Scikit-learn compatible wrapper of the Random Bits Forest program written by (Wang et al., 2016)

Overview

sklearn-compatible Random Bits Forest

Scikit-learn compatible wrapper of the Random Bits Forest program written by Wang et al., 2016, available as a binary on Sourceforge. All credits belong to the authors. This is just some quick and dirty wrapper and testing code.

The authors present "...a classification and regression algorithm called Random Bits Forest (RBF). RBF integrates neural network (for depth), boosting (for wideness) and random forest (for accuracy). It first generates and selects ~10,000 small three-layer threshold random neural networks as basis by gradient boosting scheme. These binary basis are then feed into a modified random forest algorithm to obtain predictions. In conclusion, RBF is a novel framework that performs strongly especially on data with large size."

Note: the executable supplied by the authors has been compiled for Linux, and for CPUs supporting SSE instructions.

Fig1 from Wang et al., 2016

Usage

Usage example of the Random Bits Forest:

from uci_loader import *
from randombitsforest import RandomBitsForest
X, y = getdataset('diabetes')

from sklearn.ensemble.forest import RandomForestClassifier

classifier = RandomBitsForest()
classifier.fit(X[:len(y)/2], y[:len(y)/2])
p = classifier.predict(X[len(y)/2:])
print "Random Bits Forest Accuracy:", np.mean(p == y[len(y)/2:])

classifier = RandomForestClassifier(n_estimators=20)
classifier.fit(X[:len(y)/2], y[:len(y)/2])
print "Random Forest Accuracy:", np.mean(classifier.predict(X[len(y)/2:]) == y[len(y)/2:])

Usage example for the UCI comparison:

from uci_comparison import compare_estimators
from sklearn.ensemble.forest import RandomForestClassifier, ExtraTreesClassifier
from randombitsforest import RandomBitsForest

estimators = {
              'RandomForest': RandomForestClassifier(n_estimators=200),
              'ExtraTrees': ExtraTreesClassifier(n_estimators=200),
              'RandomBitsForest': RandomBitsForest(number_of_trees=200)
            }

# optionally, pass a list of UCI dataset identifiers as the datasets parameter, e.g. datasets=['iris', 'diabetes']
# optionally, pass a dict of scoring functions as the metric parameter, e.g. metrics={'F1-score': f1_score}
compare_estimators(estimators)

"""
                          ExtraTrees F1score RandomBitsForest F1score RandomForest F1score
========================================================================================
  breastcancer (n=683)      0.960 (SE=0.003)      0.954 (SE=0.003)     *0.963 (SE=0.003)
       breastw (n=699)     *0.956 (SE=0.003)      0.951 (SE=0.003)      0.953 (SE=0.005)
      creditg (n=1000)     *0.372 (SE=0.005)      0.121 (SE=0.003)      0.371 (SE=0.005)
      haberman (n=306)      0.317 (SE=0.015)     *0.346 (SE=0.020)      0.305 (SE=0.016)
         heart (n=270)      0.852 (SE=0.004)     *0.854 (SE=0.004)      0.852 (SE=0.006)
    ionosphere (n=351)      0.740 (SE=0.037)     *0.741 (SE=0.037)      0.736 (SE=0.037)
          labor (n=57)      0.246 (SE=0.016)      0.128 (SE=0.014)     *0.361 (SE=0.018)
liverdisorders (n=345)      0.707 (SE=0.013)     *0.723 (SE=0.013)      0.713 (SE=0.012)
     tictactoe (n=958)      0.030 (SE=0.007)     *0.336 (SE=0.040)      0.030 (SE=0.007)
          vote (n=435)     *0.658 (SE=0.012)      0.228 (SE=0.017)     *0.658 (SE=0.012)
"""
Owner
Tamas Madl
Tamas Madl
The project's goal is to show a real world application of image segmentation using k means algorithm

The project's goal is to show a real world application of image segmentation using k means algorithm

2 Jan 22, 2022
A single Python file with some tools for visualizing machine learning in the terminal.

Machine Learning Visualization Tools A single Python file with some tools for visualizing machine learning in the terminal. This demo is composed of t

