Notice: scikit-multiflow works with Python 3.4+ only.
$ pip install -U numpy
Option 1. Install from source code¶
First, you need to make a copy of the
scikit-multiflow project. On the project’s github page you will find on the top-right side of the page a green button with the label “Clone or download”. By clicking on it you will get two options: clone with SSH, HTTPS or download a zip. If you opt to get a zip file then you have to unzip the project into the desired local destination before continuing.
Once numpy is installed, you can proceed with the installation of
scikit-multiflow and its other dependencies.
In a terminal, navigate to the local path of the project and run the following command (including the dot at the end):
$ pip install -U .
The -U option indicates that the package will be installed only for this user.
Optionally you can indicate to pip the remote location of the code:
$ pip install -U git+https://github.com/scikit-multiflow/scikit-multiflow
When the installation is completed (and no errors were reported), then you will be ready to use scikit-multiflow. The advantage of this option is that you can install the latest version of the code in github.
Option 2. Install from PyPI¶
scikit-multiflow is also available in the Python Package Index (PyPI). So you can install it using the following command:
$ pip install -U scikit-multiflow
This will install the latest (stable) release of the code.
Option 3. Development version¶
For people interested in contributing to scikit-multiflow we recommend to install the project in editable mode, please refer to the contributor’s page for further information.
matplotlib backend considerations¶
You may need to change your matplotlib backend, because not all backends work on all machines.
If this is the case you need to check matplotlib’s configuration. In the matplotlibrc file you will need to change the line:
backend : Qt5Agg
backend : a backend that works on your machine
The Qt5Agg backend should work with most machines, but a change may be needed.
In order to display plots from
scikit-multiflow within a Jupyter Notebook we need to define the proper
mathplotlib backend to use. This is done via a magic command at the beginning of the Notebook:
JupyterLab is Jupyter’s next-generation user interface, currently in beta it can display plots with some caveats. If you use JupyterLab then the current solution is to use the jupyter-matplotlib extension: