MBTR is a python package for multivariate boosted tree regressors trained in parameter space.

Related tags

Machine Learningmbtr
Overview

Documentation Status Build Status codecov Latest Version License: MIT

Multivariate Boosted TRee

What is MBTR

MBTR is a python package for multivariate boosted tree regressors trained in parameter space. The package can handle arbitrary multivariate losses, as long as their gradient and Hessian are known. Gradient boosted trees are competition-winning, general-purpose, non-parametric regressors, which exploit sequential model fitting and gradient descent to minimize a specific loss function. The most popular implementations are tailored to univariate regression and classification tasks, precluding the possibility of capturing multivariate target cross-correlations and applying conditional penalties to the predictions. This package allows to arbitrarily regularize the predictions, so that properties like smoothness, consistency and functional relations can be enforced.

Installation

pip install --upgrade git+https://github.com/supsi-dacd-isaac/mbtr.git

Usage

MBT regressor follows the scikit-learn syntax for regressors. Creating a default instance and training it is as simple as:

m = MBT().fit(x,y)

while predictions for the test set are obtained through

y_hat = m.predict(x_te)

The most important parameters are the number of boosts n_boost, that is, the number of fitted trees, learning_rate and the loss_type. An extensive explanation of the different parameters can be found in the documentation.

Documentation

Documentation and examples on the usage can be found at docs.

Reference

If you make use of this software for your work, we would appreciate it if you would cite us:

Lorenzo Nespoli and Vasco Medici (2020). Multivariate Boosted Trees and Applications to Forecasting and Control arXiv

@article{nespoli2020multivariate,
  title={Multivariate Boosted Trees and Applications to Forecasting and Control},
  author={Nespoli, Lorenzo and Medici, Vasco},
  journal={arXiv preprint arXiv:2003.03835},
  year={2020}
}

Acknowledgments

The authors would like to thank the Swiss Federal Office of Energy (SFOE) and the Swiss Competence Center for Energy Research - Future Swiss Electrical Infrastructure (SCCER-FURIES), for their financial and technical support to this research work.

You might also like...
Python package for stacking (machine learning technique)
Python package for stacking (machine learning technique)

vecstack Python package for stacking (stacked generalization) featuring lightweight functional API and fully compatible scikit-learn API Convenient wa

Igor Ivanov 671 Dec 25, 2022
A Python Package to Tackle the Curse of Imbalanced Datasets in Machine Learning

imbalanced-learn imbalanced-learn is a python package offering a number of re-sampling techniques commonly used in datasets showing strong between-cla

null 6.2k Jan 1, 2023
A Python package for time series classification

pyts: a Python package for time series classification pyts is a Python package for time series classification. It aims to make time series classificat

Johann Faouzi 1.4k Jan 1, 2023
ELI5 is a Python package which helps to debug machine learning classifiers and explain their predictions
ELI5 is a Python package which helps to debug machine learning classifiers and explain their predictions

A library for debugging/inspecting machine learning classifiers and explaining their predictions

null 154 Dec 17, 2022
ArviZ is a Python package for exploratory analysis of Bayesian models
ArviZ is a Python package for exploratory analysis of Bayesian models

ArviZ (pronounced "AR-vees") is a Python package for exploratory analysis of Bayesian models. Includes functions for posterior analysis, data storage, model checking, comparison and diagnostics

ArviZ 1.3k Jan 5, 2023
Python package for machine learning for healthcare using a OMOP common data model

This library was developed in order to facilitate rapid prototyping in Python of predictive machine-learning models using longitudinal medical data from an OMOP CDM-standard database.

Sontag Lab 75 Jan 3, 2023
UpliftML: A Python Package for Scalable Uplift Modeling
UpliftML: A Python Package for Scalable Uplift Modeling

UpliftML is a Python package for scalable unconstrained and constrained uplift modeling from experimental data. To accommodate working with big data, the package uses PySpark and H2O models as base learners for the uplift models. Evaluation functions expect a PySpark dataframe as input.

