Fast solver for L1-type problems: Lasso, sparse Logisitic regression, Group Lasso, weighted Lasso, Multitask Lasso, etc.

Last update: Dec 13, 2022

Related tags

Sklearn Utilities celer

Overview

celer

Fast algorithm to solve Lasso-like problems with dual extrapolation. Currently, the package handles the following problems:

Lasso
weighted Lasso
Sparse Logistic regression
Group Lasso
Multitask Lasso.

The estimators follow the scikit-learn API, come with automated parallel cross-validation, and support both sparse and dense data, with optionally feature centering, normalization, and unpenalized intercept fitting. The solvers used allow for solving large scale problems with millions of features, up to 100 times faster than scikit-learn.

Documentation

Please visit https://mathurinm.github.io/celer/ for the latest version of the documentation.

Install the released version

Assuming you have a working Python environment, e.g., with Anaconda you can install celer with pip.

From a console or terminal install celer with pip:

pip install -U celer

Install and work with the development version

From a console or terminal clone the repository and install Celer:

git clone https://github.com/mathurinm/celer.git
cd celer/
pip install -e .

To build the documentation you will need to run:

pip install -U sphinx_gallery sphinx_bootstrap_theme
cd doc/
make html

Demos & Examples

In the example section of the documentation, you will find numerous examples on real life datasets, timing comparison with other estimators, easy and fast ways to perform cross validation, etc.

Dependencies

All dependencies are in the ./requirements.txt file. They are installed automatically when pip install -e . is run.

Cite

If you use this code, please cite:

@InProceedings{pmlr-v80-massias18a,
  title =    {Celer: a Fast Solver for the Lasso with Dual Extrapolation},
  author =   {Massias, Mathurin and Gramfort, Alexandre and Salmon, Joseph},
  booktitle =        {Proceedings of the 35th International Conference on Machine Learning},
  pages =    {3321--3330},
  year =     {2018},
  volume =   {80},
}


@article{massias2020dual,
  author  = {Mathurin Massias and Samuel Vaiter and Alexandre Gramfort and Joseph Salmon},
  title   = {Dual Extrapolation for Sparse GLMs},
  journal = {Journal of Machine Learning Research},
  year    = {2020},
  volume  = {21},
  number  = {234},
  pages   = {1-33},
  url     = {http://jmlr.org/papers/v21/19-587.html}
}

ArXiv links:

Comments

n_jobs (multi-core CPU) for LassoCV function?

Thanks for these tools! Do any of the celer sklearn functions (LassoCV, etc.) have multi-cpu support like the original sklearn functions (n_jobs = 1, 2, 3, etc.)? Currently, the n_jobs argument is unrecognized as: __init__() got an unexpected keyword argument 'n_jobs'

opened by yjkimnada 14

why the three number in 'yhat' is same

Hello! I don't know why the three number in 'yhat' is same and 'w_hat' is all zero. I'd like to get 'w_hat' with a few non-zero numbers so that I can know which group of 'X' is used. Can you help me with that? Thank you very much!

###############################################################################
# Setup
# -----

import matplotlib.pyplot as plt
import numpy as np
from numpy import linalg
from skimage import io
from skimage.color.colorconv import _prepare_colorarray
from sklearn.metrics import r2_score
from sklearn.metrics import mean_squared_error

from group_lasso import GroupLasso

np.random.seed(0)     
GroupLasso.LOG_LOSSES = True

###############################################################################
# Set dataset parameters
# ----------------------
group_sizes = [3, 3, 2, 2, 2]
active_groups = np.ones(5)
active_groups[:3] = 2
np.random.shuffle(active_groups)
np.random.shuffle(active_groups)
groups = np.concatenate(
    [size * [i] for i, size in enumerate(group_sizes)]
).reshape(-1, 1)
num_coeffs = sum(group_sizes)
num_datapoints = 3

print("group_sizes:", group_sizes)
print("active_groups:", active_groups)
print("groups:", groups.shape)
print("num_coeffs:", num_coeffs)
print("____________________________________________")

###############################################################################
# Generate data matrix
# --------------------
X = np.array([[0.571, 0.095, 0.767, 0.095, 0.105, 0.767, 0.571, 0.767, 0.095, 0.767, 0.105, 0.767],
              [0.584, 0.258, 0.576, 0.258, 0.758, 0.576, 0.584, 0.576, 0.258, 0.576, 0.758, 0.576],
              [0.577, 0.961, 0.284, 0.961, 0.644, 0.284, 0.577, 0.284, 0.961, 0.284, 0.644, 0.284]])

print("X:", X.shape)
print("____________________________________________")

