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README.md

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

docs/source/tutorials/example0.rst

@@ -14,7 +14,7 @@ This dataset consists in 3 views:
 Running |platf| on digits
 -------------------------
 
-To run |platf| on digits, one has to run :
+To run |platf| on digits, we have to run :
 
 .. code-block:: python
 
@@ -25,9 +25,9 @@ This will start a benchmark with this :base_source:`config file <multiview_platf
 
 By running |platf| with this configuration, one runs :
 
-- One decision tree on each view,
-- One early fusion multiview classifier that learns on the concatenation of all the views,
-- One late fusion classifier that learn one monoview lassifier by view and aggregates their decisions in a majority vote.
+- one decision tree on each view,
+- one early fusion multiview classifier that learns on the concatenation of all the views,
+- one late fusion classifier that learn one monoview lassifier by view and aggregates their decisions in a majority vote.