-          <a href="../py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
         <li class="right" >
           <a href="multiview_platform.mono_multi_view_classifiers.html" title="multiview_platform.mono_multi_view_classifiers package"
              accesskey="N">next</a> |</li>
@@ -59,28 +56,28 @@
 <li class="toctree-l6"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.difficulty_fusion.html#submodules">Submodules</a></li>
 <li class="toctree-l6"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.difficulty_fusion.html#multiview-platform-mono-multi-view-classifiers-multiview-classifiers-difficulty-fusion-analyze-results-module">multiview_platform.mono_multi_view_classifiers.multiview_classifiers.difficulty_fusion.analyze_results module</a></li>
 <li class="toctree-l6"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.difficulty_fusion.html#multiview-platform-mono-multi-view-classifiers-multiview-classifiers-difficulty-fusion-difficulty-fusion-module">multiview_platform.mono_multi_view_classifiers.multiview_classifiers.difficulty_fusion.difficulty_fusion module</a></li>
-<li class="toctree-l6"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.difficulty_fusion.html#module-multiview_platform.mono_multi_view_classifiers.multiview_classifiers.difficulty_fusion">Module contents</a></li>
+<li class="toctree-l6"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.difficulty_fusion.html#module-contents">Module contents</a></li>
 </ul>
 </li>
 <li class="toctree-l5"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.disagree_fusion.html">multiview_platform.mono_multi_view_classifiers.multiview_classifiers.disagree_fusion package</a><ul>
 <li class="toctree-l6"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.disagree_fusion.html#submodules">Submodules</a></li>
 <li class="toctree-l6"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.disagree_fusion.html#multiview-platform-mono-multi-view-classifiers-multiview-classifiers-disagree-fusion-analyze-results-module">multiview_platform.mono_multi_view_classifiers.multiview_classifiers.disagree_fusion.analyze_results module</a></li>
 <li class="toctree-l6"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.disagree_fusion.html#multiview-platform-mono-multi-view-classifiers-multiview-classifiers-disagree-fusion-disagree-fusion-module">multiview_platform.mono_multi_view_classifiers.multiview_classifiers.disagree_fusion.disagree_fusion module</a></li>
-<li class="toctree-l6"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.disagree_fusion.html#module-multiview_platform.mono_multi_view_classifiers.multiview_classifiers.disagree_fusion">Module contents</a></li>
+<li class="toctree-l6"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.disagree_fusion.html#module-contents">Module contents</a></li>
 </ul>
 </li>
 <li class="toctree-l5"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.double_fault_fusion.html">multiview_platform.mono_multi_view_classifiers.multiview_classifiers.double_fault_fusion package</a><ul>
 <li class="toctree-l6"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.double_fault_fusion.html#submodules">Submodules</a></li>
 <li class="toctree-l6"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.double_fault_fusion.html#multiview-platform-mono-multi-view-classifiers-multiview-classifiers-double-fault-fusion-analyze-results-module">multiview_platform.mono_multi_view_classifiers.multiview_classifiers.double_fault_fusion.analyze_results module</a></li>
 <li class="toctree-l6"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.double_fault_fusion.html#multiview-platform-mono-multi-view-classifiers-multiview-classifiers-double-fault-fusion-double-fault-fusion-module">multiview_platform.mono_multi_view_classifiers.multiview_classifiers.double_fault_fusion.double_fault_fusion module</a></li>
-<li class="toctree-l6"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.double_fault_fusion.html#module-multiview_platform.mono_multi_view_classifiers.multiview_classifiers.double_fault_fusion">Module contents</a></li>
+<li class="toctree-l6"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.double_fault_fusion.html#module-contents">Module contents</a></li>
 </ul>
 </li>
 <li class="toctree-l5"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.entropy_fusion.html">multiview_platform.mono_multi_view_classifiers.multiview_classifiers.entropy_fusion package</a><ul>
 <li class="toctree-l6"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.entropy_fusion.html#submodules">Submodules</a></li>
 <li class="toctree-l6"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.entropy_fusion.html#multiview-platform-mono-multi-view-classifiers-multiview-classifiers-entropy-fusion-analyze-results-module">multiview_platform.mono_multi_view_classifiers.multiview_classifiers.entropy_fusion.analyze_results module</a></li>
 <li class="toctree-l6"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.entropy_fusion.html#multiview-platform-mono-multi-view-classifiers-multiview-classifiers-entropy-fusion-entropy-fusion-module">multiview_platform.mono_multi_view_classifiers.multiview_classifiers.entropy_fusion.entropy_fusion module</a></li>
-<li class="toctree-l6"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.entropy_fusion.html#module-multiview_platform.mono_multi_view_classifiers.multiview_classifiers.entropy_fusion">Module contents</a></li>
+<li class="toctree-l6"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.entropy_fusion.html#module-contents">Module contents</a></li>
 </ul>
 </li>
 <li class="toctree-l5"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.fat_late_fusion.html">multiview_platform.mono_multi_view_classifiers.multiview_classifiers.fat_late_fusion package</a><ul>
@@ -142,51 +139,51 @@
 </li>
 </ul>
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-<li class="toctree-l4"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.html#module-multiview_platform.mono_multi_view_classifiers.multiview_classifiers">Module contents</a></li>
+<li class="toctree-l4"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.html#module-contents">Module contents</a></li>
 </ul>
 </li>
 <li class="toctree-l3"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.utils.html">multiview_platform.mono_multi_view_classifiers.utils package</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.utils.html#submodules">Submodules</a></li>
-<li class="toctree-l4"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.utils.html#module-multiview_platform.mono_multi_view_classifiers.utils.configuration">multiview_platform.mono_multi_view_classifiers.utils.configuration module</a></li>
-<li class="toctree-l4"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.utils.html#module-multiview_platform.mono_multi_view_classifiers.utils.dataset">multiview_platform.mono_multi_view_classifiers.utils.dataset module</a></li>
-<li class="toctree-l4"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.utils.html#module-multiview_platform.mono_multi_view_classifiers.utils.execution">multiview_platform.mono_multi_view_classifiers.utils.execution module</a></li>
-<li class="toctree-l4"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.utils.html#module-multiview_platform.mono_multi_view_classifiers.utils.get_multiview_db">multiview_platform.mono_multi_view_classifiers.utils.get_multiview_db module</a></li>
-<li class="toctree-l4"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.utils.html#module-multiview_platform.mono_multi_view_classifiers.utils.hyper_parameter_search">multiview_platform.mono_multi_view_classifiers.utils.hyper_parameter_search module</a></li>
-<li class="toctree-l4"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.utils.html#module-multiview_platform.mono_multi_view_classifiers.utils.make_file_config">multiview_platform.mono_multi_view_classifiers.utils.make_file_config module</a></li>
-<li class="toctree-l4"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.utils.html#module-multiview_platform.mono_multi_view_classifiers.utils.multiclass">multiview_platform.mono_multi_view_classifiers.utils.multiclass module</a></li>
-<li class="toctree-l4"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.utils.html#module-multiview_platform.mono_multi_view_classifiers.utils.multiview_result_analysis">multiview_platform.mono_multi_view_classifiers.utils.multiview_result_analysis module</a></li>
-<li class="toctree-l4"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.utils.html#module-multiview_platform.mono_multi_view_classifiers.utils.transformations">multiview_platform.mono_multi_view_classifiers.utils.transformations module</a></li>
-<li class="toctree-l4"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.utils.html#module-multiview_platform.mono_multi_view_classifiers.utils">Module contents</a></li>
+<li class="toctree-l4"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.utils.html#multiview-platform-mono-multi-view-classifiers-utils-configuration-module">multiview_platform.mono_multi_view_classifiers.utils.configuration module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.utils.html#multiview-platform-mono-multi-view-classifiers-utils-dataset-module">multiview_platform.mono_multi_view_classifiers.utils.dataset module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.utils.html#multiview-platform-mono-multi-view-classifiers-utils-execution-module">multiview_platform.mono_multi_view_classifiers.utils.execution module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.utils.html#multiview-platform-mono-multi-view-classifiers-utils-get-multiview-db-module">multiview_platform.mono_multi_view_classifiers.utils.get_multiview_db module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.utils.html#multiview-platform-mono-multi-view-classifiers-utils-hyper-parameter-search-module">multiview_platform.mono_multi_view_classifiers.utils.hyper_parameter_search module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.utils.html#multiview-platform-mono-multi-view-classifiers-utils-make-file-config-module">multiview_platform.mono_multi_view_classifiers.utils.make_file_config module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.utils.html#multiview-platform-mono-multi-view-classifiers-utils-multiclass-module">multiview_platform.mono_multi_view_classifiers.utils.multiclass module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.utils.html#multiview-platform-mono-multi-view-classifiers-utils-multiview-result-analysis-module">multiview_platform.mono_multi_view_classifiers.utils.multiview_result_analysis module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.utils.html#multiview-platform-mono-multi-view-classifiers-utils-transformations-module">multiview_platform.mono_multi_view_classifiers.utils.transformations module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.utils.html#module-contents">Module contents</a></li>
 </ul>
 </li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.html#submodules">Submodules</a></li>
-<li class="toctree-l2"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.html#module-multiview_platform.mono_multi_view_classifiers.exec_classif">multiview_platform.mono_multi_view_classifiers.exec_classif module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.html#module-multiview_platform.mono_multi_view_classifiers.result_analysis">multiview_platform.mono_multi_view_classifiers.result_analysis module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.html#module-multiview_platform.mono_multi_view_classifiers">Module contents</a></li>
+<li class="toctree-l2"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.html#multiview-platform-mono-multi-view-classifiers-exec-classif-module">multiview_platform.mono_multi_view_classifiers.exec_classif module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.html#multiview-platform-mono-multi-view-classifiers-result-analysis-module">multiview_platform.mono_multi_view_classifiers.result_analysis module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.html#module-contents">Module contents</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="multiview_platform.tests.html">multiview_platform.tests package</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="multiview_platform.tests.html#subpackages">Subpackages</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="multiview_platform.tests.test_metrics.html">multiview_platform.tests.test_metrics package</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="multiview_platform.tests.test_metrics.html#submodules">Submodules</a></li>
-<li class="toctree-l4"><a class="reference internal" href="multiview_platform.tests.test_metrics.html#module-multiview_platform.tests.test_metrics.test_accuracy_score">multiview_platform.tests.test_metrics.test_accuracy_score module</a></li>
-<li class="toctree-l4"><a class="reference internal" href="multiview_platform.tests.test_metrics.html#module-multiview_platform.tests.test_metrics">Module contents</a></li>
+<li class="toctree-l4"><a class="reference internal" href="multiview_platform.tests.test_metrics.html#multiview-platform-tests-test-metrics-test-accuracy-score-module">multiview_platform.tests.test_metrics.test_accuracy_score module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="multiview_platform.tests.test_metrics.html#module-contents">Module contents</a></li>
 </ul>
 </li>
 <li class="toctree-l3"><a class="reference internal" href="multiview_platform.tests.test_mono_view.html">multiview_platform.tests.test_mono_view package</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="multiview_platform.tests.test_mono_view.html#module-multiview_platform.tests.test_mono_view.test_ExecClassifMonoView">multiview_platform.tests.test_mono_view.test_ExecClassifMonoView module</a></li>
-<li class="toctree-l4"><a class="reference internal" href="multiview_platform.tests.test_mono_view.html#module-multiview_platform.tests.test_mono_view.test_MonoviewUtils">multiview_platform.tests.test_mono_view.test_MonoviewUtils module</a></li>
-<li class="toctree-l4"><a class="reference internal" href="multiview_platform.tests.test_mono_view.html#module-multiview_platform.tests.test_mono_view">Module contents</a></li>
+<li class="toctree-l4"><a class="reference internal" href="multiview_platform.tests.test_mono_view.html#multiview-platform-tests-test-mono-view-test-execclassifmonoview-module">multiview_platform.tests.test_mono_view.test_ExecClassifMonoView module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="multiview_platform.tests.test_mono_view.html#multiview-platform-tests-test-mono-view-test-monoviewutils-module">multiview_platform.tests.test_mono_view.test_MonoviewUtils module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="multiview_platform.tests.test_mono_view.html#module-contents">Module contents</a></li>
 </ul>
 </li>
 <li class="toctree-l3"><a class="reference internal" href="multiview_platform.tests.test_monoview_classifiers.html">multiview_platform.tests.test_monoview_classifiers package</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="multiview_platform.tests.test_monoview_classifiers.html#submodules">Submodules</a></li>
-<li class="toctree-l4"><a class="reference internal" href="multiview_platform.tests.test_monoview_classifiers.html#module-multiview_platform.tests.test_monoview_classifiers.test_adaboost">multiview_platform.tests.test_monoview_classifiers.test_adaboost module</a></li>
-<li class="toctree-l4"><a class="reference internal" href="multiview_platform.tests.test_monoview_classifiers.html#module-multiview_platform.tests.test_monoview_classifiers.test_compatibility">multiview_platform.tests.test_monoview_classifiers.test_compatibility module</a></li>
-<li class="toctree-l4"><a class="reference internal" href="multiview_platform.tests.test_monoview_classifiers.html#module-multiview_platform.tests.test_monoview_classifiers">Module contents</a></li>
+<li class="toctree-l4"><a class="reference internal" href="multiview_platform.tests.test_monoview_classifiers.html#multiview-platform-tests-test-monoview-classifiers-test-adaboost-module">multiview_platform.tests.test_monoview_classifiers.test_adaboost module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="multiview_platform.tests.test_monoview_classifiers.html#multiview-platform-tests-test-monoview-classifiers-test-compatibility-module">multiview_platform.tests.test_monoview_classifiers.test_compatibility module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="multiview_platform.tests.test_monoview_classifiers.html#module-contents">Module contents</a></li>
 </ul>
 </li>
 <li class="toctree-l3"><a class="reference internal" href="multiview_platform.tests.test_multiview_classifiers.html">multiview_platform.tests.test_multiview_classifiers package</a><ul>
@@ -226,32 +223,32 @@
 </li>
 <li class="toctree-l5"><a class="reference internal" href="multiview_platform.tests.test_multiview_classifiers.Test_PseudoCQMeasure.html">multiview_platform.tests.test_multiview_classifiers.Test_PseudoCQMeasure package</a><ul>
 <li class="toctree-l6"><a class="reference internal" href="multiview_platform.tests.test_multiview_classifiers.Test_PseudoCQMeasure.html#submodules">Submodules</a></li>
-<li class="toctree-l6"><a class="reference internal" href="multiview_platform.tests.test_multiview_classifiers.Test_PseudoCQMeasure.html#module-multiview_platform.tests.test_multiview_classifiers.Test_PseudoCQMeasure.test_PseudoCQFusionModule">multiview_platform.tests.test_multiview_classifiers.Test_PseudoCQMeasure.test_PseudoCQFusionModule module</a></li>
-<li class="toctree-l6"><a class="reference internal" href="multiview_platform.tests.test_multiview_classifiers.Test_PseudoCQMeasure.html#module-multiview_platform.tests.test_multiview_classifiers.Test_PseudoCQMeasure">Module contents</a></li>
+<li class="toctree-l6"><a class="reference internal" href="multiview_platform.tests.test_multiview_classifiers.Test_PseudoCQMeasure.html#multiview-platform-tests-test-multiview-classifiers-test-pseudocqmeasure-test-pseudocqfusionmodule-module">multiview_platform.tests.test_multiview_classifiers.Test_PseudoCQMeasure.test_PseudoCQFusionModule module</a></li>
+<li class="toctree-l6"><a class="reference internal" href="multiview_platform.tests.test_multiview_classifiers.Test_PseudoCQMeasure.html#module-contents">Module contents</a></li>
 </ul>
 </li>
 </ul>
 </li>
 <li class="toctree-l4"><a class="reference internal" href="multiview_platform.tests.test_multiview_classifiers.html#submodules">Submodules</a></li>
-<li class="toctree-l4"><a class="reference internal" href="multiview_platform.tests.test_multiview_classifiers.html#module-multiview_platform.tests.test_multiview_classifiers.test_diversity_utils">multiview_platform.tests.test_multiview_classifiers.test_diversity_utils module</a></li>
-<li class="toctree-l4"><a class="reference internal" href="multiview_platform.tests.test_multiview_classifiers.html#module-multiview_platform.tests.test_multiview_classifiers">Module contents</a></li>
+<li class="toctree-l4"><a class="reference internal" href="multiview_platform.tests.test_multiview_classifiers.html#multiview-platform-tests-test-multiview-classifiers-test-diversity-utils-module">multiview_platform.tests.test_multiview_classifiers.test_diversity_utils module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="multiview_platform.tests.test_multiview_classifiers.html#module-contents">Module contents</a></li>
 </ul>
 </li>
 <li class="toctree-l3"><a class="reference internal" href="multiview_platform.tests.test_utils.html">multiview_platform.tests.test_utils package</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="multiview_platform.tests.test_utils.html#submodules">Submodules</a></li>
-<li class="toctree-l4"><a class="reference internal" href="multiview_platform.tests.test_utils.html#module-multiview_platform.tests.test_utils.test_GetMultiviewDB">multiview_platform.tests.test_utils.test_GetMultiviewDB module</a></li>
-<li class="toctree-l4"><a class="reference internal" href="multiview_platform.tests.test_utils.html#module-multiview_platform.tests.test_utils.test_configuration">multiview_platform.tests.test_utils.test_configuration module</a></li>
-<li class="toctree-l4"><a class="reference internal" href="multiview_platform.tests.test_utils.html#module-multiview_platform.tests.test_utils.test_execution">multiview_platform.tests.test_utils.test_execution module</a></li>
-<li class="toctree-l4"><a class="reference internal" href="multiview_platform.tests.test_utils.html#module-multiview_platform.tests.test_utils.test_multiclass">multiview_platform.tests.test_utils.test_multiclass module</a></li>
-<li class="toctree-l4"><a class="reference internal" href="multiview_platform.tests.test_utils.html#module-multiview_platform.tests.test_utils">Module contents</a></li>
+<li class="toctree-l4"><a class="reference internal" href="multiview_platform.tests.test_utils.html#multiview-platform-tests-test-utils-test-getmultiviewdb-module">multiview_platform.tests.test_utils.test_GetMultiviewDB module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="multiview_platform.tests.test_utils.html#multiview-platform-tests-test-utils-test-configuration-module">multiview_platform.tests.test_utils.test_configuration module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="multiview_platform.tests.test_utils.html#multiview-platform-tests-test-utils-test-execution-module">multiview_platform.tests.test_utils.test_execution module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="multiview_platform.tests.test_utils.html#multiview-platform-tests-test-utils-test-multiclass-module">multiview_platform.tests.test_utils.test_multiclass module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="multiview_platform.tests.test_utils.html#module-contents">Module contents</a></li>
 </ul>
 </li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="multiview_platform.tests.html#submodules">Submodules</a></li>
-<li class="toctree-l2"><a class="reference internal" href="multiview_platform.tests.html#module-multiview_platform.tests.test_ExecClassif">multiview_platform.tests.test_ExecClassif module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="multiview_platform.tests.html#module-multiview_platform.tests.test_ResultAnalysis">multiview_platform.tests.test_ResultAnalysis module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="multiview_platform.tests.html#module-multiview_platform.tests">Module contents</a></li>
+<li class="toctree-l2"><a class="reference internal" href="multiview_platform.tests.html#multiview-platform-tests-test-execclassif-module">multiview_platform.tests.test_ExecClassif module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="multiview_platform.tests.html#multiview-platform-tests-test-resultanalysis-module">multiview_platform.tests.test_ResultAnalysis module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="multiview_platform.tests.html#module-contents">Module contents</a></li>
 </ul>
 </li>
 </ul>
@@ -260,30 +257,17 @@
 <div class="section" id="submodules">
 <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this headline">¶</a></h2>
 </div>
-<div class="section" id="module-multiview_platform.execute">
-<span id="multiview-platform-execute-module"></span><h2>multiview_platform.execute module<a class="headerlink" href="#module-multiview_platform.execute" title="Permalink to this headline">¶</a></h2>
-<p>This is the execution module, used to execute the code</p>
-<dl class="function">
-<dt id="multiview_platform.execute.execute">
-<code class="descname">execute</code><span class="sig-paren">(</span><em>config_path='/home/baptiste/Documents/Gitwork/multiview-machine-learning-omis/multiview_platform/examples/config_files/config_example_1.yml'</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.execute.execute" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
+<div class="section" id="multiview-platform-execute-module">
+<h2>multiview_platform.execute module<a class="headerlink" href="#multiview-platform-execute-module" title="Permalink to this headline">¶</a></h2>
 </div>
-<div class="section" id="module-multiview_platform.tests">
-<span id="multiview-platform-tests-module"></span><h2>multiview_platform.tests module<a class="headerlink" href="#module-multiview_platform.tests" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="multiview-platform-tests-module">
+<h2>multiview_platform.tests module<a class="headerlink" href="#multiview-platform-tests-module" title="Permalink to this headline">¶</a></h2>
 </div>
-<div class="section" id="module-multiview_platform.versions">
-<span id="multiview-platform-versions-module"></span><h2>multiview_platform.versions module<a class="headerlink" href="#module-multiview_platform.versions" title="Permalink to this headline">¶</a></h2>
-<dl class="function">
-<dt id="multiview_platform.versions.test_versions">
-<code class="descname">test_versions</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.versions.test_versions" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to test if all prerequisites are installed</p>
-</dd></dl>
-
+<div class="section" id="multiview-platform-versions-module">
+<h2>multiview_platform.versions module<a class="headerlink" href="#multiview-platform-versions-module" title="Permalink to this headline">¶</a></h2>
 </div>
-<div class="section" id="module-multiview_platform">
-<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-multiview_platform" title="Permalink to this headline">¶</a></h2>
-<p>This is a test docstring to test stuff</p>
+<div class="section" id="module-contents">
+<h2>Module contents<a class="headerlink" href="#module-contents" title="Permalink to this headline">¶</a></h2>
 </div>
 </div>
 
@@ -298,10 +282,10 @@
 <li><a class="reference internal" href="#">multiview_platform references</a><ul>
 <li><a class="reference internal" href="#subpackages">Subpackages</a></li>
 <li><a class="reference internal" href="#submodules">Submodules</a></li>
-<li><a class="reference internal" href="#module-multiview_platform.execute">multiview_platform.execute module</a></li>
-<li><a class="reference internal" href="#module-multiview_platform.tests">multiview_platform.tests module</a></li>
-<li><a class="reference internal" href="#module-multiview_platform.versions">multiview_platform.versions module</a></li>
-<li><a class="reference internal" href="#module-multiview_platform">Module contents</a></li>
+<li><a class="reference internal" href="#multiview-platform-execute-module">multiview_platform.execute module</a></li>
+<li><a class="reference internal" href="#multiview-platform-tests-module">multiview_platform.tests module</a></li>
+<li><a class="reference internal" href="#multiview-platform-versions-module">multiview_platform.versions module</a></li>
+<li><a class="reference internal" href="#module-contents">Module contents</a></li>
 </ul>
 </li>
 </ul>
@@ -341,9 +325,6 @@
         <li class="right" style="margin-right: 10px">
           <a href="../genindex.html" title="General Index"
              >index</a></li>
-        <li class="right" >
-          <a href="../py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
         <li class="right" >
           <a href="multiview_platform.mono_multi_view_classifiers.html" title="multiview_platform.mono_multi_view_classifiers package"
              >next</a> |</li>
diff --git a/docs/build/references/multiview_platform.mono_multi_view_classifiers.html b/docs/build/references/multiview_platform.mono_multi_view_classifiers.html
index d1e97060cd3cc2162764d524a5718b1b372f693c..4685b97754d57ae1de3144a078689c03b24c0deb 100644
--- a/docs/build/references/multiview_platform.mono_multi_view_classifiers.html
+++ b/docs/build/references/multiview_platform.mono_multi_view_classifiers.html
@@ -27,9 +27,6 @@
         <li class="right" style="margin-right: 10px">
           <a href="../genindex.html" title="General Index"
              accesskey="I">index</a></li>
-        <li class="right" >
-          <a href="../py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
         <li class="right" >
           <a href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.html" title="multiview_platform.mono_multi_view_classifiers.multiview_classifiers package"
              accesskey="N">next</a> |</li>
@@ -58,28 +55,28 @@
 <li class="toctree-l4"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.difficulty_fusion.html#submodules">Submodules</a></li>
 <li class="toctree-l4"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.difficulty_fusion.html#multiview-platform-mono-multi-view-classifiers-multiview-classifiers-difficulty-fusion-analyze-results-module">multiview_platform.mono_multi_view_classifiers.multiview_classifiers.difficulty_fusion.analyze_results module</a></li>
 <li class="toctree-l4"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.difficulty_fusion.html#multiview-platform-mono-multi-view-classifiers-multiview-classifiers-difficulty-fusion-difficulty-fusion-module">multiview_platform.mono_multi_view_classifiers.multiview_classifiers.difficulty_fusion.difficulty_fusion module</a></li>
-<li class="toctree-l4"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.difficulty_fusion.html#module-multiview_platform.mono_multi_view_classifiers.multiview_classifiers.difficulty_fusion">Module contents</a></li>
+<li class="toctree-l4"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.difficulty_fusion.html#module-contents">Module contents</a></li>
 </ul>
 </li>
 <li class="toctree-l3"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.disagree_fusion.html">multiview_platform.mono_multi_view_classifiers.multiview_classifiers.disagree_fusion package</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.disagree_fusion.html#submodules">Submodules</a></li>
 <li class="toctree-l4"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.disagree_fusion.html#multiview-platform-mono-multi-view-classifiers-multiview-classifiers-disagree-fusion-analyze-results-module">multiview_platform.mono_multi_view_classifiers.multiview_classifiers.disagree_fusion.analyze_results module</a></li>
 <li class="toctree-l4"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.disagree_fusion.html#multiview-platform-mono-multi-view-classifiers-multiview-classifiers-disagree-fusion-disagree-fusion-module">multiview_platform.mono_multi_view_classifiers.multiview_classifiers.disagree_fusion.disagree_fusion module</a></li>
-<li class="toctree-l4"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.disagree_fusion.html#module-multiview_platform.mono_multi_view_classifiers.multiview_classifiers.disagree_fusion">Module contents</a></li>
+<li class="toctree-l4"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.disagree_fusion.html#module-contents">Module contents</a></li>
 </ul>
 </li>
 <li class="toctree-l3"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.double_fault_fusion.html">multiview_platform.mono_multi_view_classifiers.multiview_classifiers.double_fault_fusion package</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.double_fault_fusion.html#submodules">Submodules</a></li>
 <li class="toctree-l4"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.double_fault_fusion.html#multiview-platform-mono-multi-view-classifiers-multiview-classifiers-double-fault-fusion-analyze-results-module">multiview_platform.mono_multi_view_classifiers.multiview_classifiers.double_fault_fusion.analyze_results module</a></li>
 <li class="toctree-l4"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.double_fault_fusion.html#multiview-platform-mono-multi-view-classifiers-multiview-classifiers-double-fault-fusion-double-fault-fusion-module">multiview_platform.mono_multi_view_classifiers.multiview_classifiers.double_fault_fusion.double_fault_fusion module</a></li>
-<li class="toctree-l4"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.double_fault_fusion.html#module-multiview_platform.mono_multi_view_classifiers.multiview_classifiers.double_fault_fusion">Module contents</a></li>
+<li class="toctree-l4"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.double_fault_fusion.html#module-contents">Module contents</a></li>
 </ul>
 </li>
 <li class="toctree-l3"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.entropy_fusion.html">multiview_platform.mono_multi_view_classifiers.multiview_classifiers.entropy_fusion package</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.entropy_fusion.html#submodules">Submodules</a></li>
 <li class="toctree-l4"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.entropy_fusion.html#multiview-platform-mono-multi-view-classifiers-multiview-classifiers-entropy-fusion-analyze-results-module">multiview_platform.mono_multi_view_classifiers.multiview_classifiers.entropy_fusion.analyze_results module</a></li>
 <li class="toctree-l4"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.entropy_fusion.html#multiview-platform-mono-multi-view-classifiers-multiview-classifiers-entropy-fusion-entropy-fusion-module">multiview_platform.mono_multi_view_classifiers.multiview_classifiers.entropy_fusion.entropy_fusion module</a></li>
-<li class="toctree-l4"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.entropy_fusion.html#module-multiview_platform.mono_multi_view_classifiers.multiview_classifiers.entropy_fusion">Module contents</a></li>
+<li class="toctree-l4"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.entropy_fusion.html#module-contents">Module contents</a></li>
 </ul>
 </li>
 <li class="toctree-l3"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.fat_late_fusion.html">multiview_platform.mono_multi_view_classifiers.multiview_classifiers.fat_late_fusion package</a><ul>
@@ -141,21 +138,21 @@
 </li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.html#module-multiview_platform.mono_multi_view_classifiers.multiview_classifiers">Module contents</a></li>
+<li class="toctree-l2"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.html#module-contents">Module contents</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.utils.html">multiview_platform.mono_multi_view_classifiers.utils package</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.utils.html#submodules">Submodules</a></li>
-<li class="toctree-l2"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.utils.html#module-multiview_platform.mono_multi_view_classifiers.utils.configuration">multiview_platform.mono_multi_view_classifiers.utils.configuration module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.utils.html#module-multiview_platform.mono_multi_view_classifiers.utils.dataset">multiview_platform.mono_multi_view_classifiers.utils.dataset module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.utils.html#module-multiview_platform.mono_multi_view_classifiers.utils.execution">multiview_platform.mono_multi_view_classifiers.utils.execution module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.utils.html#module-multiview_platform.mono_multi_view_classifiers.utils.get_multiview_db">multiview_platform.mono_multi_view_classifiers.utils.get_multiview_db module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.utils.html#module-multiview_platform.mono_multi_view_classifiers.utils.hyper_parameter_search">multiview_platform.mono_multi_view_classifiers.utils.hyper_parameter_search module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.utils.html#module-multiview_platform.mono_multi_view_classifiers.utils.make_file_config">multiview_platform.mono_multi_view_classifiers.utils.make_file_config module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.utils.html#module-multiview_platform.mono_multi_view_classifiers.utils.multiclass">multiview_platform.mono_multi_view_classifiers.utils.multiclass module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.utils.html#module-multiview_platform.mono_multi_view_classifiers.utils.multiview_result_analysis">multiview_platform.mono_multi_view_classifiers.utils.multiview_result_analysis module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.utils.html#module-multiview_platform.mono_multi_view_classifiers.utils.transformations">multiview_platform.mono_multi_view_classifiers.utils.transformations module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.utils.html#module-multiview_platform.mono_multi_view_classifiers.utils">Module contents</a></li>
+<li class="toctree-l2"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.utils.html#multiview-platform-mono-multi-view-classifiers-utils-configuration-module">multiview_platform.mono_multi_view_classifiers.utils.configuration module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.utils.html#multiview-platform-mono-multi-view-classifiers-utils-dataset-module">multiview_platform.mono_multi_view_classifiers.utils.dataset module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.utils.html#multiview-platform-mono-multi-view-classifiers-utils-execution-module">multiview_platform.mono_multi_view_classifiers.utils.execution module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.utils.html#multiview-platform-mono-multi-view-classifiers-utils-get-multiview-db-module">multiview_platform.mono_multi_view_classifiers.utils.get_multiview_db module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.utils.html#multiview-platform-mono-multi-view-classifiers-utils-hyper-parameter-search-module">multiview_platform.mono_multi_view_classifiers.utils.hyper_parameter_search module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.utils.html#multiview-platform-mono-multi-view-classifiers-utils-make-file-config-module">multiview_platform.mono_multi_view_classifiers.utils.make_file_config module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.utils.html#multiview-platform-mono-multi-view-classifiers-utils-multiclass-module">multiview_platform.mono_multi_view_classifiers.utils.multiclass module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.utils.html#multiview-platform-mono-multi-view-classifiers-utils-multiview-result-analysis-module">multiview_platform.mono_multi_view_classifiers.utils.multiview_result_analysis module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.utils.html#multiview-platform-mono-multi-view-classifiers-utils-transformations-module">multiview_platform.mono_multi_view_classifiers.utils.transformations module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.utils.html#module-contents">Module contents</a></li>
 </ul>
 </li>
 </ul>
@@ -164,869 +161,14 @@
 <div class="section" id="submodules">
 <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this headline">¶</a></h2>
 </div>
-<div class="section" id="module-multiview_platform.mono_multi_view_classifiers.exec_classif">
-<span id="multiview-platform-mono-multi-view-classifiers-exec-classif-module"></span><h2>multiview_platform.mono_multi_view_classifiers.exec_classif module<a class="headerlink" href="#module-multiview_platform.mono_multi_view_classifiers.exec_classif" title="Permalink to this headline">¶</a></h2>
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.exec_classif.arange_metrics">
-<code class="descname">arange_metrics</code><span class="sig-paren">(</span><em>metrics</em>, <em>metric_princ</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.exec_classif.arange_metrics" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to get the metrics list in the right order so that
-the first one is the principal metric specified in args</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>metrics</strong> (<em>list of lists</em>) – The metrics that will be used in the benchmark</li>
-<li><strong>metric_princ</strong> (<em>str</em>) – The name of the metric that need to be used for the hyper-parameter
-optimization process</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>metrics</strong> – The metrics list, but arranged  so the first one is the principal one.</p>
-</td>
-</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">list of lists</p>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.exec_classif.benchmark_init">
-<code class="descname">benchmark_init</code><span class="sig-paren">(</span><em>directory</em>, <em>classification_indices</em>, <em>labels</em>, <em>labels_dictionary</em>, <em>k_folds</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.exec_classif.benchmark_init" title="Permalink to this definition">¶</a></dt>
-<dd><p>Initializes the benchmark, by saving the indices of the train
-examples and the cross validation folds.</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>directory</strong> (<em>str</em>) – The benchmark’s result directory</li>
-<li><strong>classification_indices</strong> (<em>numpy array</em>) – The indices of the examples, splitted for the train/test split</li>
-<li><strong>labels</strong> (<em>numpy array</em>) – The labels of the dataset</li>
-<li><strong>labels_dictionary</strong> (<em>dict</em>) – The dictionary with labels as keys and their names as values</li>
-<li><strong>k_folds</strong> (<em>sklearn.model_selection.Folds object</em>) – The folds for the cross validation process</li>
-</ul>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.exec_classif.exec_benchmark">
-<code class="descname">exec_benchmark</code><span class="sig-paren">(</span><em>nb_cores</em>, <em>stats_iter</em>, <em>nb_multiclass</em>, <em>benchmark_arguments_dictionaries</em>, <em>classification_indices</em>, <em>directories</em>, <em>directory</em>, <em>multi_class_labels</em>, <em>metrics</em>, <em>labels_dictionary</em>, <em>nb_labels</em>, <em>dataset_var</em>, <em>exec_one_benchmark_mono_core=&lt;function exec_one_benchmark_mono_core&gt;</em>, <em>get_results=&lt;function get_results&gt;</em>, <em>delete=&lt;function delete_HDF5&gt;</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.exec_classif.exec_benchmark" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to execute the needed benchmark(s) on multicore or mono-core functions.</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>nb_cores</strong> (<em>int</em>) – Number of threads that the benchmarks can use.</li>
-<li><strong>stats_iter</strong> (<em>int</em>) – Number of statistical iterations that have to be done.</li>
-<li><strong>benchmark_arguments_dictionaries</strong> (<em>list of dictionaries</em>) – All the needed arguments for the benchmarks.</li>
-<li><strong>classification_indices</strong> (<em>list of lists of numpy.ndarray</em>) – For each statistical iteration a couple of numpy.ndarrays is stored with the indices for the training set and
-the ones of the testing set.</li>
-<li><strong>directories</strong> (<em>list of strings</em>) – List of the paths to the result directories for each statistical iteration.</li>
-<li><strong>directory</strong> (<em>string</em>) – Path to the main results directory.</li>
-<li><strong>multi_class_labels</strong> (<em>ist of lists of numpy.ndarray</em>) – For each label couple, for each statistical iteration a triplet of numpy.ndarrays is stored with the
-indices for the biclass training set, the ones for the biclass testing set and the ones for the
-multiclass testing set.</li>
-<li><strong>metrics</strong> (<em>list of lists</em>) – metrics that will be used to evaluate the algorithms performance.</li>
-<li><strong>labels_dictionary</strong> (<em>dictionary</em>) – Dictionary mapping labels indices to labels names.</li>
-<li><strong>nb_labels</strong> (<em>int</em>) – Total number of different labels in the dataset.</li>
-<li><strong>dataset_var</strong> (<em>HDF5 dataset file</em>) – The full dataset that wil be used by the benchmark.</li>
-<li><strong>classifiers_names</strong> (<em>list of strings</em>) – List of the benchmarks’s monoview classifiers names.</li>
-<li><strong>rest_of_the_args</strong> – Just used for testing purposes</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>results</strong> – The results of the benchmark.</p>
-</td>
-</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">list of lists</p>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.exec_classif.exec_classif">
-<code class="descname">exec_classif</code><span class="sig-paren">(</span><em>arguments</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.exec_classif.exec_classif" title="Permalink to this definition">¶</a></dt>
-<dd><p>Runs the benchmark with the given arguments</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>arguments</strong> – </td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><ul class="simple">
-<li><em>&gt;&gt;&gt; exec_classif([–config_path, /path/to/config/files/])</em></li>
-<li><em>&gt;&gt;&gt;</em></li>
-</ul>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.exec_classif.exec_one_benchmark_mono_core">
-<code class="descname">exec_one_benchmark_mono_core</code><span class="sig-paren">(</span><em>dataset_var=None</em>, <em>labels_dictionary=None</em>, <em>directory=None</em>, <em>classification_indices=None</em>, <em>args=None</em>, <em>k_folds=None</em>, <em>random_state=None</em>, <em>hyper_param_search=None</em>, <em>metrics=None</em>, <em>argument_dictionaries=None</em>, <em>benchmark=None</em>, <em>views=None</em>, <em>views_indices=None</em>, <em>flag=None</em>, <em>labels=None</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.exec_classif.exec_one_benchmark_mono_core" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.exec_classif.extract_dict">
-<code class="descname">extract_dict</code><span class="sig-paren">(</span><em>classifier_config</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.exec_classif.extract_dict" title="Permalink to this definition">¶</a></dt>
-<dd><p>Reverse function of get_path_dict</p>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.exec_classif.gen_multiple_args_dictionnaries">
-<code class="descname">gen_multiple_args_dictionnaries</code><span class="sig-paren">(</span><em>nb_class</em>, <em>kwargs_init</em>, <em>classifier</em>, <em>view_name=None</em>, <em>view_index=None</em>, <em>views_dictionary=None</em>, <em>framework='monoview'</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.exec_classif.gen_multiple_args_dictionnaries" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used in the case of mutliple arguments asked in the config file.
-Will combine the arguments to explore all the possibilities.</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>nb_class</strong> (<em>int</em><em>,</em>) – The number of classes in the dataset</li>
-<li><strong>kwargs_init</strong> (<em>dict</em>) – The arguments given in the config file</li>
-<li><strong>classifier</strong> (<em>str</em>) – The name of the classifier for which multiple arguments have been asked</li>
-<li><strong>view_name</strong> (<em>str</em>) – The name of the view in consideration.</li>
-<li><strong>view_index</strong> (<em>int</em>) – The index of the view in consideration</li>
-<li><strong>views_dictionary</strong> (<em>dict</em>) – The dictionary of all the views indices and their names</li>
-<li><strong>framework</strong> (<em>str</em>) – Either monoview or multiview</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>args_dictionaries</strong> – The list of all the possible combination of asked arguments</p>
-</td>
-</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">list</p>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.exec_classif.gen_multiple_kwargs_combinations">
-<code class="descname">gen_multiple_kwargs_combinations</code><span class="sig-paren">(</span><em>cl_kwrags</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.exec_classif.gen_multiple_kwargs_combinations" title="Permalink to this definition">¶</a></dt>
-<dd><p>Generates all the possible combination of the asked args</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>cl_kwrags</strong> (<em>dict</em>) – The arguments, with one at least having multiple values</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><ul class="simple">
-<li><strong>kwargs_combination</strong> (<em>list</em>) – The list of all the combinations of arguments</li>
-<li><strong>reduced_kwargs_combination</strong> (<em>list</em>) – The reduced names and values of the arguments will be used in the naming
-process of the different classifiers</li>
-</ul>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.exec_classif.gen_single_monoview_arg_dictionary">
-<code class="descname">gen_single_monoview_arg_dictionary</code><span class="sig-paren">(</span><em>classifier_name</em>, <em>arguments</em>, <em>nb_class</em>, <em>view_index</em>, <em>view_name</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.exec_classif.gen_single_monoview_arg_dictionary" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.exec_classif.gen_single_multiview_arg_dictionary">
-<code class="descname">gen_single_multiview_arg_dictionary</code><span class="sig-paren">(</span><em>classifier_name</em>, <em>arguments</em>, <em>nb_class</em>, <em>views_dictionary=None</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.exec_classif.gen_single_multiview_arg_dictionary" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.exec_classif.get_path_dict">
-<code class="descname">get_path_dict</code><span class="sig-paren">(</span><em>multiview_classifier_args</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.exec_classif.get_path_dict" title="Permalink to this definition">¶</a></dt>
-<dd><p>This function is used to generate a dictionary with each key being
-the path to the value.
-If given {“key1”:{“key1_1”:value1}, “key2”:value2}, it will return
-{“key1.key1_1”:value1, “key2”:value2}</p>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.exec_classif.init_argument_dictionaries">
-<code class="descname">init_argument_dictionaries</code><span class="sig-paren">(</span><em>benchmark</em>, <em>views_dictionary</em>, <em>nb_class</em>, <em>init_kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.exec_classif.init_argument_dictionaries" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.exec_classif.init_benchmark">
-<code class="descname">init_benchmark</code><span class="sig-paren">(</span><em>cl_type</em>, <em>monoview_algos</em>, <em>multiview_algos</em>, <em>args</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.exec_classif.init_benchmark" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to create a list of all the algorithm packages names used for the benchmark.</p>
-<p>First this function will check if the benchmark need mono- or/and multiview
-algorithms and adds to the right
-dictionary the asked algorithms. If none is asked by the user, all will be added.</p>
-<p>If the keyword <cite>“Benchmark”</cite> is used, all mono- and multiview algorithms will be added.</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>cl_type</strong> (<em>List of string</em>) – List of types of needed benchmark</li>
-<li><strong>multiview_algos</strong> (<em>List of strings</em>) – List of multiview algorithms needed for the benchmark</li>
-<li><strong>monoview_algos</strong> (<em>Listof strings</em>) – List of monoview algorithms needed for the benchmark</li>
-<li><strong>args</strong> (<em>ParsedArgumentParser args</em>) – All the input args (used to tune the algorithms)</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>benchmark</strong> – Dictionary resuming which mono- and multiview algorithms which will be used in the benchmark.</p>
-</td>
-</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">Dictionary of dictionaries</p>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.exec_classif.init_kwargs">
-<code class="descname">init_kwargs</code><span class="sig-paren">(</span><em>args</em>, <em>classifiers_names</em>, <em>framework='monoview'</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.exec_classif.init_kwargs" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to init kwargs thanks to a function in each monoview classifier package.</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>args</strong> (<em>parsed args objects</em>) – All the args passed by the user.</li>
-<li><strong>classifiers_names</strong> (<em>list of strings</em>) – List of the benchmarks’s monoview classifiers names.</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><p><strong>kwargs</strong> – Dictionary resuming all the specific arguments for the benchmark, one dictionary for each classifier.</p>
-<p>For example, for Adaboost, the KWARGS will be <cite>{“n_estimators”:&lt;value&gt;, “base_estimator”:&lt;value&gt;}</cite></p>
-</p>
-</td>
-</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">Dictionary</p>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.exec_classif.init_kwargs_func">
-<code class="descname">init_kwargs_func</code><span class="sig-paren">(</span><em>args</em>, <em>benchmark</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.exec_classif.init_kwargs_func" title="Permalink to this definition">¶</a></dt>
-<dd><p>Dispached the kwargs initialization to monoview and multiview and creates
-the kwargs variable</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>args</strong> (<em>parsed args objects</em>) – All the args passed by the user.</li>
-<li><strong>benchmark</strong> (<em>dict</em>) – The name of the mono- and mutli-view classifiers to run in the benchmark</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>kwargs</strong> – The arguments for each mono- and multiview algorithms</p>
-</td>
-</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">dict</p>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.exec_classif.init_monoview_exps">
-<code class="descname">init_monoview_exps</code><span class="sig-paren">(</span><em>classifier_names</em>, <em>views_dictionary</em>, <em>nb_class</em>, <em>kwargs_init</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.exec_classif.init_monoview_exps" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to add each monoview exeperience args to the list of monoview experiences args.</p>
-<p>First this function will check if the benchmark need mono- or/and multiview algorithms and adds to the right
-dictionary the asked algorithms. If none is asked by the user, all will be added.</p>
-<p>If the keyword <cite>“Benchmark”</cite> is used, all mono- and multiview algorithms will be added.</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>classifier_names</strong> (<em>dictionary</em>) – All types of monoview and multiview experiments that have to be benchmarked</li>
-<li><strong>argument_dictionaries</strong> (<em>dictionary</em>) – Maps monoview and multiview experiments arguments.</li>
-<li><strong>views_dictionary</strong> (<em>dictionary</em>) – Maps the view names to their index in the HDF5 dataset</li>
-<li><strong>nb_class</strong> (<em>integer</em>) – Number of different labels in the classification</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>benchmark</strong> – Dictionary resuming which mono- and multiview algorithms which will be used in the benchmark.</p>
-</td>
-</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">Dictionary of dictionaries</p>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.exec_classif.init_multiview_exps">
-<code class="descname">init_multiview_exps</code><span class="sig-paren">(</span><em>classifier_names</em>, <em>views_dictionary</em>, <em>nb_class</em>, <em>kwargs_init</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.exec_classif.init_multiview_exps" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.exec_classif.is_dict_in">
-<code class="descname">is_dict_in</code><span class="sig-paren">(</span><em>dictionary</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.exec_classif.is_dict_in" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns True if any of the dictionary value is a dictionary itself.</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>dictionary</strong> – </td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.exec_classif.multiple_args">
-<code class="descname">multiple_args</code><span class="sig-paren">(</span><em>classifier_configuration</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.exec_classif.multiple_args" title="Permalink to this definition">¶</a></dt>
-<dd><p>Checks if multiple values were provided for at least one arg</p>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.exec_classif.set_element">
-<code class="descname">set_element</code><span class="sig-paren">(</span><em>dictionary</em>, <em>path</em>, <em>value</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.exec_classif.set_element" title="Permalink to this definition">¶</a></dt>
-<dd><p>Set value in dictionary at the location indicated by path</p>
-</dd></dl>
-
+<div class="section" id="multiview-platform-mono-multi-view-classifiers-exec-classif-module">
+<h2>multiview_platform.mono_multi_view_classifiers.exec_classif module<a class="headerlink" href="#multiview-platform-mono-multi-view-classifiers-exec-classif-module" title="Permalink to this headline">¶</a></h2>
 </div>
-<div class="section" id="module-multiview_platform.mono_multi_view_classifiers.result_analysis">
-<span id="multiview-platform-mono-multi-view-classifiers-result-analysis-module"></span><h2>multiview_platform.mono_multi_view_classifiers.result_analysis module<a class="headerlink" href="#module-multiview_platform.mono_multi_view_classifiers.result_analysis" title="Permalink to this headline">¶</a></h2>
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.result_analysis.add_new_labels_combination">
-<code class="descname">add_new_labels_combination</code><span class="sig-paren">(</span><em>iterBiclassResults</em>, <em>labelsComination</em>, <em>nbClassifiers</em>, <em>nbExamples</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.result_analysis.add_new_labels_combination" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.result_analysis.add_new_metric">
-<code class="descname">add_new_metric</code><span class="sig-paren">(</span><em>iter_biclass_results</em>, <em>metric</em>, <em>labels_combination</em>, <em>nb_classifiers</em>, <em>stats_iter</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.result_analysis.add_new_metric" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.result_analysis.analyzeMulticlass">
-<code class="descname">analyzeMulticlass</code><span class="sig-paren">(</span><em>results</em>, <em>stats_iter</em>, <em>benchmark_argument_dictionaries</em>, <em>nb_examples</em>, <em>nb_labels</em>, <em>multiclass_labels</em>, <em>metrics</em>, <em>classification_indices</em>, <em>directories</em>, <em>example_ids</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.result_analysis.analyzeMulticlass" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to transform one versus one results in multiclass results and to publish it</p>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.result_analysis.analyze_biclass">
-<code class="descname">analyze_biclass</code><span class="sig-paren">(</span><em>results</em>, <em>benchmark_argument_dictionaries</em>, <em>stats_iter</em>, <em>metrics</em>, <em>example_ids</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.result_analysis.analyze_biclass" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to extract and format the results of the different biclass experimentations performed.</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>results</strong> (<em>list</em>) – The result list returned by the bencmark execution function. For each executed benchmark, contains
-a flag &amp; a result element.
-The flag is a way to identify to which benchmark the results belong, formatted this way :
-<cite>flag = iter_index, [classifierPositive, classifierNegative]</cite> with
-- <cite>iter_index</cite> the index of the statistical iteration
-- <cite>[classifierPositive, classifierNegative]</cite> the indices of the labels considered positive and negative
-by the classifier (mainly useful for one versus one multiclass classification).</li>
-<li><strong>benchmark_argument_dictionaries</strong> (<em>list of dicts</em>) – The list of all the arguments passed to the benchmark executing functions.</li>
-<li><strong>statsIter</strong> (<em>int</em>) – The number of statistical iterations.</li>
-<li><strong>metrics</strong> (<em>list of lists</em>) – THe list containing the metrics and their configuration.</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>biclassResults</strong> – The list contains a dictionary for each statistical iteration. This dictionary contains a dictionary for each
-label combination, regrouping the scores for each metrics and the information useful to plot errors on examples.</p>
-</td>
-</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">list of dicts of dicts</p>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.result_analysis.analyze_iter_multiclass">
-<code class="descname">analyze_iter_multiclass</code><span class="sig-paren">(</span><em>multiclass_results</em>, <em>directory</em>, <em>stats_iter</em>, <em>metrics</em>, <em>data_base_name</em>, <em>nb_examples</em>, <em>example_ids</em>, <em>multiclass_labels</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.result_analysis.analyze_iter_multiclass" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to mean the multiclass results on the iterations executed with different random states</p>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.result_analysis.analyzebiclass_iter">
-<code class="descname">analyzebiclass_iter</code><span class="sig-paren">(</span><em>biclass_results</em>, <em>stats_iter</em>, <em>directory</em>, <em>labels_dictionary</em>, <em>data_base_name</em>, <em>example_ids</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.result_analysis.analyzebiclass_iter" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to format the results in order to plot the mean results on the iterations</p>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.result_analysis.autolabel">
-<code class="descname">autolabel</code><span class="sig-paren">(</span><em>rects</em>, <em>ax</em>, <em>set=1</em>, <em>std=None</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.result_analysis.autolabel" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to print the score below the bars.</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>rects</strong> (<em>pyplot bar object</em>) – THe bars.</li>
-<li><strong>ax</strong> (<em>pyplot ax object</em>) – The ax.</li>
-<li><strong>set</strong> (<em>integer</em>) – 1 means the test scores, anything else means the train score</li>
-<li><strong>std</strong> (<em>None</em><em> or </em><em>array</em>) – The standard deviations in the case of statsIter results.</li>
-</ul>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.result_analysis.format_previous_results">
-<code class="descname">format_previous_results</code><span class="sig-paren">(</span><em>biclass_results</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.result_analysis.format_previous_results" title="Permalink to this definition">¶</a></dt>
-<dd><p>Formats each statistical iteration’s result into a mean/std analysis for
-the metrics and adds the errors of each statistical iteration.</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>biclass_results</strong> (<em>The raw results</em><em>, </em><em>for each statistical iteration i contains</em>) – <ul class="simple">
-<li>biclass_results[i][“metrics_scores”] is a dictionary with a pd.dataframe
-for each metrics</li>
-<li>biclass_results[i][“example_errors”], a dicaitonary with a np.array</li>
-</ul>
-<p>for each classifier.</p>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><ul class="simple">
-<li><strong>metrics_analysis</strong> (<em>The mean and std dataframes for each metrics</em>)</li>
-<li><strong>error_analysis</strong> (<em>A dictionary containing the added errors</em>) – arrays for each classifier</li>
-</ul>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.result_analysis.gen_classifiers_dict">
-<code class="descname">gen_classifiers_dict</code><span class="sig-paren">(</span><em>results</em>, <em>metrics</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.result_analysis.gen_classifiers_dict" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.result_analysis.gen_error_data">
-<code class="descname">gen_error_data</code><span class="sig-paren">(</span><em>example_errors</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.result_analysis.gen_error_data" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to format the error data in order to plot it efficiently. The data is saves in a <cite>.csv</cite> file.</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>example_errors</strong> (<em>dict of dicts of np.arrays</em>) – A dictionary conatining all the useful data. Organized as :
-<cite>example_errors[&lt;classifier_name&gt;][“error_on_examples”]</cite> is a np.array of ints with a
-- 1 if the classifier <cite>&lt;classifier_name&gt;</cite> classifier well the example,
-- 0 if it fail to classify the example,
-- -100 if it did not classify the example (multiclass one versus one).</li>
-<li><strong>base_file_name</strong> (<em>list of str</em>) – The name of the file in which the figure will be saved (“2D_plot_data.csv” and “bar_plot_data.csv” will
-be added at the end).</li>
-<li><strong>nbCopies</strong> (<em>int</em><em>, </em><em>optinal</em><em>, </em><em>default: 2</em>) – The number of times the data is copied (classifier wise) in order for the figure to be more readable.</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last"><ul class="simple">
-<li><strong>nbClassifiers</strong> (<em>int</em>) – Number of different classifiers.</li>
-<li><strong>nbExamples</strong> (<em>int</em>) – NUmber of examples.</li>
-<li><strong>nbCopies</strong> (<em>int</em>) – The number of times the data is copied (classifier wise) in order for the figure to be more readable.</li>
-<li><strong>classifiers_names</strong> (<em>list of strs</em>) – The names fo the classifiers.</li>
-<li><strong>data</strong> (np.array of shape <cite>(nbClassifiers, nbExamples)</cite>) – A matrix with zeros where the classifier failed to classifiy the example, ones where it classified it well
-and -100 if the example was not classified.</li>
-<li><strong>error_on_examples</strong> (np.array of shape <cite>(nbExamples,)</cite>) – An array counting how many classifiers failed to classifiy each examples.</li>
-</ul>
-</p>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.result_analysis.gen_error_data_glob">
-<code class="descname">gen_error_data_glob</code><span class="sig-paren">(</span><em>combi_results</em>, <em>stats_iter</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.result_analysis.gen_error_data_glob" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.result_analysis.gen_metrics_scores_multiclass">
-<code class="descname">gen_metrics_scores_multiclass</code><span class="sig-paren">(</span><em>results</em>, <em>true_labels</em>, <em>metrics_list</em>, <em>arguments_dictionaries</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.result_analysis.gen_metrics_scores_multiclass" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to add all the metrics scores to the multiclass result structure  for each clf and each iteration</p>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.result_analysis.get_arguments">
-<code class="descname">get_arguments</code><span class="sig-paren">(</span><em>benchmark_argument_dictionaries</em>, <em>flag</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.result_analysis.get_arguments" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to get the arguments passed to the benchmark executing function corresponding to the flag of a
-biclass experimentation.</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>flag</strong> (<em>list</em>) – The needed experimentation’s flag.</li>
-<li><strong>benchmark_argument_dictionaries</strong> (<em>list of dicts</em>) – The list of all the arguments passed to the benchmark executing functions.</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>benchmarkArgumentDictionary</strong> – All the arguments passed to the benchmark executing function for the needed experimentation.</p>
-</td>
-</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">dict</p>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.result_analysis.get_error_on_labels_multiclass">
-<code class="descname">get_error_on_labels_multiclass</code><span class="sig-paren">(</span><em>multiclass_results</em>, <em>multiclass_labels</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.result_analysis.get_error_on_labels_multiclass" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to add all the arrays showing on which example there is an error for each clf and each iteration</p>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.result_analysis.get_example_errors_biclass">
-<code class="descname">get_example_errors_biclass</code><span class="sig-paren">(</span><em>groud_truth</em>, <em>results</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.result_analysis.get_example_errors_biclass" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to get for each classifier and each example whether the classifier has misclassified the example or not.</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>ground_truth</strong> (<em>numpy array of 0</em><em>, </em><em>1 and -100</em><em> (</em><em>if multiclass</em><em>)</em>) – The array with the real labels of the examples</li>
-<li><strong>results</strong> (<em>list of MonoviewResult and MultiviewResults objects</em>) – A list containing all the resluts for all the mono- &amp; multi-view experimentations.</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>example_errors</strong> – For each classifier, has an entry with a <cite>np.array</cite> over the examples, with a 1 if the examples was
-well-classified, a 0 if not and if it’s multiclass classification, a -100 if the examples was not seen during
-the one versus one classification.</p>
-</td>
-</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">dict of np.array</p>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.result_analysis.get_feature_importances">
-<code class="descname">get_feature_importances</code><span class="sig-paren">(</span><em>result</em>, <em>feature_names=None</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.result_analysis.get_feature_importances" title="Permalink to this definition">¶</a></dt>
-<dd><p>Extracts the feature importance from the monoview results and stores them in a dictionnary :
-feature_importance[view_name] is a pandas.DataFrame of size n_feature*n_clf
-containing a score of importance for each feature.</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>result</strong> (<em>list of results</em>) – </td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><strong>feature_importances</strong> – The dictionary containing all the feature importance for each view as pandas DataFrames</td>
-</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body">dict of pd.DataFrame</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.result_analysis.get_fig_size">
-<code class="descname">get_fig_size</code><span class="sig-paren">(</span><em>nb_results</em>, <em>min_size=15</em>, <em>multiplier=1.0</em>, <em>bar_width=0.35</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.result_analysis.get_fig_size" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to get the image size to save the figure and the bar width, depending on the number of scores to plot.</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>nb_results</strong> (<em>int</em>) – The number of couple of bar to plot.</li>
-<li><strong>min_size</strong> (<em>int</em>) – The minimum size of the image, if there are few classifiers to plot.</li>
-<li><strong>multiplier</strong> (<em>float</em>) – The ratio between the image size and the number of classifiers.</li>
-<li><strong>bar_width</strong> (<em>float</em>) – The width of the bars in the figure. Mainly here to centralize bar_width.</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last"><ul class="simple">
-<li><strong>fig_kwargs</strong> (<em>dict of arguments</em>) – The argument restraining the size of the figure, usable directly in the <cite>subplots</cite> function of
-<cite>matplotlib.pyplot</cite>.</li>
-<li><strong>bar_width</strong> (<em>float</em>) – The width of the bars in the figure. Mainly here to centralize bar_width.</li>
-</ul>
-</p>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.result_analysis.get_metrics_scores_biclass">
-<code class="descname">get_metrics_scores_biclass</code><span class="sig-paren">(</span><em>metrics</em>, <em>results</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.result_analysis.get_metrics_scores_biclass" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to extract metrics scores in case of biclass classification</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>metrics</strong> (<em>list of lists</em>) – The metrics names with configuration metrics[i][0] = name of metric i</li>
-<li><strong>results</strong> (<em>list of MonoviewResult and MultiviewResults objects</em>) – A list containing all the results for all the monoview experimentations.</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>metricsScores</strong> – Regroups all the scores for each metrics for each classifier and for the train and test sets.
-organized as :
--<cite>metricScores[metric_name][“classifiers_names”]</cite> is a list of all the classifiers available for this metric,
--<cite>metricScores[metric_name][“train_scores”]</cite> is a list of all the available classifiers scores on the train set,
--<cite>metricScores[metric_name][“test_scores”]</cite> is a list of all the available classifiers scores on the test set.</p>
-</td>
-</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">dict of dict of list</p>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.result_analysis.get_results">
-<code class="descname">get_results</code><span class="sig-paren">(</span><em>results</em>, <em>stats_iter</em>, <em>nb_multiclass</em>, <em>benchmark_argument_dictionaries</em>, <em>multiclass_labels</em>, <em>metrics</em>, <em>classification_indices</em>, <em>directories</em>, <em>directory</em>, <em>labels_dictionary</em>, <em>nb_examples</em>, <em>nb_labels</em>, <em>example_ids</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.result_analysis.get_results" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to analyze the results of the previous benchmarks</p>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.result_analysis.init_plot">
-<code class="descname">init_plot</code><span class="sig-paren">(</span><em>results</em>, <em>metric_name</em>, <em>metric_dataframe</em>, <em>directory</em>, <em>database_name</em>, <em>labels_names</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.result_analysis.init_plot" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.result_analysis.iterCmap">
-<code class="descname">iterCmap</code><span class="sig-paren">(</span><em>statsIter</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.result_analysis.iterCmap" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to generate a colormap that will have a tick for each iteration : the whiter the better.</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>statsIter</strong> (<em>int</em>) – The number of statistical iterations.</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><ul class="simple">
-<li><strong>cmap</strong> (<em>matplotlib.colors.ListedColorMap object</em>) – The colormap.</li>
-<li><strong>norm</strong> (<em>matplotlib.colors.BoundaryNorm object</em>) – The bounds for the colormap.</li>
-</ul>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.result_analysis.numpy_mean_and_std">
-<code class="descname">numpy_mean_and_std</code><span class="sig-paren">(</span><em>scores_array</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.result_analysis.numpy_mean_and_std" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.result_analysis.plot_2d">
-<code class="descname">plot_2d</code><span class="sig-paren">(</span><em>data</em>, <em>classifiers_names</em>, <em>nbClassifiers</em>, <em>nbExamples</em>, <em>file_name</em>, <em>minSize=10</em>, <em>labels=None</em>, <em>width_denominator=2.0</em>, <em>height_denominator=20.0</em>, <em>stats_iter=1</em>, <em>use_plotly=True</em>, <em>example_ids=None</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.result_analysis.plot_2d" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to generate a 2D plot of the errors.</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>data</strong> (np.array of shape <cite>(nbClassifiers, nbExamples)</cite>) – A matrix with zeros where the classifier failed to classifiy the example, ones where it classified it well
-and -100 if the example was not classified.</li>
-<li><strong>classifiers_names</strong> (<em>list of str</em>) – The names of the classifiers.</li>
-<li><strong>nbClassifiers</strong> (<em>int</em>) – The number of classifiers.</li>
-<li><strong>nbExamples</strong> (<em>int</em>) – The number of examples.</li>
-<li><strong>nbCopies</strong> (<em>int</em>) – The number of times the data is copied (classifier wise) in order for the figure to be more readable</li>
-<li><strong>file_name</strong> (<em>str</em>) – The name of the file in which the figure will be saved (“error_analysis_2D.png” will be added at the end)</li>
-<li><strong>minSize</strong> (<em>int</em><em>, </em><em>optinal</em><em>, </em><em>default: 10</em>) – The minimum width and height of the figure.</li>
-<li><strong>width_denominator</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default: 1.0</em>) – To obtain the image width, the number of classifiers will be divided by this number.</li>
-<li><strong>height_denominator</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default: 1.0</em>) – To obtain the image width, the number of examples will be divided by this number.</li>
-<li><strong>stats_iter</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default: 1</em>) – The number of statistical iterations realized.</li>
-</ul>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.result_analysis.plot_errors_bar">
-<code class="descname">plot_errors_bar</code><span class="sig-paren">(</span><em>error_on_examples</em>, <em>nbClassifiers</em>, <em>nbExamples</em>, <em>fileName</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.result_analysis.plot_errors_bar" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to generate a barplot of the muber of classifiers that failed to classify each examples</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>error_on_examples</strong> (np.array of shape <cite>(nbExamples,)</cite>) – An array counting how many classifiers failed to classifiy each examples.</li>
-<li><strong>classifiers_names</strong> (<em>list of str</em>) – The names of the classifiers.</li>
-<li><strong>nbClassifiers</strong> (<em>int</em>) – The number of classifiers.</li>
-<li><strong>nbExamples</strong> (<em>int</em>) – The number of examples.</li>
-<li><strong>fileName</strong> (<em>str</em>) – The name of the file in which the figure will be saved (“error_analysis_2D.png” will be added at the end)</li>
-</ul>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.result_analysis.plot_metric_scores">
-<code class="descname">plot_metric_scores</code><span class="sig-paren">(</span><em>train_scores</em>, <em>test_scores</em>, <em>names</em>, <em>nb_results</em>, <em>metric_name</em>, <em>file_name</em>, <em>tag=''</em>, <em>train_STDs=None</em>, <em>test_STDs=None</em>, <em>use_plotly=True</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.result_analysis.plot_metric_scores" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to plot and save the score barplot for a specific metric.</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>train_scores</strong> (<em>list</em><em> or </em><em>np.array of floats</em>) – The scores of each classifier on the training set.</li>
-<li><strong>test_scores</strong> (<em>list</em><em> or </em><em>np.array of floats</em>) – The scores of each classifier on the testing set.</li>
-<li><strong>names</strong> (<em>list</em><em> or </em><em>np.array of strs</em>) – The names of all the classifiers.</li>
-<li><strong>nb_results</strong> (<em>int</em>) – The number of classifiers to plot.</li>
-<li><strong>metric_name</strong> (<em>str</em>) – The plotted metric’s name</li>
-<li><strong>file_name</strong> (<em>str</em>) – The name of the file where the figure will be saved.</li>
-<li><strong>tag</strong> (<em>str</em>) – Some text to personalize the title, must start with a whitespace.</li>
-<li><strong>train_STDs</strong> (<em>np.array of floats</em><em> or </em><em>None</em>) – The array containing the standard deviations for the averaged scores on the training set.</li>
-<li><strong>test_STDs</strong> (<em>np.array of floats</em><em> or </em><em>None</em>) – The array containing the standard deviations for the averaged scores on the testing set.</li>
-</ul>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.result_analysis.plot_results_noise">
-<code class="descname">plot_results_noise</code><span class="sig-paren">(</span><em>directory</em>, <em>noise_results</em>, <em>metric_to_plot</em>, <em>name</em>, <em>width=0.1</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.result_analysis.plot_results_noise" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.result_analysis.publishExampleErrors">
-<code class="descname">publishExampleErrors</code><span class="sig-paren">(</span><em>example_errors</em>, <em>directory</em>, <em>databaseName</em>, <em>labels_names</em>, <em>example_ids</em>, <em>labels</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.result_analysis.publishExampleErrors" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.result_analysis.publishMetricsGraphs">
-<code class="descname">publishMetricsGraphs</code><span class="sig-paren">(</span><em>metrics_scores</em>, <em>directory</em>, <em>database_name</em>, <em>labels_names</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.result_analysis.publishMetricsGraphs" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to sort the results (names and both scores) in descending test score order.</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>metrics_scores</strong> (<em>dict of dicts of lists</em><em> or </em><em>np.arrays</em>) – Keys : The names of the metrics.
-Values : The scores and names of each classifier .</li>
-<li><strong>directory</strong> (<em>str</em>) – The path to the directory where the figures will be saved.</li>
-<li><strong>database_name</strong> (<em>str</em>) – The name of the database on which the experiments where conducted.</li>
-<li><strong>labels_names</strong> (<em>list of strs</em>) – The name corresponding to each numerical label.</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"></p>
-</td>
-</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">results</p>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.result_analysis.publishMulticlassExmapleErrors">
-<code class="descname">publishMulticlassExmapleErrors</code><span class="sig-paren">(</span><em>multiclass_results</em>, <em>directories</em>, <em>databaseName</em>, <em>example_ids</em>, <em>multiclass_labels</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.result_analysis.publishMulticlassExmapleErrors" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.result_analysis.publishMulticlassScores">
-<code class="descname">publishMulticlassScores</code><span class="sig-paren">(</span><em>multiclass_results</em>, <em>metrics</em>, <em>stats_iter</em>, <em>direcories</em>, <em>databaseName</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.result_analysis.publishMulticlassScores" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.result_analysis.publish_feature_importances">
-<code class="descname">publish_feature_importances</code><span class="sig-paren">(</span><em>feature_importances</em>, <em>directory</em>, <em>database_name</em>, <em>labels_names</em>, <em>feature_stds=None</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.result_analysis.publish_feature_importances" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.result_analysis.publish_iter_biclass_example_errors">
-<code class="descname">publish_iter_biclass_example_errors</code><span class="sig-paren">(</span><em>iter_results</em>, <em>directory</em>, <em>labels_dictionary</em>, <em>stats_iter</em>, <em>example_ids</em>, <em>labels</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.result_analysis.publish_iter_biclass_example_errors" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.result_analysis.publish_iter_biclass_metrics_scores">
-<code class="descname">publish_iter_biclass_metrics_scores</code><span class="sig-paren">(</span><em>iter_results</em>, <em>directory</em>, <em>labels_dictionary</em>, <em>data_base_name</em>, <em>stats_iter</em>, <em>min_size=10</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.result_analysis.publish_iter_biclass_metrics_scores" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.result_analysis.publish_iter_multiclass_example_errors">
-<code class="descname">publish_iter_multiclass_example_errors</code><span class="sig-paren">(</span><em>iter_multiclass_results</em>, <em>directory</em>, <em>classifiers_names</em>, <em>stats_iter</em>, <em>example_ids</em>, <em>multiclass_labels</em>, <em>min_size=10</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.result_analysis.publish_iter_multiclass_example_errors" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.result_analysis.publish_iter_multiclass_metrics_scores">
-<code class="descname">publish_iter_multiclass_metrics_scores</code><span class="sig-paren">(</span><em>iter_multiclass_results</em>, <em>classifiers_names</em>, <em>data_base_name</em>, <em>directory</em>, <em>stats_iter</em>, <em>min_size=10</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.result_analysis.publish_iter_multiclass_metrics_scores" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.result_analysis.publish_tracebacks">
-<code class="descname">publish_tracebacks</code><span class="sig-paren">(</span><em>directory</em>, <em>database_name</em>, <em>labels_names</em>, <em>tracebacks</em>, <em>flag</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.result_analysis.publish_tracebacks" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.result_analysis.save_dict_to_text">
-<code class="descname">save_dict_to_text</code><span class="sig-paren">(</span><em>dictionnary</em>, <em>output_file</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.result_analysis.save_dict_to_text" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.result_analysis.save_failed">
-<code class="descname">save_failed</code><span class="sig-paren">(</span><em>failed_list</em>, <em>directory</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.result_analysis.save_failed" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.result_analysis.sort_by_test_score">
-<code class="descname">sort_by_test_score</code><span class="sig-paren">(</span><em>train_scores</em>, <em>test_scores</em>, <em>names</em>, <em>train_STDs=None</em>, <em>test_STDs=None</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.result_analysis.sort_by_test_score" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to sort the results (names and both scores) in descending test score order.</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>train_scores</strong> (<em>np.array of floats</em>) – The scores of each classifier on the training set.</li>
-<li><strong>test_scores</strong> (<em>np.array of floats</em>) – The scores of each classifier on the testing set.</li>
-<li><strong>names</strong> (<em>np.array of strs</em>) – The names of all the classifiers.</li>
-<li><strong>train_STDs</strong> (<em>np.array of floats</em><em> or </em><em>None</em>) – The array containing the standard deviations for the averaged scores on the training set.</li>
-<li><strong>test_STDs</strong> (<em>np.array of floats</em><em> or </em><em>None</em>) – The array containing the standard deviations for the averaged scores on the testing set.</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last"><ul class="simple">
-<li><strong>sorted_names</strong> (<em>np.array of strs</em>) – The names of all the classifiers, sorted in descending test score order.</li>
-<li><strong>sorted_train_scores</strong> (<em>np.array of floats</em>) – The scores of each classifier on the training set, sorted in descending test score order.</li>
-<li><strong>sorted_test_scores</strong> (<em>np.array of floats</em>) – The scores of each classifier on the testing set, sorted in descending test score order.</li>
-<li><strong>sorted_train_STDs</strong> (<em>np.array of floats or None</em>) – The array containing the standard deviations for the averaged scores on the training set,
-sorted in descending test score order.</li>
-<li><strong>sorted_test_STDs</strong> (<em>np.array of floats or None</em>) – The array containing the standard deviations for the averaged scores on the testing set,
-sorted in descending test score order.</li>
-</ul>
-</p>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
+<div class="section" id="multiview-platform-mono-multi-view-classifiers-result-analysis-module">
+<h2>multiview_platform.mono_multi_view_classifiers.result_analysis module<a class="headerlink" href="#multiview-platform-mono-multi-view-classifiers-result-analysis-module" title="Permalink to this headline">¶</a></h2>
 </div>
-<div class="section" id="module-multiview_platform.mono_multi_view_classifiers">
-<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-multiview_platform.mono_multi_view_classifiers" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="module-contents">
+<h2>Module contents<a class="headerlink" href="#module-contents" title="Permalink to this headline">¶</a></h2>
 </div>
 </div>
 
