Tornado is a framework for data stream mining in Python. The framework includes various incremental/online learning algorithms as well as concept drift detection methods.

You must have Python 3.5 or above (either 32-bit or 64-bit) on your system to run the framework without any error. Note that the numpy, scipy, matplotlib, and pympler packages are used in the Tornado implementations. You may use the pip command in order to install these packages, for example:

pip install numpy

Although you can use an installer from https://www.python.org/downloads/ to install Python on your system, I highly recommend Anaconda, one of the Python distributions, since it includes the numpy, scipy, and mathplotlib packages by default. You may download one of the Anaconda's installers from https://www.anaconda.com/download/. Please note that, you still need to install the pympler package for Anaconda. For that, run the following command in a command prompt or a terminal:

conda install -c conda-forge pympler

Once you have all the packages installed, you may run the framework.

Three sample codes are prepared to show how you can use the framework. Those files are:

• github_prequential_test.py - This file lets you evaluate an adaptive algorithm, i.e. a pair of a learner and a drift detector, prequentially. In this example, Naive Bayes is the learner and Fast Hoeffding Drift Detection Method (FHDDM) is the detector. You find lists of incremental learners in tornado/classifier/ and drift detectors in tornado/drift_detection/. The outputs in the created project directory are similar to:

• github_prequential_multi_test.py - This file lets you run multiple adaptive algorithms together against a data stream. While algorithms are learning from instances of a data stream, the framework tells you which adaptive algorithm is optimal by considering classification, adaptation, and resource consumption measures. The outputs in the created project directory are similar to:

• github_generate_stream.py - The file helps you use the Tornado framework for generating synthetic data streams containing concept drifts. You find a list of stream generators in tornado/streams/generators/.

Please kindly cite the following papers, or thesis, if you plan to use Tornado or any of its components:

1. Pesaranghader, Ali. "A Reservoir of Adaptive Algorithms for Online Learning from Evolving Data Streams", Ph.D. Dissertation, Université d'Ottawa/University of Ottawa, 2018.
   DOI: http://dx.doi.org/10.20381/ruor-22444
2. Pesaranghader, Ali, et al. "Reservoir of Diverse Adaptive Learners and Stacking Fast Hoeffding Drift Detection Methods for Evolving Data Streams", Machine Learning Journal, 2018.
   Pre-print available at: https://arxiv.org/abs/1709.02457, DOI: https://doi.org/10.1007/s10994-018-5719-z
3. Pesaranghader, Ali, et al. "A framework for classification in data streams using multi-strategy learning", International Conference on Discovery Science, 2016.
   Pre-print available at: http://iwera.ir/~ali/papers/ds2016.pdf, DOI: https://doi.org/10.1007/978-3-319-46307-0_22

_{Ali Pesaranghader © 2020 | MIT License}