onelearn: Online learning in Python

Related tags

Machine Learningpythonmachine-learningrandom-forestregressionclassificationonline-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
GitHub Repository https://onelearn.readthedocs.io
A framework for building (and incrementally growing) graph-based data structures used in hierarchical or DAG-structured clustering and nearest neighbor search

A framework for building (and incrementally growing) graph-based data structures used in hierarchical or DAG-structured clustering and nearest neighbor search

Nicholas Monath 31 Nov 03, 2022
Laporan Proyek Machine Learning - Azhar Rizki Zulma

Laporan Proyek Machine Learning - Azhar Rizki Zulma Project Overview Domain proyek yang dipilih dalam proyek machine learning ini adalah mengenai hibu

Azhar Rizki Zulma 6 Mar 12, 2022
A repository to work on Machine Learning course. Select an algorithm to classify writer's gender, of Hebrew texts.

MachineLearning A repository to work on Machine Learning course. Select an algorithm to classify writer's gender, of Hebrew texts. Tested algorithms:

Haim Adrian 1 Feb 01, 2022
Open MLOps - A Production-focused Open-Source Machine Learning Framework

Open MLOps - A Production-focused Open-Source Machine Learning Framework Open MLOps is a set of open-source tools carefully chosen to ease user experi

Data Revenue 590 Dec 28, 2022
ClearML - Auto-Magical Suite of tools to streamline your ML workflow. Experiment Manager, MLOps and Data-Management

ClearML - Auto-Magical Suite of tools to streamline your ML workflow Experiment Manager, MLOps and Data-Management ClearML Formerly known as Allegro T

ClearML 4k Jan 09, 2023
Polyglot Machine Learning example for scraping similar news articles.

Polyglot Machine Learning example for scraping similar news articles In this example, we will see how we can work with Machine Learning applications w

MetaCall 15 Mar 28, 2022
This repository demonstrates the usage of hover to understand and supervise a machine learning task.

Hover Example Apps (works out-of-the-box on Binder) This repository demonstrates the usage of hover to understand and supervise a machine learning tas

Pavel 43 Dec 03, 2021
BentoML is a flexible, high-performance framework for serving, managing, and deploying machine learning models.

Model Serving Made Easy BentoML is a flexible, high-performance framework for serving, managing, and deploying machine learning models. Supports multi

BentoML 4.4k Jan 04, 2023
A quick reference guide to the most commonly used patterns and functions in PySpark SQL

Using PySpark we can process data from Hadoop HDFS, AWS S3, and many file systems. PySpark also is used to process real-time data using Streaming and

Sundar Ramamurthy 53 Dec 21, 2022
Automated Time Series Forecasting

AutoTS AutoTS is a time series package for Python designed for rapidly deploying high-accuracy forecasts at scale. There are dozens of forecasting mod

Colin Catlin 652 Jan 03, 2023
Transpile trained scikit-learn estimators to C, Java, JavaScript and others.

sklearn-porter Transpile trained scikit-learn estimators to C, Java, JavaScript and others. It's recommended for limited embedded systems and critical

Darius Morawiec 1.2k Jan 05, 2023
This repo implements a Topological SLAM: Deep Visual Odometry with Long Term Place Recognition (Loop Closure Detection)

This repo implements a topological SLAM system. Deep Visual Odometry (DF-VO) and Visual Place Recognition are combined to form the topological SLAM system.

Best of Australian Centre for Robotic Vision (ACRV) 32 Jun 23, 2022
💀mummify: a version control tool for machine learning

mummify is a version control tool for machine learning. It's simple, fast, and designed for model prototyping.

Max Humber 43 Jul 09, 2022
AutoOED: Automated Optimal Experiment Design Platform

AutoOED is an optimal experiment design platform powered with automated machine learning to accelerate the discovery of optimal solutions. Our platform solves multi-objective optimization problems an

Yunsheng Tian 107 Jan 03, 2023
Python-based implementations of algorithms for learning on imbalanced data.

ND DIAL: Imbalanced Algorithms Minimalist Python-based implementations of algorithms for imbalanced learning. Includes deep and representational learn

DIAL | Notre Dame 220 Dec 13, 2022
Machine Learning Algorithms

Machine-Learning-Algorithms In this project, the dataset was created through a survey opened on Google forms. The purpose of the form is to find the p

Göktuğ Ayar 3 Aug 10, 2022
Learn Machine Learning Algorithms by doing projects in Python and R Programming Language

Learn Machine Learning Algorithms by doing projects in Python and R Programming Language. This repo covers all aspect of Machine Learning Algorithms.

Ravi Chaubey 6 Oct 20, 2022
A high performance and generic framework for distributed DNN training

BytePS BytePS is a high performance and general distributed training framework. It supports TensorFlow, Keras, PyTorch, and MXNet, and can run on eith

Bytedance Inc. 3.3k Dec 28, 2022
Tools for mathematical optimization region

Tools for mathematical optimization region

林景 15 Nov 30, 2022
Reproducibility and Replicability of Web Measurement Studies

Reproducibility and Replicability of Web Measurement Studies This repository holds additional material to the paper "Reproducibility and Replicability

6 Dec 31, 2022