@@ -1041,9 +183,9 @@ sorted in descending test score order.</li>
 <li><a class="reference internal" href="#">multiview_platform.mono_multi_view_classifiers package</a><ul>
 <li><a class="reference internal" href="#subpackages">Subpackages</a></li>
 <li><a class="reference internal" href="#submodules">Submodules</a></li>
-<li><a class="reference internal" href="#module-multiview_platform.mono_multi_view_classifiers.exec_classif">multiview_platform.mono_multi_view_classifiers.exec_classif module</a></li>
-<li><a class="reference internal" href="#module-multiview_platform.mono_multi_view_classifiers.result_analysis">multiview_platform.mono_multi_view_classifiers.result_analysis module</a></li>
-<li><a class="reference internal" href="#module-multiview_platform.mono_multi_view_classifiers">Module contents</a></li>
+<li><a class="reference internal" href="#multiview-platform-mono-multi-view-classifiers-exec-classif-module">multiview_platform.mono_multi_view_classifiers.exec_classif module</a></li>
+<li><a class="reference internal" href="#multiview-platform-mono-multi-view-classifiers-result-analysis-module">multiview_platform.mono_multi_view_classifiers.result_analysis module</a></li>
+<li><a class="reference internal" href="#module-contents">Module contents</a></li>
 </ul>
 </li>
 </ul>
@@ -1083,9 +225,6 @@ sorted in descending test score order.</li>
         <li class="right" style="margin-right: 10px">
           <a href="../genindex.html" title="General Index"
              >index</a></li>
-        <li class="right" >
-          <a href="../py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
         <li class="right" >
           <a href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.html" title="multiview_platform.mono_multi_view_classifiers.multiview_classifiers package"
              >next</a> |</li>
diff --git a/docs/build/references/multiview_platform.mono_multi_view_classifiers.multiview_classifiers.difficulty_fusion.html b/docs/build/references/multiview_platform.mono_multi_view_classifiers.multiview_classifiers.difficulty_fusion.html
index 0566fed90f91683d9c6b06a9e104700e26c45f7c..1bbfbc99ef726790bbc378f3b587202e14987508 100644
--- a/docs/build/references/multiview_platform.mono_multi_view_classifiers.multiview_classifiers.difficulty_fusion.html
+++ b/docs/build/references/multiview_platform.mono_multi_view_classifiers.multiview_classifiers.difficulty_fusion.html
@@ -27,9 +27,6 @@
         <li class="right" style="margin-right: 10px">
           <a href="../genindex.html" title="General Index"
              accesskey="I">index</a></li>
-        <li class="right" >
-          <a href="../py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
         <li class="right" >
           <a href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.disagree_fusion.html" title="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.disagree_fusion package"
              accesskey="N">next</a> |</li>
@@ -59,19 +56,8 @@
 <div class="section" id="multiview-platform-mono-multi-view-classifiers-multiview-classifiers-difficulty-fusion-difficulty-fusion-module">
 <h2>multiview_platform.mono_multi_view_classifiers.multiview_classifiers.difficulty_fusion.difficulty_fusion module<a class="headerlink" href="#multiview-platform-mono-multi-view-classifiers-multiview-classifiers-difficulty-fusion-difficulty-fusion-module" title="Permalink to this headline">¶</a></h2>
 </div>
-<div class="section" id="module-multiview_platform.mono_multi_view_classifiers.multiview_classifiers.difficulty_fusion">
-<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-multiview_platform.mono_multi_view_classifiers.multiview_classifiers.difficulty_fusion" title="Permalink to this headline">¶</a></h2>
-<dl class="class">
-<dt id="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.difficulty_fusion.DifficultyFusion">
-<em class="property">class </em><code class="descname">DifficultyFusion</code><span class="sig-paren">(</span><em>random_state=None</em>, <em>classifier_names=None</em>, <em>monoview_estimators=None</em>, <em>classifier_configs=None</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.multiview_classifiers.difficulty_fusion.DifficultyFusion" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">multiview_platform.mono_multi_view_classifiers.multiview_classifiers.additions.diversity_utils.GlobalDiversityFusionClassifier</span></code></p>
-<dl class="method">
-<dt id="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.difficulty_fusion.DifficultyFusion.diversity_measure">
-<code class="descname">diversity_measure</code><span class="sig-paren">(</span><em>classifiers_decisions</em>, <em>combination</em>, <em>y</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.multiview_classifiers.difficulty_fusion.DifficultyFusion.diversity_measure" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-</dd></dl>
-
+<div class="section" id="module-contents">
+<h2>Module contents<a class="headerlink" href="#module-contents" title="Permalink to this headline">¶</a></h2>
 </div>
 </div>
 
@@ -87,7 +73,7 @@
 <li><a class="reference internal" href="#submodules">Submodules</a></li>
 <li><a class="reference internal" href="#multiview-platform-mono-multi-view-classifiers-multiview-classifiers-difficulty-fusion-analyze-results-module">multiview_platform.mono_multi_view_classifiers.multiview_classifiers.difficulty_fusion.analyze_results module</a></li>
 <li><a class="reference internal" href="#multiview-platform-mono-multi-view-classifiers-multiview-classifiers-difficulty-fusion-difficulty-fusion-module">multiview_platform.mono_multi_view_classifiers.multiview_classifiers.difficulty_fusion.difficulty_fusion module</a></li>
-<li><a class="reference internal" href="#module-multiview_platform.mono_multi_view_classifiers.multiview_classifiers.difficulty_fusion">Module contents</a></li>
+<li><a class="reference internal" href="#module-contents">Module contents</a></li>
 </ul>
 </li>
 </ul>
@@ -127,9 +113,6 @@
         <li class="right" style="margin-right: 10px">
           <a href="../genindex.html" title="General Index"
              >index</a></li>
-        <li class="right" >
-          <a href="../py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
         <li class="right" >
           <a href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.disagree_fusion.html" title="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.disagree_fusion package"
              >next</a> |</li>
diff --git a/docs/build/references/multiview_platform.mono_multi_view_classifiers.multiview_classifiers.disagree_fusion.html b/docs/build/references/multiview_platform.mono_multi_view_classifiers.multiview_classifiers.disagree_fusion.html
index e55d757294dcfba6429577b2a9cddb8087212e13..480edca29824b1a4157bb26f52ec37c56eddb30b 100644
--- a/docs/build/references/multiview_platform.mono_multi_view_classifiers.multiview_classifiers.disagree_fusion.html
+++ b/docs/build/references/multiview_platform.mono_multi_view_classifiers.multiview_classifiers.disagree_fusion.html
@@ -27,9 +27,6 @@
         <li class="right" style="margin-right: 10px">
           <a href="../genindex.html" title="General Index"
              accesskey="I">index</a></li>
-        <li class="right" >
-          <a href="../py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
         <li class="right" >
           <a href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.double_fault_fusion.html" title="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.double_fault_fusion package"
              accesskey="N">next</a> |</li>
@@ -59,19 +56,8 @@
 <div class="section" id="multiview-platform-mono-multi-view-classifiers-multiview-classifiers-disagree-fusion-disagree-fusion-module">
 <h2>multiview_platform.mono_multi_view_classifiers.multiview_classifiers.disagree_fusion.disagree_fusion module<a class="headerlink" href="#multiview-platform-mono-multi-view-classifiers-multiview-classifiers-disagree-fusion-disagree-fusion-module" title="Permalink to this headline">¶</a></h2>
 </div>
-<div class="section" id="module-multiview_platform.mono_multi_view_classifiers.multiview_classifiers.disagree_fusion">
-<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-multiview_platform.mono_multi_view_classifiers.multiview_classifiers.disagree_fusion" title="Permalink to this headline">¶</a></h2>
-<dl class="class">
-<dt id="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.disagree_fusion.DisagreeFusion">
-<em class="property">class </em><code class="descname">DisagreeFusion</code><span class="sig-paren">(</span><em>random_state=None</em>, <em>classifier_names=None</em>, <em>monoview_estimators=None</em>, <em>classifier_configs=None</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.multiview_classifiers.disagree_fusion.DisagreeFusion" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">multiview_platform.mono_multi_view_classifiers.multiview_classifiers.additions.diversity_utils.CoupleDiversityFusionClassifier</span></code></p>
-<dl class="method">
-<dt id="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.disagree_fusion.DisagreeFusion.diversity_measure">
-<code class="descname">diversity_measure</code><span class="sig-paren">(</span><em>first_classifier_decision</em>, <em>second_classifier_decision</em>, <em>_</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.multiview_classifiers.disagree_fusion.DisagreeFusion.diversity_measure" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-</dd></dl>
-
+<div class="section" id="module-contents">
+<h2>Module contents<a class="headerlink" href="#module-contents" title="Permalink to this headline">¶</a></h2>
 </div>
 </div>
 
@@ -87,7 +73,7 @@
 <li><a class="reference internal" href="#submodules">Submodules</a></li>
 <li><a class="reference internal" href="#multiview-platform-mono-multi-view-classifiers-multiview-classifiers-disagree-fusion-analyze-results-module">multiview_platform.mono_multi_view_classifiers.multiview_classifiers.disagree_fusion.analyze_results module</a></li>
 <li><a class="reference internal" href="#multiview-platform-mono-multi-view-classifiers-multiview-classifiers-disagree-fusion-disagree-fusion-module">multiview_platform.mono_multi_view_classifiers.multiview_classifiers.disagree_fusion.disagree_fusion module</a></li>
-<li><a class="reference internal" href="#module-multiview_platform.mono_multi_view_classifiers.multiview_classifiers.disagree_fusion">Module contents</a></li>
+<li><a class="reference internal" href="#module-contents">Module contents</a></li>
 </ul>
 </li>
 </ul>
@@ -127,9 +113,6 @@
         <li class="right" style="margin-right: 10px">
           <a href="../genindex.html" title="General Index"
              >index</a></li>
-        <li class="right" >
-          <a href="../py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
         <li class="right" >
           <a href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.double_fault_fusion.html" title="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.double_fault_fusion package"
              >next</a> |</li>
diff --git a/docs/build/references/multiview_platform.mono_multi_view_classifiers.multiview_classifiers.double_fault_fusion.html b/docs/build/references/multiview_platform.mono_multi_view_classifiers.multiview_classifiers.double_fault_fusion.html
index 34622a05bf6be9146104dc93286b4d560616438a..90f76513fc0d6c14624d9133188e7a4f83ee459e 100644
--- a/docs/build/references/multiview_platform.mono_multi_view_classifiers.multiview_classifiers.double_fault_fusion.html
+++ b/docs/build/references/multiview_platform.mono_multi_view_classifiers.multiview_classifiers.double_fault_fusion.html
@@ -27,9 +27,6 @@
         <li class="right" style="margin-right: 10px">
           <a href="../genindex.html" title="General Index"
              accesskey="I">index</a></li>
-        <li class="right" >
-          <a href="../py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
         <li class="right" >
           <a href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.entropy_fusion.html" title="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.entropy_fusion package"
              accesskey="N">next</a> |</li>
@@ -59,19 +56,8 @@
 <div class="section" id="multiview-platform-mono-multi-view-classifiers-multiview-classifiers-double-fault-fusion-double-fault-fusion-module">
 <h2>multiview_platform.mono_multi_view_classifiers.multiview_classifiers.double_fault_fusion.double_fault_fusion module<a class="headerlink" href="#multiview-platform-mono-multi-view-classifiers-multiview-classifiers-double-fault-fusion-double-fault-fusion-module" title="Permalink to this headline">¶</a></h2>
 </div>
-<div class="section" id="module-multiview_platform.mono_multi_view_classifiers.multiview_classifiers.double_fault_fusion">
-<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-multiview_platform.mono_multi_view_classifiers.multiview_classifiers.double_fault_fusion" title="Permalink to this headline">¶</a></h2>
-<dl class="class">
-<dt id="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.double_fault_fusion.DoubleFaultFusion">
-<em class="property">class </em><code class="descname">DoubleFaultFusion</code><span class="sig-paren">(</span><em>random_state=None</em>, <em>classifier_names=None</em>, <em>monoview_estimators=None</em>, <em>classifier_configs=None</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.multiview_classifiers.double_fault_fusion.DoubleFaultFusion" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">multiview_platform.mono_multi_view_classifiers.multiview_classifiers.additions.diversity_utils.CoupleDiversityFusionClassifier</span></code></p>
-<dl class="method">
-<dt id="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.double_fault_fusion.DoubleFaultFusion.diversity_measure">
-<code class="descname">diversity_measure</code><span class="sig-paren">(</span><em>first_classifier_decision</em>, <em>second_classifier_decision</em>, <em>y</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.multiview_classifiers.double_fault_fusion.DoubleFaultFusion.diversity_measure" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-</dd></dl>
-
+<div class="section" id="module-contents">
+<h2>Module contents<a class="headerlink" href="#module-contents" title="Permalink to this headline">¶</a></h2>
 </div>
 </div>
 
@@ -87,7 +73,7 @@
 <li><a class="reference internal" href="#submodules">Submodules</a></li>
 <li><a class="reference internal" href="#multiview-platform-mono-multi-view-classifiers-multiview-classifiers-double-fault-fusion-analyze-results-module">multiview_platform.mono_multi_view_classifiers.multiview_classifiers.double_fault_fusion.analyze_results module</a></li>
 <li><a class="reference internal" href="#multiview-platform-mono-multi-view-classifiers-multiview-classifiers-double-fault-fusion-double-fault-fusion-module">multiview_platform.mono_multi_view_classifiers.multiview_classifiers.double_fault_fusion.double_fault_fusion module</a></li>
-<li><a class="reference internal" href="#module-multiview_platform.mono_multi_view_classifiers.multiview_classifiers.double_fault_fusion">Module contents</a></li>
+<li><a class="reference internal" href="#module-contents">Module contents</a></li>
 </ul>
 </li>
 </ul>
@@ -127,9 +113,6 @@
         <li class="right" style="margin-right: 10px">
           <a href="../genindex.html" title="General Index"
              >index</a></li>
-        <li class="right" >
-          <a href="../py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
         <li class="right" >
           <a href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.entropy_fusion.html" title="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.entropy_fusion package"
              >next</a> |</li>
diff --git a/docs/build/references/multiview_platform.mono_multi_view_classifiers.multiview_classifiers.entropy_fusion.html b/docs/build/references/multiview_platform.mono_multi_view_classifiers.multiview_classifiers.entropy_fusion.html
index 8cecf9e32dc10ad69f027c4388fa50bb63a9a9c6..eafd720386113211b6ce053fe983a487f11194d2 100644
--- a/docs/build/references/multiview_platform.mono_multi_view_classifiers.multiview_classifiers.entropy_fusion.html
+++ b/docs/build/references/multiview_platform.mono_multi_view_classifiers.multiview_classifiers.entropy_fusion.html
@@ -27,9 +27,6 @@
         <li class="right" style="margin-right: 10px">
           <a href="../genindex.html" title="General Index"
              accesskey="I">index</a></li>
-        <li class="right" >
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-             >modules</a> |</li>
         <li class="right" >
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@@ -59,19 +56,8 @@
 <div class="section" id="multiview-platform-mono-multi-view-classifiers-multiview-classifiers-entropy-fusion-entropy-fusion-module">
 <h2>multiview_platform.mono_multi_view_classifiers.multiview_classifiers.entropy_fusion.entropy_fusion module<a class="headerlink" href="#multiview-platform-mono-multi-view-classifiers-multiview-classifiers-entropy-fusion-entropy-fusion-module" title="Permalink to this headline">¶</a></h2>
 </div>
-<div class="section" id="module-multiview_platform.mono_multi_view_classifiers.multiview_classifiers.entropy_fusion">
-<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-multiview_platform.mono_multi_view_classifiers.multiview_classifiers.entropy_fusion" title="Permalink to this headline">¶</a></h2>
-<dl class="class">
-<dt id="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.entropy_fusion.EntropyFusion">
-<em class="property">class </em><code class="descname">EntropyFusion</code><span class="sig-paren">(</span><em>random_state=None</em>, <em>classifier_names=None</em>, <em>monoview_estimators=None</em>, <em>classifier_configs=None</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.multiview_classifiers.entropy_fusion.EntropyFusion" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">multiview_platform.mono_multi_view_classifiers.multiview_classifiers.additions.diversity_utils.GlobalDiversityFusionClassifier</span></code></p>
-<dl class="method">
-<dt id="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.entropy_fusion.EntropyFusion.diversity_measure">
-<code class="descname">diversity_measure</code><span class="sig-paren">(</span><em>classifiers_decisions</em>, <em>combination</em>, <em>y</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.multiview_classifiers.entropy_fusion.EntropyFusion.diversity_measure" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-</dd></dl>
-
+<div class="section" id="module-contents">
+<h2>Module contents<a class="headerlink" href="#module-contents" title="Permalink to this headline">¶</a></h2>
 </div>
 </div>
 
@@ -87,7 +73,7 @@
 <li><a class="reference internal" href="#submodules">Submodules</a></li>
 <li><a class="reference internal" href="#multiview-platform-mono-multi-view-classifiers-multiview-classifiers-entropy-fusion-analyze-results-module">multiview_platform.mono_multi_view_classifiers.multiview_classifiers.entropy_fusion.analyze_results module</a></li>
 <li><a class="reference internal" href="#multiview-platform-mono-multi-view-classifiers-multiview-classifiers-entropy-fusion-entropy-fusion-module">multiview_platform.mono_multi_view_classifiers.multiview_classifiers.entropy_fusion.entropy_fusion module</a></li>
-<li><a class="reference internal" href="#module-multiview_platform.mono_multi_view_classifiers.multiview_classifiers.entropy_fusion">Module contents</a></li>
+<li><a class="reference internal" href="#module-contents">Module contents</a></li>
 </ul>
 </li>
 </ul>
@@ -127,9 +113,6 @@
         <li class="right" style="margin-right: 10px">
           <a href="../genindex.html" title="General Index"
              >index</a></li>
-        <li class="right" >
-          <a href="../py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
         <li class="right" >
           <a href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.fat_late_fusion.html" title="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.fat_late_fusion package"
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diff --git a/docs/build/references/multiview_platform.mono_multi_view_classifiers.multiview_classifiers.fat_late_fusion.html b/docs/build/references/multiview_platform.mono_multi_view_classifiers.multiview_classifiers.fat_late_fusion.html
index 64856de4c762a6282f7aeb4674ffdb7407086187..617b595fd9d8ff7f1ec7fb6f05be28ede9dcbf58 100644
--- a/docs/build/references/multiview_platform.mono_multi_view_classifiers.multiview_classifiers.fat_late_fusion.html
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@@ -27,9 +27,6 @@
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-             >modules</a> |</li>
         <li class="right" >
           <a href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.fat_scm_late_fusion.html" title="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.fat_scm_late_fusion package"
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@@ -116,9 +113,6 @@
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              >index</a></li>
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-          <a href="../py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
         <li class="right" >
           <a href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.fat_scm_late_fusion.html" title="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.fat_scm_late_fusion package"
              >next</a> |</li>
diff --git a/docs/build/references/multiview_platform.mono_multi_view_classifiers.multiview_classifiers.fat_scm_late_fusion.html b/docs/build/references/multiview_platform.mono_multi_view_classifiers.multiview_classifiers.fat_scm_late_fusion.html
index 6d5fe37f405b46d9b86ea035ad49c33b29fd2d37..0396862386e5b2fa6a2d71fc9098584057bb6bc2 100644
--- a/docs/build/references/multiview_platform.mono_multi_view_classifiers.multiview_classifiers.fat_scm_late_fusion.html
+++ b/docs/build/references/multiview_platform.mono_multi_view_classifiers.multiview_classifiers.fat_scm_late_fusion.html
@@ -27,9 +27,6 @@
         <li class="right" style="margin-right: 10px">
           <a href="../genindex.html" title="General Index"
              accesskey="I">index</a></li>
-        <li class="right" >
-          <a href="../py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
         <li class="right" >
           <a href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.fusion.html" title="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.fusion package"
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@@ -116,9 +113,6 @@
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-        <li class="right" >
-          <a href="../py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
         <li class="right" >
           <a href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.fusion.html" title="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.fusion package"
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diff --git a/docs/build/references/multiview_platform.mono_multi_view_classifiers.multiview_classifiers.fusion.Methods.EarlyFusionPackage.html b/docs/build/references/multiview_platform.mono_multi_view_classifiers.multiview_classifiers.fusion.Methods.EarlyFusionPackage.html
index 2316b55bb563bd279e59c56db040307d109e8176..74e655a4c0ec9a41aa9f69dc9fc512c884e13385 100644
--- a/docs/build/references/multiview_platform.mono_multi_view_classifiers.multiview_classifiers.fusion.Methods.EarlyFusionPackage.html
+++ b/docs/build/references/multiview_platform.mono_multi_view_classifiers.multiview_classifiers.fusion.Methods.EarlyFusionPackage.html
@@ -27,9 +27,6 @@
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-             >modules</a> |</li>
         <li class="right" >
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-          <a href="../py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
         <li class="right" >
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diff --git a/docs/build/references/multiview_platform.mono_multi_view_classifiers.multiview_classifiers.fusion.Methods.LateFusionPackage.html b/docs/build/references/multiview_platform.mono_multi_view_classifiers.multiview_classifiers.fusion.Methods.LateFusionPackage.html
index 842ee9c95f61628d507d4933523b04e045dd6bef..91b384a549536e855e0324bc7045f80a252d9900 100644
--- a/docs/build/references/multiview_platform.mono_multi_view_classifiers.multiview_classifiers.fusion.Methods.LateFusionPackage.html
+++ b/docs/build/references/multiview_platform.mono_multi_view_classifiers.multiview_classifiers.fusion.Methods.LateFusionPackage.html
@@ -27,9 +27,6 @@
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-             >modules</a> |</li>
         <li class="right" >
           <a href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.pseudo_cq_fusion.html" title="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.pseudo_cq_fusion package"
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-          <a href="../py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
         <li class="right" >
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diff --git a/docs/build/references/multiview_platform.mono_multi_view_classifiers.multiview_classifiers.fusion.Methods.html b/docs/build/references/multiview_platform.mono_multi_view_classifiers.multiview_classifiers.fusion.Methods.html
index 4496903cd58d0b0cec6ba30fbe4e4db9f3bdc053..5bca95d8a38e9702a5130a8e3b8e8f9b1ac953af 100644
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+++ b/docs/build/references/multiview_platform.mono_multi_view_classifiers.multiview_classifiers.fusion.Methods.html
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         <li class="right" >
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-             >modules</a> |</li>
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diff --git a/docs/build/references/multiview_platform.mono_multi_view_classifiers.multiview_classifiers.fusion.html b/docs/build/references/multiview_platform.mono_multi_view_classifiers.multiview_classifiers.fusion.html
index 0a8a4de4ad118ac0aee03b94cd857423dc224f54..9b360c2e2206c62183be8143bb7c540566f873b0 100644
--- a/docs/build/references/multiview_platform.mono_multi_view_classifiers.multiview_classifiers.fusion.html
+++ b/docs/build/references/multiview_platform.mono_multi_view_classifiers.multiview_classifiers.fusion.html
@@ -27,9 +27,6 @@
         <li class="right" style="margin-right: 10px">
           <a href="../genindex.html" title="General Index"
              accesskey="I">index</a></li>
-        <li class="right" >
-          <a href="../py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
         <li class="right" >
           <a href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.fusion.Methods.html" title="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.fusion.Methods package"
              accesskey="N">next</a> |</li>
@@ -150,9 +147,6 @@
         <li class="right" style="margin-right: 10px">
           <a href="../genindex.html" title="General Index"
              >index</a></li>
-        <li class="right" >
-          <a href="../py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
         <li class="right" >
           <a href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.fusion.Methods.html" title="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.fusion.Methods package"
              >next</a> |</li>
diff --git a/docs/build/references/multiview_platform.mono_multi_view_classifiers.multiview_classifiers.html b/docs/build/references/multiview_platform.mono_multi_view_classifiers.multiview_classifiers.html
index 91fa9125099a71e3dde1d941c7e2968190091af4..9df84d3b2a6a0ea2bae3e9e9222b2844ba34cccf 100644
--- a/docs/build/references/multiview_platform.mono_multi_view_classifiers.multiview_classifiers.html
+++ b/docs/build/references/multiview_platform.mono_multi_view_classifiers.multiview_classifiers.html
@@ -27,9 +27,6 @@
         <li class="right" style="margin-right: 10px">
           <a href="../genindex.html" title="General Index"
              accesskey="I">index</a></li>
-        <li class="right" >
-          <a href="../py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
         <li class="right" >
           <a href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.difficulty_fusion.html" title="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.difficulty_fusion package"
              accesskey="N">next</a> |</li>
@@ -57,28 +54,28 @@
 <li class="toctree-l2"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.difficulty_fusion.html#submodules">Submodules</a></li>
 <li class="toctree-l2"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.difficulty_fusion.html#multiview-platform-mono-multi-view-classifiers-multiview-classifiers-difficulty-fusion-analyze-results-module">multiview_platform.mono_multi_view_classifiers.multiview_classifiers.difficulty_fusion.analyze_results module</a></li>
 <li class="toctree-l2"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.difficulty_fusion.html#multiview-platform-mono-multi-view-classifiers-multiview-classifiers-difficulty-fusion-difficulty-fusion-module">multiview_platform.mono_multi_view_classifiers.multiview_classifiers.difficulty_fusion.difficulty_fusion module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.difficulty_fusion.html#module-multiview_platform.mono_multi_view_classifiers.multiview_classifiers.difficulty_fusion">Module contents</a></li>
+<li class="toctree-l2"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.difficulty_fusion.html#module-contents">Module contents</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.disagree_fusion.html">multiview_platform.mono_multi_view_classifiers.multiview_classifiers.disagree_fusion package</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.disagree_fusion.html#submodules">Submodules</a></li>
 <li class="toctree-l2"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.disagree_fusion.html#multiview-platform-mono-multi-view-classifiers-multiview-classifiers-disagree-fusion-analyze-results-module">multiview_platform.mono_multi_view_classifiers.multiview_classifiers.disagree_fusion.analyze_results module</a></li>
 <li class="toctree-l2"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.disagree_fusion.html#multiview-platform-mono-multi-view-classifiers-multiview-classifiers-disagree-fusion-disagree-fusion-module">multiview_platform.mono_multi_view_classifiers.multiview_classifiers.disagree_fusion.disagree_fusion module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.disagree_fusion.html#module-multiview_platform.mono_multi_view_classifiers.multiview_classifiers.disagree_fusion">Module contents</a></li>
+<li class="toctree-l2"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.disagree_fusion.html#module-contents">Module contents</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.double_fault_fusion.html">multiview_platform.mono_multi_view_classifiers.multiview_classifiers.double_fault_fusion package</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.double_fault_fusion.html#submodules">Submodules</a></li>
 <li class="toctree-l2"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.double_fault_fusion.html#multiview-platform-mono-multi-view-classifiers-multiview-classifiers-double-fault-fusion-analyze-results-module">multiview_platform.mono_multi_view_classifiers.multiview_classifiers.double_fault_fusion.analyze_results module</a></li>
 <li class="toctree-l2"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.double_fault_fusion.html#multiview-platform-mono-multi-view-classifiers-multiview-classifiers-double-fault-fusion-double-fault-fusion-module">multiview_platform.mono_multi_view_classifiers.multiview_classifiers.double_fault_fusion.double_fault_fusion module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.double_fault_fusion.html#module-multiview_platform.mono_multi_view_classifiers.multiview_classifiers.double_fault_fusion">Module contents</a></li>
+<li class="toctree-l2"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.double_fault_fusion.html#module-contents">Module contents</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.entropy_fusion.html">multiview_platform.mono_multi_view_classifiers.multiview_classifiers.entropy_fusion package</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.entropy_fusion.html#submodules">Submodules</a></li>
 <li class="toctree-l2"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.entropy_fusion.html#multiview-platform-mono-multi-view-classifiers-multiview-classifiers-entropy-fusion-analyze-results-module">multiview_platform.mono_multi_view_classifiers.multiview_classifiers.entropy_fusion.analyze_results module</a></li>
 <li class="toctree-l2"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.entropy_fusion.html#multiview-platform-mono-multi-view-classifiers-multiview-classifiers-entropy-fusion-entropy-fusion-module">multiview_platform.mono_multi_view_classifiers.multiview_classifiers.entropy_fusion.entropy_fusion module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.entropy_fusion.html#module-multiview_platform.mono_multi_view_classifiers.multiview_classifiers.entropy_fusion">Module contents</a></li>
+<li class="toctree-l2"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.entropy_fusion.html#module-contents">Module contents</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.fat_late_fusion.html">multiview_platform.mono_multi_view_classifiers.multiview_classifiers.fat_late_fusion package</a><ul>
@@ -141,8 +138,8 @@
 </ul>
 </div>
 </div>
-<div class="section" id="module-multiview_platform.mono_multi_view_classifiers.multiview_classifiers">
-<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-multiview_platform.mono_multi_view_classifiers.multiview_classifiers" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="module-contents">
+<h2>Module contents<a class="headerlink" href="#module-contents" title="Permalink to this headline">¶</a></h2>
 </div>
 </div>
 