Booking.com 254 Dec 31, 2022
scikit-multimodallearn is a Python package implementing algorithms multimodal data.

scikit-multimodallearn is a Python package implementing algorithms multimodal data. It is compatible with scikit-learn, a popul

null 12 Jun 29, 2022
MICOM is a Python package for metabolic modeling of microbial communities
MICOM is a Python package for metabolic modeling of microbial communities

Welcome MICOM is a Python package for metabolic modeling of microbial communities currently developed in the Gibbons Lab at the Institute for Systems

null 57 Dec 21, 2022
Comments
  • Is it possible to define custom loss function ?

    Is it possible to define custom loss function ?

    Dear all, First thank you for developping this tool, that I believe is of great interest. I am working with:

    • environmental variables (e.g. temperature, salinity)
    • multi-dimensional targets, that are relative abundance, with their sum = 1 for each site

    Therefore, I was wondering if it is possible to implement a custom loss function in the mbtr framework, that would be adapted for proportions. Please note that I am quite new to python.

    To do some testing, I tryed to dupplicate the mse loss function with another name in the losses.py file and adding the new loss in the LOSS_MAP in __inits__.py. Then I compiled the files. However, I have this error when trying to run the model from the multi_reg.py example:

    >>> m = MBT(loss_type = 'mse', n_boosts=30,  min_leaf=100, lambda_weights=1e-3).fit(x_tr, y_tr, do_plot=True)
  3%|▎         | 1/30 [00:03<01:45,  3.63s/it]
>>> m = MBT(loss_type = 'custom_mse', n_boosts=30,  min_leaf=100, lambda_weights=1e-3).fit(x_tr, y_tr, do_plot=True)
  0%|          | 0/30 [00:00<?, ?it/s]KeyError: 'custom_mse'

    It seems that the new loss is not recognised in LOSS_MAP:

    >>> LOSS_MAP = {'custom_mse': losses.custom_MSE,
...             'mse': losses.MSE,
...             'time_smoother': losses.TimeSmoother,
...             'latent_variable': losses.LatentVariable,
...             'linear_regression': losses.LinRegLoss,
...             'fourier': losses.FourierLoss,
...             'quantile': losses.QuantileLoss,
...             'quadratic_quantile': losses.QuadraticQuantileLoss}
AttributeError: module 'mbtr.losses' has no attribute 'custom_MSE'

    I guess that I missed something when trying to dupplicate and rename the mse loss. I would appreciate any help if the definition of a custom loss function is possible.

    Best regards,

    opened by alexschickele 2
  • Dataset cannot be reached

    Dataset cannot be reached

    Hi thank you for your effort to create this. I want to try this but i cannot download nor visit the web that you provided in example multivariate_forecas.py

    Is there any alternative link for that dataset? thank you regards!

    opened by kristfrizh 1
  • Error at import time with python 3.10.*

    Error at import time with python 3.10.*

    I want to use MBTR in a teaching module and I need to use jupyter-lab inside a conda environment for teaching purposes. While MBTR works as expected in a vanilla python 3.8, it errors out (on the same machine) in a conda environment using python 3.10

    Steps to reproduce

    conda create --name testenv
conda activate testenv

conda install -c conda-forge jupyterlab
pip install --upgrade git+https://github.com/supsi-dacd-isaac/mbtr.git
# to make sure to get the latest version; but the version on pypi gives the same error

