Multivariate Boosted TRee

Related tags

Deep 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...
Grammar Induction using a Template Tree Approach

Gitta Gitta ("Grammar Induction using a Template Tree Approach") is a method for inducing context-free grammars. It performs particularly well on data

Thomas Winters 36 Nov 15, 2022
Code for "Learning Structural Edits via Incremental Tree Transformations" (ICLR'21)

Learning Structural Edits via Incremental Tree Transformations Code for "Learning Structural Edits via Incremental Tree Transformations" (ICLR'21) 1.

NeuLab 40 Dec 23, 2022
Deep Image Search is an AI-based image search engine that includes deep transfor learning features Extraction and tree-based vectorized search.
Deep Image Search is an AI-based image search engine that includes deep transfor learning features Extraction and tree-based vectorized search.

Deep Image Search - AI-Based Image Search Engine Deep Image Search is an AI-based image search engine that includes deep transfer learning features Ex

null 139 Jan 1, 2023
Code for Graph-to-Tree Learning for Solving Math Word Problems (ACL 2020)

Graph-to-Tree Learning for Solving Math Word Problems PyTorch implementation of Graph based Math Word Problem solver described in our ACL 2020 paper G

Jipeng Zhang 66 Nov 23, 2022
NAS Benchmark in
NAS Benchmark in "Prioritized Architecture Sampling with Monto-Carlo Tree Search", CVPR2021

NAS-Bench-Macro This repository includes the benchmark and code for NAS-Bench-Macro in paper "Prioritized Architecture Sampling with Monto-Carlo Tree

null 35 Jan 3, 2023
The official code for paper "R2D2: Recursive Transformer based on Differentiable Tree for Interpretable Hierarchical Language Modeling".

R2D2 This is the official code for paper titled "R2D2: Recursive Transformer based on Differentiable Tree for Interpretable Hierarchical Language Mode

Alipay 49 Dec 17, 2022
Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks
Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks

SSTNet Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks(ICCV2021) by Zhihao Liang, Zhihao Li, Songcen Xu, Mingkui Tan, Kui J

null 83 Nov 29, 2022
Implementation of fast algorithms for Maximum Spanning Tree (MST) parsing that includes fast ArcMax+Reweighting+Tarjan algorithm for single-root dependency parsing.

Fast MST Algorithm Implementation of fast algorithms for (Maximum Spanning Tree) MST parsing that includes fast ArcMax+Reweighting+Tarjan algorithm fo

Miloš Stanojević 11 Oct 14, 2022
This is the code repository implementing the paper
This is the code repository implementing the paper "TreePartNet: Neural Decomposition of Point Clouds for 3D Tree Reconstruction".

TreePartNet This is the code repository implementing the paper "TreePartNet: Neural Decomposition of Point Clouds for 3D Tree Reconstruction". Depende

刘彦超 34 Nov 30, 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
Official PyTorch implementation of "The Center of Attention: Center-Keypoint Grouping via Attention for Multi-Person Pose Estimation" (ICCV 21).

CenterGroup This the official implementation of our ICCV 2021 paper The Center of Attention: Center-Keypoint Grouping via Attention for Multi-Person P

Dynamic Vision and Learning Group 43 Dec 25, 2022
🏆 The 1st Place Submission to AICity Challenge 2021 Natural Language-Based Vehicle Retrieval Track (Alibaba-UTS submission)

AI City 2021: Connecting Language and Vision for Natural Language-Based Vehicle Retrieval 🏆 The 1st Place Submission to AICity Challenge 2021 Natural

82 Dec 29, 2022
ICCV2021 - Mining Contextual Information Beyond Image for Semantic Segmentation

Introduction The official repository for "Mining Contextual Information Beyond Image for Semantic Segmentation". Our full code has been merged into ss

55 Nov 09, 2022
Supplementary code for the paper "Meta-Solver for Neural Ordinary Differential Equations" https://arxiv.org/abs/2103.08561

Meta-Solver for Neural Ordinary Differential Equations Towards robust neural ODEs using parametrized solvers. Main idea Each Runge-Kutta (RK) solver w

Julia Gusak 25 Aug 12, 2021
PyTorch implementation of Self-supervised Contrastive Regularization for DG (SelfReg)