@@ -156,7 +153,7 @@
   <ul>
 <li><a class="reference internal" href="#">multiview_platform.mono_multi_view_classifiers.multiview_classifiers package</a><ul>
 <li><a class="reference internal" href="#subpackages">Subpackages</a></li>
-<li><a class="reference internal" href="#module-multiview_platform.mono_multi_view_classifiers.multiview_classifiers">Module contents</a></li>
+<li><a class="reference internal" href="#module-contents">Module contents</a></li>
 </ul>
 </li>
 </ul>
@@ -196,9 +193,6 @@
         <li class="right" style="margin-right: 10px">
           <a href="../genindex.html" title="General Index"
              >index</a></li>
-        <li class="right" >
-          <a href="../py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
         <li class="right" >
           <a href="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.difficulty_fusion.html" title="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.difficulty_fusion package"
              >next</a> |</li>
diff --git a/docs/build/references/multiview_platform.mono_multi_view_classifiers.multiview_classifiers.pseudo_cq_fusion.html b/docs/build/references/multiview_platform.mono_multi_view_classifiers.multiview_classifiers.pseudo_cq_fusion.html
index 41e15752a397e45638dae91b6ad34f147a9d2ede..a7ff742ae975fd561e4d2a7f3bc07b409a5ac67c 100644
--- a/docs/build/references/multiview_platform.mono_multi_view_classifiers.multiview_classifiers.pseudo_cq_fusion.html
+++ b/docs/build/references/multiview_platform.mono_multi_view_classifiers.multiview_classifiers.pseudo_cq_fusion.html
@@ -27,9 +27,6 @@
         <li class="right" style="margin-right: 10px">
           <a href="../genindex.html" title="General Index"
              accesskey="I">index</a></li>
-        <li class="right" >
-          <a href="../py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
         <li class="right" >
           <a href="multiview_platform.mono_multi_view_classifiers.utils.html" title="multiview_platform.mono_multi_view_classifiers.utils package"
              accesskey="N">next</a> |</li>
@@ -116,9 +113,6 @@
         <li class="right" style="margin-right: 10px">
           <a href="../genindex.html" title="General Index"
              >index</a></li>
-        <li class="right" >
-          <a href="../py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
         <li class="right" >
           <a href="multiview_platform.mono_multi_view_classifiers.utils.html" title="multiview_platform.mono_multi_view_classifiers.utils package"
              >next</a> |</li>
diff --git a/docs/build/references/multiview_platform.mono_multi_view_classifiers.utils.html b/docs/build/references/multiview_platform.mono_multi_view_classifiers.utils.html
index 2d9b2723a9187507f9f21d623f6c890efa3b7461..d6acaa22a9da933dd96ca6d4aac95e7dfa4a547b 100644
--- a/docs/build/references/multiview_platform.mono_multi_view_classifiers.utils.html
+++ b/docs/build/references/multiview_platform.mono_multi_view_classifiers.utils.html
@@ -27,9 +27,6 @@
         <li class="right" style="margin-right: 10px">
           <a href="../genindex.html" title="General Index"
              accesskey="I">index</a></li>
-        <li class="right" >
-          <a href="../py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
         <li class="right" >
           <a href="multiview_platform.tests.html" title="multiview_platform.tests package"
              accesskey="N">next</a> |</li>
@@ -52,911 +49,35 @@
 <div class="section" id="submodules">
 <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this headline">¶</a></h2>
 </div>
-<div class="section" id="module-multiview_platform.mono_multi_view_classifiers.utils.configuration">
-<span id="multiview-platform-mono-multi-view-classifiers-utils-configuration-module"></span><h2>multiview_platform.mono_multi_view_classifiers.utils.configuration module<a class="headerlink" href="#module-multiview_platform.mono_multi_view_classifiers.utils.configuration" title="Permalink to this headline">¶</a></h2>
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.configuration.get_the_args">
-<code class="descname">get_the_args</code><span class="sig-paren">(</span><em>path_to_config_file='../config_files/config.yml'</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.configuration.get_the_args" title="Permalink to this definition">¶</a></dt>
-<dd><p>The function for extracting the args for a ‘.yml’ file.</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>path_to_config_file</strong> (<em>str</em><em>, </em><em>path to the yml file containing the configuration</em>) – </td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><ul class="simple">
-<li><strong>yaml_config</strong> (<em>dict, the dictionary conaining the configuration for the</em>)</li>
-<li><em>benchmark</em></li>
-</ul>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.configuration.save_config">
-<code class="descname">save_config</code><span class="sig-paren">(</span><em>directory</em>, <em>arguments</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.configuration.save_config" title="Permalink to this definition">¶</a></dt>
-<dd><p>Saves the config file in the result directory.</p>
-</dd></dl>
-
+<div class="section" id="multiview-platform-mono-multi-view-classifiers-utils-configuration-module">
+<h2>multiview_platform.mono_multi_view_classifiers.utils.configuration module<a class="headerlink" href="#multiview-platform-mono-multi-view-classifiers-utils-configuration-module" title="Permalink to this headline">¶</a></h2>
 </div>
-<div class="section" id="module-multiview_platform.mono_multi_view_classifiers.utils.dataset">
-<span id="multiview-platform-mono-multi-view-classifiers-utils-dataset-module"></span><h2>multiview_platform.mono_multi_view_classifiers.utils.dataset module<a class="headerlink" href="#module-multiview_platform.mono_multi_view_classifiers.utils.dataset" title="Permalink to this headline">¶</a></h2>
-<dl class="class">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset">
-<em class="property">class </em><code class="descname">Dataset</code><span class="sig-paren">(</span><em>views=None</em>, <em>labels=None</em>, <em>are_sparse=False</em>, <em>file_name='dataset.hdf5'</em>, <em>view_names=None</em>, <em>path=''</em>, <em>hdf5_file=None</em>, <em>labels_names=None</em>, <em>is_temp=False</em>, <em>example_ids=None</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
-<p>Class of Dataset</p>
-<p>This class is used to encapsulate the multiview dataset</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>views</strong> (<em>list of numpy arrays</em><em> or </em><em>None</em>) – The list containing each view of the dataset as a numpy array of shape
-(nb examples, nb features).</li>
-<li><strong>labels</strong> (<em>numpy array</em><em> or </em><em>None</em>) – The labels for the multiview dataset, of shape (nb examples, ).</li>
-<li><strong>are_sparse</strong> (<em>list of bool</em><em>, or </em><em>None</em>) – The list of boolean telling if each view is sparse or not.</li>
-<li><strong>file_name</strong> (<em>str</em><em>, or </em><em>None</em>) – The name of the hdf5 file that will be created to store the multiview
-dataset.</li>
-<li><strong>view_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – The name of each view.</li>
-<li><strong>path</strong> (<em>str</em><em>, or </em><em>None</em>) – The path where the hdf5 dataset file will be stored</li>
-<li><strong>hdf5_file</strong> (<em>h5py.File object</em><em>, or </em><em>None</em>) – If not None, the dataset will be imported directly from this file.</li>
-<li><strong>labels_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – The name for each unique value of the labels given in labels.</li>
-<li><strong>is_temp</strong> (<em>bool</em>) – Used if a temporary dataset has to be used by the benchmark.</li>
-</ul>
-</td>
-</tr>
-</tbody>
-</table>
-<dl class="attribute">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset.dataset">
-<code class="descname">dataset</code><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset.dataset" title="Permalink to this definition">¶</a></dt>
-<dd><p>The h5py file pbject that points to the hdf5 dataset on the disk.</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Type:</th><td class="field-body">h5py.File object</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="attribute">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset.nb_view">
-<code class="descname">nb_view</code><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset.nb_view" title="Permalink to this definition">¶</a></dt>
-<dd><p>The number of views in the dataset.</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Type:</th><td class="field-body">int</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="attribute">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset.view_dict">
-<code class="descname">view_dict</code><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset.view_dict" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="docutils">
-<dt>The dictionnary with the name of each view as the keys and their indices</dt>
-<dd>as values</dd>
-</dl>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Type:</th><td class="field-body">dict</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="method">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset.add_gaussian_noise">
-<code class="descname">add_gaussian_noise</code><span class="sig-paren">(</span><em>random_state</em>, <em>path</em>, <em>noise_std=0.15</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset.add_gaussian_noise" title="Permalink to this definition">¶</a></dt>
-<dd><p>In this function, we add a guaussian noise centered in 0 with specified
-std to each view, according to it’s range (the noise will be
-mutliplied by this range) and we crop the noisy signal according to the
-view’s attributes limits.
-This is done by creating a new dataset, to keep clean data.</p>
-</dd></dl>
-
-<dl class="method">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset.check_selected_label_names">
-<code class="descname">check_selected_label_names</code><span class="sig-paren">(</span><em>nb_labels=None</em>, <em>selected_label_names=None</em>, <em>random_state=&lt;mtrand.RandomState object&gt;</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset.check_selected_label_names" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="method">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset.copy_view">
-<code class="descname">copy_view</code><span class="sig-paren">(</span><em>target_dataset=None</em>, <em>source_view_name=None</em>, <em>target_view_index=None</em>, <em>example_indices=None</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset.copy_view" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="method">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset.filter">
-<code class="descname">filter</code><span class="sig-paren">(</span><em>labels</em>, <em>label_names</em>, <em>example_indices</em>, <em>view_names</em>, <em>path</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset.filter" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="method">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset.get_label_names">
-<code class="descname">get_label_names</code><span class="sig-paren">(</span><em>decode=True</em>, <em>example_indices=None</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset.get_label_names" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to get the list of the label names for the give set of examples</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>decode</strong> (<em>bool</em>) – If True, will decode the label names before lsiting them</li>
-<li><strong>example_indices</strong> (<em>numpy.ndarray</em>) – The array containig the indices of the needed examples</li>
-</ul>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="method">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset.get_labels">
-<code class="descname">get_labels</code><span class="sig-paren">(</span><em>example_indices=None</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset.get_labels" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="method">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset.get_name">
-<code class="descname">get_name</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset.get_name" title="Permalink to this definition">¶</a></dt>
-<dd><p>Ony works if there are not multiple dots in the files name</p>
-</dd></dl>
-
-<dl class="method">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset.get_nb_class">
-<code class="descname">get_nb_class</code><span class="sig-paren">(</span><em>example_indices=None</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset.get_nb_class" title="Permalink to this definition">¶</a></dt>
-<dd><p>Gets the number of class of the dataset</p>
-</dd></dl>
-
-<dl class="method">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset.get_nb_examples">
-<code class="descname">get_nb_examples</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset.get_nb_examples" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to get the number of examples available</p>
-</dd></dl>
-
-<dl class="method">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset.get_shape">
-<code class="descname">get_shape</code><span class="sig-paren">(</span><em>view_index=0</em>, <em>example_indices=None</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset.get_shape" title="Permalink to this definition">¶</a></dt>
-<dd><p>Gets the shape of the needed view</p>
-</dd></dl>
-
-<dl class="method">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset.get_v">
-<code class="descname">get_v</code><span class="sig-paren">(</span><em>view_index</em>, <em>example_indices=None</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset.get_v" title="Permalink to this definition">¶</a></dt>
-<dd><p>Selects the view to extract
-:param view_index: The index of the view to extract
-:type view_index: int
-:param example_indices: The array containing the indices of the examples to extract.
-:type example_indices: numpy.ndarray</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body"></td>
-</tr>
-<tr class="field-even field"><th class="field-name">Return type:</th><td class="field-body">A numpy.ndarray containing the view data for the needed examples</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="method">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset.get_view_dict">
-<code class="descname">get_view_dict</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset.get_view_dict" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns the dictionary with view indices as keys and the corresponding
-names as values</p>
-</dd></dl>
-
-<dl class="method">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset.get_view_name">
-<code class="descname">get_view_name</code><span class="sig-paren">(</span><em>view_idx</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset.get_view_name" title="Permalink to this definition">¶</a></dt>
-<dd><p>Method to get a view’s name for it’s index.</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>view_idx</strong> (<em>int</em>) – The index of the view in the dataset</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"></td>
-</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body">The view’s name.</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="method">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset.init_attrs">
-<code class="descname">init_attrs</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset.init_attrs" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to init the two attributes that are modified when self.dataset
-changes</p>
-</dd></dl>
-
-<dl class="method">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset.init_example_indces">
-<code class="descname">init_example_indces</code><span class="sig-paren">(</span><em>example_indices=None</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset.init_example_indces" title="Permalink to this definition">¶</a></dt>
-<dd><p>If no example indices are provided, selects all the examples.</p>
-</dd></dl>
-
-<dl class="method">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset.init_view_names">
-<code class="descname">init_view_names</code><span class="sig-paren">(</span><em>view_names=None</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset.init_view_names" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="method">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset.rm">
-<code class="descname">rm</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset.rm" title="Permalink to this definition">¶</a></dt>
-<dd><p>Method used to delete the dataset file on the disk if the dataset is
-temporary.</p>
-</dd></dl>
-
-<dl class="method">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset.select_labels">
-<code class="descname">select_labels</code><span class="sig-paren">(</span><em>selected_label_names</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset.select_labels" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="method">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset.select_views_and_labels">
-<code class="descname">select_views_and_labels</code><span class="sig-paren">(</span><em>nb_labels=None</em>, <em>selected_label_names=None</em>, <em>random_state=None</em>, <em>view_names=None</em>, <em>path_for_new='../data/'</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset.select_views_and_labels" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="method">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset.to_numpy_array">
-<code class="descname">to_numpy_array</code><span class="sig-paren">(</span><em>example_indices=None</em>, <em>view_indices=None</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset.to_numpy_array" title="Permalink to this definition">¶</a></dt>
-<dd><p>To concanteant the needed views in one big numpy array while saving the
-limits of each view in a list, to be bale to retrieve them later.</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>example_indices</strong> (<em>array like</em><em>,</em>) – </li>
-<li><strong>indices of the examples to extract from the dataset</strong> (<em>The</em>) – </li>
-<li><strong>view_indices</strong> (<em>array like</em><em>,</em>) – </li>
-<li><strong>indices of the view to concatenate in the numpy array</strong> (<em>The</em>) – </li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last"><ul class="simple">
-<li><strong>concat_views</strong> (<em>numpy array,</em>)</li>
-<li><em>The numpy array containing all the needed views.</em></li>
-<li><strong>view_limits</strong> (<em>list of int</em>)</li>
-<li><em>The limits of each slice used to extract the views.</em></li>
-</ul>
-</p>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="method">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset.update_hdf5_dataset">
-<code class="descname">update_hdf5_dataset</code><span class="sig-paren">(</span><em>path</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.dataset.Dataset.update_hdf5_dataset" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.dataset.confirm">
-<code class="descname">confirm</code><span class="sig-paren">(</span><em>resp=True</em>, <em>timeout=15</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.dataset.confirm" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to process answer</p>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.dataset.copy_hdf5">
-<code class="descname">copy_hdf5</code><span class="sig-paren">(</span><em>pathF</em>, <em>name</em>, <em>nbCores</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.dataset.copy_hdf5" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to copy a HDF5 database in case of multicore computing</p>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.dataset.datasets_already_exist">
-<code class="descname">datasets_already_exist</code><span class="sig-paren">(</span><em>pathF</em>, <em>name</em>, <em>nbCores</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.dataset.datasets_already_exist" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to check if it’s necessary to copy datasets</p>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.dataset.delete_HDF5">
-<code class="descname">delete_HDF5</code><span class="sig-paren">(</span><em>benchmarkArgumentsDictionaries</em>, <em>nbCores</em>, <em>dataset</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.dataset.delete_HDF5" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to delete temporary copies at the end of the benchmark</p>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.dataset.extract_subset">
-<code class="descname">extract_subset</code><span class="sig-paren">(</span><em>matrix</em>, <em>used_indices</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.dataset.extract_subset" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to extract a subset of a matrix even if it’s sparse</p>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.dataset.init_multiple_datasets">
-<code class="descname">init_multiple_datasets</code><span class="sig-paren">(</span><em>path_f</em>, <em>name</em>, <em>nb_cores</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.dataset.init_multiple_datasets" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to create copies of the dataset if multicore computation is used.</p>
-<p>This is a temporary solution to fix the sharing memory issue with HDF5 datasets.</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>path_f</strong> (<em>string</em>) – Path to the original dataset directory</li>
-<li><strong>name</strong> (<em>string</em>) – Name of the dataset</li>
-<li><strong>nb_cores</strong> (<em>int</em>) – The number of threads that the benchmark can use</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>datasetFiles</strong> – Dictionary resuming which mono- and multiview algorithms which will be used in the benchmark.</p>
-</td>
-</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">None</p>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.dataset.input_">
-<code class="descname">input_</code><span class="sig-paren">(</span><em>timeout=15</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.dataset.input_" title="Permalink to this definition">¶</a></dt>
-<dd><p>used as a UI to stop if too much HDD space will be used</p>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.dataset.is_just_number">
-<code class="descname">is_just_number</code><span class="sig-paren">(</span><em>string</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.dataset.is_just_number" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
+<div class="section" id="multiview-platform-mono-multi-view-classifiers-utils-dataset-module">
+<h2>multiview_platform.mono_multi_view_classifiers.utils.dataset module<a class="headerlink" href="#multiview-platform-mono-multi-view-classifiers-utils-dataset-module" title="Permalink to this headline">¶</a></h2>
 </div>
-<div class="section" id="module-multiview_platform.mono_multi_view_classifiers.utils.execution">
-<span id="multiview-platform-mono-multi-view-classifiers-utils-execution-module"></span><h2>multiview_platform.mono_multi_view_classifiers.utils.execution module<a class="headerlink" href="#module-multiview_platform.mono_multi_view_classifiers.utils.execution" title="Permalink to this headline">¶</a></h2>
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.execution.find_dataset_names">
-<code class="descname">find_dataset_names</code><span class="sig-paren">(</span><em>path</em>, <em>type</em>, <em>names</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.execution.find_dataset_names" title="Permalink to this definition">¶</a></dt>
-<dd><p>This function goal is to browse the dataset directory and extrats all
-the needed dataset names.</p>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.execution.gen_argument_dictionaries">
-<code class="descname">gen_argument_dictionaries</code><span class="sig-paren">(</span><em>labels_dictionary</em>, <em>directories</em>, <em>multiclass_labels</em>, <em>labels_combinations</em>, <em>indices_multiclass</em>, <em>hyper_param_search</em>, <em>args</em>, <em>k_folds</em>, <em>stats_iter_random_states</em>, <em>metrics</em>, <em>argument_dictionaries</em>, <em>benchmark</em>, <em>nb_views</em>, <em>views</em>, <em>views_indices</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.execution.gen_argument_dictionaries" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to generate a dictionary for each benchmark.</p>
-<p>One for each label combination (if multiclass), for each statistical iteration, generates an dictionary with
-all necessary information to perform the benchmark</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>labels_dictionary</strong> (<em>dictionary</em>) – Dictionary mapping labels indices to labels names.</li>
-<li><strong>directories</strong> (<em>list of strings</em>) – List of the paths to the result directories for each statistical iteration.</li>
-<li><strong>multiclass_labels</strong> (<em>list of lists of numpy.ndarray</em>) – For each label couple, for each statistical iteration a triplet of numpy.ndarrays is stored with the
-indices for the biclass training set, the ones for the biclass testing set and the ones for the
-multiclass testing set.</li>
-<li><strong>labels_combinations</strong> (<em>list of lists of numpy.ndarray</em>) – Each original couple of different labels.</li>
-<li><strong>indices_multiclass</strong> (<em>list of lists of numpy.ndarray</em>) – For each combination, contains a biclass labels numpy.ndarray with the 0/1 labels of combination.</li>
-<li><strong>hyper_param_search</strong> (<em>string</em>) – Type of hyper parameter optimization method</li>
-<li><strong>args</strong> (<em>parsed args objects</em>) – All the args passed by the user.</li>
-<li><strong>k_folds</strong> (<em>list of list of sklearn.model_selection.StratifiedKFold</em>) – For each statistical iteration a Kfold stratified (keeping the ratio between classes in each fold).</li>
-<li><strong>stats_iter_random_states</strong> (<em>list of numpy.random.RandomState objects</em>) – Multiple random states, one for each sattistical iteration of the same benchmark.</li>
-<li><strong>metrics</strong> (<em>list of lists</em>) – metrics that will be used to evaluate the algorithms performance.</li>
-<li><strong>argument_dictionaries</strong> (<em>dictionary</em>) – Dictionary resuming all the specific arguments for the benchmark, oe dictionary for each classifier.</li>
-<li><strong>benchmark</strong> (<em>dictionary</em>) – Dictionary resuming which mono- and multiview algorithms which will be used in the benchmark.</li>
-<li><strong>nb_views</strong> (<em>int</em>) – THe number of views used by the benchmark.</li>
-<li><strong>views</strong> (<em>list of strings</em>) – List of the names of the used views.</li>
-<li><strong>views_indices</strong> (<em>list of ints</em>) – List of indices (according to the dataset) of the used views.</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>benchmarkArgumentDictionaries</strong> – All the needed arguments for the benchmarks.</p>
-</td>
-</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">list of dicts</p>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.execution.gen_direcorties_names">
-<code class="descname">gen_direcorties_names</code><span class="sig-paren">(</span><em>directory</em>, <em>stats_iter</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.execution.gen_direcorties_names" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to generate the different directories of each iteration if needed.</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>directory</strong> (<em>string</em>) – Path to the results directory.</li>
-<li><strong>statsIter</strong> (<em>int</em>) – The number of statistical iterations.</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>directories</strong> – Paths to each statistical iterations result directory.</p>
-</td>
-</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">list of strings</p>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.execution.gen_k_folds">
-<code class="descname">gen_k_folds</code><span class="sig-paren">(</span><em>stats_iter</em>, <em>nb_folds</em>, <em>stats_iter_random_states</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.execution.gen_k_folds" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to generate folds indices for cross validation for each statistical iteration.</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>stats_iter</strong> (<em>integer</em>) – Number of statistical iterations of the benchmark.</li>
-<li><strong>nb_folds</strong> (<em>integer</em>) – The number of cross-validation folds for the benchmark.</li>
-<li><strong>stats_iter_random_states</strong> (<em>list of numpy.random.RandomState</em>) – The random states for each statistical iteration.</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>folds_list</strong> – For each statistical iteration a Kfold stratified (keeping the ratio between classes in each fold).</p>
-</td>
-</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">list of list of sklearn.model_selection.StratifiedKFold</p>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.execution.gen_splits">
-<code class="descname">gen_splits</code><span class="sig-paren">(</span><em>labels</em>, <em>split_ratio</em>, <em>stats_iter_random_states</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.execution.gen_splits" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to _gen the train/test splits using one or multiple random states.</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>labels</strong> (<em>numpy.ndarray</em>) – Name of the database.</li>
-<li><strong>split_ratio</strong> (<em>float</em>) – The ratio of examples between train and test set.</li>
-<li><strong>stats_iter_random_states</strong> (<em>list of numpy.random.RandomState</em>) – The random states for each statistical iteration.</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>splits</strong> – For each statistical iteration a couple of numpy.ndarrays is stored with the indices for the training set and
-the ones of the testing set.</p>
-</td>
-</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">list of lists of numpy.ndarray</p>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.execution.get_database_function">
-<code class="descname">get_database_function</code><span class="sig-paren">(</span><em>name</em>, <em>type_var</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.execution.get_database_function" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to get the right database extraction function according to the type of database and it’s name</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>name</strong> (<em>string</em>) – Name of the database.</li>
-<li><strong>type_var</strong> (<em>string</em>) – type of dataset hdf5 or csv</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>getDatabase</strong> – The function that will be used to extract the database</p>
-</td>
-</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">function</p>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.execution.init_log_file">
-<code class="descname">init_log_file</code><span class="sig-paren">(</span><em>name</em>, <em>views</em>, <em>cl_type</em>, <em>log</em>, <em>debug</em>, <em>label</em>, <em>result_directory</em>, <em>add_noise</em>, <em>noise_std</em>, <em>args</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.execution.init_log_file" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to init the directory where the preds will be stored and the log file.</p>
-<p>First this function will check if the result directory already exists (only one per minute is allowed).</p>
-<p>If the the result directory name is available, it is created, and the logfile is initiated.</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>name</strong> (<em>string</em>) – Name of the database.</li>
-<li><strong>views</strong> (<em>list of strings</em>) – List of the view names that will be used in the benchmark.</li>
-<li><strong>cl_type</strong> (<em>list of strings</em>) – Type of benchmark that will be made .</li>
-<li><strong>log</strong> (<em>bool</em>) – Whether to show the log file in console or hide it.</li>
-<li><strong>debug</strong> (<em>bool</em>) – for debug option</li>
-<li><strong>label</strong> (<em>str  for label</em>) – </li>
-<li><strong>result_directory</strong> (<em>str name of the result directory</em>) – </li>
-<li><strong>add_noise</strong> (<em>bool for add noise</em>) – </li>
-<li><strong>noise_std</strong> (<em>level of std noise</em>) – </li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>results_directory</strong> – Reference to the main results directory for the benchmark.</p>
-</td>
-</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">string</p>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.execution.init_random_state">
-<code class="descname">init_random_state</code><span class="sig-paren">(</span><em>random_state_arg</em>, <em>directory</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.execution.init_random_state" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to init a random state.
-If no random state is specified, it will generate a ‘random’ seed.
-If the <cite>randomSateArg</cite> is a string containing only numbers, it will be converted in</p>
-<blockquote>
-<div>an int to generate a seed.</div></blockquote>
-<p>If the <cite>randomSateArg</cite> is a string with letters, it must be a path to a pickled random
-state file that will be loaded.
-The function will also pickle the new random state in a file tobe able to retrieve it later.
-Tested</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>random_state_arg</strong> (<em>None</em><em> or </em><em>string</em>) – See function description.</li>
-<li><strong>directory</strong> (<em>string</em>) – Path to the results directory.</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>random_state</strong> – This random state will be used all along the benchmark .</p>
-</td>
-</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">numpy.random.RandomState object</p>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.execution.init_stats_iter_random_states">
-<code class="descname">init_stats_iter_random_states</code><span class="sig-paren">(</span><em>stats_iter</em>, <em>random_state</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.execution.init_stats_iter_random_states" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to initialize multiple random states if needed because of multiple statistical iteration of the same benchmark</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>stats_iter</strong> (<em>int</em>) – Number of statistical iterations of the same benchmark done (with a different random state).</li>
-<li><strong>random_state</strong> (<em>numpy.random.RandomState object</em>) – The random state of the whole experimentation, that will be used to generate the ones for each
-statistical iteration.</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>stats_iter_random_states</strong> – Multiple random states, one for each sattistical iteration of the same benchmark.</p>
-</td>
-</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">list of numpy.random.RandomState objects</p>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.execution.init_views">
-<code class="descname">init_views</code><span class="sig-paren">(</span><em>dataset_var</em>, <em>arg_views</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.execution.init_views" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to return the views names that will be used by the
-benchmark, their indices and all the views names.</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>dataset_var</strong> (<em>HDF5 dataset file</em>) – The full dataset that wil be used by the benchmark.</li>
-<li><strong>arg_views</strong> (<em>list of strings</em>) – The views that will be used by the benchmark (arg).</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last"><ul class="simple">
-<li><strong>views</strong> (<em>list of strings</em>) – Names of the views that will be used by the benchmark.</li>
-<li><strong>view_indices</strong> (<em>list of ints</em>) – The list of the indices of the view that will be used in the benchmark (according to the dataset).</li>
-<li><strong>all_views</strong> (<em>list of strings</em>) – Names of all the available views in the dataset.</li>
-</ul>
-</p>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.execution.parse_the_args">
-<code class="descname">parse_the_args</code><span class="sig-paren">(</span><em>arguments</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.execution.parse_the_args" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to parse the args entered by the user</p>
-</dd></dl>
-
+<div class="section" id="multiview-platform-mono-multi-view-classifiers-utils-execution-module">
+<h2>multiview_platform.mono_multi_view_classifiers.utils.execution module<a class="headerlink" href="#multiview-platform-mono-multi-view-classifiers-utils-execution-module" title="Permalink to this headline">¶</a></h2>
 </div>
-<div class="section" id="module-multiview_platform.mono_multi_view_classifiers.utils.get_multiview_db">
-<span id="multiview-platform-mono-multi-view-classifiers-utils-get-multiview-db-module"></span><h2>multiview_platform.mono_multi_view_classifiers.utils.get_multiview_db module<a class="headerlink" href="#module-multiview_platform.mono_multi_view_classifiers.utils.get_multiview_db" title="Permalink to this headline">¶</a></h2>
-<dl class="exception">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.get_multiview_db.DatasetError">
-<em class="property">exception </em><code class="descname">DatasetError</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.get_multiview_db.DatasetError" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">Exception</span></code></p>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.get_multiview_db.get_classic_db_csv">
-<code class="descname">get_classic_db_csv</code><span class="sig-paren">(</span><em>views</em>, <em>pathF</em>, <em>nameDB</em>, <em>NB_CLASS</em>, <em>askedLabelsNames</em>, <em>random_state</em>, <em>full=False</em>, <em>add_noise=False</em>, <em>noise_std=0.15</em>, <em>delimiter='</em>, <em>'</em>, <em>path_for_new='../data/'</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.get_multiview_db.get_classic_db_csv" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.get_multiview_db.get_classic_db_hdf5">
-<code class="descname">get_classic_db_hdf5</code><span class="sig-paren">(</span><em>views</em>, <em>path_f</em>, <em>name_DB</em>, <em>nb_class</em>, <em>asked_labels_names</em>, <em>random_state</em>, <em>full=False</em>, <em>add_noise=False</em>, <em>noise_std=0.15</em>, <em>path_for_new='../data/'</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.get_multiview_db.get_classic_db_hdf5" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to load a hdf5 database</p>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.get_multiview_db.get_plausible_db_hdf5">
-<code class="descname">get_plausible_db_hdf5</code><span class="sig-paren">(</span><em>features, path, file_name, nb_class=3, label_names=[b'No', b'Yes', b'Maybe'], random_state=None, full=True, add_noise=False, noise_std=0.15, nb_view=3, nb_examples=100, nb_features=10</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.get_multiview_db.get_plausible_db_hdf5" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to generate a plausible dataset to test the algorithms</p>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.get_multiview_db.make_me_noisy">
-<code class="descname">make_me_noisy</code><span class="sig-paren">(</span><em>view_data</em>, <em>random_state</em>, <em>percentage=5</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.get_multiview_db.make_me_noisy" title="Permalink to this definition">¶</a></dt>
-<dd><p>used to introduce some noise in the generated data</p>
-</dd></dl>
-
+<div class="section" id="multiview-platform-mono-multi-view-classifiers-utils-get-multiview-db-module">
+<h2>multiview_platform.mono_multi_view_classifiers.utils.get_multiview_db module<a class="headerlink" href="#multiview-platform-mono-multi-view-classifiers-utils-get-multiview-db-module" title="Permalink to this headline">¶</a></h2>
 </div>
-<div class="section" id="module-multiview_platform.mono_multi_view_classifiers.utils.hyper_parameter_search">
-<span id="multiview-platform-mono-multi-view-classifiers-utils-hyper-parameter-search-module"></span><h2>multiview_platform.mono_multi_view_classifiers.utils.hyper_parameter_search module<a class="headerlink" href="#module-multiview_platform.mono_multi_view_classifiers.utils.hyper_parameter_search" title="Permalink to this headline">¶</a></h2>
-<dl class="class">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.hyper_parameter_search.CustomRandint">
-<em class="property">class </em><code class="descname">CustomRandint</code><span class="sig-paren">(</span><em>low=0</em>, <em>high=0</em>, <em>multiplier=''</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.hyper_parameter_search.CustomRandint" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
-<p>Used as a distribution returning a integer between low and high-1.
-It can be used with a multiplier agrument to be able to perform more complex generation
-for example 10 e -(randint)</p>
-<dl class="method">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.hyper_parameter_search.CustomRandint.get_nb_possibilities">
-<code class="descname">get_nb_possibilities</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.hyper_parameter_search.CustomRandint.get_nb_possibilities" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="method">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.hyper_parameter_search.CustomRandint.rvs">
-<code class="descname">rvs</code><span class="sig-paren">(</span><em>random_state=None</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.hyper_parameter_search.CustomRandint.rvs" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-</dd></dl>
-
-<dl class="class">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.hyper_parameter_search.CustomUniform">
-<em class="property">class </em><code class="descname">CustomUniform</code><span class="sig-paren">(</span><em>loc=0</em>, <em>state=1</em>, <em>multiplier=''</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.hyper_parameter_search.CustomUniform" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
-<p>Used as a distribution returning a float between loc and loc + scale..
-It can be used with a multiplier agrument to be able to perform more complex generation
-for example 10 e -(float)</p>
-<dl class="method">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.hyper_parameter_search.CustomUniform.rvs">
-<code class="descname">rvs</code><span class="sig-paren">(</span><em>random_state=None</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.hyper_parameter_search.CustomUniform.rvs" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-</dd></dl>
-
-<dl class="class">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.hyper_parameter_search.MultiviewCompatibleRandomizedSearchCV">
-<em class="property">class </em><code class="descname">MultiviewCompatibleRandomizedSearchCV</code><span class="sig-paren">(</span><em>estimator</em>, <em>param_distributions</em>, <em>n_iter=10</em>, <em>refit=True</em>, <em>n_jobs=1</em>, <em>scoring=None</em>, <em>cv=None</em>, <em>random_state=None</em>, <em>learning_indices=None</em>, <em>view_indices=None</em>, <em>framework='monoview'</em>, <em>equivalent_draws=True</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.hyper_parameter_search.MultiviewCompatibleRandomizedSearchCV" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.model_selection._search.RandomizedSearchCV</span></code></p>
-<dl class="method">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.hyper_parameter_search.MultiviewCompatibleRandomizedSearchCV.fit">
-<code class="descname">fit</code><span class="sig-paren">(</span><em>X</em>, <em>y=None</em>, <em>groups=None</em>, <em>**fit_params</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.hyper_parameter_search.MultiviewCompatibleRandomizedSearchCV.fit" title="Permalink to this definition">¶</a></dt>
-<dd><p>Run fit with all sets of parameters.</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>X</strong> (<em>array-like</em><em>, </em><em>shape =</em><em> [</em><em>n_samples</em><em>, </em><em>n_features</em><em>]</em>) – Training vector, where n_samples is the number of samples and
-n_features is the number of features.</li>
-<li><strong>y</strong> (<em>array-like</em><em>, </em><em>shape =</em><em> [</em><em>n_samples</em><em>] or </em><em>[</em><em>n_samples</em><em>, </em><em>n_output</em><em>]</em><em>, </em><em>optional</em>) – Target relative to X for classification or regression;
-None for unsupervised learning.</li>
-<li><strong>groups</strong> (<em>array-like</em><em>, </em><em>with shape</em><em> (</em><em>n_samples</em><em>,</em><em>)</em><em>, </em><em>optional</em>) – Group labels for the samples used while splitting the dataset into
-train/test set.</li>
-<li><strong>**fit_params</strong> (<em>dict of string -&gt; object</em>) – Parameters passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method of the estimator</li>
-</ul>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="method">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.hyper_parameter_search.MultiviewCompatibleRandomizedSearchCV.fit_multiview">
-<code class="descname">fit_multiview</code><span class="sig-paren">(</span><em>X</em>, <em>y=None</em>, <em>groups=None</em>, <em>**fit_params</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.hyper_parameter_search.MultiviewCompatibleRandomizedSearchCV.fit_multiview" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="method">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.hyper_parameter_search.MultiviewCompatibleRandomizedSearchCV.get_test_folds_preds">
-<code class="descname">get_test_folds_preds</code><span class="sig-paren">(</span><em>X</em>, <em>y</em>, <em>estimator</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.hyper_parameter_search.MultiviewCompatibleRandomizedSearchCV.get_test_folds_preds" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.hyper_parameter_search.compute_possible_combinations">
-<code class="descname">compute_possible_combinations</code><span class="sig-paren">(</span><em>params_dict</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.hyper_parameter_search.compute_possible_combinations" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.hyper_parameter_search.gen_heat_maps">
-<code class="descname">gen_heat_maps</code><span class="sig-paren">(</span><em>params</em>, <em>scores_array</em>, <em>output_file_name</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.hyper_parameter_search.gen_heat_maps" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to generate a heat map for each doublet of hyperparms optimized on the previous function</p>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.hyper_parameter_search.get_test_folds_preds">
-<code class="descname">get_test_folds_preds</code><span class="sig-paren">(</span><em>X</em>, <em>y</em>, <em>cv</em>, <em>estimator</em>, <em>framework</em>, <em>available_indices=None</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.hyper_parameter_search.get_test_folds_preds" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.hyper_parameter_search.grid_search">
-<code class="descname">grid_search</code><span class="sig-paren">(</span><em>dataset</em>, <em>classifier_name</em>, <em>views_indices=None</em>, <em>k_folds=None</em>, <em>n_iter=1</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.hyper_parameter_search.grid_search" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to perfom gridsearch on the classifiers</p>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.hyper_parameter_search.randomized_search">
-<code class="descname">randomized_search</code><span class="sig-paren">(</span><em>X, y, framework, random_state, output_file_name, classifier_module, classifier_name, folds=4, nb_cores=1, metric=['accuracy_score', None], n_iter=30, classifier_kwargs=None, learning_indices=None, view_indices=None, equivalent_draws=True</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.hyper_parameter_search.randomized_search" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.hyper_parameter_search.search_best_settings">
-<code class="descname">search_best_settings</code><span class="sig-paren">(</span><em>dataset_var</em>, <em>labels</em>, <em>classifier_module</em>, <em>classifier_name</em>, <em>metrics</em>, <em>learning_indices</em>, <em>i_k_folds</em>, <em>random_state</em>, <em>directory</em>, <em>views_indices=None</em>, <em>nb_cores=1</em>, <em>searching_tool='randomized_search-equiv'</em>, <em>n_iter=1</em>, <em>classifier_config=None</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.hyper_parameter_search.search_best_settings" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to select the right hyper-parameter optimization function
-to optimize hyper parameters</p>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.hyper_parameter_search.spear_mint">
-<code class="descname">spear_mint</code><span class="sig-paren">(</span><em>dataset</em>, <em>classifier_name</em>, <em>views_indices=None</em>, <em>k_folds=None</em>, <em>n_iter=1</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.hyper_parameter_search.spear_mint" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to perform spearmint on the classifiers to optimize hyper parameters,
-longer than randomsearch (can’t be parallelized)</p>
-</dd></dl>
-
+<div class="section" id="multiview-platform-mono-multi-view-classifiers-utils-hyper-parameter-search-module">
+<h2>multiview_platform.mono_multi_view_classifiers.utils.hyper_parameter_search module<a class="headerlink" href="#multiview-platform-mono-multi-view-classifiers-utils-hyper-parameter-search-module" title="Permalink to this headline">¶</a></h2>
 </div>
-<div class="section" id="module-multiview_platform.mono_multi_view_classifiers.utils.make_file_config">
-<span id="multiview-platform-mono-multi-view-classifiers-utils-make-file-config-module"></span><h2>multiview_platform.mono_multi_view_classifiers.utils.make_file_config module<a class="headerlink" href="#module-multiview_platform.mono_multi_view_classifiers.utils.make_file_config" title="Permalink to this headline">¶</a></h2>
-<dl class="class">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.make_file_config.ConfigurationMaker">
-<em class="property">class </em><code class="descname">ConfigurationMaker</code><span class="sig-paren">(</span><em>classifier_dict=None</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.make_file_config.ConfigurationMaker" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
-<p>Find the name of the classifier from the dict classier to report</p>
-</dd></dl>
-
+<div class="section" id="multiview-platform-mono-multi-view-classifiers-utils-make-file-config-module">
+<h2>multiview_platform.mono_multi_view_classifiers.utils.make_file_config module<a class="headerlink" href="#multiview-platform-mono-multi-view-classifiers-utils-make-file-config-module" title="Permalink to this headline">¶</a></h2>
 </div>
-<div class="section" id="module-multiview_platform.mono_multi_view_classifiers.utils.multiclass">
-<span id="multiview-platform-mono-multi-view-classifiers-utils-multiclass-module"></span><h2>multiview_platform.mono_multi_view_classifiers.utils.multiclass module<a class="headerlink" href="#module-multiview_platform.mono_multi_view_classifiers.utils.multiclass" title="Permalink to this headline">¶</a></h2>
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.multiclass.gen_multiclass_labels">
-<code class="descname">gen_multiclass_labels</code><span class="sig-paren">(</span><em>labels</em>, <em>multiclass_method</em>, <em>splits</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.multiclass.gen_multiclass_labels" title="Permalink to this definition">¶</a></dt>
-<dd><p>Used to gen the train/test splits and to set up the framework of the adaptation of a multiclass dataset
-to biclass algorithms.</p>
-<p>First, the function checks whether the dataset is really multiclass.</p>
-<p>Then, it generates all the possible couples of different labels in order to perform one versus one classification.</p>
-<p>For each combination, it selects the examples in the training sets (for each statistical iteration) that have their
-label in the combination and does the same for the testing set. It also saves the multiclass testing set in order to
-use multiclass metrics on the decisions.</p>
-<p>Lastly, it creates a new array of biclass labels (0/1) for the biclass classifications used in oneVersusOne</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>labels</strong> (<em>numpy.ndarray</em>) – Name of the database.</li>
-<li><strong>multiclass_method</strong> (<em>string</em>) – The name of the multiclass method used (oneVersusOne, oneVersusAll, …).</li>
-<li><strong>splits</strong> (<em>list of lists of numpy.ndarray</em>) – For each statistical iteration a couple of numpy.ndarrays is stored with the indices for the training set and
-the ones of the testing set.</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last"><ul class="simple">
-<li><strong>multiclass_labels</strong> (<em>list of lists of numpy.ndarray</em>) – For each label couple, for each statistical iteration a triplet of numpy.ndarrays is stored with the
-indices for the biclass training set, the ones for the biclass testing set and the ones for the
-multiclass testing set.</li>
-<li><strong>labels_indices</strong> (<em>list of lists of numpy.ndarray</em>) – Each original couple of different labels.</li>
-<li><strong>indices_multiclass</strong> (<em>list of lists of numpy.ndarray</em>) – For each combination, contains a biclass labels numpy.ndarray with the 0/1 labels of combination.</li>
-</ul>
-</p>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.multiclass.gen_multiclass_monoview_decision">
-<code class="descname">gen_multiclass_monoview_decision</code><span class="sig-paren">(</span><em>monoview_result</em>, <em>classification_indices</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.multiclass.gen_multiclass_monoview_decision" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.multiclass.is_biclass">
-<code class="descname">is_biclass</code><span class="sig-paren">(</span><em>multiclass_preds</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.multiclass.is_biclass" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
+<div class="section" id="multiview-platform-mono-multi-view-classifiers-utils-multiclass-module">
+<h2>multiview_platform.mono_multi_view_classifiers.utils.multiclass module<a class="headerlink" href="#multiview-platform-mono-multi-view-classifiers-utils-multiclass-module" title="Permalink to this headline">¶</a></h2>
 </div>
-<div class="section" id="module-multiview_platform.mono_multi_view_classifiers.utils.multiview_result_analysis">
-<span id="multiview-platform-mono-multi-view-classifiers-utils-multiview-result-analysis-module"></span><h2>multiview_platform.mono_multi_view_classifiers.utils.multiview_result_analysis module<a class="headerlink" href="#module-multiview_platform.mono_multi_view_classifiers.utils.multiview_result_analysis" title="Permalink to this headline">¶</a></h2>
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.multiview_result_analysis.get_metrics_scores">
-<code class="descname">get_metrics_scores</code><span class="sig-paren">(</span><em>metrics_var</em>, <em>train_labels</em>, <em>test_labels</em>, <em>validation_indices</em>, <em>learning_indices</em>, <em>labels</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.multiview_result_analysis.get_metrics_scores" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.multiview_result_analysis.get_total_metric_scores">
-<code class="descname">get_total_metric_scores</code><span class="sig-paren">(</span><em>metric</em>, <em>train_labels</em>, <em>test_labels</em>, <em>validation_indices</em>, <em>learning_indices</em>, <em>labels</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.multiview_result_analysis.get_total_metric_scores" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.multiview_result_analysis.print_metric_score">
-<code class="descname">print_metric_score</code><span class="sig-paren">(</span><em>metric_scores</em>, <em>metrics</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.multiview_result_analysis.print_metric_score" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
+<div class="section" id="multiview-platform-mono-multi-view-classifiers-utils-multiview-result-analysis-module">
+<h2>multiview_platform.mono_multi_view_classifiers.utils.multiview_result_analysis module<a class="headerlink" href="#multiview-platform-mono-multi-view-classifiers-utils-multiview-result-analysis-module" title="Permalink to this headline">¶</a></h2>
 </div>
-<div class="section" id="module-multiview_platform.mono_multi_view_classifiers.utils.transformations">
-<span id="multiview-platform-mono-multi-view-classifiers-utils-transformations-module"></span><h2>multiview_platform.mono_multi_view_classifiers.utils.transformations module<a class="headerlink" href="#module-multiview_platform.mono_multi_view_classifiers.utils.transformations" title="Permalink to this headline">¶</a></h2>
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.transformations.sign_labels">
-<code class="descname">sign_labels</code><span class="sig-paren">(</span><em>labels</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.transformations.sign_labels" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns a label array with (-1,1) as labels.
-If labels was already made of (-1,1), returns labels.
-If labels is made of (0,1), returns labels with all
-zeros transformed in -1.</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>labels</strong> – </li>
-<li><strong>original label numpy array</strong> (<em>The</em>) – </li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"></p>
-</td>
-</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">A np.array with labels made of (-1,1)</p>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.mono_multi_view_classifiers.utils.transformations.unsign_labels">
-<code class="descname">unsign_labels</code><span class="sig-paren">(</span><em>labels</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.utils.transformations.unsign_labels" title="Permalink to this definition">¶</a></dt>
-<dd><p>The inverse function</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>labels</strong> – </td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
+<div class="section" id="multiview-platform-mono-multi-view-classifiers-utils-transformations-module">
+<h2>multiview_platform.mono_multi_view_classifiers.utils.transformations module<a class="headerlink" href="#multiview-platform-mono-multi-view-classifiers-utils-transformations-module" title="Permalink to this headline">¶</a></h2>
 </div>
-<div class="section" id="module-multiview_platform.mono_multi_view_classifiers.utils">
-<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-multiview_platform.mono_multi_view_classifiers.utils" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="module-contents">
+<h2>Module contents<a class="headerlink" href="#module-contents" title="Permalink to this headline">¶</a></h2>
 </div>
 </div>
 
