abess: Fast Best-Subset Selection in Python and R

Related tags

Deep Learningpythonmachine-learningrhigh-dimensional-datapolynomial-algorithmbest-subset-selection
Overview

abess: Fast Best-Subset Selection in Python and R

Python Build R Build codecov docs cran pypi pyversions License Codacy Badge

Overview

abess (Adaptive BEst Subset Selection) library aims to solve general best subset selection, i.e., find a small subset of predictors such that the resulting model is expected to have the highest accuracy. The selection for best subset shows great value in scientific researches and practical applications. For example, clinicians want to know whether a patient is healthy or not based on the expression levels of a few of important genes.

This library implements a generic algorithm framework to find the optimal solution in an extremely fast way. This framework now supports the detection of best subset under: linear regression, classification (binary or multi-class), counting-response modeling, censored-response modeling, multi-response modeling (multi-tasks learning), etc. It also supports the variants of best subset selection like group best subset selection, nuisance penalized regression, Especially, the time complexity of (group) best subset selection for linear regression is certifiably polynomial.

Quick start

The abess software has both Python and R's interfaces. Here a quick start will be given and for more details, please view: Installation.

Python package

Install the stable version of Python-package from Pypi with:

$ pip install abess

Best subset selection for linear regression on a simulated dataset in Python:

from abess.linear import abessLm
from abess.datasets import make_glm_data
sim_dat = make_glm_data(n = 300, p = 1000, k = 10, family = "gaussian")
model = abessLm()
model.fit(sim_dat.x, sim_dat.y)

See more examples analyzed with Python in the Python tutorials.

R package

Install the stable version of R-package from CRAN with:

install.packages("abess")

Best subset selection for linear regression on a simulated dataset in R:

library(abess)
sim_dat <- generate.data(n = 300, p = 1000)
abess(x = sim_dat[["x"]], y = sim_dat[["y"]])

See more examples analyzed with R in the R tutorials.

Runtime Performance

To show the power of abess in computation, we assess its timings of the CPU execution (seconds) on synthetic datasets, and compare to state-of-the-art variable selection methods. The variable selection and estimation results are deferred to Python performance and R performance. All computations are conducted on a Ubuntu platform with Intel(R) Core(TM) i9-9940X CPU @ 3.30GHz and 48 RAM.

Python package

We compare abess Python package with scikit-learn on linear regression and logistic regression. Results are presented in the below figure:

It can be see that abess uses the least runtime to find the solution. This results can be reproduced by running the command in shell:

$ python abess/docs/simulation/Python/timings.py

R package

We compare abess R package with three widely used R packages: glmnet, ncvreg, and L0Learn. We get the runtime comparison results:

Compared with other packages, abess shows competitive computational efficiency, and achieves the best computational power when variables have a large correlation.

Conducting the following command in shell can reproduce the above results in R:

$ Rscript abess/docs/simulation/R/timings.R

Open source software

abess is a free software and its source code is publicly available on Github. The core framework is programmed in C++, and user-friendly R and Python interfaces are offered. You can redistribute it and/or modify it under the terms of the GPL-v3 License. We welcome contributions for abess, especially stretching abess to the other best subset selection problems.

Citation

If you use abess or reference our tutorials in a presentation or publication, we would appreciate citations of our library.

Jin Zhu, Liyuan Hu, Junhao Huang, Kangkang Jiang, Yanhang Zhang, Shiyun Lin, Junxian Zhu, Xueqin Wang (2021). “abess: A Fast Best Subset Selection Library in Python and R.” arXiv:2110.09697.

The corresponding BibteX entry:

@article{zhu-abess-arxiv,
  author    = {Jin Zhu and Liyuan Hu and Junhao Huang and Kangkang Jiang and Yanhang Zhang and Shiyun Lin and Junxian Zhu and Xueqin Wang},
  title     = {abess: A Fast Best Subset Selection Library in Python and R},
  journal   = {arXiv:2110.09697},
  year      = {2021},
}

References

  • Junxian Zhu, Canhong Wen, Jin Zhu, Heping Zhang, and Xueqin Wang (2020). A polynomial algorithm for best-subset selection problem. Proceedings of the National Academy of Sciences, 117(52):33117-33123.

  • Pölsterl, S (2020). scikit-survival: A Library for Time-to-Event Analysis Built on Top of scikit-learn. J. Mach. Learn. Res., 21(212), 1-6.

  • Yanhang Zhang, Junxian Zhu, Jin Zhu, and Xueqin Wang (2021). Certifiably Polynomial Algorithm for Best Group Subset Selection. arXiv preprint arXiv:2104.12576.

  • Qiang Sun and Heping Zhang (2020). Targeted Inference Involving High-Dimensional Data Using Nuisance Penalized Regression, Journal of the American Statistical Association, DOI: 10.1080/01621459.2020.1737079.