    Then

    python

    and in python

    from mbtr.mbtr import MBT

    which outputs the following error

    Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/myself/.conda/envs/testenv/lib/python3.10/site-packages/mbtr/mbtr.py", line 317, in <module>
    def leaf_stats(y, edges, x, order):
  File "/home/myself/.conda/envs/testenv/lib/python3.10/site-packages/numba/core/decorators.py", line 219, in wrapper
    disp.compile(sig)
  File "/home/myself/.conda/envs/testenv/lib/python3.10/site-packages/numba/core/dispatcher.py", line 965, in compile
    cres = self._compiler.compile(args, return_type)
  File "/home/myself/.conda/envs/testenv/lib/python3.10/site-packages/numba/core/dispatcher.py", line 129, in compile
    raise retval
  File "/home/myself/.conda/envs/testenv/lib/python3.10/site-packages/numba/core/dispatcher.py", line 139, in _compile_cached
    retval = self._compile_core(args, return_type)
  File "/home/myself/.conda/envs/testenv/lib/python3.10/site-packages/numba/core/dispatcher.py", line 152, in _compile_core
    cres = compiler.compile_extra(self.targetdescr.typing_context,
  File "/home/myself/.conda/envs/testenv/lib/python3.10/site-packages/numba/core/compiler.py", line 716, in compile_extra
    return pipeline.compile_extra(func)
  File "/home/myself/.conda/envs/testenv/lib/python3.10/site-packages/numba/core/compiler.py", line 452, in compile_extra
    return self._compile_bytecode()
  File "/home/myself/.conda/envs/testenv/lib/python3.10/site-packages/numba/core/compiler.py", line 520, in _compile_bytecode
    return self._compile_core()
  File "/home/myself/.conda/envs/testenv/lib/python3.10/site-packages/numba/core/compiler.py", line 499, in _compile_core
    raise e
  File "/home/myself/.conda/envs/testenv/lib/python3.10/site-packages/numba/core/compiler.py", line 486, in _compile_core
    pm.run(self.state)
  File "/home/myself/.conda/envs/testenv/lib/python3.10/site-packages/numba/core/compiler_machinery.py", line 368, in run
    raise patched_exception
  File "/home/myself/.conda/envs/testenv/lib/python3.10/site-packages/numba/core/compiler_machinery.py", line 356, in run
    self._runPass(idx, pass_inst, state)
  File "/home/myself/.conda/envs/testenv/lib/python3.10/site-packages/numba/core/compiler_lock.py", line 35, in _acquire_compile_lock
    return func(*args, **kwargs)
  File "/home/myself/.conda/envs/testenv/lib/python3.10/site-packages/numba/core/compiler_machinery.py", line 311, in _runPass
    mutated |= check(pss.run_pass, internal_state)
  File "/home/myself/.conda/envs/testenv/lib/python3.10/site-packages/numba/core/compiler_machinery.py", line 273, in check
    mangled = func(compiler_state)
  File "/home/myself/.conda/envs/testenv/lib/python3.10/site-packages/numba/core/typed_passes.py", line 105, in run_pass
    typemap, return_type, calltypes, errs = type_inference_stage(
  File "/home/myself/.conda/envs/testenv/lib/python3.10/site-packages/numba/core/typed_passes.py", line 83, in type_inference_stage
    errs = infer.propagate(raise_errors=raise_errors)
  File "/home/myself/.conda/envs/testenv/lib/python3.10/site-packages/numba/core/typeinfer.py", line 1086, in propagate
    raise errors[0]
numba.core.errors.TypingError: Failed in nopython mode pipeline (step: nopython frontend)
No conversion from UniTuple(none x 2) to UniTuple(array(float64, 2d, A) x 2) for '$116return_value.7', defined at None

File ".conda/envs/testenv/lib/python3.10/site-packages/mbtr/mbtr.py", line 327:
def leaf_stats(y, edges, x, order):
    <source elided>
        s_left, s_right = None, None
    return s_left, s_right
    ^

During: typing of assignment at /home/myself/.conda/envs/testenv/lib/python3.10/site-packages/mbtr/mbtr.py (327)

File ".conda/envs/test/lib/python3.10/site-packages/mbtr/mbtr.py", line 327:
def leaf_stats(y, edges, x, order):
    <source elided>
        s_left, s_right = None, None
    return s_left, s_right
    ^

    Thanks in advance for any pointer/help. The course where I want to present this is a summer course and is closing in on me 😉

    opened by jiho 0
Releases(v0.1.3)
Owner
SUPSI-DACD-ISAAC
SUPSI-DACD-ISAAC
GitHub Repository
Kubeflow is a machine learning (ML) toolkit that is dedicated to making deployments of ML workflows on Kubernetes simple, portable, and scalable.

SDK: Overview of the Kubeflow pipelines service Kubeflow is a machine learning (ML) toolkit that is dedicated to making deployments of ML workflows on

Kubeflow 3.1k Jan 06, 2023
moDel Agnostic Language for Exploration and eXplanation

moDel Agnostic Language for Exploration and eXplanation Overview Unverified black box model is the path to the failure. Opaqueness leads to distrust.

Model Oriented 1.2k Jan 04, 2023
Pyomo is an object-oriented algebraic modeling language in Python for structured optimization problems.