@@ -970,16 +91,16 @@ zeros transformed in -1.</p>
   <ul>
 <li><a class="reference internal" href="#">multiview_platform.mono_multi_view_classifiers.utils package</a><ul>
 <li><a class="reference internal" href="#submodules">Submodules</a></li>
-<li><a class="reference internal" href="#module-multiview_platform.mono_multi_view_classifiers.utils.configuration">multiview_platform.mono_multi_view_classifiers.utils.configuration module</a></li>
-<li><a class="reference internal" href="#module-multiview_platform.mono_multi_view_classifiers.utils.dataset">multiview_platform.mono_multi_view_classifiers.utils.dataset module</a></li>
-<li><a class="reference internal" href="#module-multiview_platform.mono_multi_view_classifiers.utils.execution">multiview_platform.mono_multi_view_classifiers.utils.execution module</a></li>
-<li><a class="reference internal" href="#module-multiview_platform.mono_multi_view_classifiers.utils.get_multiview_db">multiview_platform.mono_multi_view_classifiers.utils.get_multiview_db module</a></li>
-<li><a class="reference internal" href="#module-multiview_platform.mono_multi_view_classifiers.utils.hyper_parameter_search">multiview_platform.mono_multi_view_classifiers.utils.hyper_parameter_search module</a></li>
-<li><a class="reference internal" href="#module-multiview_platform.mono_multi_view_classifiers.utils.make_file_config">multiview_platform.mono_multi_view_classifiers.utils.make_file_config module</a></li>
-<li><a class="reference internal" href="#module-multiview_platform.mono_multi_view_classifiers.utils.multiclass">multiview_platform.mono_multi_view_classifiers.utils.multiclass module</a></li>
-<li><a class="reference internal" href="#module-multiview_platform.mono_multi_view_classifiers.utils.multiview_result_analysis">multiview_platform.mono_multi_view_classifiers.utils.multiview_result_analysis module</a></li>
-<li><a class="reference internal" href="#module-multiview_platform.mono_multi_view_classifiers.utils.transformations">multiview_platform.mono_multi_view_classifiers.utils.transformations module</a></li>
-<li><a class="reference internal" href="#module-multiview_platform.mono_multi_view_classifiers.utils">Module contents</a></li>
+<li><a class="reference internal" href="#multiview-platform-mono-multi-view-classifiers-utils-configuration-module">multiview_platform.mono_multi_view_classifiers.utils.configuration module</a></li>
+<li><a class="reference internal" href="#multiview-platform-mono-multi-view-classifiers-utils-dataset-module">multiview_platform.mono_multi_view_classifiers.utils.dataset module</a></li>
+<li><a class="reference internal" href="#multiview-platform-mono-multi-view-classifiers-utils-execution-module">multiview_platform.mono_multi_view_classifiers.utils.execution module</a></li>
+<li><a class="reference internal" href="#multiview-platform-mono-multi-view-classifiers-utils-get-multiview-db-module">multiview_platform.mono_multi_view_classifiers.utils.get_multiview_db module</a></li>
+<li><a class="reference internal" href="#multiview-platform-mono-multi-view-classifiers-utils-hyper-parameter-search-module">multiview_platform.mono_multi_view_classifiers.utils.hyper_parameter_search module</a></li>
+<li><a class="reference internal" href="#multiview-platform-mono-multi-view-classifiers-utils-make-file-config-module">multiview_platform.mono_multi_view_classifiers.utils.make_file_config module</a></li>
+<li><a class="reference internal" href="#multiview-platform-mono-multi-view-classifiers-utils-multiclass-module">multiview_platform.mono_multi_view_classifiers.utils.multiclass module</a></li>
+<li><a class="reference internal" href="#multiview-platform-mono-multi-view-classifiers-utils-multiview-result-analysis-module">multiview_platform.mono_multi_view_classifiers.utils.multiview_result_analysis module</a></li>
+<li><a class="reference internal" href="#multiview-platform-mono-multi-view-classifiers-utils-transformations-module">multiview_platform.mono_multi_view_classifiers.utils.transformations module</a></li>
+<li><a class="reference internal" href="#module-contents">Module contents</a></li>
 </ul>
 </li>
 </ul>
@@ -1019,9 +140,6 @@ zeros transformed in -1.</p>
         <li class="right" style="margin-right: 10px">
           <a href="../genindex.html" title="General Index"
              >index</a></li>
-        <li class="right" >
-          <a href="../py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
         <li class="right" >
           <a href="multiview_platform.tests.html" title="multiview_platform.tests package"
              >next</a> |</li>
diff --git a/docs/build/references/multiview_platform.tests.html b/docs/build/references/multiview_platform.tests.html
index 54b2c5eb59ffeec9d02e6613bb5097653ed7a1a1..7cc0f4f97bb08231a17c9461f62b314ab244b84c 100644
--- a/docs/build/references/multiview_platform.tests.html
+++ b/docs/build/references/multiview_platform.tests.html
@@ -27,9 +27,6 @@
         <li class="right" style="margin-right: 10px">
           <a href="../genindex.html" title="General Index"
              accesskey="I">index</a></li>
-        <li class="right" >
-          <a href="../py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
         <li class="right" >
           <a href="multiview_platform.tests.test_metrics.html" title="multiview_platform.tests.test_metrics package"
              accesskey="N">next</a> |</li>
@@ -54,22 +51,22 @@
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="multiview_platform.tests.test_metrics.html">multiview_platform.tests.test_metrics package</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="multiview_platform.tests.test_metrics.html#submodules">Submodules</a></li>
-<li class="toctree-l2"><a class="reference internal" href="multiview_platform.tests.test_metrics.html#module-multiview_platform.tests.test_metrics.test_accuracy_score">multiview_platform.tests.test_metrics.test_accuracy_score module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="multiview_platform.tests.test_metrics.html#module-multiview_platform.tests.test_metrics">Module contents</a></li>
+<li class="toctree-l2"><a class="reference internal" href="multiview_platform.tests.test_metrics.html#multiview-platform-tests-test-metrics-test-accuracy-score-module">multiview_platform.tests.test_metrics.test_accuracy_score module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="multiview_platform.tests.test_metrics.html#module-contents">Module contents</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="multiview_platform.tests.test_mono_view.html">multiview_platform.tests.test_mono_view package</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="multiview_platform.tests.test_mono_view.html#submodules">Submodules</a></li>
-<li class="toctree-l2"><a class="reference internal" href="multiview_platform.tests.test_mono_view.html#module-multiview_platform.tests.test_mono_view.test_ExecClassifMonoView">multiview_platform.tests.test_mono_view.test_ExecClassifMonoView module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="multiview_platform.tests.test_mono_view.html#module-multiview_platform.tests.test_mono_view.test_MonoviewUtils">multiview_platform.tests.test_mono_view.test_MonoviewUtils module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="multiview_platform.tests.test_mono_view.html#module-multiview_platform.tests.test_mono_view">Module contents</a></li>
+<li class="toctree-l2"><a class="reference internal" href="multiview_platform.tests.test_mono_view.html#multiview-platform-tests-test-mono-view-test-execclassifmonoview-module">multiview_platform.tests.test_mono_view.test_ExecClassifMonoView module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="multiview_platform.tests.test_mono_view.html#multiview-platform-tests-test-mono-view-test-monoviewutils-module">multiview_platform.tests.test_mono_view.test_MonoviewUtils module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="multiview_platform.tests.test_mono_view.html#module-contents">Module contents</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="multiview_platform.tests.test_monoview_classifiers.html">multiview_platform.tests.test_monoview_classifiers package</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="multiview_platform.tests.test_monoview_classifiers.html#submodules">Submodules</a></li>
-<li class="toctree-l2"><a class="reference internal" href="multiview_platform.tests.test_monoview_classifiers.html#module-multiview_platform.tests.test_monoview_classifiers.test_adaboost">multiview_platform.tests.test_monoview_classifiers.test_adaboost module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="multiview_platform.tests.test_monoview_classifiers.html#module-multiview_platform.tests.test_monoview_classifiers.test_compatibility">multiview_platform.tests.test_monoview_classifiers.test_compatibility module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="multiview_platform.tests.test_monoview_classifiers.html#module-multiview_platform.tests.test_monoview_classifiers">Module contents</a></li>
+<li class="toctree-l2"><a class="reference internal" href="multiview_platform.tests.test_monoview_classifiers.html#multiview-platform-tests-test-monoview-classifiers-test-adaboost-module">multiview_platform.tests.test_monoview_classifiers.test_adaboost module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="multiview_platform.tests.test_monoview_classifiers.html#multiview-platform-tests-test-monoview-classifiers-test-compatibility-module">multiview_platform.tests.test_monoview_classifiers.test_compatibility module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="multiview_platform.tests.test_monoview_classifiers.html#module-contents">Module contents</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="multiview_platform.tests.test_multiview_classifiers.html">multiview_platform.tests.test_multiview_classifiers package</a><ul>
@@ -109,24 +106,24 @@
 </li>
 <li class="toctree-l3"><a class="reference internal" href="multiview_platform.tests.test_multiview_classifiers.Test_PseudoCQMeasure.html">multiview_platform.tests.test_multiview_classifiers.Test_PseudoCQMeasure package</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="multiview_platform.tests.test_multiview_classifiers.Test_PseudoCQMeasure.html#submodules">Submodules</a></li>
-<li class="toctree-l4"><a class="reference internal" href="multiview_platform.tests.test_multiview_classifiers.Test_PseudoCQMeasure.html#module-multiview_platform.tests.test_multiview_classifiers.Test_PseudoCQMeasure.test_PseudoCQFusionModule">multiview_platform.tests.test_multiview_classifiers.Test_PseudoCQMeasure.test_PseudoCQFusionModule module</a></li>
-<li class="toctree-l4"><a class="reference internal" href="multiview_platform.tests.test_multiview_classifiers.Test_PseudoCQMeasure.html#module-multiview_platform.tests.test_multiview_classifiers.Test_PseudoCQMeasure">Module contents</a></li>
+<li class="toctree-l4"><a class="reference internal" href="multiview_platform.tests.test_multiview_classifiers.Test_PseudoCQMeasure.html#multiview-platform-tests-test-multiview-classifiers-test-pseudocqmeasure-test-pseudocqfusionmodule-module">multiview_platform.tests.test_multiview_classifiers.Test_PseudoCQMeasure.test_PseudoCQFusionModule module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="multiview_platform.tests.test_multiview_classifiers.Test_PseudoCQMeasure.html#module-contents">Module contents</a></li>
 </ul>
 </li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="multiview_platform.tests.test_multiview_classifiers.html#submodules">Submodules</a></li>
-<li class="toctree-l2"><a class="reference internal" href="multiview_platform.tests.test_multiview_classifiers.html#module-multiview_platform.tests.test_multiview_classifiers.test_diversity_utils">multiview_platform.tests.test_multiview_classifiers.test_diversity_utils module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="multiview_platform.tests.test_multiview_classifiers.html#module-multiview_platform.tests.test_multiview_classifiers">Module contents</a></li>
+<li class="toctree-l2"><a class="reference internal" href="multiview_platform.tests.test_multiview_classifiers.html#multiview-platform-tests-test-multiview-classifiers-test-diversity-utils-module">multiview_platform.tests.test_multiview_classifiers.test_diversity_utils module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="multiview_platform.tests.test_multiview_classifiers.html#module-contents">Module contents</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="multiview_platform.tests.test_utils.html">multiview_platform.tests.test_utils package</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="multiview_platform.tests.test_utils.html#submodules">Submodules</a></li>
-<li class="toctree-l2"><a class="reference internal" href="multiview_platform.tests.test_utils.html#module-multiview_platform.tests.test_utils.test_GetMultiviewDB">multiview_platform.tests.test_utils.test_GetMultiviewDB module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="multiview_platform.tests.test_utils.html#module-multiview_platform.tests.test_utils.test_configuration">multiview_platform.tests.test_utils.test_configuration module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="multiview_platform.tests.test_utils.html#module-multiview_platform.tests.test_utils.test_execution">multiview_platform.tests.test_utils.test_execution module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="multiview_platform.tests.test_utils.html#module-multiview_platform.tests.test_utils.test_multiclass">multiview_platform.tests.test_utils.test_multiclass module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="multiview_platform.tests.test_utils.html#module-multiview_platform.tests.test_utils">Module contents</a></li>
+<li class="toctree-l2"><a class="reference internal" href="multiview_platform.tests.test_utils.html#multiview-platform-tests-test-utils-test-getmultiviewdb-module">multiview_platform.tests.test_utils.test_GetMultiviewDB module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="multiview_platform.tests.test_utils.html#multiview-platform-tests-test-utils-test-configuration-module">multiview_platform.tests.test_utils.test_configuration module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="multiview_platform.tests.test_utils.html#multiview-platform-tests-test-utils-test-execution-module">multiview_platform.tests.test_utils.test_execution module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="multiview_platform.tests.test_utils.html#multiview-platform-tests-test-utils-test-multiclass-module">multiview_platform.tests.test_utils.test_multiclass module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="multiview_platform.tests.test_utils.html#module-contents">Module contents</a></li>
 </ul>
 </li>
 </ul>
@@ -135,301 +132,14 @@
 <div class="section" id="submodules">
 <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this headline">¶</a></h2>
 </div>
-<div class="section" id="module-multiview_platform.tests.test_ExecClassif">
-<span id="multiview-platform-tests-test-execclassif-module"></span><h2>multiview_platform.tests.test_ExecClassif module<a class="headerlink" href="#module-multiview_platform.tests.test_ExecClassif" title="Permalink to this headline">¶</a></h2>
-<dl class="class">
-<dt id="multiview_platform.tests.test_ExecClassif.FakeKfold">
-<em class="property">class </em><code class="descname">FakeKfold</code><a class="headerlink" href="#multiview_platform.tests.test_ExecClassif.FakeKfold" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
-<dl class="method">
-<dt id="multiview_platform.tests.test_ExecClassif.FakeKfold.split">
-<code class="descname">split</code><span class="sig-paren">(</span><em>X</em>, <em>Y</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_ExecClassif.FakeKfold.split" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-</dd></dl>
-
-<dl class="class">
-<dt id="multiview_platform.tests.test_ExecClassif.Test_InitArgumentDictionaries">
-<em class="property">class </em><code class="descname">Test_InitArgumentDictionaries</code><span class="sig-paren">(</span><em>methodName='runTest'</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_ExecClassif.Test_InitArgumentDictionaries" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">unittest.case.TestCase</span></code></p>
-<dl class="classmethod">
-<dt id="multiview_platform.tests.test_ExecClassif.Test_InitArgumentDictionaries.setUpClass">
-<em class="property">classmethod </em><code class="descname">setUpClass</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_ExecClassif.Test_InitArgumentDictionaries.setUpClass" title="Permalink to this definition">¶</a></dt>
-<dd><p>Hook method for setting up class fixture before running tests in the class.</p>
-</dd></dl>
-
-<dl class="method">
-<dt id="multiview_platform.tests.test_ExecClassif.Test_InitArgumentDictionaries.test_init_argument_dictionaries_monoview">
-<code class="descname">test_init_argument_dictionaries_monoview</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_ExecClassif.Test_InitArgumentDictionaries.test_init_argument_dictionaries_monoview" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="method">
-<dt id="multiview_platform.tests.test_ExecClassif.Test_InitArgumentDictionaries.test_init_argument_dictionaries_multiview">
-<code class="descname">test_init_argument_dictionaries_multiview</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_ExecClassif.Test_InitArgumentDictionaries.test_init_argument_dictionaries_multiview" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="method">
-<dt id="multiview_platform.tests.test_ExecClassif.Test_InitArgumentDictionaries.test_init_argument_dictionaries_multiview_complex">
-<code class="descname">test_init_argument_dictionaries_multiview_complex</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_ExecClassif.Test_InitArgumentDictionaries.test_init_argument_dictionaries_multiview_complex" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="method">
-<dt id="multiview_platform.tests.test_ExecClassif.Test_InitArgumentDictionaries.test_init_argument_dictionaries_multiview_multiple">
-<code class="descname">test_init_argument_dictionaries_multiview_multiple</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_ExecClassif.Test_InitArgumentDictionaries.test_init_argument_dictionaries_multiview_multiple" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="method">
-<dt id="multiview_platform.tests.test_ExecClassif.Test_InitArgumentDictionaries.test_init_argument_dictionaries_multiview_multiple_complex">
-<code class="descname">test_init_argument_dictionaries_multiview_multiple_complex</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_ExecClassif.Test_InitArgumentDictionaries.test_init_argument_dictionaries_multiview_multiple_complex" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-</dd></dl>
-
-<dl class="class">
-<dt id="multiview_platform.tests.test_ExecClassif.Test_execBenchmark">
-<em class="property">class </em><code class="descname">Test_execBenchmark</code><span class="sig-paren">(</span><em>methodName='runTest'</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_ExecClassif.Test_execBenchmark" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">unittest.case.TestCase</span></code></p>
-<dl class="classmethod">
-<dt id="multiview_platform.tests.test_ExecClassif.Test_execBenchmark.setUpClass">
-<em class="property">classmethod </em><code class="descname">setUpClass</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_ExecClassif.Test_execBenchmark.setUpClass" title="Permalink to this definition">¶</a></dt>
-<dd><p>Hook method for setting up class fixture before running tests in the class.</p>
-</dd></dl>
-
-<dl class="classmethod">
-<dt id="multiview_platform.tests.test_ExecClassif.Test_execBenchmark.tearDownClass">
-<em class="property">classmethod </em><code class="descname">tearDownClass</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_ExecClassif.Test_execBenchmark.tearDownClass" title="Permalink to this definition">¶</a></dt>
-<dd><p>Hook method for deconstructing the class fixture after running all tests in the class.</p>
-</dd></dl>
-
-<dl class="method">
-<dt id="multiview_platform.tests.test_ExecClassif.Test_execBenchmark.test_multiclass_and_iter">
-<code class="descname">test_multiclass_and_iter</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_ExecClassif.Test_execBenchmark.test_multiclass_and_iter" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="method">
-<dt id="multiview_platform.tests.test_ExecClassif.Test_execBenchmark.test_multiclass_no_iter">
-<code class="descname">test_multiclass_no_iter</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_ExecClassif.Test_execBenchmark.test_multiclass_no_iter" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="method">
-<dt id="multiview_platform.tests.test_ExecClassif.Test_execBenchmark.test_no_iter_biclass_multicore">
-<code class="descname">test_no_iter_biclass_multicore</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_ExecClassif.Test_execBenchmark.test_no_iter_biclass_multicore" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="method">
-<dt id="multiview_platform.tests.test_ExecClassif.Test_execBenchmark.test_simple">
-<code class="descname">test_simple</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_ExecClassif.Test_execBenchmark.test_simple" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-</dd></dl>
-
-<dl class="class">
-<dt id="multiview_platform.tests.test_ExecClassif.Test_get_path_dict">
-<em class="property">class </em><code class="descname">Test_get_path_dict</code><span class="sig-paren">(</span><em>methodName='runTest'</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_ExecClassif.Test_get_path_dict" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">unittest.case.TestCase</span></code></p>
-<dl class="classmethod">
-<dt id="multiview_platform.tests.test_ExecClassif.Test_get_path_dict.setUpClass">
-<em class="property">classmethod </em><code class="descname">setUpClass</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_ExecClassif.Test_get_path_dict.setUpClass" title="Permalink to this definition">¶</a></dt>
-<dd><p>Hook method for setting up class fixture before running tests in the class.</p>
-</dd></dl>
-
-<dl class="classmethod">
-<dt id="multiview_platform.tests.test_ExecClassif.Test_get_path_dict.tearDownClass">
-<em class="property">classmethod </em><code class="descname">tearDownClass</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_ExecClassif.Test_get_path_dict.tearDownClass" title="Permalink to this definition">¶</a></dt>
-<dd><p>Hook method for deconstructing the class fixture after running all tests in the class.</p>
-</dd></dl>
-
-<dl class="method">
-<dt id="multiview_platform.tests.test_ExecClassif.Test_get_path_dict.test_simple">
-<code class="descname">test_simple</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_ExecClassif.Test_get_path_dict.test_simple" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-</dd></dl>
-
-<dl class="class">
-<dt id="multiview_platform.tests.test_ExecClassif.Test_initBenchmark">
-<em class="property">class </em><code class="descname">Test_initBenchmark</code><span class="sig-paren">(</span><em>methodName='runTest'</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_ExecClassif.Test_initBenchmark" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">unittest.case.TestCase</span></code></p>
-<dl class="method">
-<dt id="multiview_platform.tests.test_ExecClassif.Test_initBenchmark.test_benchmark_wanted">
-<code class="descname">test_benchmark_wanted</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_ExecClassif.Test_initBenchmark.test_benchmark_wanted" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-</dd></dl>
-
-<dl class="class">
-<dt id="multiview_platform.tests.test_ExecClassif.Test_initKWARGS">
-<em class="property">class </em><code class="descname">Test_initKWARGS</code><span class="sig-paren">(</span><em>methodName='runTest'</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_ExecClassif.Test_initKWARGS" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">unittest.case.TestCase</span></code></p>
-<dl class="method">
-<dt id="multiview_platform.tests.test_ExecClassif.Test_initKWARGS.test_initKWARGSFunc_no_monoview">
-<code class="descname">test_initKWARGSFunc_no_monoview</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_ExecClassif.Test_initKWARGS.test_initKWARGSFunc_no_monoview" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-</dd></dl>
-
-<dl class="class">
-<dt id="multiview_platform.tests.test_ExecClassif.Test_set_element">
-<em class="property">class </em><code class="descname">Test_set_element</code><span class="sig-paren">(</span><em>methodName='runTest'</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_ExecClassif.Test_set_element" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">unittest.case.TestCase</span></code></p>
-<dl class="classmethod">
-<dt id="multiview_platform.tests.test_ExecClassif.Test_set_element.setUpClass">
-<em class="property">classmethod </em><code class="descname">setUpClass</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_ExecClassif.Test_set_element.setUpClass" title="Permalink to this definition">¶</a></dt>
-<dd><p>Hook method for setting up class fixture before running tests in the class.</p>
-</dd></dl>
-
-<dl class="classmethod">
-<dt id="multiview_platform.tests.test_ExecClassif.Test_set_element.tearDownClass">
-<em class="property">classmethod </em><code class="descname">tearDownClass</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_ExecClassif.Test_set_element.tearDownClass" title="Permalink to this definition">¶</a></dt>
-<dd><p>Hook method for deconstructing the class fixture after running all tests in the class.</p>
-</dd></dl>
-
-<dl class="method">
-<dt id="multiview_platform.tests.test_ExecClassif.Test_set_element.test_simple">
-<code class="descname">test_simple</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_ExecClassif.Test_set_element.test_simple" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-</dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.tests.test_ExecClassif.fakeBenchmarkExec">
-<code class="descname">fakeBenchmarkExec</code><span class="sig-paren">(</span><em>core_index=-1</em>, <em>a=7</em>, <em>args=1</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_ExecClassif.fakeBenchmarkExec" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.tests.test_ExecClassif.fakeBenchmarkExec_monocore">
-<code class="descname">fakeBenchmarkExec_monocore</code><span class="sig-paren">(</span><em>dataset_var=1</em>, <em>a=4</em>, <em>args=1</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_ExecClassif.fakeBenchmarkExec_monocore" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.tests.test_ExecClassif.fakeBenchmarkExec_mutlicore">
-<code class="descname">fakeBenchmarkExec_mutlicore</code><span class="sig-paren">(</span><em>nb_cores=-1</em>, <em>a=6</em>, <em>args=1</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_ExecClassif.fakeBenchmarkExec_mutlicore" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.tests.test_ExecClassif.fakeDelete">
-<code class="descname">fakeDelete</code><span class="sig-paren">(</span><em>a</em>, <em>b</em>, <em>c</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_ExecClassif.fakeDelete" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.tests.test_ExecClassif.fakeExecMono">
-<code class="descname">fakeExecMono</code><span class="sig-paren">(</span><em>directory</em>, <em>name</em>, <em>labels_names</em>, <em>classification_indices</em>, <em>k_folds</em>, <em>coreIndex</em>, <em>type</em>, <em>pathF</em>, <em>random_state</em>, <em>labels</em>, <em>hyper_param_search='try'</em>, <em>metrics='try'</em>, <em>n_iter=1</em>, <em>**arguments</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_ExecClassif.fakeExecMono" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.tests.test_ExecClassif.fakeExecMulti">
-<code class="descname">fakeExecMulti</code><span class="sig-paren">(</span><em>directory</em>, <em>coreIndex</em>, <em>name</em>, <em>classification_indices</em>, <em>k_folds</em>, <em>type</em>, <em>pathF</em>, <em>labels_dictionary</em>, <em>random_state</em>, <em>labels</em>, <em>hyper_param_search=''</em>, <em>metrics=None</em>, <em>n_iter=1</em>, <em>**arguments</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_ExecClassif.fakeExecMulti" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.tests.test_ExecClassif.fakeInitMulti">
-<code class="descname">fakeInitMulti</code><span class="sig-paren">(</span><em>args</em>, <em>benchmark</em>, <em>views</em>, <em>views_indices</em>, <em>argument_dictionaries</em>, <em>random_state</em>, <em>directory</em>, <em>resultsMonoview</em>, <em>classification_indices</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_ExecClassif.fakeInitMulti" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="function">
-<dt id="multiview_platform.tests.test_ExecClassif.fakegetResults">
-<code class="descname">fakegetResults</code><span class="sig-paren">(</span><em>results</em>, <em>stats_iter</em>, <em>nb_multiclass</em>, <em>benchmark_arguments_dictionaries</em>, <em>multi_class_labels</em>, <em>metrics</em>, <em>classification_indices</em>, <em>directories</em>, <em>directory</em>, <em>labels_dictionary</em>, <em>nb_examples</em>, <em>nb_labels</em>, <em>example_ids</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_ExecClassif.fakegetResults" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
+<div class="section" id="multiview-platform-tests-test-execclassif-module">
+<h2>multiview_platform.tests.test_ExecClassif module<a class="headerlink" href="#multiview-platform-tests-test-execclassif-module" title="Permalink to this headline">¶</a></h2>
 </div>
-<div class="section" id="module-multiview_platform.tests.test_ResultAnalysis">
-<span id="multiview-platform-tests-test-resultanalysis-module"></span><h2>multiview_platform.tests.test_ResultAnalysis module<a class="headerlink" href="#module-multiview_platform.tests.test_ResultAnalysis" title="Permalink to this headline">¶</a></h2>
-<dl class="class">
-<dt id="multiview_platform.tests.test_ResultAnalysis.Test_format_previous_results">
-<em class="property">class </em><code class="descname">Test_format_previous_results</code><span class="sig-paren">(</span><em>methodName='runTest'</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_ResultAnalysis.Test_format_previous_results" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">unittest.case.TestCase</span></code></p>
-<dl class="method">
-<dt id="multiview_platform.tests.test_ResultAnalysis.Test_format_previous_results.test_simple">
-<code class="descname">test_simple</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_ResultAnalysis.Test_format_previous_results.test_simple" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-</dd></dl>
-
-<dl class="class">
-<dt id="multiview_platform.tests.test_ResultAnalysis.Test_gen_error_data">
-<em class="property">class </em><code class="descname">Test_gen_error_data</code><span class="sig-paren">(</span><em>methodName='runTest'</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_ResultAnalysis.Test_gen_error_data" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">unittest.case.TestCase</span></code></p>
-<dl class="method">
-<dt id="multiview_platform.tests.test_ResultAnalysis.Test_gen_error_data.test_simple">
-<code class="descname">test_simple</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_ResultAnalysis.Test_gen_error_data.test_simple" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-</dd></dl>
-
-<dl class="class">
-<dt id="multiview_platform.tests.test_ResultAnalysis.Test_gen_error_data_glob">
-<em class="property">class </em><code class="descname">Test_gen_error_data_glob</code><span class="sig-paren">(</span><em>methodName='runTest'</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_ResultAnalysis.Test_gen_error_data_glob" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">unittest.case.TestCase</span></code></p>
-<dl class="method">
-<dt id="multiview_platform.tests.test_ResultAnalysis.Test_gen_error_data_glob.test_simple">
-<code class="descname">test_simple</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_ResultAnalysis.Test_gen_error_data_glob.test_simple" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-</dd></dl>
-
-<dl class="class">
-<dt id="multiview_platform.tests.test_ResultAnalysis.Test_get_arguments">
-<em class="property">class </em><code class="descname">Test_get_arguments</code><span class="sig-paren">(</span><em>methodName='runTest'</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_ResultAnalysis.Test_get_arguments" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">unittest.case.TestCase</span></code></p>
-<dl class="method">
-<dt id="multiview_platform.tests.test_ResultAnalysis.Test_get_arguments.setUp">
-<code class="descname">setUp</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_ResultAnalysis.Test_get_arguments.setUp" title="Permalink to this definition">¶</a></dt>
-<dd><p>Hook method for setting up the test fixture before exercising it.</p>
-</dd></dl>
-
-<dl class="method">
-<dt id="multiview_platform.tests.test_ResultAnalysis.Test_get_arguments.test_benchmark_wanted">
-<code class="descname">test_benchmark_wanted</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_ResultAnalysis.Test_get_arguments.test_benchmark_wanted" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-</dd></dl>
-
-<dl class="class">
-<dt id="multiview_platform.tests.test_ResultAnalysis.Test_get_example_errors_biclass">
-<em class="property">class </em><code class="descname">Test_get_example_errors_biclass</code><span class="sig-paren">(</span><em>methodName='runTest'</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_ResultAnalysis.Test_get_example_errors_biclass" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">unittest.case.TestCase</span></code></p>
-<dl class="method">
-<dt id="multiview_platform.tests.test_ResultAnalysis.Test_get_example_errors_biclass.test_simple">
-<code class="descname">test_simple</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_ResultAnalysis.Test_get_example_errors_biclass.test_simple" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-</dd></dl>
-
-<dl class="class">
-<dt id="multiview_platform.tests.test_ResultAnalysis.Test_get_metrics_scores_biclass">
-<em class="property">class </em><code class="descname">Test_get_metrics_scores_biclass</code><span class="sig-paren">(</span><em>methodName='runTest'</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_ResultAnalysis.Test_get_metrics_scores_biclass" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">unittest.case.TestCase</span></code></p>
-<dl class="method">
-<dt id="multiview_platform.tests.test_ResultAnalysis.Test_get_metrics_scores_biclass.multiple_monoview_classifiers">
-<code class="descname">multiple_monoview_classifiers</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_ResultAnalysis.Test_get_metrics_scores_biclass.multiple_monoview_classifiers" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="method">
-<dt id="multiview_platform.tests.test_ResultAnalysis.Test_get_metrics_scores_biclass.mutiview_result">
-<code class="descname">mutiview_result</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_ResultAnalysis.Test_get_metrics_scores_biclass.mutiview_result" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="method">
-<dt id="multiview_platform.tests.test_ResultAnalysis.Test_get_metrics_scores_biclass.test_simple">
-<code class="descname">test_simple</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_ResultAnalysis.Test_get_metrics_scores_biclass.test_simple" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-</dd></dl>
-
-<dl class="class">
-<dt id="multiview_platform.tests.test_ResultAnalysis.Test_init_plot">
-<em class="property">class </em><code class="descname">Test_init_plot</code><span class="sig-paren">(</span><em>methodName='runTest'</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_ResultAnalysis.Test_init_plot" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">unittest.case.TestCase</span></code></p>
-<dl class="method">
-<dt id="multiview_platform.tests.test_ResultAnalysis.Test_init_plot.test_simple">
-<code class="descname">test_simple</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_ResultAnalysis.Test_init_plot.test_simple" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-</dd></dl>
-
+<div class="section" id="multiview-platform-tests-test-resultanalysis-module">
+<h2>multiview_platform.tests.test_ResultAnalysis module<a class="headerlink" href="#multiview-platform-tests-test-resultanalysis-module" title="Permalink to this headline">¶</a></h2>
 </div>
-<div class="section" id="module-multiview_platform.tests">
-<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-multiview_platform.tests" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="module-contents">
+<h2>Module contents<a class="headerlink" href="#module-contents" title="Permalink to this headline">¶</a></h2>
 </div>
 </div>
 
@@ -444,9 +154,9 @@
 <li><a class="reference internal" href="#">multiview_platform.tests package</a><ul>
 <li><a class="reference internal" href="#subpackages">Subpackages</a></li>
 <li><a class="reference internal" href="#submodules">Submodules</a></li>
-<li><a class="reference internal" href="#module-multiview_platform.tests.test_ExecClassif">multiview_platform.tests.test_ExecClassif module</a></li>
-<li><a class="reference internal" href="#module-multiview_platform.tests.test_ResultAnalysis">multiview_platform.tests.test_ResultAnalysis module</a></li>
-<li><a class="reference internal" href="#module-multiview_platform.tests">Module contents</a></li>
+<li><a class="reference internal" href="#multiview-platform-tests-test-execclassif-module">multiview_platform.tests.test_ExecClassif module</a></li>
+<li><a class="reference internal" href="#multiview-platform-tests-test-resultanalysis-module">multiview_platform.tests.test_ResultAnalysis module</a></li>
+<li><a class="reference internal" href="#module-contents">Module contents</a></li>
 </ul>
 </li>
 </ul>
@@ -486,9 +196,6 @@
         <li class="right" style="margin-right: 10px">
           <a href="../genindex.html" title="General Index"
              >index</a></li>
-        <li class="right" >
-          <a href="../py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
         <li class="right" >
           <a href="multiview_platform.tests.test_metrics.html" title="multiview_platform.tests.test_metrics package"
              >next</a> |</li>
diff --git a/docs/build/references/multiview_platform.tests.test_metrics.html b/docs/build/references/multiview_platform.tests.test_metrics.html
index 4c0c63d42e4b0bad00074b856dcb09a9dc306304..75bc98d3fa02c4dde5801cf43f4788d684a33e4a 100644
--- a/docs/build/references/multiview_platform.tests.test_metrics.html
+++ b/docs/build/references/multiview_platform.tests.test_metrics.html
@@ -27,9 +27,6 @@
         <li class="right" style="margin-right: 10px">
           <a href="../genindex.html" title="General Index"
              accesskey="I">index</a></li>
-        <li class="right" >
-          <a href="../py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
         <li class="right" >
           <a href="multiview_platform.tests.test_mono_view.html" title="multiview_platform.tests.test_mono_view package"
              accesskey="N">next</a> |</li>
@@ -52,28 +49,11 @@
 <div class="section" id="submodules">
 <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this headline">¶</a></h2>
 </div>
-<div class="section" id="module-multiview_platform.tests.test_metrics.test_accuracy_score">
-<span id="multiview-platform-tests-test-metrics-test-accuracy-score-module"></span><h2>multiview_platform.tests.test_metrics.test_accuracy_score module<a class="headerlink" href="#module-multiview_platform.tests.test_metrics.test_accuracy_score" title="Permalink to this headline">¶</a></h2>
-<dl class="class">
-<dt id="multiview_platform.tests.test_metrics.test_accuracy_score.Test_accuracy_score">
-<em class="property">class </em><code class="descname">Test_accuracy_score</code><span class="sig-paren">(</span><em>methodName='runTest'</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_metrics.test_accuracy_score.Test_accuracy_score" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">unittest.case.TestCase</span></code></p>
-<dl class="method">
-<dt id="multiview_platform.tests.test_metrics.test_accuracy_score.Test_accuracy_score.score_test">
-<code class="descname">score_test</code><span class="sig-paren">(</span><em>metric_module</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_metrics.test_accuracy_score.Test_accuracy_score.score_test" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="method">
-<dt id="multiview_platform.tests.test_metrics.test_accuracy_score.Test_accuracy_score.setUpClass">
-<code class="descname">setUpClass</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_metrics.test_accuracy_score.Test_accuracy_score.setUpClass" title="Permalink to this definition">¶</a></dt>
-<dd><p>Hook method for setting up class fixture before running tests in the class.</p>
-</dd></dl>
-
-</dd></dl>
-
+<div class="section" id="multiview-platform-tests-test-metrics-test-accuracy-score-module">
+<h2>multiview_platform.tests.test_metrics.test_accuracy_score module<a class="headerlink" href="#multiview-platform-tests-test-metrics-test-accuracy-score-module" title="Permalink to this headline">¶</a></h2>
 </div>
-<div class="section" id="module-multiview_platform.tests.test_metrics">
-<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-multiview_platform.tests.test_metrics" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="module-contents">
+<h2>Module contents<a class="headerlink" href="#module-contents" title="Permalink to this headline">¶</a></h2>
 </div>
 </div>
 
@@ -87,8 +67,8 @@
   <ul>
 <li><a class="reference internal" href="#">multiview_platform.tests.test_metrics package</a><ul>
 <li><a class="reference internal" href="#submodules">Submodules</a></li>
-<li><a class="reference internal" href="#module-multiview_platform.tests.test_metrics.test_accuracy_score">multiview_platform.tests.test_metrics.test_accuracy_score module</a></li>
-<li><a class="reference internal" href="#module-multiview_platform.tests.test_metrics">Module contents</a></li>
+<li><a class="reference internal" href="#multiview-platform-tests-test-metrics-test-accuracy-score-module">multiview_platform.tests.test_metrics.test_accuracy_score module</a></li>
+<li><a class="reference internal" href="#module-contents">Module contents</a></li>
 </ul>
 </li>
 </ul>
@@ -128,9 +108,6 @@
         <li class="right" style="margin-right: 10px">
           <a href="../genindex.html" title="General Index"
              >index</a></li>
-        <li class="right" >
-          <a href="../py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
         <li class="right" >
           <a href="multiview_platform.tests.test_mono_view.html" title="multiview_platform.tests.test_mono_view package"
              >next</a> |</li>
diff --git a/docs/build/references/multiview_platform.tests.test_mono_view.html b/docs/build/references/multiview_platform.tests.test_mono_view.html
index 797c0629dfc1788d10ea61a12c99c6cca190be44..cadba693ac8b82caf68f52838298d743a7da963b 100644
--- a/docs/build/references/multiview_platform.tests.test_mono_view.html
+++ b/docs/build/references/multiview_platform.tests.test_mono_view.html
@@ -27,9 +27,6 @@
         <li class="right" style="margin-right: 10px">
           <a href="../genindex.html" title="General Index"
              accesskey="I">index</a></li>
-        <li class="right" >
-          <a href="../py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
         <li class="right" >
           <a href="multiview_platform.tests.test_monoview_classifiers.html" title="multiview_platform.tests.test_monoview_classifiers package"
              accesskey="N">next</a> |</li>
@@ -52,94 +49,14 @@
 <div class="section" id="submodules">
 <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this headline">¶</a></h2>
 </div>
-<div class="section" id="module-multiview_platform.tests.test_mono_view.test_ExecClassifMonoView">
-<span id="multiview-platform-tests-test-mono-view-test-execclassifmonoview-module"></span><h2>multiview_platform.tests.test_mono_view.test_ExecClassifMonoView module<a class="headerlink" href="#module-multiview_platform.tests.test_mono_view.test_ExecClassifMonoView" title="Permalink to this headline">¶</a></h2>
-<dl class="class">
-<dt id="multiview_platform.tests.test_mono_view.test_ExecClassifMonoView.Test_getHPs">
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-<dl class="classmethod">
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-
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-<dd></dd></dl>
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-
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-<dt id="multiview_platform.tests.test_mono_view.test_ExecClassifMonoView.Test_initConstants.tearDownClass">
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-
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-<dd></dd></dl>
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-
-<dl class="method">
-<dt id="multiview_platform.tests.test_mono_view.test_ExecClassifMonoView.Test_initTrainTest.test_simple">
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-<dd></dd></dl>
-
-</dd></dl>
-
+<div class="section" id="multiview-platform-tests-test-mono-view-test-execclassifmonoview-module">
+<h2>multiview_platform.tests.test_mono_view.test_ExecClassifMonoView module<a class="headerlink" href="#multiview-platform-tests-test-mono-view-test-execclassifmonoview-module" title="Permalink to this headline">¶</a></h2>
 </div>
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-<dl class="class">
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-<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">unittest.case.TestCase</span></code></p>
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-<dd><p>Hook method for setting up class fixture before running tests in the class.</p>
-</dd></dl>
-
-<dl class="method">
-<dt id="multiview_platform.tests.test_mono_view.test_MonoviewUtils.Test_genTestFoldsPreds.test_simple">
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-<dd></dd></dl>
-
-</dd></dl>
-
+<div class="section" id="multiview-platform-tests-test-mono-view-test-monoviewutils-module">
+<h2>multiview_platform.tests.test_mono_view.test_MonoviewUtils module<a class="headerlink" href="#multiview-platform-tests-test-mono-view-test-monoviewutils-module" title="Permalink to this headline">¶</a></h2>
 </div>
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-<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-multiview_platform.tests.test_mono_view" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="module-contents">
+<h2>Module contents<a class="headerlink" href="#module-contents" title="Permalink to this headline">¶</a></h2>
 </div>
 </div>
 
@@ -153,9 +70,9 @@
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-<li><a class="reference internal" href="#module-multiview_platform.tests.test_mono_view">Module contents</a></li>
+<li><a class="reference internal" href="#multiview-platform-tests-test-mono-view-test-execclassifmonoview-module">multiview_platform.tests.test_mono_view.test_ExecClassifMonoView module</a></li>
+<li><a class="reference internal" href="#multiview-platform-tests-test-mono-view-test-monoviewutils-module">multiview_platform.tests.test_mono_view.test_MonoviewUtils module</a></li>
+<li><a class="reference internal" href="#module-contents">Module contents</a></li>
 </ul>
 </li>
 </ul>
@@ -195,9 +112,6 @@
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diff --git a/docs/build/references/multiview_platform.tests.test_monoview_classifiers.html b/docs/build/references/multiview_platform.tests.test_monoview_classifiers.html
index 267618ac18c1f8ca915ceb24c9d7e2c18c4609c6..560a3607f4fbe226acb8a6ecc62f9fdd36fb5d4f 100644
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@@ -52,14 +49,14 @@
 <div class="section" id="submodules">
 <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this headline">¶</a></h2>
 </div>
-<div class="section" id="module-multiview_platform.tests.test_monoview_classifiers.test_adaboost">
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+<div class="section" id="multiview-platform-tests-test-monoview-classifiers-test-adaboost-module">
+<h2>multiview_platform.tests.test_monoview_classifiers.test_adaboost module<a class="headerlink" href="#multiview-platform-tests-test-monoview-classifiers-test-adaboost-module" title="Permalink to this headline">¶</a></h2>
 </div>
-<div class="section" id="module-multiview_platform.tests.test_monoview_classifiers.test_compatibility">
-<span id="multiview-platform-tests-test-monoview-classifiers-test-compatibility-module"></span><h2>multiview_platform.tests.test_monoview_classifiers.test_compatibility module<a class="headerlink" href="#module-multiview_platform.tests.test_monoview_classifiers.test_compatibility" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="multiview-platform-tests-test-monoview-classifiers-test-compatibility-module">
+<h2>multiview_platform.tests.test_monoview_classifiers.test_compatibility module<a class="headerlink" href="#multiview-platform-tests-test-monoview-classifiers-test-compatibility-module" title="Permalink to this headline">¶</a></h2>
 </div>
-<div class="section" id="module-multiview_platform.tests.test_monoview_classifiers">
-<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-multiview_platform.tests.test_monoview_classifiers" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="module-contents">
+<h2>Module contents<a class="headerlink" href="#module-contents" title="Permalink to this headline">¶</a></h2>
 </div>
 </div>
 
@@ -73,9 +70,9 @@
   <ul>
 <li><a class="reference internal" href="#">multiview_platform.tests.test_monoview_classifiers package</a><ul>
 <li><a class="reference internal" href="#submodules">Submodules</a></li>
-<li><a class="reference internal" href="#module-multiview_platform.tests.test_monoview_classifiers.test_adaboost">multiview_platform.tests.test_monoview_classifiers.test_adaboost module</a></li>
-<li><a class="reference internal" href="#module-multiview_platform.tests.test_monoview_classifiers.test_compatibility">multiview_platform.tests.test_monoview_classifiers.test_compatibility module</a></li>
-<li><a class="reference internal" href="#module-multiview_platform.tests.test_monoview_classifiers">Module contents</a></li>
+<li><a class="reference internal" href="#multiview-platform-tests-test-monoview-classifiers-test-adaboost-module">multiview_platform.tests.test_monoview_classifiers.test_adaboost module</a></li>
+<li><a class="reference internal" href="#multiview-platform-tests-test-monoview-classifiers-test-compatibility-module">multiview_platform.tests.test_monoview_classifiers.test_compatibility module</a></li>
+<li><a class="reference internal" href="#module-contents">Module contents</a></li>
 </ul>
 </li>
 </ul>
@@ -115,9 +112,6 @@
         <li class="right" style="margin-right: 10px">
           <a href="../genindex.html" title="General Index"
              >index</a></li>
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diff --git a/docs/build/references/multiview_platform.tests.test_multiview_classifiers.Test_DifficultyMeasure.html b/docs/build/references/multiview_platform.tests.test_multiview_classifiers.Test_DifficultyMeasure.html
index 8b4625f9b6dfdad6401dca4c8be67ff0f6f8e044..2c461acbb1f55e063a9d9cb6edafedc1da139619 100644
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+++ b/docs/build/references/multiview_platform.tests.test_multiview_classifiers.Test_DifficultyMeasure.html
@@ -27,9 +27,6 @@
         <li class="right" style="margin-right: 10px">
           <a href="../genindex.html" title="General Index"
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@@ -112,9 +109,6 @@
         <li class="right" style="margin-right: 10px">
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              >index</a></li>
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diff --git a/docs/build/references/multiview_platform.tests.test_multiview_classifiers.Test_DisagreeFusion.html b/docs/build/references/multiview_platform.tests.test_multiview_classifiers.Test_DisagreeFusion.html
index e6e938a003b3fb7c6a66e9a7af4f1a40de44c550..88747905e34b5131725bbcafeab2013f14ceea67 100644
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+++ b/docs/build/references/multiview_platform.tests.test_multiview_classifiers.Test_DisagreeFusion.html
@@ -27,9 +27,6 @@
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              accesskey="N">next</a> |</li>
@@ -112,9 +109,6 @@
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diff --git a/docs/build/references/multiview_platform.tests.test_multiview_classifiers.Test_DoubleFaultFusion.html b/docs/build/references/multiview_platform.tests.test_multiview_classifiers.Test_DoubleFaultFusion.html
index 08c733c23c6775afdeb461ac462125e867928cff..8c26579d8be7f9062105959f615f73c052678192 100644
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+++ b/docs/build/references/multiview_platform.tests.test_multiview_classifiers.Test_DoubleFaultFusion.html
@@ -27,9 +27,6 @@
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              accesskey="N">next</a> |</li>
@@ -112,9 +109,6 @@
         <li class="right" style="margin-right: 10px">
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              >index</a></li>
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-             >modules</a> |</li>
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diff --git a/docs/build/references/multiview_platform.tests.test_multiview_classifiers.Test_EntropyFusion.html b/docs/build/references/multiview_platform.tests.test_multiview_classifiers.Test_EntropyFusion.html
index 6c5942c74af568ddaf2473a629ded98964119603..a88f10194277b7d38e1d824d84c14338656f7655 100644
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+++ b/docs/build/references/multiview_platform.tests.test_multiview_classifiers.Test_EntropyFusion.html
@@ -27,9 +27,6 @@
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@@ -112,9 +109,6 @@
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              >index</a></li>
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diff --git a/docs/build/references/multiview_platform.tests.test_multiview_classifiers.Test_Fusion.html b/docs/build/references/multiview_platform.tests.test_multiview_classifiers.Test_Fusion.html
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--- a/docs/build/references/multiview_platform.tests.test_multiview_classifiers.Test_Fusion.html
+++ b/docs/build/references/multiview_platform.tests.test_multiview_classifiers.Test_Fusion.html
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           <a href="multiview_platform.tests.test_multiview_classifiers.Test_PseudoCQMeasure.html" title="multiview_platform.tests.test_multiview_classifiers.Test_PseudoCQMeasure package"
              accesskey="N">next</a> |</li>
@@ -118,9 +115,6 @@
         <li class="right" style="margin-right: 10px">
           <a href="../genindex.html" title="General Index"
              >index</a></li>
-        <li class="right" >
-          <a href="../py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
         <li class="right" >
           <a href="multiview_platform.tests.test_multiview_classifiers.Test_PseudoCQMeasure.html" title="multiview_platform.tests.test_multiview_classifiers.Test_PseudoCQMeasure package"
              >next</a> |</li>
diff --git a/docs/build/references/multiview_platform.tests.test_multiview_classifiers.Test_PseudoCQMeasure.html b/docs/build/references/multiview_platform.tests.test_multiview_classifiers.Test_PseudoCQMeasure.html
index 8c5b9d7032143a6dc49503e757741ae3298ec687..41a8e5abed3966a86bf96e913555091cfe63fe6e 100644
--- a/docs/build/references/multiview_platform.tests.test_multiview_classifiers.Test_PseudoCQMeasure.html
+++ b/docs/build/references/multiview_platform.tests.test_multiview_classifiers.Test_PseudoCQMeasure.html
@@ -27,9 +27,6 @@
         <li class="right" style="margin-right: 10px">
           <a href="../genindex.html" title="General Index"
              accesskey="I">index</a></li>
-        <li class="right" >
-          <a href="../py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
         <li class="right" >
           <a href="multiview_platform.tests.test_utils.html" title="multiview_platform.tests.test_utils package"
              accesskey="N">next</a> |</li>
@@ -53,11 +50,11 @@
 <div class="section" id="submodules">
 <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this headline">¶</a></h2>
 </div>
-<div class="section" id="module-multiview_platform.tests.test_multiview_classifiers.Test_PseudoCQMeasure.test_PseudoCQFusionModule">
-<span id="multiview-platform-tests-test-multiview-classifiers-test-pseudocqmeasure-test-pseudocqfusionmodule-module"></span><h2>multiview_platform.tests.test_multiview_classifiers.Test_PseudoCQMeasure.test_PseudoCQFusionModule module<a class="headerlink" href="#module-multiview_platform.tests.test_multiview_classifiers.Test_PseudoCQMeasure.test_PseudoCQFusionModule" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="multiview-platform-tests-test-multiview-classifiers-test-pseudocqmeasure-test-pseudocqfusionmodule-module">
+<h2>multiview_platform.tests.test_multiview_classifiers.Test_PseudoCQMeasure.test_PseudoCQFusionModule module<a class="headerlink" href="#multiview-platform-tests-test-multiview-classifiers-test-pseudocqmeasure-test-pseudocqfusionmodule-module" title="Permalink to this headline">¶</a></h2>
 </div>
-<div class="section" id="module-multiview_platform.tests.test_multiview_classifiers.Test_PseudoCQMeasure">
-<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-multiview_platform.tests.test_multiview_classifiers.Test_PseudoCQMeasure" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="module-contents">
+<h2>Module contents<a class="headerlink" href="#module-contents" title="Permalink to this headline">¶</a></h2>
 </div>
 </div>
 
@@ -71,8 +68,8 @@
   <ul>
 <li><a class="reference internal" href="#">multiview_platform.tests.test_multiview_classifiers.Test_PseudoCQMeasure package</a><ul>
 <li><a class="reference internal" href="#submodules">Submodules</a></li>
-<li><a class="reference internal" href="#module-multiview_platform.tests.test_multiview_classifiers.Test_PseudoCQMeasure.test_PseudoCQFusionModule">multiview_platform.tests.test_multiview_classifiers.Test_PseudoCQMeasure.test_PseudoCQFusionModule module</a></li>
-<li><a class="reference internal" href="#module-multiview_platform.tests.test_multiview_classifiers.Test_PseudoCQMeasure">Module contents</a></li>
+<li><a class="reference internal" href="#multiview-platform-tests-test-multiview-classifiers-test-pseudocqmeasure-test-pseudocqfusionmodule-module">multiview_platform.tests.test_multiview_classifiers.Test_PseudoCQMeasure.test_PseudoCQFusionModule module</a></li>
+<li><a class="reference internal" href="#module-contents">Module contents</a></li>
 </ul>
 </li>
 </ul>
@@ -112,9 +109,6 @@
         <li class="right" style="margin-right: 10px">
           <a href="../genindex.html" title="General Index"
              >index</a></li>
-        <li class="right" >
-          <a href="../py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
         <li class="right" >
           <a href="multiview_platform.tests.test_utils.html" title="multiview_platform.tests.test_utils package"
              >next</a> |</li>
diff --git a/docs/build/references/multiview_platform.tests.test_multiview_classifiers.html b/docs/build/references/multiview_platform.tests.test_multiview_classifiers.html
index 039cf0456a19a8a6d05e531605e3dc05d0372c32..e98c4f8828e551de1582d7ac8577d944cdfad3e6 100644
--- a/docs/build/references/multiview_platform.tests.test_multiview_classifiers.html
+++ b/docs/build/references/multiview_platform.tests.test_multiview_classifiers.html
@@ -27,9 +27,6 @@
         <li class="right" style="margin-right: 10px">
           <a href="../genindex.html" title="General Index"
              accesskey="I">index</a></li>
-        <li class="right" >
-          <a href="../py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
         <li class="right" >
           <a href="multiview_platform.tests.test_multiview_classifiers.Test_DifficultyMeasure.html" title="multiview_platform.tests.test_multiview_classifiers.Test_DifficultyMeasure package"
              accesskey="N">next</a> |</li>
@@ -88,8 +85,8 @@
 </li>
 <li class="toctree-l1"><a class="reference internal" href="multiview_platform.tests.test_multiview_classifiers.Test_PseudoCQMeasure.html">multiview_platform.tests.test_multiview_classifiers.Test_PseudoCQMeasure package</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="multiview_platform.tests.test_multiview_classifiers.Test_PseudoCQMeasure.html#submodules">Submodules</a></li>
-<li class="toctree-l2"><a class="reference internal" href="multiview_platform.tests.test_multiview_classifiers.Test_PseudoCQMeasure.html#module-multiview_platform.tests.test_multiview_classifiers.Test_PseudoCQMeasure.test_PseudoCQFusionModule">multiview_platform.tests.test_multiview_classifiers.Test_PseudoCQMeasure.test_PseudoCQFusionModule module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="multiview_platform.tests.test_multiview_classifiers.Test_PseudoCQMeasure.html#module-multiview_platform.tests.test_multiview_classifiers.Test_PseudoCQMeasure">Module contents</a></li>
+<li class="toctree-l2"><a class="reference internal" href="multiview_platform.tests.test_multiview_classifiers.Test_PseudoCQMeasure.html#multiview-platform-tests-test-multiview-classifiers-test-pseudocqmeasure-test-pseudocqfusionmodule-module">multiview_platform.tests.test_multiview_classifiers.Test_PseudoCQMeasure.test_PseudoCQFusionModule module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="multiview_platform.tests.test_multiview_classifiers.Test_PseudoCQMeasure.html#module-contents">Module contents</a></li>
 </ul>
 </li>
 </ul>
@@ -98,11 +95,11 @@
 <div class="section" id="submodules">
 <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this headline">¶</a></h2>
 </div>
-<div class="section" id="module-multiview_platform.tests.test_multiview_classifiers.test_diversity_utils">
-<span id="multiview-platform-tests-test-multiview-classifiers-test-diversity-utils-module"></span><h2>multiview_platform.tests.test_multiview_classifiers.test_diversity_utils module<a class="headerlink" href="#module-multiview_platform.tests.test_multiview_classifiers.test_diversity_utils" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="multiview-platform-tests-test-multiview-classifiers-test-diversity-utils-module">
+<h2>multiview_platform.tests.test_multiview_classifiers.test_diversity_utils module<a class="headerlink" href="#multiview-platform-tests-test-multiview-classifiers-test-diversity-utils-module" title="Permalink to this headline">¶</a></h2>
 </div>
-<div class="section" id="module-multiview_platform.tests.test_multiview_classifiers">
-<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-multiview_platform.tests.test_multiview_classifiers" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="module-contents">
+<h2>Module contents<a class="headerlink" href="#module-contents" title="Permalink to this headline">¶</a></h2>
 </div>
 </div>
 
