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

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.

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

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

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

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

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

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

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

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
Fine-tuning StyleGAN2 for Cartoon Face Generation

Cartoon-StyleGAN 🙃 : Fine-tuning StyleGAN2 for Cartoon Face Generation Abstract Recent studies have shown remarkable success in the unsupervised imag

Jihye Back 520 Jan 04, 2023
An extremely simple, intuitive, hardware-friendly, and well-performing network structure for LiDAR semantic segmentation on 2D range image. IROS21

FIDNet_SemanticKITTI Motivation Implementing complicated network modules with only one or two points improvement on hardware is tedious. So here we pr

YimingZhao 54 Dec 12, 2022
Custom IMDB Dataset is extracted between 2020-2021 and custom distilBERT model is trained for movie success probability prediction

IMDB Success Predictor Project involves Web Scraping custom IMDB data between 2020 and 2021 of 10000 movies and shows sorted by number of votes ,fine

Gautam Diwan 1 Jan 18, 2022
Config files for my GitHub profile.

Canalyst Candas Data Science Library Name Canalyst Candas Description Built by a former PM / analyst to give anyone with a little bit of Python knowle

Canalyst Candas 13 Jun 24, 2022
ICNet and PSPNet-50 in Tensorflow for real-time semantic segmentation

Real-Time Semantic Segmentation in TensorFlow Perform pixel-wise semantic segmentation on high-resolution images in real-time with Image Cascade Netwo

Oles Andrienko 219 Nov 21, 2022
Official code for the CVPR 2021 paper "How Well Do Self-Supervised Models Transfer?"

How Well Do Self-Supervised Models Transfer? This repository hosts the code for the experiments in the CVPR 2021 paper How Well Do Self-Supervised Mod

Linus Ericsson 157 Dec 16, 2022
Code for the upcoming CVPR 2021 paper

The Temporal Opportunist: Self-Supervised Multi-Frame Monocular Depth Jamie Watson, Oisin Mac Aodha, Victor Prisacariu, Gabriel J. Brostow and Michael

Niantic Labs 496 Dec 30, 2022
Implementation of the paper "Self-Promoted Prototype Refinement for Few-Shot Class-Incremental Learning"

Self-Promoted Prototype Refinement for Few-Shot Class-Incremental Learning This is the implementation of the paper "Self-Promoted Prototype Refinement

Kai Zhu 78 Dec 02, 2022
This is a yolo3 implemented via tensorflow 2.7

YoloV3 - an object detection algorithm implemented via TF 2.x source code In this article I assume you've already familiar with basic computer vision

2 Jan 17, 2022
IsoGCN code for ICLR2021

IsoGCN The official implementation of IsoGCN, presented in the ICLR2021 paper Isometric Transformation Invariant and Equivariant Graph Convolutional N

horiem 39 Nov 25, 2022
RLBot Python bindings for the Rust crate rl_ball_sym

RLBot Python bindings for rl_ball_sym 0.6 Prerequisites: Rust & Cargo Build Tools for Visual Studio RLBot - Verify that the file %localappdata%\RLBotG

Eric Veilleux 2 Nov 25, 2022
Discovering Explanatory Sentences in Legal Case Decisions Using Pre-trained Language Models.

Statutory Interpretation Data Set This repository contains the data set created for the following research papers: Savelka, Jaromir, and Kevin D. Ashl

17 Dec 23, 2022
Official PyTorch implementation of the paper: DeepSIM: Image Shape Manipulation from a Single Augmented Training Sample

DeepSIM: Image Shape Manipulation from a Single Augmented Training Sample (ICCV 2021 Oral) Project | Paper Official PyTorch implementation of the pape

Eliahu Horwitz 393 Dec 22, 2022
Prototypical python implementation of the trust-region algorithm presented in Sequential Linearization Method for Bound-Constrained Mathematical Programs with Complementarity Constraints by Larson, Leyffer, Kirches, and Manns.

Prototypical python implementation of the trust-region algorithm presented in Sequential Linearization Method for Bound-Constrained Mathematical Programs with Complementarity Constraints by Larson, L

3 Dec 02, 2022
This is the official code of our paper "Diversity-based Trajectory and Goal Selection with Hindsight Experience Relay" (PRICAI 2021)

Diversity-based Trajectory and Goal Selection with Hindsight Experience Replay This is the official implementation of our paper "Diversity-based Traje

Tianhong Dai 6 Jul 18, 2022
The official implementation of the CVPR2021 paper: Decoupled Dynamic Filter Networks

Decoupled Dynamic Filter Networks This repo is the official implementation of CVPR2021 paper: "Decoupled Dynamic Filter Networks". Introduction DDF is

F.S.Fire 180 Dec 30, 2022
A PyTorch implementation of "Graph Wavelet Neural Network" (ICLR 2019)

Graph Wavelet Neural Network ⠀⠀ A PyTorch implementation of Graph Wavelet Neural Network (ICLR 2019). Abstract We present graph wavelet neural network

Benedek Rozemberczki 490 Dec 16, 2022
Constructing interpretable quadratic accuracy predictors to serve as an objective function for an IQCQP problem that represents NAS under latency constraints and solve it with efficient algorithms.

IQNAS: Interpretable Integer Quadratic programming Neural Architecture Search Realistic use of neural networks often requires adhering to multiple con

0 Oct 24, 2021
A computational block to solve entity alignment over textual attributes in a knowledge graph creation pipeline.

How to apply? Create your config.ini file following the example provided in config.ini Choose one of the options below to run: Run with Python3 pip in

Scientific Data Management Group 3 Jun 23, 2022
Implementation of the paper "Fine-Tuning Transformers: Vocabulary Transfer"

Transformer-vocabulary-transfer Implementation of the paper "Fine-Tuning Transfo

LEYA 13 Nov 30, 2022