###############################################################################
# Generate coefficients
# ---------------------k
w = np.concatenate(
    [
        np.random.standard_normal(group_size) * is_active
        for group_size, is_active in zip(group_sizes, active_groups)
    ]
)
w = w.reshape(-1, 1)
true_coefficient_mask = w != 0
intercept = 2

print("w:", w.shape)
print("true_coefficient_mask:", true_coefficient_mask.sum())
print("____________________________________________")

###############################################################################
# Generate regression targets
# ---------------------------

y_true = X @ w
y = np.array([[-0.17997138],
              [-0.15219182],
              [-0.17062552]])
y_true = X @ w
print("y:", y)
MSE1 = mean_squared_error(y, y_true)
print("MSE_yt_y:", MSE1)
print("____________________________________________")

###############################################################################
# Generate estimator and train it
# -------------------------------
gl = GroupLasso(
    groups=groups,
    group_reg=5,
    l1_reg=2,
    frobenius_lipschitz=True,
    scale_reg="inverse_group_size",
    subsampling_scheme=1,
    supress_warning=True,
    n_iter=1000,
    tol=1e-3,
)
gl.fit(X, y)

###############################################################################
# Extract results and compute performance metrics
# -----------------------------------------------

# Extract info from estimator
yhat = gl.predict(X)
sparsity_mask = gl.sparsity_mask_
w_hat = gl.coef_

print("yhat:", yhat)
print("w_hat:", w_hat.sum())
print("sparsity_mask:", sparsity_mask)
print("____________________________________________")

# Compute performance metrics
R2 = r2_score(y, yhat)
MSE_y_yh = mean_squared_error(y, yhat)
print("MSE_y_yh:", MSE_y_yh)
print("____________________________________________")

And the result of the program after running is as follows.

group_sizes: [3, 3, 2, 2, 2]
active_groups: [2. 2. 2. 1. 1.]
groups: (12, 1)
num_coeffs: 12
____________________________________________
X: (3, 12)
____________________________________________
w: (12, 1)
true_coefficient_mask: 12
____________________________________________
y: [[-0.17997138]
 [-0.15219182]
 [-0.17062552]]
MSE_yt_y: 55.67436677644974
____________________________________________
yhat: [-0.16644355 -0.16644355 -0.16644355]
w_hat: 0.0
sparsity_mask: [False False False False False False False False False False False False]
____________________________________________
MSE_y_yh: 0.0001345342966391801
____________________________________________

opened by SikangSHU 13

add MultiTaskLassoCV and MultiTaskLasso

@mathurinm I tried to add MultiTaskLassoCV and MultiTaskLasso to help @ja-che with his experiments but the test does not end. It seems like it's not converging.

@mathurinm can you have a look?

also you'll see that mtl_path is not consistent in API with sklearn lasso_path

wdyt?

opened by agramfort 9

ENH: reflexion about tol

behaviour disagrees with sklearn because the latter scales tol by norm(y) ** 2 (or norm(y) ** 2 / n_samples ?)
using tol < 1e-7 with float32 caused precision issues (found out in check_estimator, MCVE:

                  [1.9376824, 1.3127615, 2.675319, 2.8909883, 1.1503246],
                  [2.375175, 1.5866847, 1.7041336, 2.77679, 0.21310817],
                  [0.2613879, 0.06065519, 2.4978595, 2.3344703, 2.6100364],
                  [2.935855, 2.3974757, 1.384438, 2.3415875, 0.3548233],
                  [1.9197631, 0.43005985, 2.8340068, 1.565545, 1.2439859],
                  [0.79366684, 2.322701, 1.368451, 1.7053018, 0.0563694],
                  [1.8529065, 1.8362871, 1.850802, 2.8312442, 2.0454607],
                  [1.0785236, 1.3110958, 2.0928936, 0.18067642, 2.0003002],
                  [2.0119135, 0.6311477, 0.3867789, 0.946285, 1.0911323]],
                 dtype=np.float32)

    y = np.array([[1.],
                  [1.],
                  [2.],
                  [0.],
                  [2.],
                  [1.],
                  [0.],
                  [1.],
                  [1.],
                  [2.]], dtype=np.float32)

    params = dict(eps=1e-2, n_alphas=10, tol=1e-10, cv=2, n_jobs=1,
                  fit_intercept=False, verbose=2)

    clf = MultiTaskLassoCV(**params)
    clf.fit(X, y)

(casting X to float64 fixes it)

so maybe we can raise a warning if tol is low and X.dtype == np.float32

opened by mathurinm 7

Segmentation fault when fitting `GroupLasso`
I just updated to Celer 0.7dev to benefit from the weights argument in GroupLasso. After updating, I now ran into a segmentation fault error when fitting GroupLasso.