@@ -117,8 +114,8 @@
 <li><a class="reference internal" href="#">multiview_platform.tests.test_multiview_classifiers package</a><ul>
 <li><a class="reference internal" href="#subpackages">Subpackages</a></li>
 <li><a class="reference internal" href="#submodules">Submodules</a></li>
-<li><a class="reference internal" href="#module-multiview_platform.tests.test_multiview_classifiers.test_diversity_utils">multiview_platform.tests.test_multiview_classifiers.test_diversity_utils module</a></li>
-<li><a class="reference internal" href="#module-multiview_platform.tests.test_multiview_classifiers">Module contents</a></li>
+<li><a class="reference internal" href="#multiview-platform-tests-test-multiview-classifiers-test-diversity-utils-module">multiview_platform.tests.test_multiview_classifiers.test_diversity_utils module</a></li>
+<li><a class="reference internal" href="#module-contents">Module contents</a></li>
 </ul>
 </li>
 </ul>
@@ -158,9 +155,6 @@
         <li class="right" style="margin-right: 10px">
           <a href="../genindex.html" title="General Index"
              >index</a></li>
-        <li class="right" >
-          <a href="../py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
         <li class="right" >
           <a href="multiview_platform.tests.test_multiview_classifiers.Test_DifficultyMeasure.html" title="multiview_platform.tests.test_multiview_classifiers.Test_DifficultyMeasure package"
              >next</a> |</li>
diff --git a/docs/build/references/multiview_platform.tests.test_utils.html b/docs/build/references/multiview_platform.tests.test_utils.html
index fe1bd4453901f0fe060c94e5a0243d1791310877..6d13f1aa27dd0655dea2ff115b29f2259cb7d11e 100644
--- a/docs/build/references/multiview_platform.tests.test_utils.html
+++ b/docs/build/references/multiview_platform.tests.test_utils.html
@@ -26,9 +26,6 @@
         <li class="right" style="margin-right: 10px">
           <a href="../genindex.html" title="General Index"
              accesskey="I">index</a></li>
-        <li class="right" >
-          <a href="../py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
         <li class="right" >
           <a href="multiview_platform.tests.test_multiview_classifiers.Test_PseudoCQMeasure.html" title="multiview_platform.tests.test_multiview_classifiers.Test_PseudoCQMeasure package"
              accesskey="P">previous</a> |</li>
@@ -48,327 +45,20 @@
 <div class="section" id="submodules">
 <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this headline">¶</a></h2>
 </div>
-<div class="section" id="module-multiview_platform.tests.test_utils.test_GetMultiviewDB">
-<span id="multiview-platform-tests-test-utils-test-getmultiviewdb-module"></span><h2>multiview_platform.tests.test_utils.test_GetMultiviewDB module<a class="headerlink" href="#module-multiview_platform.tests.test_utils.test_GetMultiviewDB" title="Permalink to this headline">¶</a></h2>
-<dl class="class">
-<dt id="multiview_platform.tests.test_utils.test_GetMultiviewDB.Test_get_classic_db_csv">
-<em class="property">class </em><code class="descname">Test_get_classic_db_csv</code><span class="sig-paren">(</span><em>methodName='runTest'</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_utils.test_GetMultiviewDB.Test_get_classic_db_csv" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">unittest.case.TestCase</span></code></p>
-<dl class="method">
-<dt id="multiview_platform.tests.test_utils.test_GetMultiviewDB.Test_get_classic_db_csv.setUp">
-<code class="descname">setUp</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_utils.test_GetMultiviewDB.Test_get_classic_db_csv.setUp" title="Permalink to this definition">¶</a></dt>
-<dd><p>Hook method for setting up the test fixture before exercising it.</p>
-</dd></dl>
-
-<dl class="classmethod">
-<dt id="multiview_platform.tests.test_utils.test_GetMultiviewDB.Test_get_classic_db_csv.tearDown">
-<em class="property">classmethod </em><code class="descname">tearDown</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_utils.test_GetMultiviewDB.Test_get_classic_db_csv.tearDown" title="Permalink to this definition">¶</a></dt>
-<dd><p>Hook method for deconstructing the test fixture after testing it.</p>
-</dd></dl>
-
-<dl class="method">
-<dt id="multiview_platform.tests.test_utils.test_GetMultiviewDB.Test_get_classic_db_csv.test_simple">
-<code class="descname">test_simple</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_utils.test_GetMultiviewDB.Test_get_classic_db_csv.test_simple" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-</dd></dl>
-
-<dl class="class">
-<dt id="multiview_platform.tests.test_utils.test_GetMultiviewDB.Test_get_classic_db_hdf5">
-<em class="property">class </em><code class="descname">Test_get_classic_db_hdf5</code><span class="sig-paren">(</span><em>methodName='runTest'</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_utils.test_GetMultiviewDB.Test_get_classic_db_hdf5" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">unittest.case.TestCase</span></code></p>
-<dl class="method">
-<dt id="multiview_platform.tests.test_utils.test_GetMultiviewDB.Test_get_classic_db_hdf5.setUp">
-<code class="descname">setUp</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_utils.test_GetMultiviewDB.Test_get_classic_db_hdf5.setUp" title="Permalink to this definition">¶</a></dt>
-<dd><p>Hook method for setting up the test fixture before exercising it.</p>
-</dd></dl>
-
-<dl class="method">
-<dt id="multiview_platform.tests.test_utils.test_GetMultiviewDB.Test_get_classic_db_hdf5.tearDown">
-<code class="descname">tearDown</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_utils.test_GetMultiviewDB.Test_get_classic_db_hdf5.tearDown" title="Permalink to this definition">¶</a></dt>
-<dd><p>Hook method for deconstructing the test fixture after testing it.</p>
-</dd></dl>
-
-<dl class="method">
-<dt id="multiview_platform.tests.test_utils.test_GetMultiviewDB.Test_get_classic_db_hdf5.test_all_views_asked">
-<code class="descname">test_all_views_asked</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_utils.test_GetMultiviewDB.Test_get_classic_db_hdf5.test_all_views_asked" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="method">
-<dt id="multiview_platform.tests.test_utils.test_GetMultiviewDB.Test_get_classic_db_hdf5.test_asked_the_whole_dataset">
-<code class="descname">test_asked_the_whole_dataset</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_utils.test_GetMultiviewDB.Test_get_classic_db_hdf5.test_asked_the_whole_dataset" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="method">
-<dt id="multiview_platform.tests.test_utils.test_GetMultiviewDB.Test_get_classic_db_hdf5.test_simple">
-<code class="descname">test_simple</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_utils.test_GetMultiviewDB.Test_get_classic_db_hdf5.test_simple" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-</dd></dl>
-
-<dl class="class">
-<dt id="multiview_platform.tests.test_utils.test_GetMultiviewDB.Test_get_plausible_db_hdf5">
-<em class="property">class </em><code class="descname">Test_get_plausible_db_hdf5</code><span class="sig-paren">(</span><em>methodName='runTest'</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_utils.test_GetMultiviewDB.Test_get_plausible_db_hdf5" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">unittest.case.TestCase</span></code></p>
-<dl class="classmethod">
-<dt id="multiview_platform.tests.test_utils.test_GetMultiviewDB.Test_get_plausible_db_hdf5.setUpClass">
-<em class="property">classmethod </em><code class="descname">setUpClass</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_utils.test_GetMultiviewDB.Test_get_plausible_db_hdf5.setUpClass" title="Permalink to this definition">¶</a></dt>
-<dd><p>Hook method for setting up class fixture before running tests in the class.</p>
-</dd></dl>
-
-<dl class="classmethod">
-<dt id="multiview_platform.tests.test_utils.test_GetMultiviewDB.Test_get_plausible_db_hdf5.tearDownClass">
-<em class="property">classmethod </em><code class="descname">tearDownClass</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_utils.test_GetMultiviewDB.Test_get_plausible_db_hdf5.tearDownClass" title="Permalink to this definition">¶</a></dt>
-<dd><p>Hook method for deconstructing the class fixture after running all tests in the class.</p>
-</dd></dl>
-
-<dl class="method">
-<dt id="multiview_platform.tests.test_utils.test_GetMultiviewDB.Test_get_plausible_db_hdf5.test_simple">
-<code class="descname">test_simple</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_utils.test_GetMultiviewDB.Test_get_plausible_db_hdf5.test_simple" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="method">
-<dt id="multiview_platform.tests.test_utils.test_GetMultiviewDB.Test_get_plausible_db_hdf5.test_two_class">
-<code class="descname">test_two_class</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_utils.test_GetMultiviewDB.Test_get_plausible_db_hdf5.test_two_class" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-</dd></dl>
-
+<div class="section" id="multiview-platform-tests-test-utils-test-getmultiviewdb-module">
+<h2>multiview_platform.tests.test_utils.test_GetMultiviewDB module<a class="headerlink" href="#multiview-platform-tests-test-utils-test-getmultiviewdb-module" title="Permalink to this headline">¶</a></h2>
 </div>
-<div class="section" id="module-multiview_platform.tests.test_utils.test_configuration">
-<span id="multiview-platform-tests-test-utils-test-configuration-module"></span><h2>multiview_platform.tests.test_utils.test_configuration module<a class="headerlink" href="#module-multiview_platform.tests.test_utils.test_configuration" title="Permalink to this headline">¶</a></h2>
-<dl class="class">
-<dt id="multiview_platform.tests.test_utils.test_configuration.Test_get_the_args">
-<em class="property">class </em><code class="descname">Test_get_the_args</code><span class="sig-paren">(</span><em>methodName='runTest'</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_utils.test_configuration.Test_get_the_args" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">unittest.case.TestCase</span></code></p>
-<dl class="classmethod">
-<dt id="multiview_platform.tests.test_utils.test_configuration.Test_get_the_args.setUpClass">
-<em class="property">classmethod </em><code class="descname">setUpClass</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_utils.test_configuration.Test_get_the_args.setUpClass" title="Permalink to this definition">¶</a></dt>
-<dd><p>Hook method for setting up class fixture before running tests in the class.</p>
-</dd></dl>
-
-<dl class="classmethod">
-<dt id="multiview_platform.tests.test_utils.test_configuration.Test_get_the_args.tearDownClass">
-<em class="property">classmethod </em><code class="descname">tearDownClass</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_utils.test_configuration.Test_get_the_args.tearDownClass" title="Permalink to this definition">¶</a></dt>
-<dd><p>Hook method for deconstructing the class fixture after running all tests in the class.</p>
-</dd></dl>
-
-<dl class="method">
-<dt id="multiview_platform.tests.test_utils.test_configuration.Test_get_the_args.test_arguments">
-<code class="descname">test_arguments</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_utils.test_configuration.Test_get_the_args.test_arguments" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="method">
-<dt id="multiview_platform.tests.test_utils.test_configuration.Test_get_the_args.test_dict_format">
-<code class="descname">test_dict_format</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_utils.test_configuration.Test_get_the_args.test_dict_format" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="method">
-<dt id="multiview_platform.tests.test_utils.test_configuration.Test_get_the_args.test_file_loading">
-<code class="descname">test_file_loading</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_utils.test_configuration.Test_get_the_args.test_file_loading" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-</dd></dl>
-
+<div class="section" id="multiview-platform-tests-test-utils-test-configuration-module">
+<h2>multiview_platform.tests.test_utils.test_configuration module<a class="headerlink" href="#multiview-platform-tests-test-utils-test-configuration-module" title="Permalink to this headline">¶</a></h2>
 </div>
-<div class="section" id="module-multiview_platform.tests.test_utils.test_execution">
-<span id="multiview-platform-tests-test-utils-test-execution-module"></span><h2>multiview_platform.tests.test_utils.test_execution module<a class="headerlink" href="#module-multiview_platform.tests.test_utils.test_execution" title="Permalink to this headline">¶</a></h2>
-<dl class="class">
-<dt id="multiview_platform.tests.test_utils.test_execution.FakeArg">
-<em class="property">class </em><code class="descname">FakeArg</code><a class="headerlink" href="#multiview_platform.tests.test_utils.test_execution.FakeArg" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
-</dd></dl>
-
-<dl class="class">
-<dt id="multiview_platform.tests.test_utils.test_execution.Test_genArgumentDictionaries">
-<em class="property">class </em><code class="descname">Test_genArgumentDictionaries</code><span class="sig-paren">(</span><em>methodName='runTest'</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_utils.test_execution.Test_genArgumentDictionaries" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">unittest.case.TestCase</span></code></p>
-<dl class="classmethod">
-<dt id="multiview_platform.tests.test_utils.test_execution.Test_genArgumentDictionaries.setUpClass">
-<em class="property">classmethod </em><code class="descname">setUpClass</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_utils.test_execution.Test_genArgumentDictionaries.setUpClass" title="Permalink to this definition">¶</a></dt>
-<dd><p>Hook method for setting up class fixture before running tests in the class.</p>
-</dd></dl>
-
-</dd></dl>
-
-<dl class="class">
-<dt id="multiview_platform.tests.test_utils.test_execution.Test_genDirecortiesNames">
-<em class="property">class </em><code class="descname">Test_genDirecortiesNames</code><span class="sig-paren">(</span><em>methodName='runTest'</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_utils.test_execution.Test_genDirecortiesNames" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">unittest.case.TestCase</span></code></p>
-<dl class="classmethod">
-<dt id="multiview_platform.tests.test_utils.test_execution.Test_genDirecortiesNames.setUpClass">
-<em class="property">classmethod </em><code class="descname">setUpClass</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_utils.test_execution.Test_genDirecortiesNames.setUpClass" title="Permalink to this definition">¶</a></dt>
-<dd><p>Hook method for setting up class fixture before running tests in the class.</p>
-</dd></dl>
-
-<dl class="method">
-<dt id="multiview_platform.tests.test_utils.test_execution.Test_genDirecortiesNames.test_ovo_no_iter">
-<code class="descname">test_ovo_no_iter</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_utils.test_execution.Test_genDirecortiesNames.test_ovo_no_iter" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="method">
-<dt id="multiview_platform.tests.test_utils.test_execution.Test_genDirecortiesNames.test_simple_ovo">
-<code class="descname">test_simple_ovo</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_utils.test_execution.Test_genDirecortiesNames.test_simple_ovo" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-</dd></dl>
-
-<dl class="class">
-<dt id="multiview_platform.tests.test_utils.test_execution.Test_genKFolds">
-<em class="property">class </em><code class="descname">Test_genKFolds</code><span class="sig-paren">(</span><em>methodName='runTest'</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_utils.test_execution.Test_genKFolds" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">unittest.case.TestCase</span></code></p>
-<dl class="method">
-<dt id="multiview_platform.tests.test_utils.test_execution.Test_genKFolds.setUp">
-<code class="descname">setUp</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_utils.test_execution.Test_genKFolds.setUp" title="Permalink to this definition">¶</a></dt>
-<dd><p>Hook method for setting up the test fixture before exercising it.</p>
-</dd></dl>
-
-<dl class="method">
-<dt id="multiview_platform.tests.test_utils.test_execution.Test_genKFolds.test_genKFolds_iter">
-<code class="descname">test_genKFolds_iter</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_utils.test_execution.Test_genKFolds.test_genKFolds_iter" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-</dd></dl>
-
-<dl class="class">
-<dt id="multiview_platform.tests.test_utils.test_execution.Test_genSplits">
-<em class="property">class </em><code class="descname">Test_genSplits</code><span class="sig-paren">(</span><em>methodName='runTest'</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_utils.test_execution.Test_genSplits" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">unittest.case.TestCase</span></code></p>
-<dl class="method">
-<dt id="multiview_platform.tests.test_utils.test_execution.Test_genSplits.setUp">
-<code class="descname">setUp</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_utils.test_execution.Test_genSplits.setUp" title="Permalink to this definition">¶</a></dt>
-<dd><p>Hook method for setting up the test fixture before exercising it.</p>
-</dd></dl>
-
-<dl class="method">
-<dt id="multiview_platform.tests.test_utils.test_execution.Test_genSplits.test_genSplits_no_iter">
-<code class="descname">test_genSplits_no_iter</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_utils.test_execution.Test_genSplits.test_genSplits_no_iter" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="method">
-<dt id="multiview_platform.tests.test_utils.test_execution.Test_genSplits.test_simple">
-<code class="descname">test_simple</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_utils.test_execution.Test_genSplits.test_simple" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-</dd></dl>
-
-<dl class="class">
-<dt id="multiview_platform.tests.test_utils.test_execution.Test_getDatabaseFunction">
-<em class="property">class </em><code class="descname">Test_getDatabaseFunction</code><span class="sig-paren">(</span><em>methodName='runTest'</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_utils.test_execution.Test_getDatabaseFunction" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">unittest.case.TestCase</span></code></p>
-<dl class="classmethod">
-<dt id="multiview_platform.tests.test_utils.test_execution.Test_getDatabaseFunction.setUpClass">
-<em class="property">classmethod </em><code class="descname">setUpClass</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_utils.test_execution.Test_getDatabaseFunction.setUpClass" title="Permalink to this definition">¶</a></dt>
-<dd><p>Hook method for setting up class fixture before running tests in the class.</p>
-</dd></dl>
-
-<dl class="method">
-<dt id="multiview_platform.tests.test_utils.test_execution.Test_getDatabaseFunction.test_hdf5">
-<code class="descname">test_hdf5</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_utils.test_execution.Test_getDatabaseFunction.test_hdf5" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="method">
-<dt id="multiview_platform.tests.test_utils.test_execution.Test_getDatabaseFunction.test_plausible_hdf5">
-<code class="descname">test_plausible_hdf5</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_utils.test_execution.Test_getDatabaseFunction.test_plausible_hdf5" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="method">
-<dt id="multiview_platform.tests.test_utils.test_execution.Test_getDatabaseFunction.test_simple">
-<code class="descname">test_simple</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_utils.test_execution.Test_getDatabaseFunction.test_simple" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-</dd></dl>
-
-<dl class="class">
-<dt id="multiview_platform.tests.test_utils.test_execution.Test_initRandomState">
-<em class="property">class </em><code class="descname">Test_initRandomState</code><span class="sig-paren">(</span><em>methodName='runTest'</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_utils.test_execution.Test_initRandomState" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">unittest.case.TestCase</span></code></p>
-<dl class="method">
-<dt id="multiview_platform.tests.test_utils.test_execution.Test_initRandomState.setUp">
-<code class="descname">setUp</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_utils.test_execution.Test_initRandomState.setUp" title="Permalink to this definition">¶</a></dt>
-<dd><p>Hook method for setting up the test fixture before exercising it.</p>
-</dd></dl>
-
-<dl class="method">
-<dt id="multiview_platform.tests.test_utils.test_execution.Test_initRandomState.tearDown">
-<code class="descname">tearDown</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_utils.test_execution.Test_initRandomState.tearDown" title="Permalink to this definition">¶</a></dt>
-<dd><p>Hook method for deconstructing the test fixture after testing it.</p>
-</dd></dl>
-
-<dl class="method">
-<dt id="multiview_platform.tests.test_utils.test_execution.Test_initRandomState.test_random_state_42">
-<code class="descname">test_random_state_42</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_utils.test_execution.Test_initRandomState.test_random_state_42" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="method">
-<dt id="multiview_platform.tests.test_utils.test_execution.Test_initRandomState.test_random_state_pickle">
-<code class="descname">test_random_state_pickle</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_utils.test_execution.Test_initRandomState.test_random_state_pickle" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-</dd></dl>
-
-<dl class="class">
-<dt id="multiview_platform.tests.test_utils.test_execution.Test_initStatsIterRandomStates">
-<em class="property">class </em><code class="descname">Test_initStatsIterRandomStates</code><span class="sig-paren">(</span><em>methodName='runTest'</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_utils.test_execution.Test_initStatsIterRandomStates" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">unittest.case.TestCase</span></code></p>
-<dl class="classmethod">
-<dt id="multiview_platform.tests.test_utils.test_execution.Test_initStatsIterRandomStates.setUpClass">
-<em class="property">classmethod </em><code class="descname">setUpClass</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_utils.test_execution.Test_initStatsIterRandomStates.setUpClass" title="Permalink to this definition">¶</a></dt>
-<dd><p>Hook method for setting up class fixture before running tests in the class.</p>
-</dd></dl>
-
-<dl class="method">
-<dt id="multiview_platform.tests.test_utils.test_execution.Test_initStatsIterRandomStates.test_multiple_iter">
-<code class="descname">test_multiple_iter</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_utils.test_execution.Test_initStatsIterRandomStates.test_multiple_iter" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="method">
-<dt id="multiview_platform.tests.test_utils.test_execution.Test_initStatsIterRandomStates.test_one_statiter">
-<code class="descname">test_one_statiter</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_utils.test_execution.Test_initStatsIterRandomStates.test_one_statiter" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-</dd></dl>
-
-<dl class="class">
-<dt id="multiview_platform.tests.test_utils.test_execution.Test_parseTheArgs">
-<em class="property">class </em><code class="descname">Test_parseTheArgs</code><span class="sig-paren">(</span><em>methodName='runTest'</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_utils.test_execution.Test_parseTheArgs" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">unittest.case.TestCase</span></code></p>
-<dl class="method">
-<dt id="multiview_platform.tests.test_utils.test_execution.Test_parseTheArgs.setUp">
-<code class="descname">setUp</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_utils.test_execution.Test_parseTheArgs.setUp" title="Permalink to this definition">¶</a></dt>
-<dd><p>Hook method for setting up the test fixture before exercising it.</p>
-</dd></dl>
-
-<dl class="method">
-<dt id="multiview_platform.tests.test_utils.test_execution.Test_parseTheArgs.test_empty_args">
-<code class="descname">test_empty_args</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_utils.test_execution.Test_parseTheArgs.test_empty_args" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-</dd></dl>
-
+<div class="section" id="multiview-platform-tests-test-utils-test-execution-module">
+<h2>multiview_platform.tests.test_utils.test_execution module<a class="headerlink" href="#multiview-platform-tests-test-utils-test-execution-module" title="Permalink to this headline">¶</a></h2>
 </div>
-<div class="section" id="module-multiview_platform.tests.test_utils.test_multiclass">
-<span id="multiview-platform-tests-test-utils-test-multiclass-module"></span><h2>multiview_platform.tests.test_utils.test_multiclass module<a class="headerlink" href="#module-multiview_platform.tests.test_utils.test_multiclass" title="Permalink to this headline">¶</a></h2>
-<dl class="class">
-<dt id="multiview_platform.tests.test_utils.test_multiclass.Test_genMulticlassLabels">
-<em class="property">class </em><code class="descname">Test_genMulticlassLabels</code><span class="sig-paren">(</span><em>methodName='runTest'</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_utils.test_multiclass.Test_genMulticlassLabels" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">unittest.case.TestCase</span></code></p>
-<dl class="classmethod">
-<dt id="multiview_platform.tests.test_utils.test_multiclass.Test_genMulticlassLabels.setUpClass">
-<em class="property">classmethod </em><code class="descname">setUpClass</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_utils.test_multiclass.Test_genMulticlassLabels.setUpClass" title="Permalink to this definition">¶</a></dt>
-<dd><p>Hook method for setting up class fixture before running tests in the class.</p>
-</dd></dl>
-
-<dl class="method">
-<dt id="multiview_platform.tests.test_utils.test_multiclass.Test_genMulticlassLabels.test_one_versus_one">
-<code class="descname">test_one_versus_one</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.tests.test_utils.test_multiclass.Test_genMulticlassLabels.test_one_versus_one" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-</dd></dl>
-
+<div class="section" id="multiview-platform-tests-test-utils-test-multiclass-module">
+<h2>multiview_platform.tests.test_utils.test_multiclass module<a class="headerlink" href="#multiview-platform-tests-test-utils-test-multiclass-module" title="Permalink to this headline">¶</a></h2>
 </div>
-<div class="section" id="module-multiview_platform.tests.test_utils">
-<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-multiview_platform.tests.test_utils" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="module-contents">
+<h2>Module contents<a class="headerlink" href="#module-contents" title="Permalink to this headline">¶</a></h2>
 </div>
 </div>
 
@@ -382,11 +72,11 @@
   <ul>
 <li><a class="reference internal" href="#">multiview_platform.tests.test_utils package</a><ul>
 <li><a class="reference internal" href="#submodules">Submodules</a></li>
-<li><a class="reference internal" href="#module-multiview_platform.tests.test_utils.test_GetMultiviewDB">multiview_platform.tests.test_utils.test_GetMultiviewDB module</a></li>
-<li><a class="reference internal" href="#module-multiview_platform.tests.test_utils.test_configuration">multiview_platform.tests.test_utils.test_configuration module</a></li>
-<li><a class="reference internal" href="#module-multiview_platform.tests.test_utils.test_execution">multiview_platform.tests.test_utils.test_execution module</a></li>
-<li><a class="reference internal" href="#module-multiview_platform.tests.test_utils.test_multiclass">multiview_platform.tests.test_utils.test_multiclass module</a></li>
-<li><a class="reference internal" href="#module-multiview_platform.tests.test_utils">Module contents</a></li>
+<li><a class="reference internal" href="#multiview-platform-tests-test-utils-test-getmultiviewdb-module">multiview_platform.tests.test_utils.test_GetMultiviewDB module</a></li>
+<li><a class="reference internal" href="#multiview-platform-tests-test-utils-test-configuration-module">multiview_platform.tests.test_utils.test_configuration module</a></li>
+<li><a class="reference internal" href="#multiview-platform-tests-test-utils-test-execution-module">multiview_platform.tests.test_utils.test_execution module</a></li>
+<li><a class="reference internal" href="#multiview-platform-tests-test-utils-test-multiclass-module">multiview_platform.tests.test_utils.test_multiclass module</a></li>
+<li><a class="reference internal" href="#module-contents">Module contents</a></li>
 </ul>
 </li>
 </ul>
@@ -423,9 +113,6 @@
         <li class="right" style="margin-right: 10px">
           <a href="../genindex.html" title="General Index"
              >index</a></li>
-        <li class="right" >
-          <a href="../py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
         <li class="right" >
           <a href="multiview_platform.tests.test_multiview_classifiers.Test_PseudoCQMeasure.html" title="multiview_platform.tests.test_multiview_classifiers.Test_PseudoCQMeasure package"
              >previous</a> |</li>
diff --git a/docs/build/search.html b/docs/build/search.html
index 2b02fed2b6832e81ea68156c295c92270fd80540..f0c641e0eb478915217ef66acdfa1a55580bab48 100644
--- a/docs/build/search.html
+++ b/docs/build/search.html
@@ -34,9 +34,6 @@
         <li class="right" style="margin-right: 10px">
           <a href="genindex.html" title="General Index"
              accesskey="I">index</a></li>
-        <li class="right" >
-          <a href="py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
         <li class="nav-item nav-item-0"><a href="index.html">MultiviewPlatform 0 documentation</a> &#187;</li> 
       </ul>
     </div>  
@@ -85,9 +82,6 @@
         <li class="right" style="margin-right: 10px">
           <a href="genindex.html" title="General Index"
              >index</a></li>
-        <li class="right" >
-          <a href="py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
         <li class="nav-item nav-item-0"><a href="index.html">MultiviewPlatform 0 documentation</a> &#187;</li> 
       </ul>
     </div>
diff --git a/docs/build/searchindex.js b/docs/build/searchindex.js
index fa03a8b9a3858f1e726dfd82e4c210a83259848c..a1fdf32d99e1a48b1c80814aea5e6c2da5907e8b 100644
--- a/docs/build/searchindex.js
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diff --git a/docs/build/tutorials/example1.html b/docs/build/tutorials/example1.html
index 81b085b2ebd7c22214e72c89919fb6b1835b6fc3..10e689c3335521257aa8229162f0c0afa4907e35 100644
--- a/docs/build/tutorials/example1.html
+++ b/docs/build/tutorials/example1.html
@@ -27,9 +27,6 @@
         <li class="right" style="margin-right: 10px">
           <a href="../genindex.html" title="General Index"
              accesskey="I">index</a></li>
-        <li class="right" >
-          <a href="../py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
         <li class="right" >
           <a href="example2.html" title="Example 2 : Understanding the hyper-parameter optimization"
              accesskey="N">next</a> |</li>
@@ -52,7 +49,7 @@
 <h2>Context<a class="headerlink" href="#context" title="Permalink to this headline">¶</a></h2>
 <p>This platform aims at running multiple state-of-the-art classifiers on a multiview dataset in a classification context.
 It has been developed in order to get a baseline on common algorithms for any classification task.</p>
-<p>Adding a new classifier (monoview and/or multiview) as been made as simple as possible in order for users to be able to
+<p>Adding a new classifier (monoview and/or multiview) to the benchmark as been made as simple as possible in order for users to be able to
 customize the set of classifiers and test their performances in a controlled environment.</p>
 </div>
 <div class="section" id="introduction-to-this-tutorial">
@@ -70,110 +67,118 @@ customize the set of classifiers and test their performances in a controlled env
 <p>The config file that will be used in this example is located in <code class="docutils literal notranslate"><span class="pre">multiview-machine-learning-omis/multiview_platform/examples/config_files/config_exmaple_1.yml</span></code></p>
 <p>We will decrypt the main arguments :</p>
 <ul>
-<li><p class="first">The first part of the arguments are the basic ones :</p>
+<li><p class="first">The first part regroups the basics :</p>
 <blockquote>
 <div><ul class="simple">
 <li><code class="docutils literal notranslate"><span class="pre">log:</span> <span class="pre">True</span></code> allows to print the log in the terminal,</li>
 <li><code class="docutils literal notranslate"><span class="pre">name:</span> <span class="pre">[&quot;plausible&quot;]</span></code> uses the plausible simulated dataset,</li>
-<li><code class="docutils literal notranslate"><span class="pre">random_state:</span> <span class="pre">42</span></code> fixes the random state of this benchmark, it is useful for reproductibility,</li>
-<li><code class="docutils literal notranslate"><span class="pre">full:</span> <span class="pre">True</span></code> the benchmark will used the full dataset,</li>
-<li><code class="docutils literal notranslate"><span class="pre">res_dir:</span> <span class="pre">&quot;examples/results/example_1/&quot;</span></code> the results will be saved in <code class="docutils literal notranslate"><span class="pre">multiview-machine-learning-omis/multiview_platform/examples/results/example_1</span></code></li>
+<li><code class="docutils literal notranslate"><span class="pre">random_state:</span> <span class="pre">42</span></code> fixes the seed of the random state for this benchmark, it is useful for reproductibility,</li>
+<li><code class="docutils literal notranslate"><span class="pre">full:</span> <span class="pre">True</span></code> means the benchmark will use the full dataset,</li>
+<li><code class="docutils literal notranslate"><span class="pre">res_dir:</span> <span class="pre">&quot;examples/results/example_1/&quot;</span></code> saves the results in <code class="docutils literal notranslate"><span class="pre">multiview-machine-learning-omis/multiview_platform/examples/results/example_1</span></code></li>
 </ul>
 </div></blockquote>
 </li>
 <li><p class="first">Then the classification-related arguments :</p>
 <blockquote>
-<div><ul class="simple">
-<li><code class="docutils literal notranslate"><span class="pre">split:</span> <span class="pre">0.8</span></code> means that 80% of the dataset will be used to test the different classifiers and 20% to train them</li>
-<li><code class="docutils literal notranslate"><span class="pre">type:</span> <span class="pre">[&quot;monoview&quot;,</span> <span class="pre">&quot;multiview&quot;]</span></code> allows for monoview and multiview algorithms to be used in the benchmark</li>
-<li><code class="docutils literal notranslate"><span class="pre">algos_monoview:</span> <span class="pre">[&quot;all&quot;]</span></code> runs on all the available monoview algorithms (same for <code class="docutils literal notranslate"><span class="pre">algos_muliview</span></code>)</li>
-<li><code class="docutils literal notranslate"><span class="pre">metrics:</span> <span class="pre">[&quot;accuracy_score&quot;,</span> <span class="pre">&quot;f1_score&quot;]</span></code> means that the benchmark will evaluate the performance of each algortihms on these two metrics.</li>
+<div><ul>
+<li><p class="first"><code class="docutils literal notranslate"><span class="pre">split:</span> <span class="pre">0.8</span></code> means that 80% of the dataset will be used to test the different classifiers and 20% to train them,</p>
+</li>
+<li><p class="first"><code class="docutils literal notranslate"><span class="pre">type:</span> <span class="pre">[&quot;monoview&quot;,</span> <span class="pre">&quot;multiview&quot;]</span></code> allows for monoview and multiview algorithms to be used in the benchmark,</p>
+</li>
+<li><p class="first"><code class="docutils literal notranslate"><span class="pre">algos_monoview:</span> <span class="pre">[&quot;all&quot;]</span></code> runs on all the available monoview algorithms (same for <code class="docutils literal notranslate"><span class="pre">algos_muliview</span></code>)</p>
+</li>
+<li><p class="first">The metrics configuration</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">metrics</span><span class="p">:</span>
+  <span class="n">accuracy_score</span><span class="p">:{}</span>
+  <span class="n">f1_score</span><span class="p">:</span>
+    <span class="n">average</span><span class="p">:</span><span class="s2">&quot;binary&quot;</span>
+</pre></div>
+</div>
+<p>means that the benchmark will evaluate the performance of each algorithms on accuracy, and f1-score with a binary average.</p>
+</li>
 </ul>
 </div></blockquote>
 </li>
-<li><p class="first">Then, the two following categories are algorithm-related and contain all the default values for the hyper-parameters.</p>
-</li>
 </ul>
 <p><strong>Start the benchmark</strong></p>
-<p>During the whole benchmark, the log file will be printed in the terminal. To start the benchmark run :</p>
+<p>During the whole benchmark, the log file will be printed in the terminal. To start the benchmark, run :</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">execute</span><span class="p">()</span>
 </pre></div>
 </div>
 <p>The execution should take less than five minutes. We will first analyze the results and parse through the information the platform output.</p>
 <p><strong>Understanding the results</strong></p>
-<p>The result structure can be startling at first, but as the platform provides a lot of information, it has to be organized.</p>
+<p>The result structure can be startling at first, but, as the platform provides a lot of information, it has to be organized.</p>
 <p>The results are stored in <code class="docutils literal notranslate"><span class="pre">multiview_platform/examples/results/example_1/</span></code>. Here, you will find a directory with the name of the database used for the benchmark, here : <code class="docutils literal notranslate"><span class="pre">plausible/</span></code>
 Then, a directory with the amount of noise in the experiments, we didn’t add any, so <code class="docutils literal notranslate"><span class="pre">n_0/</span></code> finally, a directory with
 the date and time of the beginning of the experiment. Let’s say you started the benchmark on the 25th of December 1560,
 at 03:42 PM, the directory’s name should be <code class="docutils literal notranslate"><span class="pre">started_1560_12_25-15_42/</span></code>.</p>
 <p>From here the result directory has the structure that follows  :</p>
 <div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="p">|</span> started_1560_12_25-15_42
-<span class="p">|</span> ├── No-vs-Yes
-<span class="p">|</span> <span class="p">|</span> ├── adaboost
-<span class="p">|</span> <span class="p">|</span> <span class="p">|</span>   ├── ViewNumber0
-<span class="p">|</span> <span class="p">|</span> <span class="p">|</span>   <span class="p">|</span>   ├── 1560_12_25-15_42-*-summary.txt
-<span class="p">|</span> <span class="p">|</span> <span class="p">|</span>   <span class="p">|</span>   ├── &lt;other classifier dependant files&gt;
-<span class="p">|</span> <span class="p">|</span> <span class="p">|</span>   ├── ViewNumber1
-<span class="p">|</span> <span class="p">|</span> <span class="p">|</span>   <span class="p">|</span>   ├── 1560_12_25-15_42-*-summary.txt
-<span class="p">|</span> <span class="p">|</span> <span class="p">|</span>   <span class="p">|</span>   ├── &lt;other classifier dependant files&gt;
-<span class="p">|</span> <span class="p">|</span> <span class="p">|</span>   ├── ViewNumber2
-<span class="p">|</span> <span class="p">|</span> <span class="p">|</span>   <span class="p">|</span>   ├── 1560_12_25-15_42-*-summary.txt
-<span class="p">|</span> <span class="p">|</span> <span class="p">|</span>   <span class="p">|</span>   ├── &lt;other classifier dependant files&gt;
-<span class="p">|</span> <span class="p">|</span> ├── decision_tree
-<span class="p">|</span> <span class="p">|</span> <span class="p">|</span>   ├── ViewNumber0
-<span class="p">|</span> <span class="p">|</span> <span class="p">|</span>   <span class="p">|</span>  ├── &lt;summary <span class="p">&amp;</span> classifier dependant files&gt;
-<span class="p">|</span> <span class="p">|</span> <span class="p">|</span>   ├── ViewNumber1
-<span class="p">|</span> <span class="p">|</span> <span class="p">|</span>   <span class="p">|</span>  ├── &lt;summary <span class="p">&amp;</span> classifier dependant files&gt;
-<span class="p">|</span> <span class="p">|</span> <span class="p">|</span>   ├── ViewNumber2
-<span class="p">|</span> <span class="p">|</span> <span class="p">|</span>   <span class="p">|</span>  ├── &lt;summary <span class="p">&amp;</span> classifier dependant files&gt;
-<span class="p">|</span> <span class="p">|</span> ├── <span class="o">[</span>..
-<span class="p">|</span> <span class="p">|</span> ├── ..<span class="o">]</span>
-<span class="p">|</span> <span class="p">|</span> ├── weighted_linear_late_fusion
-<span class="p">|</span> <span class="p">|</span> <span class="p">|</span>   ├── &lt;summary <span class="p">&amp;</span> classifier dependant files&gt;
-<span class="p">|</span> <span class="p">|</span> ├── <span class="o">[</span>..
-<span class="p">|</span> <span class="p">|</span> ├── ..<span class="o">]</span>
-<span class="p">|</span> <span class="p">|</span> ├── train_labels.csv
-<span class="p">|</span> │ └── train_indices.csv
+<span class="p">|</span> ├── adaboost
+<span class="p">|</span> <span class="p">|</span>   ├── ViewNumber0
+<span class="p">|</span> <span class="p">|</span>   <span class="p">|</span>   ├── *-summary.txt
+<span class="p">|</span> <span class="p">|</span>   <span class="p">|</span>   ├── &lt;other classifier dependant files&gt;
+<span class="p">|</span> <span class="p">|</span>   ├── ViewNumber1
+<span class="p">|</span> <span class="p">|</span>   <span class="p">|</span>   ├── *-summary.txt
+<span class="p">|</span> <span class="p">|</span>   <span class="p">|</span>   ├── &lt;other classifier dependant files&gt;
+<span class="p">|</span> <span class="p">|</span>   ├── ViewNumber2
+<span class="p">|</span> <span class="p">|</span>   <span class="p">|</span>   ├── *-summary.txt
+<span class="p">|</span> <span class="p">|</span>   <span class="p">|</span>   ├── &lt;other classifier dependant files&gt;
+<span class="p">|</span> ├── decision_tree
+<span class="p">|</span> <span class="p">|</span>   ├── ViewNumber0
+<span class="p">|</span> <span class="p">|</span>   <span class="p">|</span>  ├── &lt;summary <span class="p">&amp;</span> classifier dependant files&gt;
+<span class="p">|</span> <span class="p">|</span>   ├── ViewNumber1
+<span class="p">|</span> <span class="p">|</span>   <span class="p">|</span>  ├── &lt;summary <span class="p">&amp;</span> classifier dependant files&gt;
+<span class="p">|</span> <span class="p">|</span>   ├── ViewNumber2
+<span class="p">|</span> <span class="p">|</span>   <span class="p">|</span>  ├── &lt;summary <span class="p">&amp;</span> classifier dependant files&gt;
+<span class="p">|</span> ├── <span class="o">[</span>..
+<span class="p">|</span> ├── ..<span class="o">]</span>
+<span class="p">|</span> ├── weighted_linear_late_fusion
+<span class="p">|</span> <span class="p">|</span>   ├── &lt;summary <span class="p">&amp;</span> classifier dependant files&gt;
+<span class="p">|</span> ├── <span class="o">[</span>..
+<span class="p">|</span> ├── ..<span class="o">]</span>
+<span class="p">|</span> ├── train_labels.csv
+<span class="p">|</span> └── train_indices.csv
 <span class="p">|</span> ├── 1560_12_25-15_42-*-LOG.log
 <span class="p">|</span> ├── config_file.yml
-<span class="p">|</span> ├── 1560_12_25-15_42-*-accuracy_score.png
-<span class="p">|</span> ├── 1560_12_25-15_42-*-accuracy_score.csv
-<span class="p">|</span> ├── 1560_12_25-15_42-*-f1_score.png
-<span class="p">|</span> ├── 1560_12_25-15_42-*-f1_score.csv
-<span class="p">|</span> ├── 1560_12_25-15_42-*-error_analysis_2D.png
-<span class="p">|</span> ├── 1560_12_25-15_42-*-error_analysis_2D.html
-<span class="p">|</span> ├── 1560_12_25-15_42-*-error_analysis_bar.png
-<span class="p">|</span> ├── 1560_12_25-15_42-*-ViewNumber0-feature_importance.html
-<span class="p">|</span> ├── 1560_12_25-15_42-*-ViewNumber0-feature_importance_dataframe.csv
-<span class="p">|</span> ├── 1560_12_25-15_42-*-ViewNumber1-feature_importance.html
-<span class="p">|</span> ├── 1560_12_25-15_42-*-ViewNumber1-feature_importance_dataframe.csv
-<span class="p">|</span> ├── 1560_12_25-15_42-*-ViewNumber2-feature_importance.html
-<span class="p">|</span> ├── 1560_12_25-15_42-*-ViewNumber2-feature_importance_dataframe.csv
-<span class="p">|</span> ├── 1560_12_25-15_42-*-bar_plot_data.csv
-<span class="p">|</span> ├── 1560_12_25-15_42-*-2D_plot_data.csv
+<span class="p">|</span> ├── *-accuracy_score*.png
+<span class="p">|</span> ├── *-accuracy_score*.html
+<span class="p">|</span> ├── *-accuracy_score*-class.html
+<span class="p">|</span> ├── *-accuracy_score*.csv
+<span class="p">|</span> ├── *-f1_score.png
+<span class="p">|</span> ├── *-f1_score.html
+<span class="p">|</span> ├── *-f1_score-class.html
+<span class="p">|</span> ├── *-f1_score.csv
+<span class="p">|</span> ├── *-error_analysis_2D.png
+<span class="p">|</span> ├── *-error_analysis_2D.html
+<span class="p">|</span> ├── *-error_analysis_bar.png
+<span class="p">|</span> ├── *-error_analysis_bar.html
+<span class="p">|</span> ├── feature_importances
+<span class="p">|</span> <span class="p">|</span> ├── *-ViewNumber0-feature_importance.html
+<span class="p">|</span> <span class="p">|</span> ├── *-ViewNumber0-feature_importance_dataframe.csv
+<span class="p">|</span> <span class="p">|</span> ├── *-ViewNumber1-feature_importance.html
+<span class="p">|</span> <span class="p">|</span> ├── *-ViewNumber1-feature_importance_dataframe.csv
+<span class="p">|</span> <span class="p">|</span> ├── *-ViewNumber2-feature_importance.html
+<span class="p">|</span> <span class="p">|</span> ├── *-ViewNumber2-feature_importance_dataframe.csv
+<span class="p">|</span> ├── *-bar_plot_data.csv
+<span class="p">|</span> ├── *-2D_plot_data.csv
 <span class="p">|</span> └── random_state.pickle
 </pre></div>
 </div>
-<p>The structure can seem complex, but it priovides a lot of information, from the most general to the most precise.</p>
+<p>The structure can seem complex, but it provides a lot of information, from the most general to the most precise.</p>
 <p>Let’s comment each file :</p>
-<div class="section" id="accuracy-score-png-and-1560-12-25-15-42-accuracy-score-csv">
-<h3><code class="docutils literal notranslate"><span class="pre">1560_12_25-15_42-*-accuracy_score.png</span></code> and <code class="docutils literal notranslate"><span class="pre">1560_12_25-15_42-*-accuracy_score.csv</span></code><a class="headerlink" href="#accuracy-score-png-and-1560-12-25-15-42-accuracy-score-csv" title="Permalink to this headline">¶</a></h3>
+<div class="section" id="accuracy-score-html-accuracy-score-png-and-accuracy-score-csv">
+<h3><code class="docutils literal notranslate"><span class="pre">*-accuracy_score*.html</span></code>, <code class="docutils literal notranslate"><span class="pre">*-accuracy_score*.png</span></code> and <code class="docutils literal notranslate"><span class="pre">*-accuracy_score*.csv</span></code><a class="headerlink" href="#accuracy-score-html-accuracy-score-png-and-accuracy-score-csv" title="Permalink to this headline">¶</a></h3>
 <p>These files contain the scores of each classifier for the accuracy metric, ordered with le best ones on the right and
-the worst ones on the left, as an image or as as csv matrix.
+the worst ones on the left, as an interactive html page, an image or a csv matrix. The star after <code class="docutils literal notranslate"><span class="pre">accuracy_score*</span></code> means that it is the principal metric.
 The image version is as follows :</p>
-<div class="figure" id="id1">
-<a class="reference internal image-reference" href="../_images/accuracy.png"><img alt="../_images/accuracy.png" src="../_images/accuracy.png" style="width: 950.0px; height: 316.5px;" /></a>
-<p class="caption"><span class="caption-text">This is a bar plot showing the score on the training set (light gray), and testing set (dark gray). For each
-monoview classifier, on each view and or each multiview classifier, the scores are printed over the name, under each bar.
-It is highly recommended to click on the image to be able to zoom.</span></p>
-</div>
 <p>The csv file is a matrix with the score on train stored in the first row and the score on test stored in the second one. Each classifier is presented in a row. It is loadable with pandas.</p>
-<p>Similar files have been generated for the f1 metric (<code class="docutils literal notranslate"><span class="pre">1560_12_25-15_42-*-f1_score.png</span></code> and <code class="docutils literal notranslate"><span class="pre">1560_12_25-15_42-*-f1_score.csv</span></code>).</p>
+<p>Similar files have been generated for the f1 metric.</p>
 </div>
-<div class="section" id="error-analysis-2d-png-and-1560-12-25-15-42-error-analysis-2d-html">
-<h3><code class="docutils literal notranslate"><span class="pre">1560_12_25-15_42-*-error_analysis_2D.png</span></code> and <code class="docutils literal notranslate"><span class="pre">1560_12_25-15_42-*-error_analysis_2D.html</span></code><a class="headerlink" href="#error-analysis-2d-png-and-1560-12-25-15-42-error-analysis-2d-html" title="Permalink to this headline">¶</a></h3>
+<div class="section" id="error-analysis-2d-png-and-error-analysis-2d-html">
+<h3><code class="docutils literal notranslate"><span class="pre">*-error_analysis_2D.png</span></code> and <code class="docutils literal notranslate"><span class="pre">*-error_analysis_2D.html</span></code><a class="headerlink" href="#error-analysis-2d-png-and-error-analysis-2d-html" title="Permalink to this headline">¶</a></h3>
 <p>In these files, one can visualize the success or failure of each classifier on each example.</p>
-<p>Below, <code class="docutils literal notranslate"><span class="pre">1560_12_25-15_42-*-error_analysis_2D.html</span></code> is displayed.</p>
+<p>Below, <code class="docutils literal notranslate"><span class="pre">*-error_analysis_2D.html</span></code> is displayed.</p>
 <p>It is the representation of a matrix, where the rows are the examples, and the columns are the classifiers.</p>
 <p>If a classifier (Lasso on the first view for example) missclassified an example (example number 75 for examples),
 a black rectangle is printed in the row corresponding to example 75 and the column corresponding to Lasso-ViewNumber0,
@@ -220,17 +225,17 @@ It could mean that the example is incorrectly labeled in the dataset or is very
 It is an understandable behaviour as the Plausible dataset’s view are generated and noised independently.
 Morever, as confirmed by the accuracy graph, four monoview classifiers classified all the example to the same class,
 and then, display a black half-column.</p>
-<p>The data used to generate those matrices is available in <code class="docutils literal notranslate"><span class="pre">1560_12_25-15_42-*-2D_plot_data.csv</span></code></p>
+<p>The data used to generate those matrices is available in <code class="docutils literal notranslate"><span class="pre">*-2D_plot_data.csv</span></code></p>
 </div>
-<div class="section" id="error-analysis-bar-png">
-<h3><code class="docutils literal notranslate"><span class="pre">1560_12_25-15_42-*-error_analysis_bar.png</span></code><a class="headerlink" href="#error-analysis-bar-png" title="Permalink to this headline">¶</a></h3>
+<div class="section" id="error-analysis-bar-png-and-error-analysis-bar-html">
+<h3><code class="docutils literal notranslate"><span class="pre">*-error_analysis_bar.png</span></code> and <code class="docutils literal notranslate"><span class="pre">*-error_analysis_bar.html</span></code><a class="headerlink" href="#error-analysis-bar-png-and-error-analysis-bar-html" title="Permalink to this headline">¶</a></h3>
 <p>This file is a different way to visualize the same information as the two previous ones. Indeed, it is a bar plot,
 with a bar for each example, counting the number of classifiers that failed to classify this particular example.</p>
-<div class="figure" id="id2">
+<div class="figure" id="id1">
 <a class="reference internal image-reference" href="../_images/bar_error.png"><img alt="../_images/bar_error.png" src="../_images/bar_error.png" style="width: 160.0px; height: 120.0px;" /></a>
 <p class="caption"><span class="caption-text">The bar plot showing for each example how many classifiers failed on it.</span></p>
 </div>
-<p>The data used to generate this graph is available in <code class="docutils literal notranslate"><span class="pre">1560_12_25-15_42-*-bar_plot_data.csv</span></code></p>
+<p>The data used to generate this graph is available in <code class="docutils literal notranslate"><span class="pre">*-bar_plot_data.csv</span></code></p>
 </div>
 <div class="section" id="config-file-yml">
 <h3><code class="docutils literal notranslate"><span class="pre">config_file.yml</span></code><a class="headerlink" href="#config-file-yml" title="Permalink to this headline">¶</a></h3>
@@ -246,7 +251,7 @@ with a bar for each example, counting the number of classifiers that failed to c
 </div>
 <div class="section" id="classifier-dependant-files">
 <h3>Classifier-dependant files<a class="headerlink" href="#classifier-dependant-files" title="Permalink to this headline">¶</a></h3>
-<p>For each classifier, at least one file is generated, called <code class="docutils literal notranslate"><span class="pre">1560_12_25-15_42-*-summary.txt</span></code>.</p>
+<p>For each classifier, at least one file is generated, called <code class="docutils literal notranslate"><span class="pre">*-summary.txt</span></code>.</p>
 <pre class="literal-block">
 Classification on plausible database for ViewNumber0 with svm_linear.
 
@@ -279,7 +284,7 @@ Classifier configuration :
  Classifier Interpretation : 
 
 </pre>
-<p>This regroups the useful information on the classifiers configuration and it’s performance. An interpretation section is
+<p>This regroups the useful information on the classifier’s configuration and it’s performance. An interpretation section is
 available for classifiers that present some interpretation-related information (as feature importance).</p>
 </div>
 </div>
@@ -297,9 +302,9 @@ available for classifiers that present some interpretation-related information (
 <li><a class="reference internal" href="#context">Context</a></li>
 <li><a class="reference internal" href="#introduction-to-this-tutorial">Introduction to this tutorial</a></li>
 <li><a class="reference internal" href="#getting-started">Getting started</a><ul>
-<li><a class="reference internal" href="#accuracy-score-png-and-1560-12-25-15-42-accuracy-score-csv"><code class="docutils literal notranslate"><span class="pre">1560_12_25-15_42-*-accuracy_score.png</span></code> and <code class="docutils literal notranslate"><span class="pre">1560_12_25-15_42-*-accuracy_score.csv</span></code></a></li>
-<li><a class="reference internal" href="#error-analysis-2d-png-and-1560-12-25-15-42-error-analysis-2d-html"><code class="docutils literal notranslate"><span class="pre">1560_12_25-15_42-*-error_analysis_2D.png</span></code> and <code class="docutils literal notranslate"><span class="pre">1560_12_25-15_42-*-error_analysis_2D.html</span></code></a></li>
-<li><a class="reference internal" href="#error-analysis-bar-png"><code class="docutils literal notranslate"><span class="pre">1560_12_25-15_42-*-error_analysis_bar.png</span></code></a></li>
+<li><a class="reference internal" href="#accuracy-score-html-accuracy-score-png-and-accuracy-score-csv"><code class="docutils literal notranslate"><span class="pre">*-accuracy_score*.html</span></code>, <code class="docutils literal notranslate"><span class="pre">*-accuracy_score*.png</span></code> and <code class="docutils literal notranslate"><span class="pre">*-accuracy_score*.csv</span></code></a></li>
+<li><a class="reference internal" href="#error-analysis-2d-png-and-error-analysis-2d-html"><code class="docutils literal notranslate"><span class="pre">*-error_analysis_2D.png</span></code> and <code class="docutils literal notranslate"><span class="pre">*-error_analysis_2D.html</span></code></a></li>
+<li><a class="reference internal" href="#error-analysis-bar-png-and-error-analysis-bar-html"><code class="docutils literal notranslate"><span class="pre">*-error_analysis_bar.png</span></code> and <code class="docutils literal notranslate"><span class="pre">*-error_analysis_bar.html</span></code></a></li>
 <li><a class="reference internal" href="#config-file-yml"><code class="docutils literal notranslate"><span class="pre">config_file.yml</span></code></a></li>
 <li><a class="reference internal" href="#random-state-pickle"><code class="docutils literal notranslate"><span class="pre">random_state.pickle</span></code></a></li>
 <li><a class="reference internal" href="#log-log"><code class="docutils literal notranslate"><span class="pre">1560_12_25-15_42-*-LOG.log</span></code></a></li>
@@ -345,9 +350,6 @@ available for classifiers that present some interpretation-related information (
         <li class="right" style="margin-right: 10px">
           <a href="../genindex.html" title="General Index"
              >index</a></li>
-        <li class="right" >
-          <a href="../py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
         <li class="right" >
           <a href="example2.html" title="Example 2 : Understanding the hyper-parameter optimization"
              >next</a> |</li>
diff --git a/docs/build/tutorials/example2.html b/docs/build/tutorials/example2.html
index 8fdf6b576f0f73c46ae10744fa37b3ae62749e42..0ccdbfc38914ffde3eac7b7a5d318aec1e3c0935 100644
--- a/docs/build/tutorials/example2.html
+++ b/docs/build/tutorials/example2.html
@@ -27,9 +27,6 @@
         <li class="right" style="margin-right: 10px">
           <a href="../genindex.html" title="General Index"
              accesskey="I">index</a></li>
-        <li class="right" >
-          <a href="../py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
         <li class="right" >
           <a href="example3.html" title="Example 3 : Understanding the statistical iterations"
              accesskey="N">next</a> |</li>
@@ -48,51 +45,81 @@
             
