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/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