Here is a minimal reproducible example:

import numpy as np from celer import GroupLasso X = np.random.normal(0, 1, (30, 60)) y = np.random.normal(0, 1, (30,)) clf = GroupLasso(3, 0.001, tol=1e-10, fit_intercept=False) clf.fit(X, y)

@Badr-MOUFAD Are you able to reproduce this error on your machine?

Note that Lasso is working perfectly, and that the error occurs only with GroupLasso with or without the weights argument.
opened by PABannier 6
Warm starts and CPUs in use for the GroupLasso

Hi! I was wondering how you were leveraging warm starts in GroupLassoCV? Perhaps between folds in for a given alpha? By default, it seems many cores are being used by GroupLasso or GroupLassoCV? In the case of GroupLassoCV, performance seems to decrease when njobs is set to -1.

Thanks for your work in any case !

opened by georgestod 6
[READY] FIX - more than one iteration done when fitting with alpha > alpha_max
closes #252

We get such behavior because of how we construct a dual feasible vector.

Indeed, when alpha > alpha_max (let say alpha = m * alpha_max where m > 1) , we start the solver at theta = m * y / n_samples, wich is feasible yet not optimal.

...

This proceeds as follows:

[x] fix bug

[x] add unit testing
opened by Badr-MOUFAD 5
ENH add weights to GroupLasso to provide support for an Adaptive GroupLasso

Hi, thanks for this great package! Does the group-lasso method allow for different penalizations for different groups, such that one could fit an adaptive group lasso, i.e., applying different weights in the second call to the method? (reference for adapative group lasso: https://www.sciencedirect.com/science/article/abs/pii/S0167947308002582)

If not, would it be possible to extend the method (easily)?

Thank you.

opened by sehoff 5
Different output than scikit-learn's LASSO on a weird example
Hi !

I am not sure this is the best place to report this, but I noticed a difference in the ouptut produced by your solver and scikit-learn's. I had a really hard time coming up with some minimal example, sorry if the one below is not really informative. I am using Python 3.7.4 and celer development version on Ubuntu.

import numpy as np import sklearn.linear_model import celer X = np.array([[0, 0, 0, 0, 0, 0, 0, 0.001, 0, 0, 0.015, 0, 0, 0.046, 0, 0, 0.061, 0, 0, 0.062]]).T y = np.array([0.008, 0, 0.001, 0.02, 0, 0.001, 0.024, 0.001, 0.001, 0.023, 0.006, 0, 0.011, 0.032, 0, 0.002, 0.056, 0.001, 0.001, 0.062]) lasso_sklearn = sklearn.linear_model.Lasso(alpha=1e-4) lasso_celer = celer.Lasso(alpha=1e-4) lasso_sklearn.fit(X, y) lasso_celer.fit(X, y)

So the coefficient I get from scikit-learn's solver is approximately 0.55 (dual gap is 0) and the one I get from your solver is 0 (dual gap is approximately 1e-4).

I know this is a really degenerate use case, so maybe there is no need to worry about it, but I wanted to report this just in case, and ask if there was any reason celer should not be used in such situation.

