Installation guide¶

Prerequisites¶

Before starting the installation process, ensure to have python 3.9 or newer (3.11 is recommended) installed on your machine. Please refer to the Python official website for further information concerning the installation process.

This can be achieved on Windows, Linux, and macOS.

python --version

The output of the command above should tell you what version is installed on your machine.

Then, check if you also have pip installed. Note you might need to install python3-pip on some Linux distributions.

WindowsLinuxmacOS

python -m pip --version

pip --version

pip --version

It should respond with an output similar to the example below:

pip 22.0.2 from /usr/lib/python3/dist-packages/pip (python 3.10)

You are now ready to continue with the desired installation guide below.

Virtual environment¶

Create a virtual environment for your project and activate it. Note you might need to install python3-venv on some Linux distributions.

WindowsLinuxmacOS

python3 -m venv .venv
.venv/Scripts/activate

python3 -m venv .venv
source .venv/bin/activate

python3 -m venv .venv
source .venv/bin/activate

MLxplain installation¶

Here you can find the only step to follow to install the mlxplain package on your machine.

Install the mlxplain package using pip:

python -m pip install mlxplain

Now, you should be ready to use the mlxplain library. You can continue to follow the mlxplain examples to learn more about how to use this tool.

Rules extraction installation¶

Here you can find the only step to follow to install the rules-extraction project on your machine.

Install the rule-extraction package using pip:

python -m pip install rules-extraction

Now, you should be ready to use the rules-extraction library. You can continue to follow the rules extraction examples to learn more about how to use this tool.

Dimlpfidex installation¶

Here you can find the only step to follow to install the dimlpfidex project on your machine.

Install the dimlpfidex package using pip:

python -m pip install dimlpfidex

To validate all the above steps, try to call one of the algorithms from the dimlpfidex package. To do so, import Fidex from dimlpfidex and call the FidexGloRules function inside your python interpreter:

>>> from dimlpfidex import fidex
>>> fidex.fidexGloRules()

Ensure you have the same output as below:

---------------------------------------------------------------------

Warning! The files are located with respect to the root folder dimlpfidex.
The arguments can be specified in the command or in a json configuration file with --json_config_file your_config_file.json.

----------------------------

Required parameters:

--train_data_file <str>       Path to the file containing the train portion of the dataset
--train_class_file <str>      Path to the file containing the train true classes of the dataset, not mandatory if classes are specified in train data file
(...)

You are now set to use all dimlpfidex tools! You can continue to follow the getting-started tutorial to learn more about how to use the different tools available.