@@ -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
...
...
@@ -45,10 +45,10 @@ In order to run it you'll need to try on **simulated** data with the command
frommultiview_platform.executeimportexecute
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 :
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).
...
...
@@ -93,28 +94,20 @@ 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
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
## Running the tests
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.
**/!\ 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
@@ -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).