Thanks in advance for your help !
opened by rpetit 5

BUG - unable to install ``celer`` in an empty python virtual environment

minimal steps to reproduce,

cd to a new directory
create a python environment python -m venv venv
install celer pip install -U celer

Error logs

(click to expend)

  import numpy.distutils.command.sdist
  Traceback (most recent call last):
    File "C:\Users\HP\AppData\Local\Temp\easy_install-unrz350b\numpy-1.23.0rc3\setup.py", line 251, in generate_cython
  ModuleNotFoundError: No module named 'Cython'
 
  The above exception was the direct cause of the following exception:
 
  Traceback (most recent call last):
    File "C:\Users\HP\Desktop\install-celer\venv\lib\site-packages\setuptools\sandbox.py", line 154, in save_modules
      yield saved
    File "C:\Users\HP\Desktop\install-celer\venv\lib\site-packages\setuptools\sandbox.py", line 195, in setup_context
      yield
    File "C:\Users\HP\Desktop\install-celer\venv\lib\site-packages\setuptools\sandbox.py", line 250, in run_setup
      _execfile(setup_script, ns)
    File "C:\Users\HP\Desktop\install-celer\venv\lib\site-packages\setuptools\sandbox.py", line 45, in _execfile
      exec(code, globals, locals)
    File "C:\Users\HP\AppData\Local\Temp\easy_install-unrz350b\numpy-1.23.0rc3\setup.py", line 493, in <module>
    File "C:\Users\HP\AppData\Local\Temp\easy_install-unrz350b\numpy-1.23.0rc3\setup.py", line 475, in setup_package
    File "C:\Users\HP\AppData\Local\Temp\easy_install-unrz350b\numpy-1.23.0rc3\setup.py", line 258, in generate_cython
  OSError: Cython needs to be installed in Python as a module
 
  During handling of the above exception, another exception occurred:
 
  Traceback (most recent call last):
    File "<string>", line 2, in <module>
    File "<pip-setuptools-caller>", line 34, in <module>
    File "C:\Users\HP\AppData\Local\Temp\pip-req-build-87kkcnrt\setup.py", line 4, in <module>
      dist.Distribution().fetch_build_eggs(['numpy>=1.12'])
    File "C:\Users\HP\Desktop\install-celer\venv\lib\site-packages\setuptools\dist.py", line 716, in fetch_build_eggs
      resolved_dists = pkg_resources.working_set.resolve(
    File "C:\Users\HP\Desktop\install-celer\venv\lib\site-packages\pkg_resources\__init__.py", line 780, in resolve
      dist = best[req.key] = env.best_match(
    File "C:\Users\HP\Desktop\install-celer\venv\lib\site-packages\pkg_resources\__init__.py", line 1065, in best_match
      return self.obtain(req, installer)
    File "C:\Users\HP\Desktop\install-celer\venv\lib\site-packages\pkg_resources\__init__.py", line 1077, in obtain
      return installer(requirement)
    File "C:\Users\HP\Desktop\install-celer\venv\lib\site-packages\setuptools\dist.py", line 786, in fetch_build_egg
      return cmd.easy_install(req)
    File "C:\Users\HP\Desktop\install-celer\venv\lib\site-packages\setuptools\command\easy_install.py", line 679, in easy_install      
      return self.install_item(spec, dist.location, tmpdir, deps)
    File "C:\Users\HP\Desktop\install-celer\venv\lib\site-packages\setuptools\command\easy_install.py", line 705, in install_item      
      dists = self.install_eggs(spec, download, tmpdir)
    File "C:\Users\HP\Desktop\install-celer\venv\lib\site-packages\setuptools\command\easy_install.py", line 890, in install_eggs      
      return self.build_and_install(setup_script, setup_base)
    File "C:\Users\HP\Desktop\install-celer\venv\lib\site-packages\setuptools\command\easy_install.py", line 1158, in 
   build_and_install          self.run_setup(setup_script, setup_base, args)
    File "C:\Users\HP\Desktop\install-celer\venv\lib\site-packages\setuptools\command\easy_install.py", line 1144, in run_setup        
      run_setup(setup_script, args)
    File "C:\Users\HP\Desktop\install-celer\venv\lib\site-packages\setuptools\sandbox.py", line 253, in run_setup
      raise
    File "C:\Users\HP\AppData\Local\Programs\Python\Python38-32\lib\contextlib.py", line 131, in __exit__
      self.gen.throw(type, value, traceback)
    File "C:\Users\HP\Desktop\install-celer\venv\lib\site-packages\setuptools\sandbox.py", line 195, in setup_context
      yield
    File "C:\Users\HP\AppData\Local\Programs\Python\Python38-32\lib\contextlib.py", line 131, in __exit__
      self.gen.throw(type, value, traceback)
    File "C:\Users\HP\Desktop\install-celer\venv\lib\site-packages\setuptools\sandbox.py", line 166, in save_modules
      saved_exc.resume()
    File "C:\Users\HP\Desktop\install-celer\venv\lib\site-packages\setuptools\sandbox.py", line 141, in resume
      six.reraise(type, exc, self._tb)
    File "C:\Users\HP\Desktop\install-celer\venv\lib\site-packages\setuptools\_vendor\six.py", line 685, in reraise
      raise value.with_traceback(tb)
    File "C:\Users\HP\Desktop\install-celer\venv\lib\site-packages\setuptools\sandbox.py", line 154, in save_modules
      yield saved
    File "C:\Users\HP\Desktop\install-celer\venv\lib\site-packages\setuptools\sandbox.py", line 195, in setup_context
      yield
    File "C:\Users\HP\Desktop\install-celer\venv\lib\site-packages\setuptools\sandbox.py", line 250, in run_setup
      _execfile(setup_script, ns)
    File "C:\Users\HP\Desktop\install-celer\venv\lib\site-packages\setuptools\sandbox.py", line 45, in _execfile
      exec(code, globals, locals)
    File "C:\Users\HP\AppData\Local\Temp\easy_install-unrz350b\numpy-1.23.0rc3\setup.py", line 493, in <module>
    File "C:\Users\HP\AppData\Local\Temp\easy_install-unrz350b\numpy-1.23.0rc3\setup.py", line 475, in setup_package
    File "C:\Users\HP\AppData\Local\Temp\easy_install-unrz350b\numpy-1.23.0rc3\setup.py", line 258, in generate_cython
  OSError: Cython needs to be installed in Python as a module
  [end of output]