   <div class="section" id="example-2-understanding-the-hyper-parameter-optimization">
 <h1>Example 2 : Understanding the hyper-parameter optimization<a class="headerlink" href="#example-2-understanding-the-hyper-parameter-optimization" title="Permalink to this headline">¶</a></h1>
-<div class="section" id="intuitive-explanation-on-hyper-parameters">
-<h2>Intuitive explanation on hyper-parameters<a class="headerlink" href="#intuitive-explanation-on-hyper-parameters" title="Permalink to this headline">¶</a></h2>
-<p>Hyper-parameters are parameters of a classifier (monoview or multiview) that are task-dependant and have a huge part in the performance of the algorithm for a given task.</p>
-<p>The simplest example is the decision tree. One of it’s hyper-parameter is the depth of the tree. The deeper the tree is,
-the most it will fit on the learning data. However a tree too deep will most likely overfit and won’t have any value on
+<div class="section" id="hyper-parameters-intuition">
+<h2>Hyper-parameters intuition<a class="headerlink" href="#hyper-parameters-intuition" title="Permalink to this headline">¶</a></h2>
+<p>Hyper-parameters are parameters of a classifier (monoview or multiview) that are
+task-dependant and have a huge part in the performance of the algorithm for a given task.</p>
+<p>The simplest example is the decision tree. One of it’s hyper-parameter is the
+depth of the tree. The deeper the tree is,
+the most it will fit on the learning data. However, a tree too deep will most
+likely overfit and won’t have any relevance on
 unseen testing data.</p>
-<p>This platform proposes a randomized search for optimizing hyperparamter on the given task. In this example,
-we first will analyze how it works and then how to use it.</p>
+<p>This platform proposes a randomized search and a grid search to optimize
+hyper-parameters. In this example, we first will analyze the theory and
+then how to use it.</p>
 </div>
 <div class="section" id="understanding-train-test-split">
 <h2>Understanding train/test split<a class="headerlink" href="#understanding-train-test-split" title="Permalink to this headline">¶</a></h2>
-<p>In order to provide robust results, this platform splits the dataset in a training set, tha will be used by the
-classifier to optimize their hyper-parameter and learn a relevant model, and a testing set that will take no part in
-the learning process and serve as unseen data to estimate each model’s generalization capacity.</p>
-<p>This split is controlled by the config file’s argument <code class="docutils literal notranslate"><span class="pre">split:</span></code>. It uses a float to pass the ratio between the size of the testing set and the training set  :
-<img class="math" src="../_images/math/db7e01742f2503f433edf60a93d678b26b5c756b.png" alt="\text{split} = \frac{\text{test size}}{\text{train size}}"/>. In order to be as fare as possible, this split is made by keeping the ratio btween each class in the training set and in the testing set.</p>
+<p>In order to provide robust results, this platform splits the dataset in a
+training set, that will be used by the classifier to optimize their
+hyper-parameter and learn a relevant model, and a testing set that will take
+no part in the learning process and serve as unseen data to estimate each
+model’s generalization capacity.</p>
+<p>This split ratio is controlled by the config file’s argument <code class="docutils literal notranslate"><span class="pre">split:</span></code>. It uses a float to pass the ratio between the size of the testing set and the training set  :
+<img class="math" src="../_images/math/6d5e78587eeb09a4cdc8542b46dfcabdaa51f335.png" alt="\text{split} = \frac{\text{test size}}{\text{dataset size}}"/>. In order to be as fair as possible, this split is made by keeping the ratio between each class in the training set and in the testing set.</p>
 <p>So if a dataset has 100 examples with 60% of them in class A, and 40% of them in class B, using <code class="docutils literal notranslate"><span class="pre">split:</span> <span class="pre">0.2</span></code>
 will generate a training set with 48 examples of class A and 32 examples of class B and a testing set
 with 12 examples of class A and 8 examples of class B.</p>
-<p>Ths process uses sklearn’s <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.StratifiedShuffleSplit.html">StratifiedShuffleSplit</a> to split the dataset at random while being reproductilbe thanks to the random state.</p>
+<p>Ths process uses sklearn’s <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.StratifiedShuffleSplit.html">StratifiedShuffleSplit</a> to split the dataset at random while being reproductible thanks to the random_state.</p>
 </div>
 <div class="section" id="understanding-hyper-parameter-optimization">
 <h2>Understanding hyper-parameter optimization<a class="headerlink" href="#understanding-hyper-parameter-optimization" title="Permalink to this headline">¶</a></h2>
-<p>As hyper-paramters are task dependant, there are two ways in the platform to set their value :</p>
+<p>As hyper-parameters are task dependant, there are three ways in the platform to set their value :</p>
 <ul class="simple">
-<li>If you know the value (or a set of values), specify them at the end of the config file for each algorithm you want to test, and use <code class="docutils literal notranslate"><span class="pre">hps_type:</span> <span class="pre">None</span></code> in the classifiaction section of the config file. This will set the Hyper Parameter Search to None, and bypass the optimization process to run the algorithm on the specified values.</li>
-<li>If you don’t know the value, the platform proposes a random search for hyper-parameter optimization.</li>
+<li>If you know the value (or a set of values), specify them at the end of the config file for each algorithm you want to test, and use <code class="docutils literal notranslate"><span class="pre">hps_type:</span> <span class="pre">None</span></code> in the classifiaction section of the config file. This will bypass the optimization process to run the algorithm on the specified values.</li>
+<li>If you have several possible values in mind, specify them in the config file and use <code class="docutils literal notranslate"><span class="pre">hps_type:</span> <span class="pre">'Grid'</span></code> to run a grid search on the possible values.</li>
+<li>If you have no ideas on the values, the platform proposes a random search for hyper-parameter optimization.</li>
 </ul>
+<div class="section" id="grid-search">
+<h3>Grid search<a class="headerlink" href="#grid-search" title="Permalink to this headline">¶</a></h3>
+<p>The grid search is useful when one has several possible sets of hyper-parameters to test, as it is faster thant random-search but requires a relevant prior on the classification task.</p>
+<p>In order to use grid search in SuMMIT, one has to specify <code class="docutils literal notranslate"><span class="pre">hps_type:</span> <span class="pre">&quot;Grid&quot;</span></code> in the config file and provide the values for each parameter of each algorithm in <code class="docutils literal notranslate"><span class="pre">hps_args:</span></code>.
+For example, let us suppose that one wants to run a decision tree but wants to try several depth values (1,5,10), then one has to specify in the config file :</p>
+<div class="highlight-yaml notranslate"><div class="highlight"><pre><span></span><span class="nt">hps_type</span><span class="p">:</span> <span class="s">&quot;Grid&quot;</span>
+<span class="nt">hps_args</span><span class="p">:</span>
+  <span class="nt">decision_tree</span><span class="p">:</span>
+    <span class="nt">max_depth</span><span class="p">:</span> <span class="p p-Indicator">[</span><span class="nv">1</span><span class="p p-Indicator">,</span><span class="nv">5</span><span class="p p-Indicator">,</span><span class="nv">10</span><span class="p p-Indicator">]</span>
+</pre></div>
+</div>
+<p>For more complex classifiers this process can be quite long, but it allows a shorter computational time.</p>
+</div>
 <div class="section" id="random-search">
 <h3>Random search<a class="headerlink" href="#random-search" title="Permalink to this headline">¶</a></h3>
-<p>The random search is one of the most efficient while fairest method to optimize hyper-parameter.
-Thus, for each algorithm in the platform, each of its hyper-paramter is provided with distribution of possible values,
+<p>The random search is one of the most efficient while fairest method to optimize hyper-parameter without any prior knowledge on the dataset.
+Thus, for each algorithm in the platform, each of its hyper-parameter is provided with a distribution of possible values,
 (for example, the decision tree’s max depth parameter is provided with a uniform distribution between 1 and 300).
 The random search method will randomly select hyper-parameters within this distribution and evaluate the performance of
 the classifier with this configuration. It will repeat that process with different randomly selected sets of
 hyper-parameter and keep the best configuration performance-wise.</p>
-<p>In the config file, to enable random search, set the <code class="docutils literal notranslate"><span class="pre">hps_type:</span></code> line to <code class="docutils literal notranslate"><span class="pre">hps_type:</span> <span class="pre">&quot;randomized_search&quot;</span></code> and to
-control the number of draws, use the <code class="docutils literal notranslate"><span class="pre">hps_iter:</span></code> line.</p>
+<p>In the config file, to enable random search, set the <code class="docutils literal notranslate"><span class="pre">hps_type:</span></code> line to <code class="docutils literal notranslate"><span class="pre">hps_type:</span> <span class="pre">&quot;Random&quot;</span></code>.
+The the randomized search can be configured with two arguments :</p>
+<div class="highlight-yaml notranslate"><div class="highlight"><pre><span></span><span class="nt">hps_type</span><span class="p">:</span> <span class="s">&quot;Random&quot;</span>
+<span class="nt">hps_args</span><span class="p">:</span>
+  <span class="nt">n_iter</span><span class="p">:</span> <span class="l l-Scalar l-Scalar-Plain">5</span>
+  <span class="nt">equivalent_draws</span><span class="p">:</span> <span class="l l-Scalar l-Scalar-Plain">True</span>
+</pre></div>
+</div>
+<p>The <code class="docutils literal notranslate"><span class="pre">n_iter</span></code> parameter controls the number of random draws for each classifier
+and if <code class="docutils literal notranslate"><span class="pre">equivalent_draws</span></code> is set to <code class="docutils literal notranslate"><span class="pre">True</span></code>, then the multiview classifiers
+will be allowed <img class="math" src="../_images/math/a41d9126a6756c30058d2933450d07fdaf813e69.png" alt="\text{n\_iter} \times \text{n\_views}"/> iterations,
+to compensate the fact that they have to solve a musch more complex problem than the monoview ones.</p>
 </div>
 <div class="section" id="k-folds-cross-validation">
 <h3>K-folds cross-validation<a class="headerlink" href="#k-folds-cross-validation" title="Permalink to this headline">¶</a></h3>
-<p>During the process of optimizing the hyper-parameter, the random serach has to estimate the perofmance of the classifier.</p>
-<p>In order to do so, the platform uses k-folds cross-validation. This method consists in splitting the training set in
-<img class="math" src="../_images/math/0b7c1e16a3a8a849bb8ffdcdbf86f65fd1f30438.png" alt="k"/> equal sub-sets, training the classifier (with the randomly chose hyper-parameters) on <img class="math" src="../_images/math/61e28425012ff04c501a81720cdd9f9b5afb962e.png" alt="k-1"/> subsets an
-testing it on the last one, evaluating it’s predictive performance.</p>
+<p>During the process of optimizing the hyper-parameters, the random search has to estimate the performance of each classifier.</p>
+<p>To do so, the platform uses k-folds cross-validation. This method consists in splitting the training set in
+<img class="math" src="../_images/math/0b7c1e16a3a8a849bb8ffdcdbf86f65fd1f30438.png" alt="k"/> equal sub-sets, training the classifier (with the hyper-parameters to evaluate) on <img class="math" src="../_images/math/61e28425012ff04c501a81720cdd9f9b5afb962e.png" alt="k-1"/> subsets an
+testing it on the last one, evaluating it’s predictive performance on unseen data.</p>
 <p>This learning-and-testing process is repeated <img class="math" src="../_images/math/0b7c1e16a3a8a849bb8ffdcdbf86f65fd1f30438.png" alt="k"/> times and the estimated performance is the mean of the
 performance on each testing set.</p>
 <p>In the platform, the training set (the 48 examples of class A and 32 examples of class B from last example) will be
@@ -116,7 +143,7 @@ thanks to the <code class="docutils literal notranslate"><span class="pre">metri
 <ul class="simple">
 <li><code class="docutils literal notranslate"><span class="pre">split:</span></code>, controlling the ration of size between the testing set and the training set,</li>
 <li><code class="docutils literal notranslate"><span class="pre">hps_type:</span></code>, controlling the type of hyper-parameter search,</li>
-<li><code class="docutils literal notranslate"><span class="pre">hps_iter:</span></code>, controlling the number of random draws during the hyper-parameter search,</li>
+<li><code class="docutils literal notranslate"><span class="pre">hps_args:</span></code>, controlling the parameters of the hyper-parameters search method,</li>
 <li><code class="docutils literal notranslate"><span class="pre">nb_folds:</span></code>, controlling the number of folds in the cross-validation process.</li>
 </ul>
 <div class="section" id="example-2-1-no-hyper-parameter-optimization-impact-of-split-size">
@@ -129,32 +156,32 @@ three lines in the configuration file are useful :</p>
 <ul class="simple">
 <li><code class="docutils literal notranslate"><span class="pre">type:</span></code> in which one has to specify which type of algorithms are needed, here we used  <code class="docutils literal notranslate"><span class="pre">type:</span> <span class="pre">[&quot;monoview&quot;,&quot;multiview&quot;]</span></code>,</li>
 <li><code class="docutils literal notranslate"><span class="pre">algos_monoview:</span></code> in which one specifies the names of the monoview algorithms to run, here we used : <code class="docutils literal notranslate"><span class="pre">algos_monoview:</span> <span class="pre">[&quot;decision_tree&quot;,</span> <span class="pre">&quot;adaboost&quot;,</span> <span class="pre">]</span></code></li>
-<li><code class="docutils literal notranslate"><span class="pre">algos_multiview:</span></code> is the same but with multiview algorithms, here we used : `` algos_multiview: [“majority_voting_fusion”, ]``</li>
+<li><code class="docutils literal notranslate"><span class="pre">algos_multiview:</span></code> is the same but with multiview algorithms, here we used : <code class="docutils literal notranslate"><span class="pre">algos_multiview:</span> <span class="pre">[&quot;majority_voting_fusion&quot;,</span> <span class="pre">]</span></code></li>
 </ul>
-<p>In order for the platofrm to understand the names, the user has to give the name of the python module in which the classifier is implemented in the platform.</p>
+<p>In order for the platform to understand the names, the user has to give the name of the python module in which the classifier is implemented in the platform.</p>
 <p>In the config file, the default values for adaboost’s hyper-parameters are :</p>
 <div class="highlight-yaml notranslate"><div class="highlight"><pre><span></span><span class="nt">adaboost</span><span class="p">:</span>
-    <span class="nt">n_estimators</span><span class="p">:</span> <span class="p p-Indicator">[</span><span class="nv">50</span><span class="p p-Indicator">]</span>
-    <span class="nt">base_estimator</span><span class="p">:</span> <span class="p p-Indicator">[</span><span class="s">&quot;DecisionTreeClassifier&quot;</span><span class="p p-Indicator">]</span>
+  <span class="nt">n_estimators</span><span class="p">:</span> <span class="l l-Scalar l-Scalar-Plain">50</span>
+  <span class="nt">base_estimator</span><span class="p">:</span> <span class="s">&quot;DecisionTreeClassifier&quot;</span>
 </pre></div>
 </div>
 <p>(see <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.AdaBoostClassifier.html#sklearn.ensemble.AdaBoostClassifier">adaboost’s sklearn’s page</a> for more information)</p>
 <p>For decision_tree :</p>
 <div class="highlight-yaml notranslate"><div class="highlight"><pre><span></span><span class="nt">decision_tree</span><span class="p">:</span>
-    <span class="nt">max_depth</span><span class="p">:</span> <span class="p p-Indicator">[</span><span class="nv">10</span><span class="p p-Indicator">]</span>
-    <span class="nt">criterion</span><span class="p">:</span> <span class="p p-Indicator">[</span><span class="s">&quot;gini&quot;</span><span class="p p-Indicator">]</span>
-    <span class="nt">splitter</span><span class="p">:</span> <span class="p p-Indicator">[</span><span class="s">&quot;best&quot;</span><span class="p p-Indicator">]</span>
+  <span class="nt">max_depth</span><span class="p">:</span> <span class="l l-Scalar l-Scalar-Plain">10</span>
+  <span class="nt">criterion</span><span class="p">:</span> <span class="s">&quot;gini&quot;</span>
+  <span class="nt">splitter</span><span class="p">:</span> <span class="s">&quot;best&quot;</span>
 </pre></div>
 </div>
 <p>(<a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html">sklearn’s decision tree</a>)</p>
 <p>And for the late fusion majority vote :</p>
 <div class="highlight-yaml notranslate"><div class="highlight"><pre><span></span><span class="nt">majority_voting_fusion</span><span class="p">:</span>
-    <span class="nt">classifier_names</span><span class="p">:</span> <span class="p p-Indicator">[[</span><span class="s">&quot;decision_tree&quot;</span><span class="p p-Indicator">,</span> <span class="s">&quot;decision_tree&quot;</span><span class="p p-Indicator">,</span> <span class="s">&quot;decision_tree&quot;</span><span class="p p-Indicator">,</span> <span class="p p-Indicator">]]</span>
+    <span class="nt">classifier_names</span><span class="p">:</span> <span class="p p-Indicator">[</span><span class="s">&quot;decision_tree&quot;</span><span class="p p-Indicator">,</span> <span class="s">&quot;decision_tree&quot;</span><span class="p p-Indicator">,</span> <span class="s">&quot;decision_tree&quot;</span><span class="p p-Indicator">,</span> <span class="p p-Indicator">]</span>
     <span class="nt">classifier_configs</span><span class="p">:</span>
         <span class="nt">decision_tree</span><span class="p">:</span>
-            <span class="nt">max_depth</span><span class="p">:</span> <span class="p p-Indicator">[</span><span class="nv">1</span><span class="p p-Indicator">]</span>
-            <span class="nt">criterion</span><span class="p">:</span> <span class="p p-Indicator">[</span><span class="s">&quot;gini&quot;</span><span class="p p-Indicator">]</span>
-            <span class="nt">splitter</span><span class="p">:</span> <span class="p p-Indicator">[</span><span class="s">&quot;best&quot;</span><span class="p p-Indicator">]</span>
+            <span class="nt">max_depth</span><span class="p">:</span> <span class="l l-Scalar l-Scalar-Plain">1</span>
+            <span class="nt">criterion</span><span class="p">:</span> <span class="s">&quot;gini&quot;</span>
+            <span class="nt">splitter</span><span class="p">:</span> <span class="s">&quot;best&quot;</span>
 </pre></div>
 </div>
 <p>(It will build a vote with one decision tree on each view, with the specified configuration for the decision trees)</p>
@@ -266,10 +293,10 @@ In the config file called <code class="docutils literal notranslate"><span class
 <p>The first difference between these two examples is the time to run the benchmrak, as in the first on more examples are given to learn the algorithms, it is longer. However, the right amount of training examples depends on the available dataset and the task’s complexity.</p>
 <p>TODO COMMENT</p>
 <p><strong>Conclusion</strong></p>
-<p>THe impact of split ratio : dataset related.</p>
+<p>The impact of split ratio : dataset related.</p>
 </div>
-<div class="section" id="example-2-2-usage-of-hyper-parameter-optimization">
-<h3>Example 2.2 : Usage of hyper-parameter optimization :<a class="headerlink" href="#example-2-2-usage-of-hyper-parameter-optimization" title="Permalink to this headline">¶</a></h3>
+<div class="section" id="example-2-2-usage-of-randomized-hyper-parameter-optimization">
+<h3>Example 2.2 : Usage of randomized hyper-parameter optimization :<a class="headerlink" href="#example-2-2-usage-of-randomized-hyper-parameter-optimization" title="Permalink to this headline">¶</a></h3>
 <p>In the previous example, we have seen that the split ratio has an impact on the computational time.
 But the most time-consuming task is optimizing the hyper parameters.
 Up to now, the platform used the hyper-parameters values given in the config file.
@@ -278,7 +305,8 @@ However, most of the time, they are unknown to the user, and then have to be opt
 <p>In this example, we will use the hyper-parameter optimization method implemented in the platform, to do so we will use three lines of the config file :</p>
 <ul class="simple">
 <li><code class="docutils literal notranslate"><span class="pre">hps_type:</span></code>, controlling the type of hyper-parameter search,</li>
-<li><code class="docutils literal notranslate"><span class="pre">hps_iter:</span></code>, controlling the number of random draws during the hyper-parameter search,</li>
+<li><code class="docutils literal notranslate"><span class="pre">n_iter:</span></code>, controlling the number of random draws during the hyper-parameter search,</li>
+<li><code class="docutils literal notranslate"><span class="pre">equivalent_draws</span></code>, controlling the number fo draws for multiview algorithms,</li>
 <li><code class="docutils literal notranslate"><span class="pre">nb_folds:</span></code>, controlling the number of folds in the cross-validation process.</li>
 </ul>
 <p>So if you run <code class="docutils literal notranslate"><span class="pre">example</span> <span class="pre">2.2.1</span></code> with :</p>
@@ -286,56 +314,11 @@ However, most of the time, they are unknown to the user, and then have to be opt
 <span class="gp">&gt;&gt;&gt; </span><span class="n">execute</span><span class="p">(</span><span class="s2">&quot;example2.2.1&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
-<p>The <code class="docutils literal notranslate"><span class="pre">hps_type</span></code> argument is set to <code class="docutils literal notranslate"><span class="pre">&quot;randomised_search&quot;</span></code>, which is at the moment the only hyper-parameter optimization method implemented in the platform.
-The <code class="docutils literal notranslate"><span class="pre">hps_iter</span></code> argument is set to <code class="docutils literal notranslate"><span class="pre">5</span></code>,
+<p>The <code class="docutils literal notranslate"><span class="pre">hps_type</span></code> argument is set to <code class="docutils literal notranslate"><span class="pre">&quot;Random&quot;</span></code>, which is at the moment the only hyper-parameter optimization method implemented in the platform.
+The <code class="docutils literal notranslate"><span class="pre">n_iter</span></code> argument is set to <code class="docutils literal notranslate"><span class="pre">5</span></code>,
+The <code class="docutils literal notranslate"><span class="pre">equivalent_draws</span></code> argument  is set to <code class="docutils literal notranslate"><span class="pre">True</span></code>,
 The <code class="docutils literal notranslate"><span class="pre">nb_folds</span></code> argument is set o <code class="docutils literal notranslate"><span class="pre">5</span></code>.</p>
-<p><strong>WARNING : The “csv-table” directive’s “:file:” and “:url:” options represent a potential security holes. They can be disabled with the “file_insertion_enabled” runtime setting.</strong></p>
-<table border="1" class="docutils">
-<colgroup>
-<col width="11%" />
-<col width="11%" />
-<col width="11%" />
-<col width="11%" />
-<col width="11%" />
-<col width="11%" />
-<col width="11%" />
-<col width="11%" />
-<col width="11%" />
-</colgroup>
-<tbody valign="top">
-<tr class="row-odd"><td>&#160;</td>
-<td>double_fault_fusion</td>
-<td>difficulty_fusion</td>
-<td>lasso-ViewNumber1</td>
-<td>sgd-ViewNumber0</td>
-<td>svm_poly-ViewNumber1</td>
-<td>svm_poly-ViewNumber2</td>
-<td>svm_poly-ViewNumber0</td>
-<td>&#160;</td>
-</tr>
-<tr class="row-even"><td>0</td>
-<td>0.5</td>
-<td>0.5</td>
-<td>0.5</td>
-<td>0.5</td>
-<td>0.7</td>
-<td>0.7</td>
-<td>0.65</td>
-<td>&#160;</td>
-</tr>
-<tr class="row-odd"><td>1</td>
-<td>0.5</td>
-<td>0.5</td>
-<td>0.5</td>
-<td>0.5</td>
-<td>0.7125</td>
-<td>0.7125</td>
-<td>0.8</td>
-<td>&#160;</td>
-</tr>
-</tbody>
-</table>
-<p>Here, we used <code class="docutils literal notranslate"><span class="pre">split:</span> <span class="pre">0.2</span></code> and the results are far better than with the preset of hyper paramters, as the classifiers are able to fit the task.</p>
+<p>Here, we used <code class="docutils literal notranslate"><span class="pre">split:</span> <span class="pre">0.2</span></code> and the results are far better than with the preset of hyper parameters, as the classifiers are able to fit the task.</p>
 <p>The computing time should be longer than the previous examples. Let’s see the pseudo code of the benchmark, while using the hyper-parameter optimization:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span>for each monoview classifier:
     for each view:
@@ -359,18 +342,23 @@ for each multiview classifier:
     learn on the whole training set
 </pre></div>
 </div>
-<p>The instructions inside the brackets are the one that the HP optimization adds. So for the monoview algorithms,
+<p>The instructions inside the brackets are the one that the hyper-parameter
+optimization adds. So for the monoview algorithms,
 the computational impact of the HPO is bigger than for the multiview algorithms.</p>
-<p>The choice made here is to allow the same amount of draws for each HPO. However, as many of the multiview algorithms
+<p>The choice made here is to allow the same amount of draws for each HPO. However,
+as many of the multiview algorithms
 are more complex and have bigger HP spaces, allowing them more draws, can be a defendable idea.</p>
-<p>However, for most of the tasks, using the HPO is a necessity to be able to get the most of each classifier in terms
-of performance.</p>
-<p>The HPO is a matter of tradeoff between precision and computational demand. For most algorithms the more draws you
-allow, the closer to ideal the outputted HP will be, however, many draws mean much longer computational time.</p>
-<p>Similarly, the number of folds has a great importance in estimating the performance of a specific Hp combination,
-and the more folds the but more folds take also more time, as one has to train more times and on bigger parts of the
+<p>However, for most of the tasks, using the HPO is a necessity to be able to
+get the most of each classifier in terms of performance.</p>
+<p>The HPO is a matter of trade-off between precision and computational demand.
+For most algorithms the more draws you allow, the closer to ideal the outputted
+HP will be, however, many draws mean much longer computational time.</p>
+<p>Similarly, the number of folds has a great importance in estimating the
+performance of a specific HP combination, and the more folds the but more folds
+take also more time, as one has to train more times and on bigger parts of the
 dataset.</p>
-<p>The figure below represents the duration of the execution on a personal computer with different fold/draws settings :</p>
+<p>The figure below represents the duration of the execution on a personal computer
+with different fold/draws settings :</p>
 <html>
 <head><meta charset="utf-8" /></head>
 <body>
@@ -393,7 +381,7 @@ dataset.</p>
                     Plotly.newPlot(
                         'a34ab35c-7722-424b-b26f-05ce9018d050',
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                         {"responsive": true}
                     )
                 };
@@ -401,12 +389,34 @@ dataset.</p>
             </script>
         </div>
 </body>
-</html><p>The duration is in seconds, and we used 2,5,10,15,20 as values for <code class="docutils literal notranslate"><span class="pre">nb_folds</span></code> and 2,5,10,20,30,50,100 for <code class="docutils literal notranslate"><span class="pre">hps_iter</span></code> with two monoview classifiers and one multiview classifier on simulated data.</p>
+</html><p>The duration is in seconds, and we used 2,5,10,15,20 as values for <code class="docutils literal notranslate"><span class="pre">nb_folds</span></code>
+and 2,5,10,20,30,50,100 for <code class="docutils literal notranslate"><span class="pre">n_iter</span></code> with two monoview classifiers and one
+multiview classifier on simulated data.</p>
 <div class="admonition note">
 <p class="first admonition-title">Note</p>
-<p class="last">In order to compensate the fact that the multiview classifiers have more complex problems to solve, it is possible to use <code class="docutils literal notranslate"><span class="pre">&quot;randomized_search-equiv&quot;</span></code> as the HPS optimization method to allow
-<code class="docutils literal notranslate"><span class="pre">hps_iter</span></code> draws for the monoview classifiers and <code class="docutils literal notranslate"><span class="pre">hps_iter</span> <span class="pre">*</span> <span class="pre">nb_view</span></code> draws for the ones that are multiview.</p>
+<p class="last">The hyper-parameter optimization process generates a report for each
+classifier, providing each set of parameters and its cross-validation score,
+to be able to extract the relevant parameters for a future benchmark on the
+same dataset.</p>
+</div>
 </div>
+<div class="section" id="example-2-3-usage-of-grid-search">
+<h3>Example 2.3 : Usage of grid search :<a class="headerlink" href="#example-2-3-usage-of-grid-search" title="Permalink to this headline">¶</a></h3>
+<p>In SuMMIT, it is possible to use a grid search if one has several possible
+hyper-parameter values in mind to test.</p>
+<p>In order to set up the grid search one has to provide in the <code class="docutils literal notranslate"><span class="pre">hps_args:</span></code>
+argument the names, parameters and values to test. Let us say we want to try
+several depths for a decision tree, and several <code class="docutils literal notranslate"><span class="pre">C</span></code> values for a
+linear <a class="reference external" href="ttps://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html">SVM</a>:</p>
+<div class="highlight-yaml notranslate"><div class="highlight"><pre><span></span><span class="nt">hps_type</span><span class="p">:</span> <span class="s">&quot;Grid&quot;</span>
+<span class="nt">hps_args</span><span class="p">:</span>
+  <span class="nt">decision_tree</span><span class="p">:</span>
+    <span class="nt">max_depth</span><span class="p">:</span> <span class="p p-Indicator">[</span><span class="nv">1</span><span class="p p-Indicator">,</span><span class="nv">2</span><span class="p p-Indicator">,</span><span class="nv">3</span><span class="p p-Indicator">,</span><span class="nv">4</span><span class="p p-Indicator">,</span><span class="nv">5</span><span class="p p-Indicator">]</span>
+  <span class="nt">svm_linear</span><span class="p">:</span>
+    <span class="nt">C</span><span class="p">:</span> <span class="p p-Indicator">[</span><span class="nv">0.1</span><span class="p p-Indicator">,</span><span class="nv">0.2</span><span class="p p-Indicator">,</span><span class="nv">0.3</span><span class="p p-Indicator">,</span><span class="nv">0.4</span><span class="p p-Indicator">,</span><span class="nv">0.5</span><span class="p p-Indicator">]</span>
+</pre></div>
+</div>
+<p>TODO : a more complex example</p>
 </div>
 </div>
 </div>
@@ -420,9 +430,10 @@ dataset.</p>
   <h3><a href="../index.html">Table of Contents</a></h3>
   <ul>
 <li><a class="reference internal" href="#">Example 2 : Understanding the hyper-parameter optimization</a><ul>
-<li><a class="reference internal" href="#intuitive-explanation-on-hyper-parameters">Intuitive explanation on hyper-parameters</a></li>
+<li><a class="reference internal" href="#hyper-parameters-intuition">Hyper-parameters intuition</a></li>
 <li><a class="reference internal" href="#understanding-train-test-split">Understanding train/test split</a></li>
 <li><a class="reference internal" href="#understanding-hyper-parameter-optimization">Understanding hyper-parameter optimization</a><ul>
+<li><a class="reference internal" href="#grid-search">Grid search</a></li>
 <li><a class="reference internal" href="#random-search">Random search</a></li>
 <li><a class="reference internal" href="#k-folds-cross-validation">K-folds cross-validation</a></li>
 <li><a class="reference internal" href="#metric-choice">Metric choice</a></li>
@@ -430,7 +441,8 @@ dataset.</p>
 </li>
 <li><a class="reference internal" href="#hands-on-experience">Hands-on experience</a><ul>
 <li><a class="reference internal" href="#example-2-1-no-hyper-parameter-optimization-impact-of-split-size">Example 2.1 : No hyper-parameter optimization, impact of split size</a></li>
-<li><a class="reference internal" href="#example-2-2-usage-of-hyper-parameter-optimization">Example 2.2 : Usage of hyper-parameter optimization :</a></li>
+<li><a class="reference internal" href="#example-2-2-usage-of-randomized-hyper-parameter-optimization">Example 2.2 : Usage of randomized hyper-parameter optimization :</a></li>
+<li><a class="reference internal" href="#example-2-3-usage-of-grid-search">Example 2.3 : Usage of grid search :</a></li>
 </ul>
 </li>
 </ul>
@@ -472,9 +484,6 @@ dataset.</p>
         <li class="right" style="margin-right: 10px">
           <a href="../genindex.html" title="General Index"
              >index</a></li>
-        <li class="right" >
-          <a href="../py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
         <li class="right" >
           <a href="example3.html" title="Example 3 : Understanding the statistical iterations"
              >next</a> |</li>
diff --git a/docs/build/tutorials/index.html b/docs/build/tutorials/index.html
index b34289634878464fb5df952ed9c1db2162fb6ad7..e33bc50dc598353d08034fca21c20a09a43d07a9 100644
--- a/docs/build/tutorials/index.html
+++ b/docs/build/tutorials/index.html
@@ -27,9 +27,6 @@
         <li class="right" style="margin-right: 10px">
           <a href="../genindex.html" title="General Index"
              accesskey="I">index</a></li>
-        <li class="right" >
-          <a href="../py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
         <li class="right" >
           <a href="installation.html" title="Install Multiview Platform"
              accesskey="N">next</a> |</li>
@@ -101,9 +98,6 @@
         <li class="right" style="margin-right: 10px">
           <a href="../genindex.html" title="General Index"
              >index</a></li>
-        <li class="right" >
-          <a href="../py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
         <li class="right" >
           <a href="installation.html" title="Install Multiview Platform"
              >next</a> |</li>
diff --git a/docs/build/tutorials/installation.html b/docs/build/tutorials/installation.html
index 059ff8431f16693382f3864c1157e2afce4ef38c..f4415572375e5fbecb62a2e6aeea96cee490f560 100644
--- a/docs/build/tutorials/installation.html
+++ b/docs/build/tutorials/installation.html
@@ -27,9 +27,6 @@
         <li class="right" style="margin-right: 10px">
           <a href="../genindex.html" title="General Index"
              accesskey="I">index</a></li>
-        <li class="right" >
-          <a href="../py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
         <li class="right" >
           <a href="example1.html" title="Example 1 : First steps with Multiview Platform"
              accesskey="N">next</a> |</li>
@@ -111,9 +108,6 @@
         <li class="right" style="margin-right: 10px">
           <a href="../genindex.html" title="General Index"
              >index</a></li>
-        <li class="right" >
-          <a href="../py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
         <li class="right" >
           <a href="example1.html" title="Example 1 : First steps with Multiview Platform"
              >next</a> |</li>
diff --git a/docs/source/index.rst b/docs/source/index.rst
index d60e4a57b8a4f24cf839f717562d1f43e70fff89..b30d339a1f8c5b7b7447bf6e76bf4634888d6ed9 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -3,15 +3,15 @@ sphinx-quickstart on Mon Jan 29 17:13:09 2018.
 You can adapt this file completely to your liking, but it should at least
 contain the root `toctree` directive.
 
-Welcome to MultiviewPlatform's documentation!
+Welcome to SuMMIT's documentation!
 =============================================
 
-This package is used as an easy-to-use platform to estimate different mono- and multi-view classifiers' performance on a multiview dataset.
+This package ha been designed as an easy-to-use platform to estimate different mono- and multi-view classifiers' performances on a multiview dataset.
 
 The main advantage of the platform is that it allows to add and remove a classifier without modifying its core code (the procedure is described thoroughly in this documentation).
 
 .. toctree::
-   :maxdepth: 3
+   :maxdepth: 1
    :caption: Contents:
 
    readme_link
diff --git a/docs/source/tutorials/example1.rst b/docs/source/tutorials/example1.rst
index a7f3f1c8e46b5fe880e26d17bb34c36653a89993..07d53f7851ed3c99a5f278913f4e240ad924772d 100644
--- a/docs/source/tutorials/example1.rst
+++ b/docs/source/tutorials/example1.rst
@@ -10,7 +10,7 @@ Context
 This platform aims at running multiple state-of-the-art classifiers on a multiview dataset in a classification context.
 It has been developed in order to get a baseline on common algorithms for any classification task.
 
-Adding a new classifier (monoview and/or multiview) as been made as simple as possible in order for users to be able to
+Adding a new classifier (monoview and/or multiview) to the benchmark as been made as simple as possible in order for users to be able to
 customize the set of classifiers and test their performances in a controlled environment.
 
 
@@ -39,26 +39,31 @@ The config file that will be used in this example is located in ``multiview-mach
 
 We will decrypt the main arguments :
 
-+ The first part of the arguments are the basic ones :
++ The first part regroups the basics :
 
     - ``log: True`` allows to print the log in the terminal,
     - ``name: ["plausible"]`` uses the plausible simulated dataset,
-    - ``random_state: 42`` fixes the random state of this benchmark, it is useful for reproductibility,
-    - ``full: True`` the benchmark will used the full dataset,
-    - ``res_dir: "examples/results/example_1/"`` the results will be saved in ``multiview-machine-learning-omis/multiview_platform/examples/results/example_1``
+    - ``random_state: 42`` fixes the seed of the random state for this benchmark, it is useful for reproductibility,
+    - ``full: True`` means the benchmark will use the full dataset,
+    - ``res_dir: "examples/results/example_1/"`` saves the results in ``multiview-machine-learning-omis/multiview_platform/examples/results/example_1``
 
 + Then the classification-related arguments :
 
-    - ``split: 0.8`` means that 80% of the dataset will be used to test the different classifiers and 20% to train them
-    - ``type: ["monoview", "multiview"]`` allows for monoview and multiview algorithms to be used in the benchmark
+    - ``split: 0.8`` means that 80% of the dataset will be used to test the different classifiers and 20% to train them,
+    - ``type: ["monoview", "multiview"]`` allows for monoview and multiview algorithms to be used in the benchmark,
     - ``algos_monoview: ["all"]`` runs on all the available monoview algorithms (same for ``algos_muliview``)
-    - ``metrics: ["accuracy_score", "f1_score"]`` means that the benchmark will evaluate the performance of each algortihms on these two metrics.
+    - The metrics configuration ::
 
-+ Then, the two following categories are algorithm-related and contain all the default values for the hyper-parameters.
+                        metrics:
+                          accuracy_score:{}
+                          f1_score:
+                            average:"binary"
+
+      means that the benchmark will evaluate the performance of each algorithms on accuracy, and f1-score with a binary average.
 
 **Start the benchmark**
 
-During the whole benchmark, the log file will be printed in the terminal. To start the benchmark run :
+During the whole benchmark, the log file will be printed in the terminal. To start the benchmark, run :
 
 .. code-block:: python
 
@@ -69,7 +74,7 @@ The execution should take less than five minutes. We will first analyze the resu
 
 **Understanding the results**
 
-The result structure can be startling at first, but as the platform provides a lot of information, it has to be organized.
+The result structure can be startling at first, but, as the platform provides a lot of information, it has to be organized.
 
 The results are stored in ``multiview_platform/examples/results/example_1/``. Here, you will find a directory with the name of the database used for the benchmark, here : ``plausible/``
 Then, a directory with the amount of noise in the experiments, we didn't add any, so ``n_0/`` finally, a directory with
@@ -108,13 +113,18 @@ From here the result directory has the structure that follows  :
     | └── train_indices.csv
     | ├── 1560_12_25-15_42-*-LOG.log
     | ├── config_file.yml
-    | ├── *-accuracy_score.png
-    | ├── *-accuracy_score.csv
+    | ├── *-accuracy_score*.png
+    | ├── *-accuracy_score*.html
+    | ├── *-accuracy_score*-class.html
+    | ├── *-accuracy_score*.csv
     | ├── *-f1_score.png
+    | ├── *-f1_score.html
+    | ├── *-f1_score-class.html
     | ├── *-f1_score.csv
     | ├── *-error_analysis_2D.png
     | ├── *-error_analysis_2D.html
     | ├── *-error_analysis_bar.png
+    | ├── *-error_analysis_bar.html
     | ├── feature_importances
     | | ├── *-ViewNumber0-feature_importance.html
     | | ├── *-ViewNumber0-feature_importance_dataframe.csv
@@ -127,19 +137,19 @@ From here the result directory has the structure that follows  :
     | └── random_state.pickle
 
 
-The structure can seem complex, but it priovides a lot of information, from the most general to the most precise.
+The structure can seem complex, but it provides a lot of information, from the most general to the most precise.
 
 Let's comment each file :
 
-``*-accuracy_score.png`` and ``*-accuracy_score.csv``
+``*-accuracy_score*.html``, ``*-accuracy_score*.png`` and ``*-accuracy_score*.csv``
 <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
 
 These files contain the scores of each classifier for the accuracy metric, ordered with le best ones on the right and
-the worst ones on the left, as an image or as as csv matrix.
+the worst ones on the left, as an interactive html page, an image or a csv matrix. The star after ``accuracy_score*`` means that it is the principal metric.
 The image version is as follows :
 
-.. figure:: ./images/accuracy.png
-    :scale: 25
+.. raw:: html
+    :file: ./images/accuracy_score.html
 
     This is a bar plot showing the score on the training set (light gray), and testing set (dark gray). For each
     monoview classifier, on each view and or each multiview classifier, the scores are printed over the name, under each bar.
@@ -147,7 +157,7 @@ The image version is as follows :
 
 The csv file is a matrix with the score on train stored in the first row and the score on test stored in the second one. Each classifier is presented in a row. It is loadable with pandas.
 
-Similar files have been generated for the f1 metric (``*-f1_score.png`` and ``*-f1_score.csv``).
+Similar files have been generated for the f1 metric.
 
 
 ``*-error_analysis_2D.png`` and ``*-error_analysis_2D.html``
@@ -187,8 +197,8 @@ and then, display a black half-column.
 
 The data used to generate those matrices is available in ``*-2D_plot_data.csv``
 
-``*-error_analysis_bar.png``
-<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
+``*-error_analysis_bar.png`` and ``*-error_analysis_bar.html``
+<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
 
 This file is a different way to visualize the same information as the two previous ones. Indeed, it is a bar plot,
 with a bar for each example, counting the number of classifiers that failed to classify this particular example.
@@ -223,5 +233,5 @@ For each classifier, at least one file is generated, called ``*-summary.txt``.
 .. include:: ./images/summary.txt
     :literal:
 
-This regroups the useful information on the classifiers configuration and it's performance. An interpretation section is
+This regroups the useful information on the classifier's configuration and it's performance. An interpretation section is
 available for classifiers that present some interpretation-related information (as feature importance).
\ No newline at end of file
diff --git a/docs/source/tutorials/example2.rst b/docs/source/tutorials/example2.rst
index 16f8381363b9ef182ea3d1fe68f6dfe16e1966c2..b12a318e969a94ab9bc268807f939843c3abd9f8 100644
--- a/docs/source/tutorials/example2.rst
+++ b/docs/source/tutorials/example2.rst
@@ -2,66 +2,103 @@
 Example 2 : Understanding the hyper-parameter optimization
 ==========================================================
 
-Intuitive explanation on hyper-parameters
+Hyper-parameters intuition
 -----------------------------------------
 
-Hyper-parameters are parameters of a classifier (monoview or multiview) that are task-dependant and have a huge part in the performance of the algorithm for a given task.
+Hyper-parameters are parameters of a classifier (monoview or multiview) that are
+task-dependant and have a huge part in the performance of the algorithm for a given task.
 
-The simplest example is the decision tree. One of it's hyper-parameter is the depth of the tree. The deeper the tree is,
-the most it will fit on the learning data. However a tree too deep will most likely overfit and won't have any value on
+The simplest example is the decision tree. One of it's hyper-parameter is the
+depth of the tree. The deeper the tree is,
+the most it will fit on the learning data. However, a tree too deep will most
+likely overfit and won't have any relevance on
 unseen testing data.
 
-This platform proposes a randomized search for optimizing hyperparamter on the given task. In this example,
-we first will analyze how it works and then how to use it.
+This platform proposes a randomized search and a grid search to optimize
+hyper-parameters. In this example, we first will analyze the theory and
+then how to use it.
 
 
 Understanding train/test split
 ------------------------------
 
-In order to provide robust results, this platform splits the dataset in a training set, tha will be used by the
-classifier to optimize their hyper-parameter and learn a relevant model, and a testing set that will take no part in
-the learning process and serve as unseen data to estimate each model's generalization capacity.
+In order to provide robust results, this platform splits the dataset in a
+training set, that will be used by the classifier to optimize their
+hyper-parameter and learn a relevant model, and a testing set that will take
+no part in the learning process and serve as unseen data to estimate each
+model's generalization capacity.
 
-This split is controlled by the config file's argument ``split:``. It uses a float to pass the ratio between the size of the testing set and the training set  :
-:math:`\text{split} = \frac{\text{test size}}{\text{train size}}`. In order to be as fare as possible, this split is made by keeping the ratio btween each class in the training set and in the testing set.
+This split ratio is controlled by the config file's argument ``split:``. It uses a float to pass the ratio between the size of the testing set and the training set  :
+:math:`\text{split} = \frac{\text{test size}}{\text{dataset size}}`. In order to be as fair as possible, this split is made by keeping the ratio between each class in the training set and in the testing set.
 
 So if a dataset has 100 examples with 60% of them in class A, and 40% of them in class B, using ``split: 0.2``
 will generate a training set with 48 examples of class A and 32 examples of class B and a testing set
 with 12 examples of class A and 8 examples of class B.
 
-Ths process uses sklearn's StratifiedShuffleSplit_ to split the dataset at random while being reproductilbe thanks to the random state.
+Ths process uses sklearn's StratifiedShuffleSplit_ to split the dataset at random while being reproductible thanks to the random_state.
 
 .. _StratifiedShuffleSplit: https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.StratifiedShuffleSplit.html
 
 Understanding hyper-parameter optimization
 ------------------------------------------
 
-As hyper-paramters are task dependant, there are two ways in the platform to set their value :
+As hyper-parameters are task dependant, there are three ways in the platform to set their value :
+
+- If you know the value (or a set of values), specify them at the end of the config file for each algorithm you want to test, and use ``hps_type: None`` in the classifiaction section of the config file. This will bypass the optimization process to run the algorithm on the specified values.
+- If you have several possible values in mind, specify them in the config file and use ``hps_type: 'Grid'`` to run a grid search on the possible values.
+- If you have no ideas on the values, the platform proposes a random search for hyper-parameter optimization.
+
+Grid search
+<<<<<<<<<<<
+
+The grid search is useful when one has several possible sets of hyper-parameters to test, as it is faster thant random-search but requires a relevant prior on the classification task.
+
+In order to use grid search in SuMMIT, one has to specify ``hps_type: "Grid"`` in the config file and provide the values for each parameter of each algorithm in ``hps_args:``.
+For example, let us suppose that one wants to run a decision tree but wants to try several depth values (1,5,10), then one has to specify in the config file :
+
+.. code-block:: yaml
+
+    hps_type: "Grid"
+    hps_args:
+      decision_tree:
+        max_depth: [1,5,10]
+
+For more complex classifiers this process can be quite long, but it allows a shorter computational time.
 
-- If you know the value (or a set of values), specify them at the end of the config file for each algorithm you want to test, and use ``hps_type: None`` in the classifiaction section of the config file. This will set the Hyper Parameter Search to None, and bypass the optimization process to run the algorithm on the specified values.
-- If you don't know the value, the platform proposes a random search for hyper-parameter optimization.
 
 Random search
 <<<<<<<<<<<<<
 
-The random search is one of the most efficient while fairest method to optimize hyper-parameter.
-Thus, for each algorithm in the platform, each of its hyper-paramter is provided with distribution of possible values,
+The random search is one of the most efficient while fairest method to optimize hyper-parameter without any prior knowledge on the dataset.
+Thus, for each algorithm in the platform, each of its hyper-parameter is provided with a distribution of possible values,
 (for example, the decision tree's max depth parameter is provided with a uniform distribution between 1 and 300).
 The random search method will randomly select hyper-parameters within this distribution and evaluate the performance of
 the classifier with this configuration. It will repeat that process with different randomly selected sets of
 hyper-parameter and keep the best configuration performance-wise.
 
-In the config file, to enable random search, set the ``hps_type:`` line to ``hps_type: "randomized_search"`` and to
-control the number of draws, use the ``hps_iter:`` line.
+In the config file, to enable random search, set the ``hps_type:`` line to ``hps_type: "Random"``.
+The the randomized search can be configured with two arguments :
+
+.. code-block:: yaml
+
+    hps_type: "Random"
+    hps_args:
+      n_iter: 5
+      equivalent_draws: True
+
+The ``n_iter`` parameter controls the number of random draws for each classifier
+and if ``equivalent_draws`` is set to ``True``, then the multiview classifiers
+will be allowed :math:`\text{n\_iter} \times \text{n\_views}` iterations,
+to compensate the fact that they have to solve a musch more complex problem than the monoview ones.
 
 K-folds cross-validation
 <<<<<<<<<<<<<<<<<<<<<<<<
 
-During the process of optimizing the hyper-parameter, the random serach has to estimate the perofmance of the classifier.
+During the process of optimizing the hyper-parameters, the random search has to estimate the performance of each classifier.
 
-In order to do so, the platform uses k-folds cross-validation. This method consists in splitting the training set in
-:math:`k` equal sub-sets, training the classifier (with the randomly chose hyper-parameters) on :math:`k-1` subsets an
-testing it on the last one, evaluating it's predictive performance.
+To do so, the platform uses k-folds cross-validation. This method consists in splitting the training set in
+:math:`k` equal sub-sets, training the classifier (with the hyper-parameters to evaluate) on :math:`k-1` subsets an
+testing it on the last one, evaluating it's predictive performance on unseen data.
 
 This learning-and-testing process is repeated :math:`k` times and the estimated performance is the mean of the
 performance on each testing set.
@@ -90,7 +127,7 @@ This example will focus only on some lines of the configuration file :
 
 - ``split:``, controlling the ration of size between the testing set and the training set,
 - ``hps_type:``, controlling the type of hyper-parameter search,
-- ``hps_iter:``, controlling the number of random draws during the hyper-parameter search,
+- ``hps_args:``, controlling the parameters of the hyper-parameters search method,
 - ``nb_folds:``, controlling the number of folds in the cross-validation process.
 
 Example 2.1 : No hyper-parameter optimization, impact of split size
@@ -107,17 +144,17 @@ three lines in the configuration file are useful :
 
 - ``type:`` in which one has to specify which type of algorithms are needed, here we used  ``type: ["monoview","multiview"]``,
 - ``algos_monoview:`` in which one specifies the names of the monoview algorithms to run, here we used : ``algos_monoview: ["decision_tree", "adaboost", ]``
-- ``algos_multiview:`` is the same but with multiview algorithms, here we used : `` algos_multiview: ["majority_voting_fusion", ]``
+- ``algos_multiview:`` is the same but with multiview algorithms, here we used : ``algos_multiview: ["majority_voting_fusion", ]``
 
-In order for the platofrm to understand the names, the user has to give the name of the python module in which the classifier is implemented in the platform.
+In order for the platform to understand the names, the user has to give the name of the python module in which the classifier is implemented in the platform.
 
 In the config file, the default values for adaboost's hyper-parameters are :
 
 .. code-block:: yaml
 
     adaboost:
-        n_estimators: [50]
-        base_estimator: ["DecisionTreeClassifier"]
+      n_estimators: 50
+      base_estimator: "DecisionTreeClassifier"
 
 (see `adaboost's sklearn's page <https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.AdaBoostClassifier.html#sklearn.ensemble.AdaBoostClassifier>`_ for more information)
 
@@ -126,9 +163,9 @@ For decision_tree :
 .. code-block:: yaml
 
     decision_tree:
-        max_depth: [10]
-        criterion: ["gini"]
-        splitter: ["best"]
+      max_depth: 10
+      criterion: "gini"
+      splitter: "best"
 
 (`sklearn's decision tree <https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html>`_)
 
@@ -137,12 +174,12 @@ And for the late fusion majority vote :
 .. code-block:: yaml
 
     majority_voting_fusion:
-        classifier_names: [["decision_tree", "decision_tree", "decision_tree", ]]
+        classifier_names: ["decision_tree", "decision_tree", "decision_tree", ]
         classifier_configs:
             decision_tree:
-                max_depth: [1]
-                criterion: ["gini"]
-                splitter: ["best"]
+                max_depth: 1
+                criterion: "gini"
+                splitter: "best"
 
 (It will build a vote with one decision tree on each view, with the specified configuration for the decision trees)
 
@@ -182,10 +219,10 @@ TODO COMMENT
 
 **Conclusion**
 
-THe impact of split ratio : dataset related.
+The impact of split ratio : dataset related.
 
-Example 2.2 : Usage of hyper-parameter optimization :
-<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
+Example 2.2 : Usage of randomized hyper-parameter optimization :
+<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
 
 In the previous example, we have seen that the split ratio has an impact on the computational time.
 But the most time-consuming task is optimizing the hyper parameters.
@@ -196,7 +233,8 @@ However, most of the time, they are unknown to the user, and then have to be opt
 In this example, we will use the hyper-parameter optimization method implemented in the platform, to do so we will use three lines of the config file :
 
 - ``hps_type:``, controlling the type of hyper-parameter search,
-- ``hps_iter:``, controlling the number of random draws during the hyper-parameter search,
+- ``n_iter:``, controlling the number of random draws during the hyper-parameter search,
+- ``equivalent_draws``, controlling the number fo draws for multiview algorithms,
 - ``nb_folds:``, controlling the number of folds in the cross-validation process.
 
 So if you run ``example 2.2.1`` with :
@@ -206,16 +244,12 @@ So if you run ``example 2.2.1`` with :
    >>> from multiview_platform.execute import execute
    >>> execute("example2.2.1")
 
-The ``hps_type`` argument is set to ``"randomised_search"``, which is at the moment the only hyper-parameter optimization method implemented in the platform.
-The ``hps_iter`` argument is set to ``5``,
+The ``hps_type`` argument is set to ``"Random"``, which is at the moment the only hyper-parameter optimization method implemented in the platform.
+The ``n_iter`` argument is set to ``5``,
+The ``equivalent_draws`` argument  is set to ``True``,
 The ``nb_folds`` argument is set o ``5``.
 
-**WARNING : The "csv-table" directive's ":file:" and ":url:" options represent a potential security holes. They can be disabled with the "file_insertion_enabled" runtime setting.**
-
-.. csv-table::
-    :file: ./images/result_default_hp_high_train.csv
-
-Here, we used ``split: 0.2`` and the results are far better than with the preset of hyper paramters, as the classifiers are able to fit the task.
+Here, we used ``split: 0.2`` and the results are far better than with the preset of hyper parameters, as the classifiers are able to fit the task.
 
 
 The computing time should be longer than the previous examples. Let's see the pseudo code of the benchmark, while using the hyper-parameter optimization::
@@ -241,32 +275,61 @@ The computing time should be longer than the previous examples. Let's see the ps
         └
         learn on the whole training set
 
-The instructions inside the brackets are the one that the HP optimization adds. So for the monoview algorithms,
+The instructions inside the brackets are the one that the hyper-parameter
+optimization adds. So for the monoview algorithms,
 the computational impact of the HPO is bigger than for the multiview algorithms.
 
-The choice made here is to allow the same amount of draws for each HPO. However, as many of the multiview algorithms
+The choice made here is to allow the same amount of draws for each HPO. However,
+as many of the multiview algorithms
 are more complex and have bigger HP spaces, allowing them more draws, can be a defendable idea.
 
-However, for most of the tasks, using the HPO is a necessity to be able to get the most of each classifier in terms
-of performance.
+However, for most of the tasks, using the HPO is a necessity to be able to
+get the most of each classifier in terms of performance.
 
-The HPO is a matter of tradeoff between precision and computational demand. For most algorithms the more draws you
-allow, the closer to ideal the outputted HP will be, however, many draws mean much longer computational time.
+The HPO is a matter of trade-off between precision and computational demand.
+For most algorithms the more draws you allow, the closer to ideal the outputted
+HP will be, however, many draws mean much longer computational time.
 
-Similarly, the number of folds has a great importance in estimating the performance of a specific Hp combination,
-and the more folds the but more folds take also more time, as one has to train more times and on bigger parts of the
+Similarly, the number of folds has a great importance in estimating the
+performance of a specific HP combination, and the more folds the but more folds
+take also more time, as one has to train more times and on bigger parts of the
 dataset.
 
-The figure below represents the duration of the execution on a personal computer with different fold/draws settings :
+The figure below represents the duration of the execution on a personal computer
+with different fold/draws settings :
 
 .. raw:: html
     :file: ./images/durations.html
 
-The duration is in seconds, and we used 2,5,10,15,20 as values for ``nb_folds`` and 2,5,10,20,30,50,100 for ``hps_iter`` with two monoview classifiers and one multiview classifier on simulated data.
+The duration is in seconds, and we used 2,5,10,15,20 as values for ``nb_folds``
+and 2,5,10,20,30,50,100 for ``n_iter`` with two monoview classifiers and one
+multiview classifier on simulated data.
 
 .. note::
-    In order to compensate the fact that the multiview classifiers have more complex problems to solve, it is possible to use ``"randomized_search-equiv"`` as the HPS optimization method to allow
-    ``hps_iter`` draws for the monoview classifiers and ``hps_iter * nb_view`` draws for the ones that are multiview.
+    The hyper-parameter optimization process generates a report for each
+    classifier, providing each set of parameters and its cross-validation score,
+    to be able to extract the relevant parameters for a future benchmark on the
+    same dataset.
 
 
+Example 2.3 : Usage of grid search :
+<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
+
+In SuMMIT, it is possible to use a grid search if one has several possible
+hyper-parameter values in mind to test.
+
+In order to set up the grid search one has to provide in the ``hps_args:``
+argument the names, parameters and values to test. Let us say we want to try
+several depths for a decision tree, and several ``C`` values for a
+linear `SVM <ttps://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html>`_:
+
+.. code-block:: yaml
+
+    hps_type: "Grid"
+    hps_args:
+      decision_tree:
+        max_depth: [1,2,3,4,5]
+      svm_linear:
+        C: [0.1,0.2,0.3,0.4,0.5]
 
+TODO : a more complex example
diff --git a/docs/source/tutorials/example3.rst b/docs/source/tutorials/example3.rst
index c81e31ef4aa5d334c13a6ccc6b88929443963a21..590f8d2a67b5f08b424f63540b8a9d3856646991 100644
--- a/docs/source/tutorials/example3.rst
+++ b/docs/source/tutorials/example3.rst
@@ -13,7 +13,7 @@ To settle this issue, the platform can run on multiple splits and return the mea
 How to use it
 -------------
 
-This feature is controlled by a single argument : ``stats_iter:`` in the ``Classification`` section of the config file.
+This feature is controlled by a single argument : ``stats_iter:`` in the config file.
 Modifying this argument and setting more than one ``stats_iter`` will slightly modify the result directory's structure.
 Indeed, as the platform will perform a benchmark on multiple train/test split, the result directory will be larger in order to keep all the individual results.
 In terms of pseudo-code, if one uses HPO, it adds a for loop on the pseudo code displayed in example 2 ::
@@ -49,15 +49,23 @@ The result directory will be structured as :
     | | └── train_indices.csv
     | | ├── 1560_12_25-15_42-*-LOG.log
     | | ├── config_file.yml
-    | | ├── *-accuracy_score.png
+    | | ├── *-accuracy_score.
+    | | ├── *-accuracy_score-class.html
+    | | ├── *-accuracy_score.html
     | | ├── *-accuracy_score.csv
     | | ├── *-f1_score.png
     | | ├── *-f1_score.csv
+    | | ├── *-f1_score-class.html
+    | | ├── *-f1_score.html
     | | ├── *-error_analysis_2D.png
     | | ├── *-error_analysis_2D.html
     | | ├── *-error_analysis_bar.png
+    | | ├── *-error_analysis_bar.HTML
     | | ├── *-bar_plot_data.csv
     | | ├── *-2D_plot_data.csv
+    | | ├── feature_importances
+    | | ├── [..
+    | | ├── ..]
     | | ├── adaboost
     | | |   ├── ViewNumber0
     | | |   |   ├── *-summary.txt
@@ -65,7 +73,7 @@ The result directory will be structured as :
     | | |   ├── ViewNumber1
     | | |   |   ├── *-summary.txt
     | | |   |   ├── <other classifier dependant files>
-    | | | |   ├── ViewNumber2
+    | | |   ├── ViewNumber2
     | | |   |   ├── *-summary.txt
     | | |   |   ├── <other classifier dependant files>
     | | ├── decision_tree
@@ -92,11 +100,16 @@ The result directory will be structured as :
     | ├── config_file.yml
     | ├── *-accuracy_score.png
     | ├── *-accuracy_score.csv
+    | ├── *-accuracy_score.html
+    | ├── *-accuracy_score-class.html
     | ├── *-f1_score.png
     | ├── *-f1_score.csv
+    | ├── *-f1_score.html
+    | ├── *-f1_score-class.html
     | ├── *-error_analysis_2D.png
     | ├── *-error_analysis_2D.html
     | ├── *-error_analysis_bar.png
+    | ├── *-error_analysis_bar.html
     | ├── *-bar_plot_data.csv
     | ├── *-2D_plot_data.csv
     | ├── feature_importances
@@ -112,8 +125,8 @@ If you look closely, nearly all the files from Example 1 are in each ``iter_`` d
 So, the files stored in ``started_1560_12_25-15_42/`` are the one that show the mean results on all the statistical iterations.
 For example, ``started_1560_12_25-15_42/*-accuracy_score.png`` looks like :
 
-.. figure:: ./images/accuracy_mean.png
-    :scale: 25
+.. raw:: html
+    ./images/accuracy_mean.html
 
     The main difference between this plot an the one from Example 1 is that here, the scores are means over all the statistical iterations, and the standard deviations are plotted as vertical lines on top of the bars and printed after each score under the bars as "± <std>".
 
@@ -121,9 +134,13 @@ Then, each iteration's directory regroups all the results, structured as in Exam
 
 
 
-**Example with stats iter**
+Example
+<<<<<<<
+
 
-**Duration ??**
+Duration
+<<<<<<<<
 
+Increasing the number of statistical iterations can be costly in terms of computational resources
 
 
diff --git a/docs/source/tutorials/example4.rst b/docs/source/tutorials/example4.rst
index 8c1104095b38883c6005d4f3c461cc3ed2c9ff53..a25e7e12ce7666c0f92f41ac656f9dcd5073a5e2 100644
--- a/docs/source/tutorials/example4.rst
+++ b/docs/source/tutorials/example4.rst
@@ -5,7 +5,7 @@ Taking control : Use your own dataset
 .. role:: python(code)
     :language: python
 
-While developping this platform, the goal has been to bea able to use it relatively easily on different datasets.
+While developping this platform, the goal has been to be able to use it relatively easily on different datasets.
 In order to do so, a fixed input format is used, and we choosed HDF5 as it allows to store a multiview dataset and its metadata in a single file, while being able to load it partially.
 
 The bare necessities
@@ -14,7 +14,6 @@ The bare necessities
 At the moment, in order for the platform to work, the dataset must satisfy the following minimum requirements :
 
 - Each example must be described in each view, with no missing data (you can use external tools to fill the gaps, or use only the fully-described examples of you dataset)
-- ?
 
 The dataset structure
 ---------------------
@@ -25,80 +24,107 @@ Let's suppose that one has a multiview dataset consisting of 3 views describing
 2. An image of each example, described by 40 features,
 3. A written commentary for each example, described by 55 features.
 
