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

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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.

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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
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