 note: This error originates from a subprocess, and is likely not a problem with pip.
 error: metadata-generation-failed

  × Encountered error while generating package metadata.
  ╰─> See above for output.

Additional comments

It seems that the error is related to fetching NumPy builds

https://github.com/mathurinm/celer/blob/bd11a44471ff5b688eea30bb43030ec547dd3a4d/setup.py#L4

Also, a portion of the error, namely

OSError: Cython needs to be installed in Python as a module
      [end of output]

is related to https://github.com/numpy/numpy/blob/705244444526804860e9f8d52459e2ca5c255366/setup.py#L257-L258

opened by Badr-MOUFAD 4

FIX - Compatibility with ``scikit-learn`` 1.2.dev
scikit-learn 1.2.dev breaks down the actual code. debug_script.py provides a small snippet to reproduce.

investigation

scikit-learn seems to have added a validation step of the class parameters at the fit moment. our Lasso estimator doesn't have the same signature as the scikit-learn (e.g. copy_X and random_state), though we inherit from it.

Therefore we get an error when comparing the constructor arguments with the parent class

click to expend error

raise ValueError( ValueError: The parameter constraints ['alpha', 'fit_intercept', 'precompute', 'max_iter', 'copy_X', 'tol', 'warm_start', 'positive', 'random_state', 'selection'] contain unexpected parameters {'copy_X', 'precompute', 'random_state', 'selection'}

potential fix

A straightforward fix would be to override _validate_params. But I don't think it's a reliable way to do it.

click to expend code

def _validate_params(self): pass
opened by Badr-MOUFAD 0
`climate._target_region` incorrectly extracts misaligned column

There seems to be a problem with the climate.target_region function which incorrectly extracts one column to the right of the intended one. https://github.com/mathurinm/celer/blob/main/celer/datasets/climate.py#L60-L72

The pos_Lx value range is supposed to be 0~143, but it is 0~144. For example, if Lx is 359, pos_Lx will be 144.

Shouldn't we use np.floor insted of np.ceil?

opened by shimon-sato 2
MAINT prune default value different between celer_path and Lasso

Thank you again for this remarkably fast library.

In the celer_path the default value of prune is 0 while in Lasso, MultiTaskLasso, and GroupLasso it's set to 1 (True).

opened by cruyffturn 1
Feature Request: MultiTask GroupLasso

I was wondering whether you could (easily) implement the MultiTask GroupLasso as in, or similar to, equation 1 here: http://psb.stanford.edu/psb-online/proceedings/psb22/nouira.pdf

Thanks !

opened by sehoff 0
MAINT - Use ``create_dual_point`` in group and multitask lasso

https://github.com/mathurinm/celer/blob/4230db117a916e5158cfae85b2cd1a7249cf5475/celer/group_fast.pyx#L237-L238

https://github.com/mathurinm/celer/blob/4230db117a916e5158cfae85b2cd1a7249cf5475/celer/multitask_fast.pyx#L332-L333

opened by Badr-MOUFAD 0
DOC - Add examples for ``ElasticNet``
Example(s) should exhibit the advantages of the ElasticNet estimator over the Lasso and OLS estimators, namely

[x] Feature selection (case p >> n)

[ ] Grouping effects

[ ] Generalization to unseen data.