Pyomo is a Python-based open-source software package that supports a diverse set of optimization capabilities for formulating and analyzing optimization models. Pyomo can be used to define symbolic p

Pyomo 1.4k Dec 28, 2022
Accelerating model creation and evaluation.

EmeraldML A machine learning library for streamlining the process of (1) cleaning and splitting data, (2) training, optimizing, and testing various mo

Yusuf 0 Dec 06, 2021
A mindmap summarising Machine Learning concepts, from Data Analysis to Deep Learning.

A mindmap summarising Machine Learning concepts, from Data Analysis to Deep Learning.

Daniel Formoso 5.7k Dec 30, 2022
Classification based on Fuzzy Logic(C-Means).

CMeans_fuzzy Classification based on Fuzzy Logic(C-Means). Table of Contents About The Project Fuzzy CMeans Algorithm Built With Getting Started Insta

Armin Zolfaghari Daryani 3 Feb 08, 2022
Home repository for the Regularized Greedy Forest (RGF) library. It includes original implementation from the paper and multithreaded one written in C++, along with various language-specific wrappers.

Regularized Greedy Forest Regularized Greedy Forest (RGF) is a tree ensemble machine learning method described in this paper. RGF can deliver better r

RGF-team 363 Dec 14, 2022
Machine Learning e Data Science com Python

Machine Learning e Data Science com Python Arquivos do curso de Data Science e Machine Learning com Python na Udemy, cliqe aqui para acessá-lo. O prin

Renan Barbosa 1 Jan 27, 2022
Python package for machine learning for healthcare using a OMOP common data model

This library was developed in order to facilitate rapid prototyping in Python of predictive machine-learning models using longitudinal medical data from an OMOP CDM-standard database.

Sontag Lab 75 Jan 03, 2023
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
Predict profitability of trades based on indicator buy / sell signals

Predict profitability of trades based on indicator buy / sell signals Trade profitability analysis for trades based on various indicators signals: MAC

Tomasz Porzycki 1 Dec 15, 2021
Titanic Traveller Survivability Prediction

The aim of the mini project is predict whether or not a passenger survived based on attributes such as their age, sex, passenger class, where they embarked and more.

John Phillip 0 Jan 20, 2022
YouTube Spam Detection with python

YouTube Spam Detection This code deletes spam comment on youtube videos based on two characteristics (currently) If the author of the comment has a se

MohamadReza Taalebi 5 Sep 27, 2022
Tribuo - A Java machine learning library

Tribuo - A Java prediction library (v4.1) Tribuo is a machine learning library in Java that provides multi-class classification, regression, clusterin

Oracle 1.1k Dec 28, 2022
Meerkat provides fast and flexible data structures for working with complex machine learning datasets.

Meerkat makes it easier for ML practitioners to interact with high-dimensional, multi-modal data. It provides simple abstractions for data inspection, model evaluation and model training supported by

Robustness Gym 115 Dec 12, 2022
A Python package to preprocess time series

Disclaimer: This package is WIP. Do not take any APIs for granted. tspreprocess Time series can contain noise, may be sampled under a non fitting rate

Maximilian Christ 57 Dec 17, 2022
Provide an input CSV and a target field to predict, generate a model + code to run it.

automl-gs Give an input CSV file and a target field you want to predict to automl-gs, and get a trained high-performing machine learning or deep learn

Max Woolf 1.8k Jan 04, 2023
A toolkit for geo ML data processing and model evaluation (fork of solaris)

An open source ML toolkit for overhead imagery. This is a beta version of lunular which may continue to develop. Please report any bugs through issues

Ryan Avery 4 Nov 04, 2021
OptaPy is an AI constraint solver for Python to optimize planning and scheduling problems.

OptaPy is an AI constraint solver for Python to optimize the Vehicle Routing Problem, Employee Rostering, Maintenance Scheduling, Task Assignment, School Timetabling, Cloud Optimization, Conference S

OptaPy 208 Dec 27, 2022
Lightweight Machine Learning Experiment Logging 📖

Simple logging of statistics, model checkpoints, plots and other objects for your Machine Learning Experiments (MLE). Furthermore, the MLELogger comes with smooth multi-seed result aggregation and co

Robert Lange 65 Dec 08, 2022