onelearn: Online learning in Python

Related tags

Machine Learning python machine-learning random-forest regression classification online-learning-algorithms
Overview

Build Status Documentation Status PyPI - Python Version PyPI - Wheel GitHub stars GitHub issues GitHub license Coverage Status

onelearn: Online learning in Python

Documentation | Reproduce experiments |

onelearn stands for ONE-shot LEARNning. It is a small python package for online learning with Python. It provides :

  • online (or one-shot) learning algorithms: each sample is processed once, only a single pass is performed on the data
  • including multi-class classification and regression algorithms
  • For now, only ensemble methods, namely Random Forests

Installation

The easiest way to install onelearn is using pip

pip install onelearn

But you can also use the latest development from github directly with

pip install git+https://github.com/onelearn/onelearn.git

References

@article{mourtada2019amf,
  title={AMF: Aggregated Mondrian Forests for Online Learning},
  author={Mourtada, Jaouad and Ga{\"\i}ffas, St{\'e}phane and Scornet, Erwan},
  journal={arXiv preprint arXiv:1906.10529},
  year={2019}
}
Comments
  • Unable to pickle AMFClassifier.

    Unable to pickle AMFClassifier.

    I would like to save the AMFClassifier, but am unable to pickle it. I have also tried to use dill or joblib, but they also don't seem to work.

    Is there maybe another way to somehow export the AMFClassifier in any way, such that I can save it and load it in another kernel?

    Below I added a snippet of code which reproduces the error. Note that only after the partial_fit method an error occurs when pickling. When the AMFClassifier has not been fit yet, pickling happens without problems, however, exporting an empty model is pretty useless.

    Any help or tips is much appreciated.

    from onelearn import AMFClassifier
import dill as pickle
from sklearn import datasets


iris = datasets.load_iris()
X = iris.data
y = iris.target

amf = AMFClassifier(n_classes=3)

dump = pickle.dumps(amf)
amf = pickle.loads(dump)

amf.partial_fit(X,y)

dump = pickle.dumps(amf)
amf = pickle.loads(dump)
    opened by w-feijen 1
  • Move experiments of the paper in a experiments folder

    Move experiments of the paper in a experiments folder

    • Update the documentation
    • Explain that we must clone the repo

    Move also the short experiments to a examples folder and build a sphinx gallery with it

    enhancement 
    opened by stephanegaiffas 1
  • Add some extra tests

    Add some extra tests

    • Test that batch versus online training leads to the exact same forest
    • Test the behavior of reserve_samples, with several calls to partial_fit to check that memory is correctly allocated and
    tests 
    opened by stephanegaiffas 1
  • What if predict_proba receives a single sample

    What if predict_proba receives a single sample

    get_amf_decision_online amf.partial_fit(X_train[iteration - 1], y_train[iteration - 1]) File "/Users/stephanegaiffas/Code/onelearn/onelearn/forest.py", line 259, in partial_fit n_samples, n_features = X.shape

    opened by stephanegaiffas 1
  • Improve coverage

    Improve coverage

    A problem is that @jit functions don't work with coverage... a workaround is to disable using the NUMBA_DISABLE_JIT environment variable, but breaks the code that use @jitclass and .class_type.instance_type attributes

    enhancement bug fix 
    opened by stephanegaiffas 1
Releases(v0.3)

  • v0.3(Sep 29, 2021)

    This release adds the following improvements

    • AMFClassifier and AMFRegressor can be serialized to files (using internally pickle) using the save and load methods
    Source code(tar.gz)
    Source code(zip)

  • v0.2.0(Apr 6, 2020)

    This release adds the following improvements

    • SampleCollection pre-allocates more samples instead of the bare minimum for faster computation
    • The playground can be launched from the library
    • A documentation on readthedocs
    • Faster computations and a lot of code cleaning
    • Unittests for python 3.6-3.8
    Source code(tar.gz)
    Source code(zip)
Owner
null
GitHub https://onelearn.readthedocs.io
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