(refer toH Zou · 2005)
opened by Badr-MOUFAD 0

Releases(v0.5.1)

v0.5.1(Oct 12, 2020)

Source code(tar.gz)
Source code(zip)
v0.3(Nov 28, 2018)

Source code(tar.gz)
Source code(zip)
v0.2(Sep 12, 2018)

Source code(tar.gz)
Source code(zip)

Owner

GitHub https://mathurinm.github.io/celer/

Code for paper: Group-CAM: Group Score-Weighted Visual Explanations for Deep Convolutional Networks

Group-CAM By Zhang, Qinglong and Rao, Lu and Yang, Yubin [State Key Laboratory for Novel Software Technology at Nanjing University] This repo is the o

98 Nov 16, 2022

SparseLasso: Sparse Solutions for the Lasso

SparseLasso: Sparse Solutions for the Lasso Introduction SparseLasso provides a Scikit-Learn based estimation of the Lasso with cross-validation tunin

1 Nov 8, 2021

Code for paper "Energy-Constrained Compression for Deep Neural Networks via Weighted Sparse Projection and Layer Input Masking"

model_based_energy_constrained_compression Code for paper "Energy-Constrained Compression for Deep Neural Networks via Weighted Sparse Projection and

16 Jun 15, 2022

Implementation of Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning

advantage-weighted-regression Implementation of Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning, by Peng et al. (

1 Dec 2, 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 Scheduling, Job Shop Scheduling, Bin Packing and many more planning problems.

208 Dec 27, 2022

Sudoku solver - A sudoku solver with python

sudoku_solver A sudoku solver What is Sudoku? Sudoku (Japanese: 数独, romanized: s

0 May 22, 2022

Simple Linear 2nd ODE Solver GUI - A 2nd constant coefficient linear ODE solver with simple GUI using euler's method

Simple_Linear_2nd_ODE_Solver_GUI Description It is a 2nd constant coefficient li

4 Feb 5, 2022

Wordle Solver: A simple script which is also called Wordle solver

wordle-solver this code is a simple script which is also called Wordle solver. t

1 Feb 15, 2022

Multitask Learning Strengthens Adversarial Robustness

15 Jun 10, 2022

Code for paper Multitask-Finetuning of Zero-shot Vision-Language Models

2 Jul 15, 2022

Python scripts for performing road segemtnation and car detection using the HybridNets multitask model in ONNX.

ONNX-HybridNets-Multitask-Road-Detection Python scripts for performing road segemtnation and car detection using the HybridNets multitask model in ONN

45 Jan 1, 2023

Differentiable Neural Computers, Sparse Access Memory and Sparse Differentiable Neural Computers, for Pytorch

Differentiable Neural Computers and family, for Pytorch Includes: Differentiable Neural Computers (DNC) Sparse Access Memory (SAM) Sparse Differentiab

302 Dec 14, 2022

A collection of various RL algorithms like policy gradients, DQN and PPO. The goal of this repo will be to make it a go-to resource for learning about RL. How to visualize, debug and solve RL problems. I've additionally included playground.py for learning more about OpenAI gym, etc.

Reinforcement Learning (PyTorch) ?? + ?? = ❤️ This repo will contain PyTorch implementation of various fundamental RL algorithms. It's aimed at making

123 Dec 23, 2022

Quantile Regression DQN a Minimal Working Example, Distributional Reinforcement Learning with Quantile Regression

Quantile Regression DQN Quantile Regression DQN a Minimal Working Example, Distributional Reinforcement Learning with Quantile Regression (https://arx

80 Sep 17, 2022

Hitters Linear Regression - Hitters Linear Regression With Python

Hitters_Linear_Regression Kullanacağımız veri seti Carnegie Mellon Üniversitesi'

2 Jan 26, 2022

Transformers-regression - Regression Bugs Are In Your Model! Measuring, Reducing and Analyzing Regressions In NLP Model Updates

Regression Free Model Update Code for the paper: Regression Bugs Are In Your Mod

2 Feb 17, 2022