SelfReg PyTorch official implementation of Self-supervised Contrastive Regularization for Domain Generalization (SelfReg, https://arxiv.org/abs/2104.0

64 Dec 16, 2022
CodeContests is a competitive programming dataset for machine-learning

CodeContests CodeContests is a competitive programming dataset for machine-learning. This dataset was used when training AlphaCode. It consists of pro

DeepMind 1.6k Jan 08, 2023
PyTorch code for ICLR 2021 paper Unbiased Teacher for Semi-Supervised Object Detection

Unbiased Teacher for Semi-Supervised Object Detection This is the PyTorch implementation of our paper: Unbiased Teacher for Semi-Supervised Object Detection

Facebook Research 366 Dec 28, 2022
An investigation project for SISR.

SISR-Survey An investigation project for SISR. This repository is an official project of the paper "From Beginner to Master: A Survey for Deep Learnin

Juncheng Li 79 Oct 20, 2022
Relative Uncertainty Learning for Facial Expression Recognition

Relative Uncertainty Learning for Facial Expression Recognition The official implementation of the following paper at NeurIPS2021: Title: Relative Unc

35 Dec 28, 2022
Code for the paper: On Pathologies in KL-Regularized Reinforcement Learning from Expert Demonstrations

Non-Parametric Prior Actor-Critic (N-PPAC) This repository contains the code for On Pathologies in KL-Regularized Reinforcement Learning from Expert D

Cong Lu 5 May 13, 2022
🔎 Monitor deep learning model training and hardware usage from your mobile phone 📱

Monitor deep learning model training and hardware usage from mobile. 🔥 Features Monitor running experiments from mobile phone (or laptop) Monitor har

labml.ai 1.2k Dec 25, 2022
Scripts and misc. stuff related to the PortSwigger Web Academy

PortSwigger Web Academy Notes Mostly scripts to automate the exploits. Going in the order of the recomended learning path - starting with SQLi. Commun

pageinsec 17 Dec 30, 2022
This script scrapes and stores the availability of timeslots for Car Driving Test at all RTA Serivce NSW centres in the state.

This script scrapes and stores the availability of timeslots for Car Driving Test at all RTA Serivce NSW centres in the state. Dependencies Account wi

Balamurugan Soundararaj 21 Dec 14, 2022
An official reimplementation of the method described in the INTERSPEECH 2021 paper - Speech Resynthesis from Discrete Disentangled Self-Supervised Representations.

Speech Resynthesis from Discrete Disentangled Self-Supervised Representations Implementation of the method described in the Speech Resynthesis from Di

Facebook Research 253 Jan 06, 2023
Example for AUAV 2022 with obstacle avoidance.

AUAV 2022 Sample This is a sample PX4 based quadrotor path planning framework based on Ubuntu 20.04 and ROS noetic for the IEEE Autonomous UAS 2022 co

James Goppert 11 Sep 16, 2022
Place holder for HOPE: a human-centric and task-oriented MT evaluation framework using professional post-editing

HOPE: A Task-Oriented and Human-Centric Evaluation Framework Using Professional Post-Editing Towards More Effective MT Evaluation Place holder for dat

Lifeng Han 1 Apr 25, 2022
AttGAN: Facial Attribute Editing by Only Changing What You Want (IEEE TIP 2019)

News 11 Jan 2020: We clean up the code to make it more readable! The old version is here: v1. AttGAN TIP Nov. 2019, arXiv Nov. 2017 TensorFlow impleme

Zhenliang He 568 Dec 14, 2022
Image reconstruction done with untrained neural networks.

PyTorch Deep Image Prior An implementation of image reconstruction methods from Deep Image Prior (Ulyanov et al., 2017) in PyTorch. The point of the p

Atiyo Ghosh 192 Nov 30, 2022
SparseML is a libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models

SparseML is a toolkit that includes APIs, CLIs, scripts and libraries that apply state-of-the-art sparsification algorithms such as pruning and quantization to any neural network. General, recipe-dri

Neural Magic 1.5k Dec 30, 2022
CLNTM - Contrastive Learning for Neural Topic Model

Contrastive Learning for Neural Topic Model This repository contains the impleme

Thong Thanh Nguyen 25 Nov 24, 2022