-So three matrices (200x100 ; 200x40 ; 200x55) make up the dataset. THe most usual way to save matrices are `.csv` files. So let's suppose that one has
+So three matrices (200x100 ; 200x40 ; 200x55) make up the dataset. The most usual way to save matrices are `.csv` files. So let us suppose that one has
 
 1. ``sound.csv``,
 2. ``image.csv``
 3. ``commentary.csv``.
 
-Let's suppose that all this data should be used to classify the examples in two classes : Animal or Object and that on has a ``labels.csv`` file wit one value for each example, a 0 if the example is an Animal and a 1 if it is an Object.
+Let us suppose that all this data should be used to classify the examples in three classes : "Human", "Animal" or "Object"  and that on has a ``labels.csv`` file with one value for each example, 0 if the example is a human, 1 if it is an animal an 2 if it is an object.
 
 In order to run a benchmark on this dataset, one has to format it using HDF5.
 
 HDF5 conversion
 ---------------
 
-We will use here a python script to convert the dataset in the right format :
+We will use here a python script, provided with the platform (``./format_dataset.py``) to convert the dataset in the right format :
 
 .. code-block:: python
 
     import h5py
     import numpy as np
 
-Let's load the csv matrices :
+Let's define the variables that will be used to load the csv matrices :
 
 .. code-block:: python
 
-    sound_matrix = np.genfromtxt("path/to/sound.csv", delimiter=",")
-    image_matrix = np.genfromtxt("path/to/image.csv", delimiter=",")
-    commentary_matrix = np.genfromtxt("path/to/commentary.csv", delimiter=",")
-    labels = np.genfromtxt("path/to/labels.csv", delimiter=",")
+    # The following variables are defined as an example, you should modify them to fit your dataset files.
+    view_names = ["sound", "image", "commentary", ]
+    data_file_paths = ["path/to/sound.csv", "path/to/image.csv", "path/to/commentary.csv",]
+    labels_file_path = "path/to/labels/file.csv"
+    example_ids_path = "path/to/example_ids/file.csv"
+    labels_names = ["Human", "Animal", "Object"]
 
 Let's create the HDF5 file :
 
 .. code-block:: python
 
-    hdf5_file = h5py.File("path/to/database_name.hdf5", "w")
+    # HDF5 dataset initialization :
+    hdf5_file = h5py.File("path/to/file.hdf5", "w")
 
-Now, let's create the first dataset : the one with the sound view :
+Now, for each view, create an HDF5 dataset :
 
 .. code-block:: python
 
-    sound_dataset = hdf5_file.create_dataset("View0", data=sound_matrix)
-    sound_dataset.attrs["name"] = "Sound"
-    sound_dataset.attrs["sparse"] = "False"
+    for view_index, (file_path, view_name) in enumerate(zip(data_file_paths, view_names)):
+        # Get the view's data from the csv file
+        view_data = np.genfromtxt(file_path, delimiter=",")
 
-**Be sure to use View0 as the name of the dataset**, as it is mandatory for the platform to run (the following datasets will be named :python:`"View1"`, :python:`"View2"`, ...)
+        # Store it in a dataset in the hdf5 file,
+        # do not modify the name of the dataset
+        view_dataset = hdf5_file.create_dataset(name="View{}".format(view_index),
+                                                shape=view_data.shape,
+                                                data=view_data)
+        # Store the name of the view in an attribute,
+        # do not modify the attribute's key
+        view_dataset.attrs["name"] = view_name
 
-For each view available, let's create a dataset similarly (be sure that the examples are described in the same order : line 1 of the sound matrix describes the same example as line 1 of the image one and the commentary one)
-
-.. code-block:: python
-
-    image_dataset = hdf5_file.create_dataset("View1", data=image_matrix)
-    image_dataset.attrs["name"] = "Image"
-    image_dataset.attrs["sparse"] = "False"
-
-    commentary_dataset = hdf5_file.create_dataset("View2", data=commentary_matrix)
-    commentary_dataset.attrs["name"] = "Commentary"
-    commentary_dataset.attrs["sparse"] = "False"
+        # This is an artifact of work in progress for sparse support, not available ATM,
+        # do not modify the attribute's key
+        view_dataset.attrs["sparse"] = False
 
 Let's now create the labels dataset (here also, be sure that the labels are correctly ordered).
 
 .. code-block:: python
 
-    labels_dataset = hdf5_file.create_dataset("Labels", data=labels)
-    labels_dataset.attrs["name"] = ["Animal".encode(), "Object".encode()]
+    # Get le labels data from a csv file
+    labels_data = np.genfromtxt(labels_file_path, delimiter=',')
+
+    # Here, we supposed that the labels file contained numerical labels (0,1,2)
+    # that refer to the label names of label_names.
+    # The Labels HDF5 dataset must contain only integers that represent the
+    # different classes, the names of each class are saved in an attribute
+
+    # Store the integer labels in the HDF5 dataset,
+    # do not modify the name of the dataset
+    labels_dset = hdf5_file.create_dataset(name="Labels",
+                                           shape=labels_data.shape,
+                                           data=labels_data)
+    # Save the labels names in an attribute as encoded strings,
+    # do not modify the attribute's key
+    labels_dset.attrs["names"] = [label_name.encode() for label_name in labels_names]
 
-Be sure to sort the label names in the right order (the label must be the same as the list's index, here 0 is Animal, and also :python:`labels_dataset.attrs["name"][0]`)
+Be sure to sort the label names in the right order (the label must be the same as the list's index, here 0 is "Human", and also :python:`labels_dataset.attrs["name"][0]`)
 
 Let's now store the metadata :
 
 .. code-block:: python
 
-    metadata_group = hdf5_file.create_group("Metadata")
-    metadata_group.attrs["nbView"] = 3
-    metadata_group.attrs["nbClass"] = 2
-    metadata_group.attrs["datasetLength"] = 200
+    # Create a Metadata HDF5 group to store the metadata,
+    # do not modify the name of the group
+    metadata_group = hdf5_file.create_group(name="Metadata")
+
+    # Store the number of views in the dataset,
+    # do not modify the attribute's key
+    metadata_group.attrs["nbView"] = len(view_names)
+
+    # Store the number of classes in the dataset,
+    # do not modify the attribute's key
+    metadata_group.attrs["nbClass"] = np.unique(labels_data)
+
+    # Store the number of examples in the dataset,
+    # do not modify the attribute's key
+    metadata_group.attrs["datasetLength"] = labels_data.shape[0]
 
 Here, we store
 
@@ -107,8 +133,8 @@ Here, we store
 - The number of examples in the :python:`"datasetLength"` attribute.
 
 Now, the dataset is ready to be used in the platform.
-Let's suppose it is stored in ``path/to/database_name.hdf5``, then by setting the ``pathf:`` line of the config file to
-``pathf: path/to/`` and the ``name:`` line to ``name: ["database_name.hdf5"]``, the benchmark will run on the created dataset.
+Let's suppose it is stored in ``path/to/file.hdf5``, then by setting the ``pathf:`` line of the config file to
+``pathf: path/to/`` and the ``name:`` line to ``name: ["file.hdf5"]``, the benchmark will run on the created dataset.
 
 
 Adding additional information on the examples
@@ -116,15 +142,23 @@ Adding additional information on the examples
 
 In order to be able to analyze the results with more clarity, one can add the examples IDs to the dataset, by adding a dataset to the metadata group.
 
-Let's suppose that the objects we are trying to classify between 'Animal' and 'Object' are all bears, cars, planes, and birds. And that one has a ``.csv`` file with an ID for each of them (:python:`"bear_112", "plane_452", "bird_785", "car_369", ...` for example)
+Let's suppose that the objects we are trying to classify between "Human", "Animal" and "Object" are all people, bears, cars, planes, and birds. And that one has a ``.csv`` file with an ID for each of them (:python:`"john_115", "doe_562", "bear_112", "plane_452", "bird_785", "car_369", ...` for example)
 
 Then as long as the IDs order corresponds to the example order in the lines of the previous matrices, to add the IDs in the hdf5 file, just add :
 
 .. code-block:: python
 
-    id_table = np.genfromtxt("path.to/id.csv", delimiter=",").astype(np.dtype('S10'))
-    metadata_group.create_dataset("example_ids", data=id_table, dtype=np.dtype('S10'))
+    # Let us suppose that the examples have string ids, available in a csv file,
+    # they can be stored in the HDF5 and will be used in the result analysis.
+    example_ids = np.genfromtxt(example_ids_path, delimiter=',')
+
+    # To sore the strings in an HDF5 dataset, be sure to use the S<max_length> type,
+    # do not modify the name of the dataset.
+    metadata_group.create_dataset("example_ids",
+                                  data=np.array(example_ids).astype(np.dtype("S100")),
+                                  dtype=np.dtype("S100"))
+
 
-Be sure to keep the name :python:`"example_ids"`, as it is mandatory for the platform to find the IDs dataset in the file.
+Be sure to keep the name :python:`"example_ids"`, as it is mandatory for the platform to find the dataset in the file.
 
 
diff --git a/docs/source/tutorials/example5.rst b/docs/source/tutorials/example5.rst
index 3d59d30f59c35936f7acc5bc99ade69b9c326b49..97de4c7d800b8530ab75b33149f99f5b6bb37808 100644
--- a/docs/source/tutorials/example5.rst
+++ b/docs/source/tutorials/example5.rst
@@ -1,4 +1,5 @@
 .. |algo| replace:: name_me
+
 ========================================
 Taking control : Use your own algorithms
 ========================================
@@ -6,27 +7,34 @@ Taking control : Use your own algorithms
 .. role:: python(code)
     :language: python
 
-One of the main goals of this platform is to be able to add a classifier to it without modifying the code.
+One of the main goals of this platform is to be able to add a classifier to it without modifying the main code.
 
 Simple task : Adding a monoview classifier
 ------------------------------------------
 
+Make it work
+<<<<<<<<<<<<
+
 Let's say we want to add a monoview classifier called "algo" to the platform in order to compare it to the other available ones.
-Let's suppose that we have a python module ``algo_module.py`` in which algo is defined in the class :python:`Algo` with the guidelines of ``sklearn``.
+Let's suppose that we have a python module ``algo_module.py`` in which algo is defined in the class :python:`Algo` following ``scikit-learn``'s `guidelines <https://scikit-learn.org/stable/developers/index.html>`_ .
 
-To add algo to the platform, let's create a file called ``algo.py`` in ``multiview_platform/mono_multi_view_classifiers/monoview_classifiers/``
+To add "algo" to the platform, let's create a file called ``algo.py`` in ``multiview_platform/mono_multi_view_classifiers/monoview_classifiers/``
 
 In this file let's define the class :python:`AlgoClassifier`, inheriting from :python:`Algo` and :python:`BaseMonoviewClassifier` that contains the required methods for the platfrom.
 
+Moreover, one has to add a variable called :python:`classifier_class_name` that contains the class name (here ``'AlgoClassifier'``)
+
 .. code-block:: python
 
     import Algo
     from ..monoview.monoview_utils import BaseMonoviewClassifier
 
+    classifier_class_name = "AlgoClassifier"
+
     class AlgoClassifier(Algo, BaseMonoviewClassifier):
 
 
-To be able to use the hyper-parameter optimization of the platform, we need to provide some information in the :python:`__init__()` method.
+To be able to use the randomized hyper-parameter optimization, we need to provide some information in the :python:`__init__()` method.
 Indeed, all the algorithms included in the platform must provide two hyper-parameter-related attributes :
 
 - :python:`self.param_names` that contain the name of the hyper-parameters that have to be optimized (they must correspond to the name of the attributes of the class :python:`Algo`)
@@ -61,53 +69,93 @@ Then, the :python:`__init__()` method of the :python:`AlgoClassifier` class wil
                              ["l1", "l2"],
                              CustomRandint()]
 
-In this method, we added the needed attributes. See REF TO DOC OF DISTRIBS for the dicumentation on the used distributions.
+In this method, we added the needed attributes. See REF TO DOC OF DISTRIBS for the documentation on the used distributions.
 
 If "algo" is implemented in a sklearn fashion, it is now usable in the platform.
 
-TODO interpretation
+Interpretation
+<<<<<<<<<<<<<<
+
+It is possible to provide some information about the decision process of the algorithm in the :python:`get_interpretation` method.
+
+It inputs four arguments :
+
+* :python:`directory`, a string containing the directory where figures should be sotred
+* :python:`base_file_name`, a string containing the file name prefix that shoul be used to sotre figures
+* :python:`y_test`, an array containing the labels of the test set
+* :python:`multiclass` a boolean that is True if the target is multiclass
+
+This method must return a string that will be appended to the summary file.
+
+An example of method can be :
+
+.. code-block:: python
+
+    def get_interpretation(self, directory, base_file_name, y_test,
+                           multiclass=False):
+        interpret_string = "Algo is a very relevant algorithm that used all the features to classify"
+        # Save a figure in os.path.join(directory, base_file_name+figure_name.png")
+        return interpretString
+
 
 More complex task : Adding a multiview classifier
 -------------------------------------------------
 
+This part is a bit more complex as to the best of our knowledge, there is no consensus regarding a multiview input for a classifier.
+
+The first step of the integration of a multiview classifier is very similar to the monoview one let us suppose one wants to add "new mv algo", that is implemented in the class `NewMVAlgo`. To do so, create a "new_mv_algo.py" file in ``multiview_platform/mono_multi_view_classifiers/multiview_classifiers/``.
 
+In this file let's define the class :python:`NewMVAlgoClassifier`, inheriting from :python:`NewMVAlgo` and :python:`BaseMultiviewClassifier` that contains the required methods for the platform.
+
+Moreover, one has to add a variable called :python:`classifier_class_name` that contains the class name (here ``'NewMVAlgoClassifier'``)
 
 .. code-block:: python
 
-    from mimbo import MimboClassifier
-    from ..multiview.multiview_utils import BaseMultiviewClassifier, \
-                                            get_examples_views_indices
+    from new_mv_algo_module import NewMVAlgo
+    from ..multiview.multiview_utils import BaseMultiviewClassifier
+
     from ..utils.hyper_parameter_search import CustomRandint
 
-    classifier_class_name = "Mimbo"
-
-    class Mimbo(BaseMultiviewClassifier, MimboClassifier):
-
-        def __init__(self, n_estimators=50,
-                     random_state=None,
-                     best_view_mode="edge"):
-            super().__init__(random_state)
-            super(BaseMultiviewClassifier, self).__init__(n_estimators=n_estimators,
-                                        random_state=random_state,
-                                        best_view_mode=best_view_mode)
-            self.param_names = ["n_estimators", "random_state", "best_view_mode"]
-            self.distribs = [CustomRandint(5,200), [random_state], ["edge", "error"]]
-
-        def fit(self, X, y, train_indices=None, view_indices=None):
-            train_indices, view_indices = get_examples_views_indices(X,
-                                                                     train_indices,
-                                                                     view_indices)
-            numpy_X, view_limits = X.to_numpy_array(example_indices=train_indices,
-                                                    view_indices=view_indices)
-            return super(Mimbo, self).fit(numpy_X, y[train_indices],
-                                                    view_limits)
-
-        def predict(self, X, example_indices=None, view_indices=None):
-            example_indices, view_indices = get_examples_views_indices(X,
-                                                                     example_indices,
-                                                                     view_indices)
-            numpy_X, view_limits = X.to_numpy_array(example_indices=example_indices,
-                                                    view_indices=view_indices)
-            return super(Mimbo, self).predict(numpy_X)
+    classifier_class_name = "NewMVAlgoClassifier"
+
+    class NewMVAlgoClassifier(BaseMultiviewClassifier, NewMVAlgo):
+
+        def __init__(self, param_1=50,
+                         random_state=None,
+                         param_2="edge"):
+                BaseMultiviewClassifier.__init__(self, random_state)
+                NewMVAlgo.__init__(self, param_1=param_1,
+                                            random_state=random_state,
+                                            param_2=param_2)
+                self.param_names = ["param_1", "random_state", "param_2"]
+                self.distribs = [CustomRandint(5,200), [random_state], ["val_1", "val_2"]]
+
+In SuMMIT the input of the :python:`fit()` method is `X`, a dataset object that provide access to each view with a method : :python:`dataset_var.get_v(view_index, example_indices)`,
+so in order to add a mutliview classifier to SuMMIT, one will probably have to add a data-transformation step before using the class's :python:`fit()` method.
 
+Moreover, to get restrain the examples and descriptors used in the method, SuMMIT provides two supplementary arguments :
+
+- ``train_indices`` is an array of examples indices that compose the training set,
+- ``view_indices`` is an array of view indices to restrain the number of views on which the algorithm will train.
+
+These two arguments are useful to reduce memory usage. Indeed, `X`, the dataset object is just a wrapper for an HDF5 file object, so the data will only be loaded once the `get_v` method is called, so the train and test set are not loaded at the same time.
+
+
+
+.. code-block:: python
 
+    def fit(self, X, y, train_indices=None, view_indices=None):
+        train_indices, view_indices = get_examples_views_indices(X,
+                                                                 train_indices,
+                                                                 view_indices)
+        needed_input = transform_data_if_needed(X, train_indices, view_indices)
+        return NewMVAlgo.fit(self, needed_input, y[train_indices])
+
+    def predict(self, X, example_indices=None, view_indices=None):
+        example_indices, view_indices = get_examples_views_indices(X,
+                                                                 example_indices,
+                                                                 view_indices)
+        needed_input = transform_data_if_needed(X, example_indices, view_indices)
+        return NewMVAlgo.predict(self, needed_input)
+
+Similarly to monoview algorithms, it is possible to add an interpretation method.
\ No newline at end of file
diff --git a/docs/source/tutorials/images/durations.html b/docs/source/tutorials/images/durations.html
index 43acdd3e7465b5b26dec64faaa4ca7d0b84df801..75194011e209569929e807b58e037fedd8bf90f1 100644
--- a/docs/source/tutorials/images/durations.html
+++ b/docs/source/tutorials/images/durations.html
@@ -20,7 +20,7 @@
                     Plotly.newPlot(
                         'a34ab35c-7722-424b-b26f-05ce9018d050',
                         [{"contours": {"z": {"highlightcolor": "limegreen", "project": {"z": true}, "show": true, "usecolormap": true}}, "type": "surface", "x": [2, 5, 10, 20, 30, 50, 100], "y": [2, 5, 10, 15, 20], "z": [[12.30792236328125, 14.539308309555054, 25.204104900360107, 40.63256311416626, 57.830570936203, 94.06869697570801, 177.3781054019928], [23.060000896453857, 38.69007134437561, 55.38164758682251, 112.99745631217957, 154.44376754760742, 249.8409869670868, 444.60806703567505], [31.165709257125854, 75.43783736228943, 112.19536924362183, 184.5037076473236, 290.94096207618713, 461.04882979393005, 932.619647026062], [73.37707662582397, 100.57551407814026, 183.70872330665588, 331.44516921043396, 433.04977560043335, 637.6133468151093, 1335.7918057441711], [79.04550528526306, 153.53510761260986, 199.00436758995056, 341.0050745010376, 608.0813648700714, 918.9630422592163, 1716.7227251529694]]}],
-                        {"scene": {"xaxis": {"title": {"text": "x : Number of draws"}}, "yaxis": {"title": {"text": "y : Number of folds"}}, "zaxis": {"title": {"text": "z : Benchmark duration (s)"}}}, "template": {"data": {"bar": [{"error_x": {"color": "#2a3f5f"}, "error_y": {"color": "#2a3f5f"}, "marker": {"line": {"color": "#E5ECF6", "width": 0.5}}, "type": "bar"}], "barpolar": [{"marker": {"line": {"color": "#E5ECF6", "width": 0.5}}, "type": "barpolar"}], "carpet": [{"aaxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "baxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "type": "carpet"}], "choropleth": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "choropleth"}], "contour": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "contour"}], "contourcarpet": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "contourcarpet"}], "heatmap": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "heatmap"}], "heatmapgl": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "heatmapgl"}], "histogram": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "histogram"}], "histogram2d": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "histogram2d"}], "histogram2dcontour": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "histogram2dcontour"}], "mesh3d": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "mesh3d"}], "parcoords": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "parcoords"}], "scatter": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatter"}], "scatter3d": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatter3d"}], "scattercarpet": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattercarpet"}], "scattergeo": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergeo"}], "scattergl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergl"}], "scattermapbox": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattermapbox"}], "scatterpolar": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolar"}], "scatterpolargl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolargl"}], "scatterternary": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterternary"}], "surface": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "surface"}], "table": [{"cells": {"fill": {"color": "#EBF0F8"}, "line": {"color": "white"}}, "header": {"fill": {"color": "#C8D4E3"}, "line": {"color": "white"}}, "type": "table"}]}, "layout": {"annotationdefaults": {"arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1}, "colorscale": {"diverging": [[0, "#8e0152"], [0.1, "#c51b7d"], [0.2, "#de77ae"], [0.3, "#f1b6da"], [0.4, 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true, "showland": true, "subunitcolor": "white"}, "hoverlabel": {"align": "left"}, "hovermode": "closest", "mapbox": {"style": "light"}, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": {"angularaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "radialaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "scene": {"xaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Benchmark durations for multiple HPO settings"}},
+                        {"scene": {"xaxis": {"title": {"text": "n_draws"}}, "yaxis": {"title": {"text": "n_folds"}}, "zaxis": {"title": {"text": "Duration (s)"}}}, "template": {"data": {"bar": [{"error_x": {"color": "#2a3f5f"}, "error_y": {"color": "#2a3f5f"}, "marker": {"line": {"color": "#E5ECF6", "width": 0.5}}, "type": "bar"}], "barpolar": [{"marker": {"line": {"color": "#E5ECF6", "width": 0.5}}, "type": "barpolar"}], "carpet": [{"aaxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "baxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "type": "carpet"}], "choropleth": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "choropleth"}], "contour": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, 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[0.8, "#7fbc41"], [0.9, "#4d9221"], [1, "#276419"]], "sequential": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "sequentialminus": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]]}, "colorway": ["#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52"], "font": {"color": "#2a3f5f"}, "geo": {"bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white"}, "hoverlabel": {"align": "left"}, "hovermode": "closest", "mapbox": {"style": "light"}, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": {"angularaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "radialaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "scene": {"xaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Benchmark durations for multiple HPO settings"}},
                         {"responsive": true}
                     )
                 };
diff --git a/format_dataset.py b/format_dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..02ef73996cbdae89b035c3be7d4b860a9a962523
--- /dev/null
+++ b/format_dataset.py
@@ -0,0 +1,86 @@
+"""
+This file is provided as an example of dataset formatting, using a csv-stored
+mutliview dataset to build a SuMMIT-compatible hdf5 file.
+Please see http://baptiste.bauvin.pages.lis-lab.fr/multiview-machine-learning-omis/tutorials/example4.html
+for complementary information the example given here is fully described in the
+documentation.
+"""
+
+import numpy as np
+import h5py
+
+
+# The following variables are defined as an example, you should modify them to fit your dataset files.
+view_names = ["sound", "image", "commentary", ]
+data_file_paths = ["path/to/sound.csv", "path/to/image.csv", "path/to/commentary.csv",]
+labels_file_path = "path/to/labels/file.csv"
+example_ids_path = "path/to/example_ids/file.csv"
+labels_names = ["Human", "Animal", "Object"]
+
+
+# HDF5 dataset initialization :
+hdf5_file = h5py.File("path/to/file.hdf5", "w")
+
+# Store each view in a hdf5 dataset :
+for view_index, (file_path, view_name) in enumerate(zip(data_file_paths, view_names)):
+    # Get the view's data from the csv file
+    view_data = np.genfromtxt(file_path, delimiter=",")
+
+    # Store it in a dataset in the hdf5 file,
+    # do not modify the name of the dataset
+    view_dataset = hdf5_file.create_dataset(name="View{}".format(view_index),
+                                            shape=view_data.shape,
+                                            data=view_data)
+    # Store the name of the view in an attribute,
+    # do not modify the attribute's key
+    view_dataset.attrs["name"] = view_name
+
+    # This is an artifact of work in progress for sparse support, not available ATM,
+    # do not modify the attribute's key
+    view_dataset.attrs["sparse"] = False
+
+# Get le labels data from a csv file
+labels_data = np.genfromtxt(labels_file_path, delimiter=',')
+
+# Here, we supposed that the labels file contained numerical labels (0,1,2)
+# that refer to the label names of label_names.
+# The Labels HDF5 dataset must contain only integers that represent the
+# different classes, the names of each class are saved in an attribute
+
+# Store the integer labels in the HDF5 dataset,
+# do not modify the name of the dataset
+labels_dset = hdf5_file.create_dataset(name="Labels",
+                                       shape=labels_data.shape,
+                                       data=labels_data)
+
+# Save the labels names in an attribute as encoded strings,
+# do not modify the attribute's key
+labels_dset.attrs["names"] = [label_name.encode() for label_name in labels_names]
+
+# Create a Metadata HDF5 group to store the metadata,
+# do not modify the name of the group
+metadata_group = hdf5_file.create_group(name="Metadata")
+
+# Store the number of views in the dataset,
+# do not modify the attribute's key
+metadata_group.attrs["nbView"] = len(view_names)
+
+# Store the number of classes in the dataset,
+# do not modify the attribute's key
+metadata_group.attrs["nbClass"] = np.unique(labels_data)
+
+# Store the number of examples in the dataset,
+# do not modify the attribute's key
+metadata_group.attrs["datasetLength"] = labels_data.shape[0]
+
+# Let us suppose that the examples have string ids, available in a csv file,
+# they can be stored in the HDF5 and will be used in the result analysis.
+example_ids = np.genfromtxt(example_ids_path, delimiter=',')
+
+# To sore the strings in an HDF5 dataset, be sure to use the S<max_length> type,
+# do not modify the name of the dataset.
+metadata_group.create_dataset("example_ids",
+                              data=np.array(example_ids).astype(np.dtype("S100")),
+                              dtype=np.dtype("S100"))
+
+hdf5_file.close()
\ No newline at end of file
diff --git a/multiview-machine-learning-omis.iml b/multiview-machine-learning-omis.iml
index 44fd3b74fc86530cd201eaebb7cfa795f2c2ecca..f124452660c9d4909c004b2d55a95d8fde056580 100644
--- a/multiview-machine-learning-omis.iml
+++ b/multiview-machine-learning-omis.iml
@@ -10,6 +10,7 @@
     <orderEntry type="library" name="R Skeletons" level="application" />
     <orderEntry type="module" module-name="Datasets" />
     <orderEntry type="module" module-name="scikit-multimodallearn" />
+    <orderEntry type="module" module-name="lives_dataset" />
   </component>
   <component name="TestRunnerService">
     <option name="projectConfiguration" value="pytest" />
diff --git a/multiview_platform/examples/config_files/config_example_1.yml b/multiview_platform/examples/config_files/config_example_1.yml
index 46c27f97a83512bc12caaf658920fe5287ea4f5e..3a151b62c483ac5d09784ff8a2987b76ef1f080d 100644
--- a/multiview_platform/examples/config_files/config_example_1.yml
+++ b/multiview_platform/examples/config_files/config_example_1.yml
@@ -5,7 +5,7 @@ log: True
 # The name of each dataset in the directory on which the benchmark should be run
 name: ["plausible"]
 # A label for the resul directory
-label: "_"
+label: ""
 # The type of dataset, currently supported ".hdf5", and ".csv"
 file_type: ".hdf5"
 # The views to use in the banchmark, an empty value will result in using all the views
@@ -34,9 +34,7 @@ track_tracebacks: True
 
 # All the classification-realted configuration options
 
-# If the dataset is multiclass, will use this multiclass-to-biclass method
-multiclass_method: "oneVersusOne"
-# The ratio number of test exmaples/number of train examples
+# The ratio of test examples/number of train examples
 split: 0.8
 # The nubmer of folds in the cross validation process when hyper-paramter optimization is performed
 nb_folds: 2
@@ -54,17 +52,13 @@ algos_multiview: ["all"]
 # split, to have more statistically significant results
 stats_iter: 1
 # The metrics that will be use din the result analysis
-metrics: ["accuracy_score", "f1_score"]
+metrics:
+  accuracy_score: {}
+  f1_score:
+    average: "binary"
 # The metric that will be used in the hyper-parameter optimization process
 metric_princ: "accuracy_score"
 # The type of hyper-parameter optimization method
-hps_type: None
+hps_type: "None"
 # The number of iteration in the hyper-parameter optimization process
-hps_iter: 2
-
-
-# The following arguments are classifier-specific, and are documented in each
-# of the corresponding modules.
-
-# In order to run multiple sets of parameters, use multiple values in the
-# following lists, and set hps_type to None.
+hps_args: {}
diff --git a/multiview_platform/mono_multi_view_classifiers/exec_classif.py b/multiview_platform/mono_multi_view_classifiers/exec_classif.py
index 8d10f2e5718e2374fba1626e0c1f8a07e8409c60..a05a86078dd18383f9b0ae1860346330f17cc82b 100644
--- a/multiview_platform/mono_multi_view_classifiers/exec_classif.py
+++ b/multiview_platform/mono_multi_view_classifiers/exec_classif.py
@@ -501,7 +501,7 @@ def arange_metrics(metrics, metric_princ):
 
     Parameters
     ----------
-    metrics : list of lists
+    metrics : dict
         The metrics that will be used in the benchmark
 
     metric_princ : str
@@ -512,13 +512,11 @@ def arange_metrics(metrics, metric_princ):
     -------
     metrics : list of lists
         The metrics list, but arranged  so the first one is the principal one."""
-    if [metric_princ] in metrics:
-        metric_index = metrics.index([metric_princ])
-        first_metric = metrics[0]
-        metrics[0] = [metric_princ]
-        metrics[metric_index] = first_metric
+    if metric_princ in metrics:
+        metrics = dict((key, value) if not key==metric_princ else (key+"*", value) for key, value in metrics.items())
     else:
-        raise AttributeError(metric_princ + " not in metric pool")
+        raise AttributeError("{} not in metric pool ({})".format(metric_princ,
+                                                                 metrics))
     return metrics
 
 
@@ -874,8 +872,8 @@ def exec_classif(arguments):
     dataset_list = execution.find_dataset_names(args["pathf"],
                                                 args["file_type"],
                                                 args["name"])
-    if not args["add_noise"]:
-        args["noise_std"] = [0.0]
+    # if not args["add_noise"]:
+        # args["noise_std"] = [0.0]
     for dataset_name in dataset_list:
         noise_results = []
         for noise_std in args["noise_std"]:
diff --git a/multiview_platform/mono_multi_view_classifiers/monoview/analyze_result.py b/multiview_platform/mono_multi_view_classifiers/monoview/analyze_result.py
deleted file mode 100644
index 04c405b0d7edeedf50238534d6c54a648da333ee..0000000000000000000000000000000000000000
--- a/multiview_platform/mono_multi_view_classifiers/monoview/analyze_result.py
+++ /dev/null
@@ -1,103 +0,0 @@
-# from datetime import timedelta as hms
-#
-# from .. import metrics
-# from ..utils.base import get_metric
-#
-#
-# def get_db_config_string(name, feat, classification_indices, shape,
-#                          class_labels_names, k_folds):
-#     """
-#
-#     Parameters
-#     ----------
-#     name
-#     feat
-#     classification_indices
-#     shape
-#     class_labels_names
-#     k_folds
-#
-#     Returns
-#     -------
-#
-#     """
-#     learning_rate = float(len(classification_indices[0])) / (
-#             len(classification_indices[0]) + len(classification_indices[1]))
-#     db_config_string = "Database configuration : \n"
-#     db_config_string += "\t- Database name : " + name + "\n"
-#     db_config_string += "\t- View name : " + feat + "\t View shape : " + str(
-#         shape) + "\n"
-#     db_config_string += "\t- Learning Rate : " + str(learning_rate) + "\n"
-#     db_config_string += "\t- Labels used : " + ", ".join(
-#         class_labels_names) + "\n"
-#     db_config_string += "\t- Number of cross validation folds : " + str(
-#         k_folds.n_splits) + "\n\n"
-#     return db_config_string
-#
-#
-# def get_classifier_config_string(grid_search, nb_cores, n_iter, cl_kwargs,
-#                                  classifier,
-#                                  output_file_name, y_test):
-#     classifier_config_string = "Classifier configuration : \n"
-#     classifier_config_string += "\t- " + classifier.get_config()[5:] + "\n"
-#     classifier_config_string += "\t- Executed on " + str(
-#         nb_cores) + " core(s) \n"
-#     if grid_search:
-#         classifier_config_string += "\t- Got configuration using randomized search with " + str(
-#             n_iter) + " iterations \n"
-#     classifier_config_string += "\n\n"
-#     classifier_interpret_string = classifier.get_interpretation(
-#         output_file_name,
-#         y_test)
-#     return classifier_config_string, classifier_interpret_string
-#
-#
-# def get_metric_score(metric, y_train, y_train_pred, y_test, y_test_pred):
-#     metric_module = getattr(metrics, metric[0])
-#     if metric[1] is not None:
-#         metric_kwargs = dict((index, metricConfig) for index, metricConfig in
-#                              enumerate(metric[1]))
-#     else:
-#         metric_kwargs = {}
-#     metric_score_train = metric_module.score(y_train, y_train_pred)
-#     metric_score_test = metric_module.score(y_test, y_test_pred)
-#     metric_score_string = "\tFor " + metric_module.get_config(
-#         **metric_kwargs) + " : "
-#     metric_score_string += "\n\t\t- Score on train : " + str(metric_score_train)
-#     metric_score_string += "\n\t\t- Score on test : " + str(metric_score_test)
-#     metric_score_string += "\n"
-#     return metric_score_string, [metric_score_train, metric_score_test]
-#
-#
-# def execute(name, learning_rate, k_folds, nb_cores, grid_search, metrics_list,
-#             n_iter,
-#             feat, cl_type, cl_kwargs, class_labels_names,
-#             shape, y_train, y_train_pred, y_test, y_test_pred, time,
-#             random_state, classifier, output_file_name):
-#     metric_module, metric_kwargs = get_metric(metrics_list)
-#     train_score = metric_module.score(y_train, y_train_pred)
-#     test_score = metric_module.score(y_test, y_test_pred)
-#     string_analysis = "Classification on " + name + " database for " + feat + " with " + cl_type + ".\n\n"
-#     string_analysis += metrics_list[0][0] + " on train : " + str(
-#         train_score) + "\n" + \
-#                        metrics_list[0][0] + " on test : " + str(
-#         test_score) + "\n\n"
-#     string_analysis += get_db_config_string(name, feat, learning_rate, shape,
-#                                             class_labels_names, k_folds)
-#     classifier_config_string, classifier_intepret_string = get_classifier_config_string(
-#         grid_search, nb_cores, n_iter, cl_kwargs, classifier, output_file_name,
-#         y_test)
-#     string_analysis += classifier_config_string
-#     metrics_scores = {}
-#     for metric in metrics_list:
-#         metric_string, metric_score = get_metric_score(metric, y_train,
-#                                                        y_train_pred, y_test,
-#                                                        y_test_pred)
-#         string_analysis += metric_string
-#         metrics_scores[metric[0]] = metric_score
-#     string_analysis += "\n\n Classification took " + str(hms(seconds=int(time)))
-#     string_analysis += "\n\n Classifier Interpretation : \n"
-#     string_analysis += classifier_intepret_string
-#
-#     image_analysis = {}
-#     return string_analysis, image_analysis, metrics_scores
diff --git a/multiview_platform/mono_multi_view_classifiers/monoview/exec_classif_mono_view.py b/multiview_platform/mono_multi_view_classifiers/monoview/exec_classif_mono_view.py
index 0e1cd183b0e19ce4bf5b1d0dd015cce845376388..418356507531feb9b46194122d3963635534ff89 100644
--- a/multiview_platform/mono_multi_view_classifiers/monoview/exec_classif_mono_view.py
+++ b/multiview_platform/mono_multi_view_classifiers/monoview/exec_classif_mono_view.py
@@ -53,7 +53,7 @@ def exec_monoview_multicore(directory, name, labels_names,
 def exec_monoview(directory, X, Y, database_name, labels_names, classification_indices,
                   k_folds, nb_cores, databaseType, path,
                   random_state, hyper_param_search="randomized_search",
-                  metrics=[["accuracy_score", None]], n_iter=30, view_name="",
+                  metrics={"accuracy_score":{}}, n_iter=30, view_name="",
                   hps_kwargs={}, **args):
     logging.debug("Start:\t Loading data")
     kwargs, \
@@ -140,30 +140,30 @@ def exec_monoview(directory, X, Y, database_name, labels_names, classification_i
                                              classification_indices=classification_indices,
                                              k_folds=k_folds,
                                              hps_method=hyper_param_search,
-                                             metrics_list=metrics,
+                                             metrics_dict=metrics,
                                              n_iter=n_iter,
                                              class_label_names=labels_names,
-                                             train_pred=train_pred,
-                                             test_pred=test_pred,
+                                             pred=full_pred,
                                              directory=directory,
                                              base_file_name=base_file_name,
                                              labels=Y,
                                              database_name=database_name,
                                              nb_cores=nb_cores,
                                              duration=whole_duration)
-    string_analysis, images_analysis, metrics_scores = result_analyzer.analyze()
+    string_analysis, images_analysis, metrics_scores, class_metrics_scores, \
+    confusion_matrix = result_analyzer.analyze()
     logging.debug("Done:\t Getting results")
 
     logging.debug("Start:\t Saving preds")
     save_results(string_analysis, output_file_name, full_pred, train_pred,
-                 y_train, images_analysis, y_test)
+                 y_train, images_analysis, y_test, confusion_matrix)
     logging.info("Done:\t Saving results")
 
     view_index = args["view_index"]
     return MonoviewResult(view_index, classifier_name, view_name,
                           metrics_scores, full_pred, cl_kwargs,
                           classifier, X_train.shape[1],
-                          hyper_param_duration, fit_duration, pred_duration)
+                          hyper_param_duration, fit_duration, pred_duration, class_metrics_scores)
 
 
 def init_constants(args, X, classification_indices, labels_names,
@@ -223,11 +223,13 @@ def get_hyper_params(classifier_module, search_method, classifier_module_name,
 
 def save_results(string_analysis, output_file_name, full_labels_pred,
                  y_train_pred,
-                 y_train, images_analysis, y_test):
+                 y_train, images_analysis, y_test, confusion_matrix):
     logging.info(string_analysis)
     output_text_file = open(output_file_name + 'summary.txt', 'w')
     output_text_file.write(string_analysis)
     output_text_file.close()
+    np.savetxt(output_file_name+"confusion_matrix.csv", confusion_matrix,
+               delimiter=', ')
     np.savetxt(output_file_name + "full_pred.csv",
                full_labels_pred.astype(np.int16), delimiter=",")
     np.savetxt(output_file_name + "train_pred.csv",
diff --git a/multiview_platform/mono_multi_view_classifiers/monoview/monoview_utils.py b/multiview_platform/mono_multi_view_classifiers/monoview/monoview_utils.py
index 84540ab38d984bdafba615f235477e690d0a1ce4..076044b8426e3aefd409f9c8fe0286c3f2e103c8 100644
--- a/multiview_platform/mono_multi_view_classifiers/monoview/monoview_utils.py
+++ b/multiview_platform/mono_multi_view_classifiers/monoview/monoview_utils.py
@@ -159,7 +159,7 @@ class MonoviewResult(object):
     def __init__(self, view_index, classifier_name, view_name, metrics_scores,
                  full_labels_pred, classifier_config,
                  classifier, n_features, hps_duration, fit_duration,
-                 pred_duration):
+                 pred_duration, class_metric_scores):
         self.view_index = view_index
         self.classifier_name = classifier_name
         self.view_name = view_name
@@ -171,6 +171,7 @@ class MonoviewResult(object):
         self.hps_duration = hps_duration
         self.fit_duration = fit_duration
         self.pred_duration = pred_duration
+        self.class_metric_scores = class_metric_scores
 
     def get_classifier_name(self):
         return self.classifier_name + "-" + self.view_name
@@ -207,12 +208,12 @@ def get_accuracy_graph(plotted_data, classifier_name, file_name,
 class MonoviewResultAnalyzer(ResultAnalyser):
 
     def __init__(self, view_name, classifier_name, shape, classifier,
-                 classification_indices, k_folds, hps_method, metrics_list,
-                 n_iter, class_label_names, train_pred, test_pred,
+                 classification_indices, k_folds, hps_method, metrics_dict,
+                 n_iter, class_label_names, pred,
                  directory, base_file_name, labels, database_name, nb_cores, duration):
         ResultAnalyser.__init__(self, classifier, classification_indices,
-                                k_folds, hps_method, metrics_list, n_iter,
-                                class_label_names, train_pred, test_pred,
+                                k_folds, hps_method, metrics_dict, n_iter,
+                                class_label_names, pred,
                                 directory, base_file_name, labels,
                                 database_name, nb_cores, duration)
         self.view_name = view_name
diff --git a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/adaboost.py b/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/adaboost.py
index 8f5e55a376582a90b784354408b43d5e38553f30..6fbfbd8b5998a54df863e26aab82e515a74269dc 100644
--- a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/adaboost.py
+++ b/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/adaboost.py
@@ -124,10 +124,8 @@ class Adaboost(AdaBoostClassifier, BaseMonoviewClassifier):
         pred = AdaBoostClassifier.predict(self, X)
         end = time.time()
         self.pred_time = end - begin
-        # TODO : mauvaise verif
-        if X.shape != self.train_shape:
-            self.step_predictions = np.array(
-                [step_pred for step_pred in self.staged_predict(X)])
+        self.step_predictions = np.array(
+            [step_pred for step_pred in self.staged_predict(X)])
         return pred
 
     def get_interpretation(self, directory, base_file_name, y_test, multi_class=False):
diff --git a/multiview_platform/mono_multi_view_classifiers/multiview/analyze_results.py b/multiview_platform/mono_multi_view_classifiers/multiview/analyze_results.py
deleted file mode 100644
index b24cdc3fcfb17c01e975bca843ce8653b02efc31..0000000000000000000000000000000000000000
--- a/multiview_platform/mono_multi_view_classifiers/multiview/analyze_results.py
+++ /dev/null
@@ -1,152 +0,0 @@
-# from .. import metrics
-#
-# from ..utils.base import get_metric
-#
-# # Author-Info
-# __author__ = "Baptiste Bauvin"
-# __status__ = "Prototype"  # Production, Development, Prototype
-#
-#
-# def print_metric_score(metric_scores, metric_list):
-#     """
-#     this function print the metrics scores
-#
-#     Parameters
-#     ----------
-#     metric_scores : the score of metrics
-#
-#     metric_list : list of metrics
-#
-#     Returns
-#     -------
-#     metric_score_string string constaining all metric results
-#     """
-#     metric_score_string = "\n\n"
-#     for metric in metric_list:
-#         metric_module = getattr(metrics, metric[0])
-#         if metric[1] is not None:
-#             metric_kwargs = dict(
-#                 (index, metricConfig) for index, metricConfig in
-#                 enumerate(metric[1]))
-#         else:
-#             metric_kwargs = {}
-#         metric_score_string += "\tFor " + metric_module.get_config(
-#             **metric_kwargs) + " : "
-#         metric_score_string += "\n\t\t- Score on train : " + str(
-#             metric_scores[metric[0]][0])
-#         metric_score_string += "\n\t\t- Score on test : " + str(
-#             metric_scores[metric[0]][1])
-#         metric_score_string += "\n\n"
-#     return metric_score_string
-#
-#
-# def get_total_metric_scores(metric, train_labels, test_labels,
-#                             validation_indices,
-#                             learning_indices, labels):
-#     """
-#
-#     Parameters
-#     ----------
-#
-#     metric :
-#
-#     train_labels : labels of train
-#
-#     test_labels :  labels of test
-#
-#     validation_indices :
-#
-#     learning_indices :
-#
-#     labels :
-#
-#     Returns
-#     -------
-#     list of [train_score, test_score]
-#     """
-#     metric_module = getattr(metrics, metric[0])
-#     if metric[1] is not None:
-#         metric_kwargs = dict((index, metricConfig) for index, metricConfig in
-#                              enumerate(metric[1]))
-#     else:
-#         metric_kwargs = {}
-#     train_score = metric_module.score(labels[learning_indices], train_labels,
-#                                       **metric_kwargs)
-#     test_score = metric_module.score(labels[validation_indices], test_labels,
-#                                      **metric_kwargs)
-#     return [train_score, test_score]
-#
-#
-# def get_metrics_scores(metrics, train_labels, test_labels,
-#                        validation_indices, learning_indices, labels):
-#     metrics_scores = {}
-#     for metric in metrics:
-#         metrics_scores[metric[0]] = get_total_metric_scores(metric,
-#                                                             train_labels,
-#                                                             test_labels,
-#                                                             validation_indices,
-#                                                             learning_indices,
-#                                                             labels)
-#     return metrics_scores
-#
-#
-# def execute(classifier, pred_train_labels, pred_test_labels,
-#             classification_indices, labels_dictionary, views, name, k_folds,
-#             metrics_list, labels, directory):
-#     """
-#
-#     Parameters
-#     ----------
-#     classifier : classifier used
-#
-#     pred_train_labels : labels of train
-#
-#     pred_test_labels : labels of test
-#
-#     classification_indices
-#
-#     labels_dictionary
-#
-#     views
-#
-#     name
-#
-#     k_folds
-#
-#     metrics_list
-#
-#     labels
-#
-#     Returns
-#     -------
-#     return tuple of (string_analysis, images_analysis, metricsScore)
-#     """
-#     classifier_name = classifier.short_name
-#     learning_indices, validation_indices = classification_indices
-#     metric_module, metric_kwargs = get_metric(metrics_list)
-#     score_on_train = metric_module.score(labels[learning_indices],
-#                                          pred_train_labels,
-#                                          **metric_kwargs)
-#     score_on_test = metric_module.score(labels[validation_indices],
-#                                         pred_test_labels, **metric_kwargs)
-#
-#     string_analysis = "\t\tResult for multiview classification with " + classifier_name + \
-#                       "\n\n" + metrics_list[0][0] + " :\n\t-On Train : " + str(
-#         score_on_train) + "\n\t-On Test : " + str(
-#         score_on_test) + \
-#                       "\n\nDataset info :\n\t-Database name : " + name + "\n\t-Labels : " + \
-#                       ', '.join(
-#                           labels_dictionary.values()) + "\n\t-Views : " + ', '.join(
-#         views) + "\n\t-" + str(
-#         k_folds.n_splits) + \
-#                       " folds\n\nClassification configuration : \n\t-Algorithm used : " + classifier_name + " with : " + classifier.get_config()
-#
-#     metrics_scores = get_metrics_scores(metrics_list, pred_train_labels,
-#                                         pred_test_labels,
-#                                         validation_indices, learning_indices,
-#                                         labels)
-#     string_analysis += print_metric_score(metrics_scores, metrics_list)
-#     string_analysis += "\n\n Interpretation : \n\n" + classifier.get_interpretation(
-#         directory, labels)
-#     images_analysis = {}
-#     return string_analysis, images_analysis, metrics_scores
diff --git a/multiview_platform/mono_multi_view_classifiers/multiview/exec_multiview.py b/multiview_platform/mono_multi_view_classifiers/multiview/exec_multiview.py
index f1b23d9a5ceb3e6bf26505e05841b943d43d61f1..f300d63ad89b52970f124a63a8af697a0260cb91 100644
--- a/multiview_platform/mono_multi_view_classifiers/multiview/exec_multiview.py
+++ b/multiview_platform/mono_multi_view_classifiers/multiview/exec_multiview.py
@@ -71,7 +71,8 @@ def init_constants(kwargs, classification_indices, metrics,
            directory, base_file_name
 
 
-def save_results(string_analysis, images_analysis, output_file_name):
+def save_results(string_analysis, images_analysis, output_file_name,
+                 confusion_matrix):
     """
     Save results in derectory
 
@@ -104,6 +105,8 @@ def save_results(string_analysis, images_analysis, output_file_name):
     output_text_file = open(output_file_name + 'summary.txt', 'w')
     output_text_file.write(string_analysis)
     output_text_file.close()
+    np.savetxt(output_file_name+"confusion_matrix.csv", confusion_matrix,
+               delimiter=',')
 
     if images_analysis is not None:
         for image_name in images_analysis.keys():
@@ -311,9 +314,9 @@ def exec_multiview(directory, dataset_var, name, classification_indices,
                                           example_indices=validation_indices,
                                           view_indices=views_indices)
     pred_duration = time.monotonic() - pred_beg
-    full_labels = np.zeros(dataset_var.get_labels().shape, dtype=int) - 100
-    full_labels[learning_indices] = train_pred
-    full_labels[validation_indices] = test_pred
+    full_pred = np.zeros(dataset_var.get_labels().shape, dtype=int) - 100
+    full_pred[learning_indices] = train_pred
+    full_pred[validation_indices] = test_pred
     logging.info("Done:\t Pertidcting")
 
     whole_duration = time.time() - t_start
@@ -329,24 +332,24 @@ def exec_multiview(directory, dataset_var, name, classification_indices,
                                               classification_indices=classification_indices,
                                               k_folds=k_folds,
                                               hps_method=hps_method,
-                                              metrics_list=metrics,
+                                              metrics_dict=metrics,
                                               n_iter=n_iter,
                                               class_label_names=list(labels_dictionary.values()),
-                                              train_pred=train_pred,
-                                              test_pred=test_pred,
+                                              pred=full_pred,
                                               directory=directory,
                                               base_file_name=base_file_name,
                                               labels=labels,
                                               database_name=dataset_var.get_name(),
                                               nb_cores=nb_cores,
                                               duration=whole_duration)
-    string_analysis, images_analysis, metrics_scores = result_analyzer.analyze()
+    string_analysis, images_analysis, metrics_scores, class_metrics_scores, \
+    confusion_matrix = result_analyzer.analyze()
     logging.info("Done:\t Result Analysis for " + cl_type)
 
     logging.debug("Start:\t Saving preds")
-    save_results(string_analysis, images_analysis, output_file_name)
+    save_results(string_analysis, images_analysis, output_file_name, confusion_matrix)
     logging.debug("Start:\t Saving preds")
 
     return MultiviewResult(cl_type, classifier_config, metrics_scores,
-                           full_labels, hps_duration, fit_duration,
-                           pred_duration)
+                           full_pred, hps_duration, fit_duration,
+                           pred_duration, class_metrics_scores)
diff --git a/multiview_platform/mono_multi_view_classifiers/multiview/multiview_utils.py b/multiview_platform/mono_multi_view_classifiers/multiview/multiview_utils.py
index 46ffe37a843c90b39d9bfede14a191484de78f1c..fd53ed5c8ee365c1956788c2a5edf096bf589fd9 100644
--- a/multiview_platform/mono_multi_view_classifiers/multiview/multiview_utils.py
+++ b/multiview_platform/mono_multi_view_classifiers/multiview/multiview_utils.py
@@ -1,6 +1,8 @@
 from abc import abstractmethod
 
 import numpy as np
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.base import BaseEstimator
 
 from .. import monoview_classifiers
 from ..utils.base import BaseClassifier, ResultAnalyser
@@ -31,6 +33,34 @@ class BaseMultiviewClassifier(BaseClassifier):
         self.weird_strings = {}
         self.used_views = None
 
+    def set_base_estim_from_dict(self, base_estim_dict, **kwargs):
+        if base_estim_dict is None:
+            base_estimator = DecisionTreeClassifier()
+        elif isinstance(base_estim_dict, str) and kwargs is not None:
+            estim_name = base_estim_dict
+            estim_module = getattr(monoview_classifiers, estim_name)
+            estim_class = getattr(estim_module,
+                                  estim_module.classifier_class_name)
+            base_estim_params = {}
+            for key, value in kwargs.items():
+                key, delim, sub_key = key.partition('__')
+                if key == "base_estimator":
+                    base_estim_params[sub_key] = value
+            base_estimator = estim_class(**base_estim_params)
+        elif isinstance(base_estim_dict, dict):
+            estim_name = next(iter(base_estim_dict))
+            estim_module = getattr(monoview_classifiers, estim_name)
+            estim_class = getattr(estim_module,
+                                  estim_module.classifier_class_name)
+            base_estimator = estim_class(**base_estim_dict[estim_name])
+        elif isinstance(base_estim_dict, BaseEstimator):
+            base_estimator = base_estim_dict
+        else:
+            raise ValueError("base_estimator should be either None, a dictionary"
+                             " or a BaseEstimator child object, "
+                             "here it is {}".format(type(base_estim_dict)))
+        return base_estimator
+
     @abstractmethod
     def fit(self, X, y, train_indices=None, view_indices=None):
         pass
@@ -62,7 +92,7 @@ class BaseMultiviewClassifier(BaseClassifier):
                             n_classes=3, n_views=2):
         if int(n_samples / n_classes) < 1:
             raise ValueError(
-                "n_samples ({}) / n_classe ({}) must be over 1".format(
+                "n_samples ({}) / n_classes ({}) must be over 1".format(
                     n_samples,
                     n_classes))
         fake_mc_X = RAMDataset(
@@ -152,7 +182,7 @@ from .. import multiview_classifiers
 class MultiviewResult(object):
     def __init__(self, classifier_name, classifier_config,
                  metrics_scores, full_labels, hps_duration, fit_duration,
-                 pred_duration):
+                 pred_duration, class_metric_scores):
         self.classifier_name = classifier_name
         self.classifier_config = classifier_config
         self.metrics_scores = metrics_scores
@@ -160,6 +190,7 @@ class MultiviewResult(object):
         self.hps_duration = hps_duration
         self.fit_duration = fit_duration
         self.pred_duration = pred_duration
+        self.class_metric_scores = class_metric_scores
 
     def get_classifier_name(self):
         try:
@@ -176,14 +207,14 @@ class MultiviewResult(object):
 class MultiviewResultAnalyzer(ResultAnalyser):
 
     def __init__(self, view_names, classifier, classification_indices, k_folds,
-                 hps_method, metrics_list, n_iter, class_label_names,
-                 train_pred, test_pred, directory, base_file_name, labels,
+                 hps_method, metrics_dict, n_iter, class_label_names,
+                 pred, directory, base_file_name, labels,
                  database_name, nb_cores, duration):
         if hps_method.endswith("equiv"):
             n_iter = n_iter*len(view_names)
         ResultAnalyser.__init__(self, classifier, classification_indices, k_folds,
-                                hps_method, metrics_list, n_iter, class_label_names,
-                                train_pred, test_pred, directory,
+                                hps_method, metrics_dict, n_iter, class_label_names,
+                                pred, directory,
                                 base_file_name, labels, database_name,
                                 nb_cores, duration)
         self.classifier_name = classifier.short_name
diff --git a/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/additions/fusion_utils.py b/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/additions/fusion_utils.py
index 7ad8a76bb9678127919d2aa6b9210f7147f03675..17ec74012dc729ba67bf43723b3a7acb29edbcc7 100644
--- a/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/additions/fusion_utils.py
+++ b/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/additions/fusion_utils.py
@@ -9,7 +9,7 @@ class BaseFusionClassifier():
     def init_monoview_estimator(self, classifier_name, classifier_config,
                                 classifier_index=None, multiclass=False):
         if classifier_index is not None:
-            if classifier_config is not None:
+            if classifier_config is not None and classifier_name in classifier_config:
                 classifier_configs = classifier_config[classifier_name]
             else:
                 classifier_configs = None
diff --git a/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/mucombo.py b/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/mucombo.py
index d787e41cf99c7b20d105e74c6b6a83462a4275a6..ec4973e342c66e36d38c2ea9c15420fc8363cbd9 100644
--- a/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/mucombo.py
+++ b/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/mucombo.py
@@ -5,6 +5,7 @@ from multimodal.boosting.cumbo import MuCumboClassifier
 from ..multiview.multiview_utils import BaseMultiviewClassifier
 from ..utils.hyper_parameter_search import CustomRandint
 from ..utils.dataset import get_examples_views_indices
+from ..utils.base import base_boosting_estimators
 
 classifier_class_name = "MuCumbo"
 