Bram Wasti 35 Dec 29, 2022
Machine learning algorithms implementation

Machine learning algorithms implementation This repository consisits of implementation of various machine learning algorithms. The algorithms implemen

Karun Dawadi 1 Jan 03, 2022
CorrProxies - Optimizing Machine Learning Inference Queries with Correlative Proxy Models

CorrProxies - Optimizing Machine Learning Inference Queries with Correlative Proxy Models

ZhihuiYangCS 8 Jun 07, 2022
DistML is a Ray extension library to support large-scale distributed ML training on heterogeneous multi-node multi-GPU clusters

DistML is a Ray extension library to support large-scale distributed ML training on heterogeneous multi-node multi-GPU clusters

27 Aug 19, 2022
TensorFlowOnSpark brings TensorFlow programs to Apache Spark clusters.

TensorFlowOnSpark TensorFlowOnSpark brings scalable deep learning to Apache Hadoop and Apache Spark clusters. By combining salient features from the T

Yahoo 3.8k Jan 04, 2023
Nixtla is an open-source time series forecasting library.

Nixtla Nixtla is an open-source time series forecasting library. We are helping data scientists and developers to have access to open source state-of-

Nixtla 401 Jan 08, 2023
Unofficial pytorch implementation of the paper "Context Reasoning Attention Network for Image Super-Resolution (ICCV 2021)"

CRAN Unofficial pytorch implementation of the paper "Context Reasoning Attention Network for Image Super-Resolution (ICCV 2021)" This code doesn't exa

4 Nov 11, 2021
Vowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques

Vowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learn

Vowpal Wabbit 8.1k Dec 30, 2022
XGBoost + Optuna

AutoXGB XGBoost + Optuna: no brainer auto train xgboost directly from CSV files auto tune xgboost using optuna auto serve best xgboot model using fast

abhishek thakur 517 Dec 31, 2022
Scikit-Learn useful pre-defined Pipelines Hub

Scikit-Pipes Scikit-Learn useful pre-defined Pipelines Hub Usage: Install scikit-pipes It's advised to install sklearn-genetic using a virtual env, in

Rodrigo Arenas 1 Apr 26, 2022
Apache (Py)Spark type annotations (stub files).

PySpark Stubs A collection of the Apache Spark stub files. These files were generated by stubgen and manually edited to include accurate type hints. T

Maciej 114 Nov 22, 2022
Combines MLflow with a database (PostgreSQL) and a reverse proxy (NGINX) into a multi-container Docker application

Combines MLflow with a database (PostgreSQL) and a reverse proxy (NGINX) into a multi-container Docker application (with docker-compose).

Philip May 2 Dec 03, 2021
Quantum Machine Learning

The Machine Learning package simply contains sample datasets at present. It has some classification algorithms such as QSVM and VQC (Variational Quantum Classifier), where this data can be used for e

Qiskit 364 Jan 08, 2023
Bottleneck a collection of fast, NaN-aware NumPy array functions written in C.

Bottleneck Bottleneck is a collection of fast, NaN-aware NumPy array functions written in C. As one example, to check if a np.array has any NaNs using

Python for Data 835 Dec 27, 2022
Real-time stream processing for python

Streamz Streamz helps you build pipelines to manage continuous streams of data. It is simple to use in simple cases, but also supports complex pipelin

Python Streamz 1.1k Dec 28, 2022
Cool Python features for machine learning that I used to be too afraid to use. Will be updated as I have more time / learn more.

python-is-cool A gentle guide to the Python features that I didn't know existed or was too afraid to use. This will be updated as I learn more and bec

Chip Huyen 3.3k Jan 05, 2023
Simple, light-weight config handling through python data classes with to/from JSON serialization/deserialization.

Simple but maybe too simple config management through python data classes. We use it for machine learning.

Eren Gölge 67 Nov 29, 2022
A linear regression model for house price prediction

Linear_Regression_Model A linear regression model for house price prediction. This code is using these packages, so please make sure your have install

ShawnWang 1 Nov 29, 2021
Microsoft contributing libraries, tools, recipes, sample codes and workshop contents for machine learning & deep learning.

Microsoft contributing libraries, tools, recipes, sample codes and workshop contents for machine learning & deep learning.

Microsoft 366 Jan 03, 2023