@@ -13,13 +14,14 @@ class MuCumbo(BaseMultiviewClassifier, MuCumboClassifier):
 
     def __init__(self, base_estimator=None,
                  n_estimators=50,
-                 random_state=None,):
+                 random_state=None,**kwargs):
         BaseMultiviewClassifier.__init__(self, random_state)
+        base_estimator = self.set_base_estim_from_dict(base_estimator, **kwargs)
         MuCumboClassifier.__init__(self, base_estimator=base_estimator,
                                     n_estimators=n_estimators,
                                     random_state=random_state,)
         self.param_names = ["base_estimator", "n_estimators", "random_state",]
-        self.distribs = [[DecisionTreeClassifier(max_depth=1)],
+        self.distribs = [base_boosting_estimators,
                          CustomRandint(5,200), [random_state],]
 
     def fit(self, X, y, train_indices=None, view_indices=None):
@@ -40,3 +42,7 @@ class MuCumbo(BaseMultiviewClassifier, MuCumboClassifier):
         numpy_X, view_limits = X.to_numpy_array(example_indices=example_indices,
                                                 view_indices=view_indices)
         return MuCumboClassifier.predict(self, numpy_X)
+
+    def get_interpretation(self, directory, base_file_name, labels,
+                           multiclass=False):
+        return ""
diff --git a/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/mumbo.py b/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/mumbo.py
index 024c0e7cd75aaee29c9cd61e5d67a1394e79ab27..0fc63fb4416bbd68a7af74e6d6cc6e4620dde32e 100644
--- a/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/mumbo.py
+++ b/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/mumbo.py
@@ -1,5 +1,4 @@
 from sklearn.tree import DecisionTreeClassifier
-from  sklearn.base import BaseEstimator
 import numpy as np
 import os
 
@@ -19,9 +18,9 @@ class Mumbo(BaseMultiviewClassifier, MumboClassifier):
     def __init__(self, base_estimator=None,
                  n_estimators=50,
                  random_state=None,
-                 best_view_mode="edge"):
+                 best_view_mode="edge", **kwargs):
         BaseMultiviewClassifier.__init__(self, random_state)
-        base_estimator = self.set_base_estim_from_dict(base_estimator)
+        base_estimator = self.set_base_estim_from_dict(base_estimator, **kwargs)
         MumboClassifier.__init__(self, base_estimator=base_estimator,
                                     n_estimators=n_estimators,
                                     random_state=random_state,
@@ -30,23 +29,6 @@ class Mumbo(BaseMultiviewClassifier, MumboClassifier):
         self.distribs = [base_boosting_estimators,
                          CustomRandint(5,200), [random_state], ["edge", "error"]]
 
-    def set_base_estim_from_dict(self, base_estim_dict):
-        if base_estim_dict is None:
-            base_estimator = DecisionTreeClassifier()
-        elif isinstance(base_estim_dict, dict):
-            estim_name = next(iter(base_estim_dict))
-            estim_module = getattr(monoview_classifiers, estim_name)
-            estim_class = getattr(estim_module,
-                                  estim_module.classifier_class_name)
-            base_estimator = estim_class(**base_estim_dict[estim_name])
-        elif isinstance(base_estim_dict, BaseEstimator):
-            base_estimator = base_estim_dict
-        else:
-            raise ValueError("base_estimator should be either None, a dictionary"
-                             " or a BaseEstimator child object, "
-                             "here it is {}".format(type(base_estim_dict)))
-        return base_estimator
-
     def set_params(self, base_estimator=None, **params):
         """
         Sets the base estimator from a dict.
diff --git a/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/weighted_linear_early_fusion.py b/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/weighted_linear_early_fusion.py
index eaa8ce34e2ffd6ab4e811638d94d1211b5c94faf..d04b2ef71c7dff45a7e1a2123979de5309941ade 100644
--- a/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/weighted_linear_early_fusion.py
+++ b/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/weighted_linear_early_fusion.py
@@ -33,7 +33,7 @@ class WeightedLinearEarlyFusion(BaseMultiviewClassifier, BaseFusionClassifier):
         BaseMultiviewClassifier.__init__(self, random_state=random_state)
         self.view_weights = view_weights
         self.monoview_classifier_name = monoview_classifier_name
-        self.short_name = "early_fusion_" + self.monoview_classifier_name
+        self.short_name = "early_fusion"
         if monoview_classifier_name in monoview_classifier_config:
             self.monoview_classifier_config = monoview_classifier_config[
                 monoview_classifier_name]
@@ -57,7 +57,7 @@ class WeightedLinearEarlyFusion(BaseMultiviewClassifier, BaseFusionClassifier):
             monoview_classifier_name,
             monoview_classifier_config)
         self.monoview_classifier_config = self.monoview_classifier.get_params()
-        self.short_name = "early_fusion_" + self.monoview_classifier_name
+        self.short_name = "early_fusion"
         return self
 
     def get_params(self, deep=True):
diff --git a/multiview_platform/mono_multi_view_classifiers/result_analysis/error_analysis.py b/multiview_platform/mono_multi_view_classifiers/result_analysis/error_analysis.py
index fd18a3b02edc250479a688084ec4759c7dde6e5f..103dd50d2cc4d93a655bc63eb455127277acc867 100644
--- a/multiview_platform/mono_multi_view_classifiers/result_analysis/error_analysis.py
+++ b/multiview_platform/mono_multi_view_classifiers/result_analysis/error_analysis.py
@@ -50,8 +50,7 @@ def publish_example_errors(example_errors, directory, databaseName,
                            labels_names, example_ids, labels):
     logging.debug("Start:\t Biclass Label analysis figure generation")
 
-    base_file_name = os.path.join(directory, databaseName + "-" + "_vs_".join(
-        labels_names) + "-")
+    base_file_name = os.path.join(directory, databaseName + "-" )
 
     nb_classifiers, nb_examples, classifiers_names, \
     data_2d, error_on_examples = gen_error_data(example_errors)
diff --git a/multiview_platform/mono_multi_view_classifiers/result_analysis/execution.py b/multiview_platform/mono_multi_view_classifiers/result_analysis/execution.py
index c62425c945d6f26747d67d860b89155913a33fb8..88392cd673ef4a701b8e796f513d27f47db6001d 100644
--- a/multiview_platform/mono_multi_view_classifiers/result_analysis/execution.py
+++ b/multiview_platform/mono_multi_view_classifiers/result_analysis/execution.py
@@ -12,7 +12,7 @@ def analyze(results, stats_iter, benchmark_argument_dictionaries,
     """Used to analyze the results of the previous benchmarks"""
     data_base_name = benchmark_argument_dictionaries[0]["args"]["name"]
 
-    results_means_std, iter_results, flagged_failed = analyze_iterations(
+    results_means_std, iter_results, flagged_failed, label_names = analyze_iterations(
         results, benchmark_argument_dictionaries,
         stats_iter, metrics, example_ids, labels)
     if flagged_failed:
@@ -21,7 +21,7 @@ def analyze(results, stats_iter, benchmark_argument_dictionaries,
     if stats_iter > 1:
         results_means_std = analyze_all(
             iter_results, stats_iter, directory,
-            data_base_name, example_ids)
+            data_base_name, example_ids, label_names)
     return results_means_std
 
 
@@ -62,6 +62,7 @@ def analyze_iterations(results, benchmark_argument_dictionaries, stats_iter,
     """
     logging.debug("Start:\t Analyzing all results")
     iter_results = {"metrics_scores": [i for i in range(stats_iter)],
+                    "class_metrics_scores": [i for i in range(stats_iter)],
                     "example_errors": [i for i in range(stats_iter)],
                     "feature_importances": [i for i in range(stats_iter)],
                     "durations":[i for i in range(stats_iter)]}
@@ -69,22 +70,22 @@ def analyze_iterations(results, benchmark_argument_dictionaries, stats_iter,
     fig_errors = []
     for iter_index, result, tracebacks in results:
         arguments = get_arguments(benchmark_argument_dictionaries, iter_index)
+        labels_names = list(arguments["labels_dictionary"].values())
 
-        metrics_scores = get_metrics_scores(metrics, result)
+        metrics_scores, class_metric_scores = get_metrics_scores(metrics, result, labels_names)
         example_errors = get_example_errors(labels, result)
         feature_importances = get_feature_importances(result)
         durations = get_duration(result)
         directory = arguments["directory"]
 
         database_name = arguments["args"]["name"]
-        labels_names = [arguments["labels_dictionary"][0],
-                        arguments["labels_dictionary"][1]]
+
 
         flagged_tracebacks_list += publish_tracebacks(directory, database_name,
                                                       labels_names, tracebacks,
                                                       iter_index)
         res = publish_metrics_graphs(metrics_scores, directory, database_name,
-                                     labels_names)
+                                     labels_names, class_metric_scores)
         publish_example_errors(example_errors, directory, database_name,
                                labels_names, example_ids, labels)
         publish_feature_importances(feature_importances, directory,
@@ -92,6 +93,7 @@ def analyze_iterations(results, benchmark_argument_dictionaries, stats_iter,
         plot_durations(durations, directory, database_name)
 
         iter_results["metrics_scores"][iter_index] = metrics_scores
+        iter_results["class_metrics_scores"][iter_index] = class_metric_scores
         iter_results["example_errors"][iter_index] = example_errors
         iter_results["feature_importances"][iter_index] = feature_importances
         iter_results["labels"] = labels
@@ -99,19 +101,20 @@ def analyze_iterations(results, benchmark_argument_dictionaries, stats_iter,
 
     logging.debug("Done:\t Analyzing all results")
 
-    return res, iter_results, flagged_tracebacks_list
+    return res, iter_results, flagged_tracebacks_list, labels_names
+
 
 def analyze_all(iter_results, stats_iter, directory, data_base_name,
-                example_ids):
+                example_ids, label_names):
     """Used to format the results in order to plot the mean results on
     the iterations"""
-    metrics_analysis, error_analysis, feature_importances, \
+    metrics_analysis, class_metrics_analysis, error_analysis, feature_importances, \
     feature_importances_stds, labels, duration_means, \
     duration_stds = format_previous_results(iter_results)
 
-    results = publish_all_metrics_scores(metrics_analysis,
+    results = publish_all_metrics_scores(metrics_analysis, class_metrics_analysis,
                                          directory,
-                                         data_base_name, stats_iter)
+                                         data_base_name, stats_iter, label_names)
     publish_all_example_errors(error_analysis, directory, stats_iter,
                                example_ids, labels)
     publish_feature_importances(feature_importances, directory,
@@ -166,6 +169,7 @@ def format_previous_results(iter_results_lists):
 
     """
     metrics_analysis = {}
+    class_metrics_analysis = {}
     feature_importances_analysis = {}
     feature_importances_stds = {}
 
@@ -186,6 +190,23 @@ def format_previous_results(iter_results_lists):
         metrics_analysis[metric_name][
             "std"] = dataframe.groupby(dataframe.index).std(ddof=0)
 
+    class_metric_concat_dict = {}
+    for iter_index, class_metrics_score in enumerate(
+            iter_results_lists["class_metrics_scores"]):
+        for metric_name, dataframe in class_metrics_score.items():
+            if metric_name not in class_metric_concat_dict:
+                class_metric_concat_dict[metric_name] = dataframe
+            else:
+                class_metric_concat_dict[metric_name] = pd.concat(
+                    [class_metric_concat_dict[metric_name], dataframe])
+
+    for metric_name, dataframe in class_metric_concat_dict.items():
+        class_metrics_analysis[metric_name] = {}
+        class_metrics_analysis[metric_name][
+            "mean"] = dataframe.groupby(dataframe.index).mean()
+        class_metrics_analysis[metric_name][
+            "std"] = dataframe.groupby(dataframe.index).std(ddof=0)
+
     durations_df_concat = pd.DataFrame(dtype=float)
     for iter_index, durations_df in enumerate(iter_results_lists["durations"]):
         durations_df_concat = pd.concat((durations_df_concat, durations_df),
@@ -220,6 +241,7 @@ def format_previous_results(iter_results_lists):
             else:
                 added_example_errors[classifier_name] += errors
     error_analysis = added_example_errors
-    return metrics_analysis, error_analysis, feature_importances_analysis, \
+    return metrics_analysis, class_metrics_analysis ,error_analysis, \
+           feature_importances_analysis, \
            feature_importances_stds, iter_results_lists["labels"], \
            duration_means, duration_stds
diff --git a/multiview_platform/mono_multi_view_classifiers/result_analysis/metric_analysis.py b/multiview_platform/mono_multi_view_classifiers/result_analysis/metric_analysis.py
index 3ab3f8a156aac6632ec23e6dea08c448e9464156..32ac48301385abe797db5b9ffe893bb9ca853045 100644
--- a/multiview_platform/mono_multi_view_classifiers/result_analysis/metric_analysis.py
+++ b/multiview_platform/mono_multi_view_classifiers/result_analysis/metric_analysis.py
@@ -7,12 +7,13 @@ import logging
 
 from ..utils.organization import secure_file_path
 
-def get_metrics_scores(metrics, results):
+
+def get_metrics_scores(metrics, results, label_names):
     r"""Used to extract metrics scores in case of classification
 
     Parameters
     ----------
-    metrics : list of lists
+    metrics : dict
         The metrics names with configuration metrics[i][0] = name of metric i
     results : list of MonoviewResult and MultiviewResults objects
         A list containing all the results for all the monoview experimentations.
@@ -35,27 +36,38 @@ def get_metrics_scores(metrics, results):
                         for classifier_result in results
                         if classifier_result.get_classifier_name()
                         not in classifier_names]
-    metrics_scores = dict((metric[0], pd.DataFrame(data=np.zeros((2,
+    metrics_scores = dict((metric, pd.DataFrame(data=np.zeros((2,
                                                                   len(
                                                                       classifier_names))),
                                                    index=["train", "test"],
                                                    columns=classifier_names))
-                          for metric in metrics)
-
-    for metric in metrics:
-        for classifierResult in results:
-            metrics_scores[metric[0]].loc[
-                "train", classifierResult.get_classifier_name()] = \
-            classifierResult.metrics_scores[metric[0]][0]
-            metrics_scores[metric[0]].loc[
-                "test", classifierResult.get_classifier_name()] = \
-                classifierResult.metrics_scores[metric[0]][1]
-
-    return metrics_scores
+                          for metric in metrics.keys())
+    class_metric_scores = dict((metric, pd.DataFrame(
+        index=pd.MultiIndex.from_product([["train", "test"], label_names]),
+        columns=classifier_names, dtype=float))
+                               for metric in metrics)
+
+    for metric in metrics.keys():
+        for classifier_result in results:
+            metrics_scores[metric].loc[
+                "train", classifier_result.get_classifier_name()] = \
+            classifier_result.metrics_scores[metric][0]
+            metrics_scores[metric].loc[
+                "test", classifier_result.get_classifier_name()] = \
+                classifier_result.metrics_scores[metric][1]
+            for label_index, label_name in enumerate(label_names):
+                class_metric_scores[metric].loc[(
+                    "train", label_name),classifier_result.get_classifier_name()] = \
+                classifier_result.class_metric_scores[metric][0][label_index]
+                class_metric_scores[metric].loc[(
+                    "test", label_name), classifier_result.get_classifier_name()] = \
+                    classifier_result.class_metric_scores[metric][1][label_index]
+
+    return metrics_scores, class_metric_scores
 
 
 def publish_metrics_graphs(metrics_scores, directory, database_name,
-                           labels_names):
+                           labels_names, class_metric_scores):
     r"""Used to sort the results (names and both scores) in descending test
     score order.
 
@@ -76,24 +88,32 @@ def publish_metrics_graphs(metrics_scores, directory, database_name,
     results
     """
     results = []
-    for metric_name, metric_dataframe in metrics_scores.items():
+    for metric_name in metrics_scores.keys():
         logging.debug(
-            "Start:\t Biclass score graph generation for " + metric_name)
+            "Start:\t Score graph generation for " + metric_name)
         train_scores, test_scores, classifier_names, \
-        file_name, nb_results, results = init_plot(results, metric_name,
-                                                   metric_dataframe, directory,
-                                                   database_name, labels_names)
+        file_name, nb_results, results,\
+        class_test_scores = init_plot(results, metric_name,
+                                      metrics_scores[metric_name],
+                                      directory,
+                                      database_name,
+                                      class_metric_scores[metric_name])
 
         plot_metric_scores(train_scores, test_scores, classifier_names,
                            nb_results, metric_name, file_name,
                            tag=" " + " vs ".join(labels_names))
+
+        class_file_name = os.path.join(directory, database_name + "-"
+                             + metric_name+"-class")
+        plot_class_metric_scores(class_test_scores, class_file_name,
+                                 labels_names, classifier_names, metric_name)
         logging.debug(
-            "Done:\t Biclass score graph generation for " + metric_name)
+            "Done:\t Score graph generation for " + metric_name)
     return results
 
 
-def publish_all_metrics_scores(iter_results, directory,
-                               data_base_name, stats_iter,
+def publish_all_metrics_scores(iter_results, class_iter_results, directory,
+                               data_base_name, stats_iter, label_names,
                                min_size=10):
     results = []
     secure_file_path(os.path.join(directory, "a"))
@@ -101,38 +121,49 @@ def publish_all_metrics_scores(iter_results, directory,
     for metric_name, scores in iter_results.items():
         train = np.array(scores["mean"].loc["train"])
         test = np.array(scores["mean"].loc["test"])
-        names = np.array(scores["mean"].columns)
+        classifier_names = np.array(scores["mean"].columns)
         train_std = np.array(scores["std"].loc["train"])
         test_std = np.array(scores["std"].loc["test"])
 
-        file_name = os.path.join(directory, data_base_name + "-Mean_on_" + str(
+        file_name = os.path.join(directory, data_base_name + "-mean_on_" + str(
             stats_iter) + "_iter-" + metric_name)
-        nbResults = names.shape[0]
+        nb_results = classifier_names.shape[0]
 
-        plot_metric_scores(train, test, names, nbResults,
+        plot_metric_scores(train, test, classifier_names, nb_results,
                            metric_name, file_name, tag=" averaged",
                            train_STDs=train_std, test_STDs=test_std)
         results += [[classifier_name, metric_name, test_mean, test_std]
                     for classifier_name, test_mean, test_std
-                    in zip(names, test, test_std)]
-    return results
+                    in zip(classifier_names, test, test_std)]
+
+    for metric_name, scores in class_iter_results.items():
+        test = np.array([np.array(scores["mean"].iloc[i, :]) for i in range(scores["mean"].shape[0]) if scores["mean"].iloc[i, :].name[0]=='test'])
+        classifier_names = np.array(scores["mean"].columns)
+        test_std = np.array([np.array(scores["std"].iloc[i, :]) for i in range(scores["std"].shape[0]) if scores["std"].iloc[i, :].name[0]=='test'])
 
+        file_name = os.path.join(directory, data_base_name + "-mean_on_" + str(
+            stats_iter) + "_iter-" + metric_name+"-class")
+
+        plot_class_metric_scores(test, file_name, label_names, classifier_names, metric_name, stds=test_std, tag="averaged")
+    return results
 
 def init_plot(results, metric_name, metric_dataframe,
-              directory, database_name, labels_names):
+              directory, database_name, class_metric_scores):
     train = np.array(metric_dataframe.loc["train"])
     test = np.array(metric_dataframe.loc["test"])
+    class_test = np.array(class_metric_scores.loc["test"])
     classifier_names = np.array(metric_dataframe.columns)
 
     nb_results = metric_dataframe.shape[1]
 
-    file_name = os.path.join(directory, database_name + "-" + "_vs_".join(
-        labels_names) + "-" + metric_name)
+    file_name = os.path.join(directory, database_name + "-" + metric_name)
 
-    results += [[classifiers_name, metric_name, testMean, testSTD]
-                for classifiers_name, testMean, testSTD in
-                zip(classifier_names, test, np.zeros(len(test)))]
-    return train, test, classifier_names, file_name, nb_results, results
+    results += [[classifiers_name, metric_name, test_mean, test_std, class_mean]
+                for classifiers_name, test_mean, class_mean, test_std in
+                zip(classifier_names, test, np.transpose(class_test),
+                    np.zeros(len(test)))]
+    return train, test, classifier_names, file_name, nb_results, results, \
+           class_test
 
 
 def plot_metric_scores(train_scores, test_scores, names, nb_results,
@@ -230,6 +261,28 @@ def plot_metric_scores(train_scores, test_scores, names, nb_results,
         del fig
 
 
+def plot_class_metric_scores(class_test_scores, class_file_name,
+                             labels_names, classifier_names, metric_name,
+                             stds=None, tag=""):
+    fig = plotly.graph_objs.Figure()
+    for lab_index, scores in enumerate(class_test_scores):
+        if stds is None:
+            std = None
+        else:
+            std = stds[lab_index]
+        fig.add_trace(plotly.graph_objs.Bar(
+            name=labels_names[lab_index],
+            x=classifier_names, y=scores,
+            error_y=dict(type='data', array=std),
+            ))
+    fig.update_layout(
+        title=metric_name + "<br>" + tag + " scores for each classifier")
+    fig.update_layout(paper_bgcolor='rgba(0,0,0,0)',
+                      plot_bgcolor='rgba(0,0,0,0)')
+    plotly.offline.plot(fig, filename=class_file_name + ".html", auto_open=False)
+    del fig
+
+
 def get_fig_size(nb_results, min_size=15, multiplier=1.0, bar_width=0.35):
     r"""Used to get the image size to save the figure and the bar width, depending on the number of scores to plot.
 
diff --git a/multiview_platform/mono_multi_view_classifiers/utils/base.py b/multiview_platform/mono_multi_view_classifiers/utils/base.py
index 6530eeac96f599c3d55e9643ace5b07d56b2642b..d35c40fc1c3cd72105d629accc56966661ddbe75 100644
--- a/multiview_platform/mono_multi_view_classifiers/utils/base.py
+++ b/multiview_platform/mono_multi_view_classifiers/utils/base.py
@@ -2,7 +2,8 @@ import numpy as np
 from sklearn.base import BaseEstimator
 from abc import abstractmethod
 from datetime import timedelta as hms
-
+from tabulate import tabulate
+from sklearn.metrics import confusion_matrix as confusion
 from sklearn.tree import DecisionTreeClassifier
 from sklearn.ensemble import AdaBoostClassifier, RandomForestClassifier
 
@@ -139,17 +140,16 @@ def get_names(classed_list):
     return np.array([object_.__class__.__name__ for object_ in classed_list])
 
 
-def get_metric(metric_list):
+def get_metric(metrics_dict):
     """
     Fetches the metric module in the metrics package
     """
-    metric_module = getattr(metrics, metric_list[0][0])
-    if metric_list[0][1] is not None:
-        metric_kwargs = dict((index, metricConfig) for index, metricConfig in
-                             enumerate(metric_list[0][1]))
-    else:
-        metric_kwargs = {}
-    return metric_module, metric_kwargs
+    for metric_name, metric_kwargs in metrics_dict.items():
+        if metric_name.endswith("*"):
+            princ_metric_name = metric_name[:-1]
+            princ_metric_kwargs = metric_kwargs
+    metric_module = getattr(metrics, princ_metric_name)
+    return metric_module, princ_metric_kwargs
 
 
 class ResultAnalyser():
@@ -160,8 +160,8 @@ class ResultAnalyser():
     """
 
     def __init__(self, classifier, classification_indices, k_folds,
-                 hps_method, metrics_list, n_iter, class_label_names,
-                 train_pred, test_pred, directory, base_file_name, labels,
+                 hps_method, metrics_dict, n_iter, class_label_names,
+                 pred, directory, base_file_name, labels,
                  database_name, nb_cores, duration):
         """
 
@@ -175,7 +175,7 @@ class ResultAnalyser():
 
         hps_method: string naming the hyper-parameter search method
 
-        metrics_list: list of the metrics to compute on the results
+        metrics_dict: list of the metrics to compute on the results
 
         n_iter: number of HPS iterations
 
@@ -199,11 +199,10 @@ class ResultAnalyser():
         self.train_indices, self.test_indices = classification_indices
         self.k_folds = k_folds
         self.hps_method = hps_method
-        self.metrics_list = metrics_list
+        self.metrics_dict = metrics_dict
         self.n_iter = n_iter
         self.class_label_names = class_label_names
-        self.train_pred = train_pred
-        self.test_pred = test_pred
+        self.pred = pred
         self.directory = directory
         self.base_file_name = base_file_name
         self.labels = labels
@@ -212,6 +211,7 @@ class ResultAnalyser():
         self.nb_cores = nb_cores
         self.duration = duration
         self.metric_scores = {}
+        self.class_metric_scores = {}
 
     def get_all_metrics_scores(self, ):
         """
@@ -219,11 +219,14 @@ class ResultAnalyser():
         Returns
         -------
         """
-        for metric, metric_args in self.metrics_list:
-            self.metric_scores[metric] = self.get_metric_scores(metric,
-                                                                    metric_args)
-
-    def get_metric_scores(self, metric, metric_kwargs):
+        for metric, metric_args in self.metrics_dict.items():
+            class_train_scores, class_test_scores, train_score, test_score\
+                = self.get_metric_score(metric, metric_args)
+            self.class_metric_scores[metric] = (class_train_scores,
+                                                class_test_scores)
+            self.metric_scores[metric] = (train_score, test_score)
+
+    def get_metric_score(self, metric, metric_kwargs):
         """
         Get the train and test scores for a specific metric and its arguments
 
@@ -238,14 +241,28 @@ class ResultAnalyser():
         -------
         train_score, test_score
         """
-        metric_module = getattr(metrics, metric)
+        if not metric.endswith("*"):
+            metric_module = getattr(metrics, metric)
+        else:
+            metric_module = getattr(metrics, metric[:-1])
+        class_train_scores = []
+        class_test_scores = []
+        for label_value in np.unique(self.labels):
+            train_example_indices = self.train_indices[np.where(self.labels[self.train_indices]==label_value)[0]]
+            test_example_indices = self.test_indices[np.where(self.labels[self.test_indices] == label_value)[0]]
+            class_train_scores.append(metric_module.score(y_true=self.labels[train_example_indices],
+                                              y_pred=self.pred[train_example_indices],
+                                              **metric_kwargs))
+            class_test_scores.append(metric_module.score(y_true=self.labels[test_example_indices],
+                                             y_pred=self.pred[test_example_indices],
+                                             **metric_kwargs))
         train_score = metric_module.score(y_true=self.labels[self.train_indices],
-                                          y_pred=self.train_pred,
-                                          **metric_kwargs)
+                                              y_pred=self.pred[self.train_indices],
+                                              **metric_kwargs)
         test_score = metric_module.score(y_true=self.labels[self.test_indices],
-                                         y_pred=self.test_pred,
-                                         **metric_kwargs)
-        return train_score, test_score
+                                              y_pred=self.pred[self.test_indices],
+                                              **metric_kwargs)
+        return class_train_scores, class_test_scores, train_score, test_score
 
     def print_metric_score(self,):
         """
@@ -262,13 +279,21 @@ class ResultAnalyser():
         metric_score_string string formatting all metric results
         """
         metric_score_string = "\n\n"
-        for metric, metric_kwargs in self.metrics_list:
-            metric_module = getattr(metrics, metric)
+        for metric, metric_kwargs in self.metrics_dict.items():
+            if metric.endswith("*"):
+                metric_module = getattr(metrics, metric[:-1])
+            else:
+                metric_module = getattr(metrics, metric)
             metric_score_string += "\tFor {} : ".format(metric_module.get_config(
                 **metric_kwargs))
             metric_score_string += "\n\t\t- Score on train : {}".format(self.metric_scores[metric][0])
             metric_score_string += "\n\t\t- Score on test : {}".format(self.metric_scores[metric][1])
             metric_score_string += "\n\n"
+        metric_score_string += "Test set confusion matrix : \n\n"
+        self.confusion_matrix = confusion(y_true=self.labels[self.test_indices], y_pred=self.pred[self.test_indices])
+        formatted_conf = [[label_name]+list(row) for label_name, row in zip(self.class_label_names, self.confusion_matrix)]
+        metric_score_string+=tabulate(formatted_conf, headers= ['']+self.class_label_names, tablefmt='fancy_grid')
+        metric_score_string += "\n\n"
         return metric_score_string
 
     @abstractmethod
@@ -341,7 +366,8 @@ class ResultAnalyser():
             self.directory, self.base_file_name,
             self.labels[self.test_indices])
         image_analysis = {}
-        return string_analysis, image_analysis, self.metric_scores
+        return string_analysis, image_analysis, self.metric_scores, \
+               self.class_metric_scores, self.confusion_matrix
 
 
 base_boosting_estimators = [DecisionTreeClassifier(max_depth=1),
diff --git a/multiview_platform/mono_multi_view_classifiers/utils/configuration.py b/multiview_platform/mono_multi_view_classifiers/utils/configuration.py
index 9f3490847129dc3fc72e60103f6df95ab8834221..fcd62c6d94ef3f24dec3dc80aa7a992400b7fa67 100644
--- a/multiview_platform/mono_multi_view_classifiers/utils/configuration.py
+++ b/multiview_platform/mono_multi_view_classifiers/utils/configuration.py
@@ -45,7 +45,7 @@ def pass_default_config(log=True,
                         algos_monoview=["all"],
                         algos_multiview=["svm_jumbo_fusion", ],
                         stats_iter=2,
-                        metrics=["accuracy_score", "f1_score"],
+                        metrics={"accuracy_score":{}, "f1_score":{}},
                         metric_princ="accuracy_score",
                         hps_type="Random",
                         hps_iter=1,
diff --git a/multiview_platform/mono_multi_view_classifiers/utils/execution.py b/multiview_platform/mono_multi_view_classifiers/utils/execution.py
index ec783b662b7aa608f1b7391f00ee059f75056e68..163b75812a5785ec7a7f1337d9ad358c6e790206 100644
--- a/multiview_platform/mono_multi_view_classifiers/utils/execution.py
+++ b/multiview_platform/mono_multi_view_classifiers/utils/execution.py
@@ -119,7 +119,7 @@ def get_database_function(name, type_var):
 
 
 def init_log_file(name, views, cl_type, log, debug, label,
-                  result_directory, add_noise, noise_std, args):
+                  result_directory, args):
     r"""Used to init the directory where the preds will be stored and the log file.
 
     First this function will check if the result directory already exists (only one per minute is allowed).
@@ -153,16 +153,15 @@ def init_log_file(name, views, cl_type, log, debug, label,
     """
     if views is None:
         views = []
-    noise_string = "n_" + str(int(noise_std * 100))
     result_directory = os.path.join(os.path.dirname(
         os.path.dirname(os.path.dirname(os.path.realpath(__file__)))),
                                     result_directory)
     if debug:
-        result_directory = os.path.join(result_directory, name, noise_string,
+        result_directory = os.path.join(result_directory, name,
                                         "debug_started_" + time.strftime(
                                             "%Y_%m_%d-%H_%M_%S") + "_" + label)
     else:
-        result_directory = os.path.join(result_directory, name, noise_string,
+        result_directory = os.path.join(result_directory, name,
                                         "started_" + time.strftime(
                                             "%Y_%m_%d-%H_%M") + "_" + label)
     log_file_name = time.strftime("%Y_%m_%d-%H_%M") + "-" + ''.join(
diff --git a/multiview_platform/mono_multi_view_classifiers/utils/hyper_parameter_search.py b/multiview_platform/mono_multi_view_classifiers/utils/hyper_parameter_search.py
index d65af38d05c9d67ff071c67e1eac7c97a937c765..8d126bbbd7ccf27ec4044f9e2dbf987a724a03cb 100644
--- a/multiview_platform/mono_multi_view_classifiers/utils/hyper_parameter_search.py
+++ b/multiview_platform/mono_multi_view_classifiers/utils/hyper_parameter_search.py
@@ -47,7 +47,7 @@ from .. import metrics
 class HPSearch:
 
     def get_scoring(self, metric):
-        if isinstance(metric, list):
+        if isinstance(metric, dict):
             metric_module, metric_kwargs = get_metric(metric)
             return metric_module.get_scorer(**metric_kwargs)
         else:
diff --git a/multiview_platform/tests/test_exec_classif.py b/multiview_platform/tests/test_exec_classif.py
index 5187ad89a1e4c687eaea5550060d9ad54d588a47..062761391e9f5fb0616d14b8627a3a23f4b497ee 100644
--- a/multiview_platform/tests/test_exec_classif.py
+++ b/multiview_platform/tests/test_exec_classif.py
@@ -33,10 +33,10 @@ class Test_InitArgumentDictionaries(unittest.TestCase):
         cls.nb_class = 2
         cls.monoview_classifier_name = "fake_monoview_classifier"
         cls.monoview_classifier_arg_name = "fake_arg"
-        cls.monoview_classifier_arg_value = ["fake_value_1"]
+        cls.monoview_classifier_arg_value = "fake_value_1"
         cls.multiview_classifier_name = "fake_multiview_classifier"
         cls.multiview_classifier_arg_name = "fake_arg_mv"
-        cls.multiview_classifier_arg_value = ["fake_value_2"]
+        cls.multiview_classifier_arg_value = "fake_value_2"
         cls.init_kwargs = {
             'monoview':{
                 cls.monoview_classifier_name:
@@ -56,14 +56,14 @@ class Test_InitArgumentDictionaries(unittest.TestCase):
                                                             "None", {})
         expected_output = [{
                 self.monoview_classifier_name: {
-                    self.monoview_classifier_arg_name:self.monoview_classifier_arg_value[0]},
+                    self.monoview_classifier_arg_name:self.monoview_classifier_arg_value},
                 "view_name": "test_view_0",
                 'hps_kwargs': {},
                 "classifier_name": self.monoview_classifier_name,
                 "nb_class": self.nb_class,
                 "view_index": 0},
                 {self.monoview_classifier_name: {
-                    self.monoview_classifier_arg_name: self.monoview_classifier_arg_value[0]},
+                    self.monoview_classifier_arg_name: self.monoview_classifier_arg_value},
                 "view_name": "test_view",
                 'hps_kwargs': {},
                 "classifier_name": self.monoview_classifier_name,
@@ -89,7 +89,7 @@ class Test_InitArgumentDictionaries(unittest.TestCase):
                 "labels_names":None,
                 self.multiview_classifier_name: {
                     self.multiview_classifier_arg_name:
-                        self.multiview_classifier_arg_value[0]},
+                        self.multiview_classifier_arg_value},
         },]
         self.assertEqual(arguments["multiview"][0], expected_output[0])
 
diff --git a/multiview_platform/tests/test_mono_view/test_ExecClassifMonoView.py b/multiview_platform/tests/test_mono_view/test_ExecClassifMonoView.py
index c717411688e4f70fc0c17f995b39ff598215b59f..e51c9e7b27bfdf29834588895e73e9e229588a30 100644
--- a/multiview_platform/tests/test_mono_view/test_ExecClassifMonoView.py
+++ b/multiview_platform/tests/test_mono_view/test_ExecClassifMonoView.py
@@ -115,7 +115,7 @@ class Test_getHPs(unittest.TestCase):
         cls.output_file_name = tmp_path
         cls.cv = StratifiedKFold(n_splits=2, random_state=cls.random_state, shuffle=True)
         cls.nb_cores = 1
-        cls.metrics = [["accuracy_score", None]]
+        cls.metrics = {"accuracy_score*": {}}
         cls.kwargs = {"decision_tree" : {"max_depth": 1,
                       "criterion": "gini",
                       "splitter": "best"}}
diff --git a/multiview_platform/tests/test_result_analysis/test_error_analysis.py b/multiview_platform/tests/test_result_analysis/test_error_analysis.py
index 3168048ae04f61a305f7b79e0b134ef48ae5abfc..07ec87c2e4b8d96dc93db8d92e63d346ddcfcf2b 100644
--- a/multiview_platform/tests/test_result_analysis/test_error_analysis.py
+++ b/multiview_platform/tests/test_result_analysis/test_error_analysis.py
@@ -14,14 +14,14 @@ class Test_get_example_errors(unittest.TestCase):
         results = [MultiviewResult("mv", "", {"accuracy_score": [0.7, 0.75],
                                               "f1_score": [0.71, 0.76]},
                                    np.array([0,0,0,0,1,1,1,1,1]),
-                                   0,0,0),
+                                   0,0,0, {}),
                    MonoviewResult(0,
                                   "dt",
                                   "1",
                                   {"accuracy_score": [0.8, 0.85],
                                    "f1_score": [0.81, 0.86]}
                                   , np.array([0,0,1,1,0,0,1,1,0]), "", "",
-                                  "", "",0,0)
+                                  "", "",0,0, {})
                    ]
         example_errors = get_example_errors(ground_truth,
                                                             results)
@@ -45,7 +45,7 @@ class Test_gen_error_data(unittest.TestCase):
         self.assertEqual(nb_examples, 7)
         self.assertEqual(classifiers_names, ["ada-1", "mv"])
         np.testing.assert_array_equal(data_2d, np.array([ada_data, mv_data]).transpose())
-        np.testing.assert_array_equal(error_on_examples, -1*(ada_data+mv_data)/nb_classifiers)
+        np.testing.assert_array_equal(error_on_examples, (ada_data+mv_data)/nb_classifiers)
 
 
 
@@ -72,5 +72,5 @@ class Test_gen_error_data_glob(unittest.TestCase):
         self.assertEqual(nb_examples, 7)
         self.assertEqual(nb_classifiers, 2)
         np.testing.assert_array_equal(data, np.array([ada_sum, mv_sum]).transpose())
-        np.testing.assert_array_equal(error_on_examples, -1*np.sum(np.array([ada_sum, mv_sum]), axis=0)+(nb_classifiers*stats_iter))
+        np.testing.assert_array_equal(error_on_examples, np.sum(np.array([ada_sum, mv_sum]), axis=0)/(nb_classifiers*stats_iter))
         self.assertEqual(classifier_names, ["ada-1", "mv"])
\ No newline at end of file
diff --git a/multiview_platform/tests/test_result_analysis/test_execution.py b/multiview_platform/tests/test_result_analysis/test_execution.py
index 3d11cb564d352747589066a8d23a5d0ba51bd00e..cafd6c90e2c73402d1ad68c4121580ff1d8dd86e 100644
--- a/multiview_platform/tests/test_result_analysis/test_execution.py
+++ b/multiview_platform/tests/test_result_analysis/test_execution.py
@@ -10,7 +10,7 @@ from multiview_platform.mono_multi_view_classifiers.result_analysis.execution im
 class Test_format_previous_results(unittest.TestCase):
 
     def test_simple(self):
-        iter_results = {"metrics_scores":[], "example_errors":[], "feature_importances":[], "labels":[], "durations":[]}
+        iter_results = {"metrics_scores":[], "example_errors":[], "feature_importances":[], "labels":[], "durations":[], "class_metrics_scores":[]}
         random_state = np.random.RandomState(42)
 
         # Gen metrics data
@@ -46,7 +46,7 @@ class Test_format_previous_results(unittest.TestCase):
                                                          data=np.ones((2, 2))))
 
         # Running the function
-        metric_analysis, error_analysis, \
+        metric_analysis, class_met, error_analysis, \
         feature_importances, feature_stds, \
         labels, durations_mean, duration_std = format_previous_results(iter_results)
         mean_df = pd.DataFrame(data=np.mean(np.array([metrics_1_data,
diff --git a/multiview_platform/tests/test_result_analysis/test_metric_analysis.py b/multiview_platform/tests/test_result_analysis/test_metric_analysis.py
index 17f8cee856262b19222ab06965090b51b6e453df..800b916e87cafe07b2fe66717f3778a9cfe6a4f3 100644
--- a/multiview_platform/tests/test_result_analysis/test_metric_analysis.py
+++ b/multiview_platform/tests/test_result_analysis/test_metric_analysis.py
@@ -12,20 +12,20 @@ class Test_get_metrics_scores(unittest.TestCase):
 
 
     def test_simple(self):
-        metrics = [["accuracy_score"], ["f1_score"]]
+        metrics = {"accuracy_score*":{},"f1_score":{}}
         results = [MonoviewResult(0,
                                   "ada",
                                   "0",
-                                  {"accuracy_score":[0.9, 0.95],
+                                  {"accuracy_score*":[0.9, 0.95],
                                    "f1_score":[0.91, 0.96]}
-                                  , "", "", "", "", "",0,0)]
-        metrics_scores = get_metrics_scores(metrics,
-                                                            results)
+                                  , "", "", "", "", "",0,0,{})]
+        metrics_scores, class_met = get_metrics_scores(metrics,
+                                                            results, [])
         self.assertIsInstance(metrics_scores, dict)
-        self.assertIsInstance(metrics_scores["accuracy_score"], pd.DataFrame)
-        np.testing.assert_array_equal(np.array(metrics_scores["accuracy_score"].loc["train"]), np.array([0.9]))
+        self.assertIsInstance(metrics_scores["accuracy_score*"], pd.DataFrame)
+        np.testing.assert_array_equal(np.array(metrics_scores["accuracy_score*"].loc["train"]), np.array([0.9]))
         np.testing.assert_array_equal(
-            np.array(metrics_scores["accuracy_score"].loc["test"]),
+            np.array(metrics_scores["accuracy_score*"].loc["test"]),
             np.array([0.95]))
         np.testing.assert_array_equal(
             np.array(metrics_scores["f1_score"].loc["train"]),
@@ -37,11 +37,11 @@ class Test_get_metrics_scores(unittest.TestCase):
                                       np.array(["ada-0"]))
 
     def test_multiple_monoview_classifiers(self):
-        metrics = [["accuracy_score"], ["f1_score"]]
+        metrics = {"accuracy_score*":{},"f1_score":{}}
         results = [MonoviewResult(view_index=0,
                                   classifier_name="ada",
                                   view_name="0",
-                                  metrics_scores={"accuracy_score": [0.9, 0.95],
+                                  metrics_scores={"accuracy_score*": [0.9, 0.95],
                                    "f1_score": [0.91, 0.96]},
                                   full_labels_pred="",
                                   classifier_config="",
@@ -49,11 +49,12 @@ class Test_get_metrics_scores(unittest.TestCase):
                                   n_features="",
                                   hps_duration=0,
                                   fit_duration=0,
-                                  pred_duration=0),
+                                  pred_duration=0,
+                                  class_metric_scores={}),
                    MonoviewResult(view_index=0,
                                   classifier_name="dt",
                                   view_name="1",
-                                  metrics_scores={"accuracy_score": [0.8, 0.85],
+                                  metrics_scores={"accuracy_score*": [0.8, 0.85],
                                    "f1_score": [0.81, 0.86]},
                                   full_labels_pred="",
                                   classifier_config="",
@@ -61,17 +62,18 @@ class Test_get_metrics_scores(unittest.TestCase):
                                   n_features="",
                                   hps_duration=0,
                                   fit_duration=0,
-                                  pred_duration=0)
+                                  pred_duration=0,
+                                  class_metric_scores={})
                    ]
-        metrics_scores = get_metrics_scores(metrics,
-                                                            results)
+        metrics_scores, class_met = get_metrics_scores(metrics,
+                                                            results, [])
         self.assertIsInstance(metrics_scores, dict)
-        self.assertIsInstance(metrics_scores["accuracy_score"], pd.DataFrame)
+        self.assertIsInstance(metrics_scores["accuracy_score*"], pd.DataFrame)
         np.testing.assert_array_equal(
-            np.array(metrics_scores["accuracy_score"].loc["train"]),
+            np.array(metrics_scores["accuracy_score*"].loc["train"]),
             np.array([0.9, 0.8]))
         np.testing.assert_array_equal(
-            np.array(metrics_scores["accuracy_score"].loc["test"]),
+            np.array(metrics_scores["accuracy_score*"].loc["test"]),
             np.array([0.95, 0.85]))
         np.testing.assert_array_equal(
             np.array(metrics_scores["f1_score"].loc["train"]),
@@ -84,13 +86,13 @@ class Test_get_metrics_scores(unittest.TestCase):
             np.array(["ada-0", "dt-1"]))
 
     def test_mutiview_result(self):
-        metrics = [["accuracy_score"], ["f1_score"]]
-        results = [MultiviewResult("mv", "", {"accuracy_score": [0.7, 0.75],
-                                   "f1_score": [0.71, 0.76]}, "",0,0,0 ),
+        metrics = {"accuracy_score*":{},"f1_score":{}}
+        results = [MultiviewResult("mv", "", {"accuracy_score*": [0.7, 0.75],
+                                   "f1_score": [0.71, 0.76]}, "",0,0,0, {}),
                    MonoviewResult(view_index=0,
                                   classifier_name="dt",
                                   view_name="1",
-                                  metrics_scores={"accuracy_score": [0.8, 0.85],
+                                  metrics_scores={"accuracy_score*": [0.8, 0.85],
                                                   "f1_score": [0.81, 0.86]},
                                   full_labels_pred="",
                                   classifier_config="",
@@ -98,17 +100,18 @@ class Test_get_metrics_scores(unittest.TestCase):
                                   n_features="",
                                   hps_duration=0,
                                   fit_duration=0,
-                                  pred_duration=0)
+                                  pred_duration=0,
+                                  class_metric_scores={})
                    ]
-        metrics_scores = get_metrics_scores(metrics,
-                                                            results)
+        metrics_scores, class_met = get_metrics_scores(metrics,
+                                                            results, [])
         self.assertIsInstance(metrics_scores, dict)
-        self.assertIsInstance(metrics_scores["accuracy_score"], pd.DataFrame)
+        self.assertIsInstance(metrics_scores["accuracy_score*"], pd.DataFrame)
         np.testing.assert_array_equal(
-            np.array(metrics_scores["accuracy_score"].loc["train"]),
+            np.array(metrics_scores["accuracy_score*"].loc["train"]),
             np.array([0.7, 0.8]))
         np.testing.assert_array_equal(
-            np.array(metrics_scores["accuracy_score"].loc["test"]),
+            np.array(metrics_scores["accuracy_score*"].loc["test"]),
             np.array([0.75, 0.85]))
         np.testing.assert_array_equal(
             np.array(metrics_scores["f1_score"].loc["train"]),
@@ -132,17 +135,19 @@ class Test_init_plot(unittest.TestCase):
         directory = "dir"
         database_name = 'db'
         labels_names = ['lb1', "lb2"]
+        class_met = metric_dataframe = pd.DataFrame(index=["train", "test"],
+                                        columns=["dt-1", "mv"], data=data)
         train, test, classifier_names, \
-        file_name, nb_results, results = init_plot(results,
+        file_name, nb_results, results, class_test = init_plot(results,
                                                                    metric_name,
                                                                    metric_dataframe,
                                                                    directory,
                                                                    database_name,
-                                                                   labels_names)
-        self.assertEqual(file_name, os.path.join("dir", "db-lb1_vs_lb2-acc"))
+                                                                   class_met)
+        self.assertEqual(file_name, os.path.join("dir", "db-acc"))
         np.testing.assert_array_equal(train, data[0,:])
         np.testing.assert_array_equal(test, data[1, :])
         np.testing.assert_array_equal(classifier_names, np.array(["dt-1", "mv"]))
         self.assertEqual(nb_results, 2)
-        self.assertEqual(results, [["dt-1", "acc", data[1,0], 0],
-                                   ["mv", "acc", data[1,1], 0]])
\ No newline at end of file
+        self.assertEqual(results, [["dt-1", "acc", data[1,0], 0.0, data[1,0]],
+                                   ["mv", "acc", data[1,1], 0.0, data[1,1]]])
\ No newline at end of file
diff --git a/multiview_platform/tests/test_utils/test_base.py b/multiview_platform/tests/test_utils/test_base.py
index 41186fcd58c8b2cd9a20ed75ddce7c444637d098..90532ce06444b5567b3207ed26453742523d6705 100644
--- a/multiview_platform/tests/test_utils/test_base.py
+++ b/multiview_platform/tests/test_utils/test_base.py
@@ -32,14 +32,12 @@ class Test_ResultAnalyzer(unittest.TestCase):
         cls.n_splits = 5
         cls.k_folds = StratifiedKFold(n_splits=cls.n_splits, )
         cls.hps_method = "randomized_search"
-        cls.metrics_list = [("accuracy_score", {}), ("f1_score", {})]
+        cls.metrics_list = {"accuracy_score": {}, "f1_score":{}}
         cls.n_iter = 6
         cls.class_label_names = ["class{}".format(ind+1)
                                   for ind in range(cls.n_classes)]
-        cls.train_pred = np.random.randint(0, cls.n_classes,
-                                           size=cls.train_length)
-        cls.test_pred = np.random.randint(0, cls.n_classes,
-                                          size=cls.test_length)
+        cls.pred = np.random.randint(0, cls.n_classes,
+                                          size=cls.n_examples)
         cls.directory = "fake_directory"
         cls.base_file_name = "fake_file"
         cls.labels = np.random.randint(0, cls.n_classes,
@@ -48,19 +46,19 @@ class Test_ResultAnalyzer(unittest.TestCase):
         cls.nb_cores = 0.5
         cls.duration = -4
         cls.train_accuracy = accuracy_score(cls.labels[cls.train_indices],
-                                            cls.train_pred)
+                                            cls.pred[cls.train_indices])
         cls.test_accuracy = accuracy_score(cls.labels[cls.test_indices],
-                                            cls.test_pred)
+                                            cls.pred[cls.test_indices])
         cls.train_f1 = f1_score(cls.labels[cls.train_indices],
-                                            cls.train_pred, average='micro')
+                                cls.pred[cls.train_indices], average='micro')
         cls.test_f1 = f1_score(cls.labels[cls.test_indices],
-                                           cls.test_pred, average='micro')
+                               cls.pred[cls.test_indices], average='micro')
 
     def test_simple(self):
         RA = base.ResultAnalyser(self.classifier, self.classification_indices,
                                  self.k_folds, self.hps_method, self.metrics_list,
                                  self.n_iter, self.class_label_names,
-                                 self.train_pred, self.test_pred, self.directory,
+                                 self.pred, self.directory,
                                  self.base_file_name, self.labels,
                                  self.database_name, self.nb_cores,
                                  self.duration)
@@ -70,20 +68,20 @@ class Test_ResultAnalyzer(unittest.TestCase):
                                  self.k_folds, self.hps_method,
                                  self.metrics_list,
                                  self.n_iter, self.class_label_names,
-                                 self.train_pred, self.test_pred,
+                                 self.pred,
                                  self.directory, self.base_file_name,
                                  self.labels, self.database_name,
                                  self.nb_cores, self.duration)
-        train_score, test_score = RA.get_metric_scores("accuracy_score", {})
-        self.assertEqual(train_score, self.train_accuracy)
-        self.assertEqual(test_score, self.test_accuracy)
+        cl_train, cl_test,train_score, test_score = RA.get_metric_score("accuracy_score", {})
+        np.testing.assert_array_equal(train_score, self.train_accuracy)
+        np.testing.assert_array_equal(test_score, self.test_accuracy)
 
     def test_get_all_metrics_scores(self):
         RA = base.ResultAnalyser(self.classifier, self.classification_indices,
                                  self.k_folds, self.hps_method,
                                  self.metrics_list,
                                  self.n_iter, self.class_label_names,
-                                 self.train_pred, self.test_pred,
+                                 self.pred,
                                  self.directory, self.base_file_name,
                                  self.labels, self.database_name,
                                  self.nb_cores, self.duration)
diff --git a/requirements.txt b/requirements.txt
index a5c90c624487b2a1bd404248cacfc11002cbf035..7b79ed9d04c61bbeb96f9ce4a18ce3a0eafef6e0 100755
--- a/requirements.txt
+++ b/requirements.txt
@@ -17,3 +17,4 @@ cvxopt==1.2.0
 plotly==4.2.1
 matplotlib==3.1.1
 -e git+https://gitlab.lis-lab.fr/dominique.benielli/multiviewmetriclearning
+tabulate==0.8.6