Comments
  • Import error

    Import error

    When I import abess in each python version I try,such as ( 3.5 3.6 3.7 ) all report the error as follow'

    Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "C:\ProgramData\Anaconda3\envs\cp3710\lib\site-packages\abess\__init__.py", line 9, in <module>
    from abess.linear import abessLogistic, abessLm, abessCox, abessPoisson, abessMultigaussian, abessMultinomial
  File "C:\ProgramData\Anaconda3\envs\cp3710\lib\site-packages\abess\linear.py", line 3, in <module>
    from .bess_base import bess_base
  File "C:\ProgramData\Anaconda3\envs\cp3710\lib\site-packages\abess\bess_base.py", line 7, in <module>
    from abess.cabess import pywrap_abess
  File "C:\ProgramData\Anaconda3\envs\cp3710\lib\site-packages\abess\cabess.py", line 13, in <module>
    from . import _cabess
ImportError: DLL load failed: 找不到指定的模块。

    I try all I can find in the web,but not works, cloud anyone can help me .

    opened by bored2020 12
  • Problem of computing GIC

    Problem of computing GIC

    I want to compute GIC to select the true model. But I gain different results from the abess packages and manual calculation.

       set.seed(2)
    p = 250
    N = 2500
    X = matrix(rnorm(N * p), ncol = p)
    A = sort(sample(p, 10))
    beta = rep(0, p)
    beta = replace(beta, A, rnorm(10, mean = 6))
    xbeta <- X %*% beta
    Y <- xbeta + rnorm(N)

    Compute the estimator by abess packages.

    
    C = abess(X, Y, family = "gaussian", tune.path="sequence",tune.type = "gic")
    k = C$best.size
    mid=coef(abess(X, Y, family = "gaussian",support.size =k))
    Central =mid[2:(p+1)]
    intercept=mid[1]
    #compute GIC[10]=131.3686
    GIC= N*log(1/(2*N)*t(Y-X%*%Central-intercept)%*%(Y-X%*%Central-intercept))+k*log(p)*(log(log(N)))
    #GIC=-1601.499
    good first issue invalid 
    opened by yannstory 9
  • zsh: illegal hardware instruction python (mac m1 silicon)

    zsh: illegal hardware instruction python (mac m1 silicon)

    I run the following code in the terminal, I get the error "zsh: illegal hardware instruction python"

    Python 3.9.7 (default, Sep 16 2021, 08:50:36) 
[Clang 10.0.0 ] :: Anaconda, Inc. on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> from abess.linear import abessLogistic
>>> from sklearn.datasets import load_breast_cancer
>>> from sklearn.pipeline import Pipeline
>>> from sklearn.metrics import make_scorer, roc_auc_score
>>> from sklearn.preprocessing import PolynomialFeatures
>>> from sklearn.model_selection import GridSearchCV
>>> pipe = Pipeline([('poly', PolynomialFeatures(include_bias=False)), ('alogistic', abessLogistic())])
>>> param_grid = {'poly__interaction_only': [True, False],'poly__degree': [1, 2, 3]}
>>> scorer = make_scorer(roc_auc_score, greater_is_better=True)
>>> grid_search = GridSearchCV(pipe, param_grid, scoring=scorer, cv=5)
>>> X, y = load_breast_cancer(return_X_y=True)
>>> grid_search.fit(X, y)
    documentation 
    opened by JiaqiHu2021 6
  • cross validation in R package

    cross validation in R package

    Describe the bug The cross validation result is not the same as the result written in R.

    The code to reproduce

    library(abess)
n <- 100
p <- 200
support.size <- 3
dataset <- generate.data(n, p, support.size, seed = 1)
ss <- 0:10

nfolds <- 5
foldid <- rep(1:nfolds, ceiling(n / nfolds))[1:n]
abess_fit <- abess(dataset[["x"]], dataset[["y"]], 
                   tune.type = "cv", nfolds = nfolds, 
                   foldid = foldid, support.size = ss, num.threads = 1)

cv <- rep(0, length(ss))
for (k in 1:nfolds) {
  abess_fit_k <- abess(dataset[["x"]][foldid != k, ], 
                       dataset[["y"]][foldid != k], support.size = ss)
  y_hat_k <- predict(abess_fit_k, dataset[["x"]][foldid == k, ], 
                     support.size = ss)
  fold_cv <- apply(y_hat_k, 2, function(yh) {
    mean((dataset[["y"]][foldid == k] - yh)^2)
  })
  fold_cv <- round(fold_cv, digits = 2)
  print(fold_cv)
  cv <- cv + fold_cv
}
cv <- cv / nfolds
names(cv) <- NULL
all.equal(cv, abess_fit$tune.value, digits = 2)

    Expected behavior The output of all.equal(cv, abess_fit$tune.value, digits = 2) is TRUE. However, the output is "Mean relative difference: 0.0008444762".

    System info

    platform       x86_64-apple-darwin17.0     
arch           x86_64                      
os             darwin17.0                  
system         x86_64, darwin17.0          
status                                     
major          4                           
minor          1.0                         
year           2021                        
month          05                          
day            18                          
svn rev        80317                       
language       R                           
version.string R version 4.1.0 (2021-05-18)
nickname       Camp Pontanezen
    wontfix 
    opened by Mamba413 5
  • Could not install python package on m1 pro silicon

    Could not install python package on m1 pro silicon

    I run pip install abess in the terminal

    Last login: Tue Jan 18 20:32:17 on ttys000 (base) [email protected] ~ % pip install abess

    Collecting abess
  Using cached abess-0.3.6.tar.gz (1.5 MB)
Requirement already satisfied: numpy in ./opt/anaconda3/lib/python3.9/site-packages (from abess) (1.20.3)
Requirement already satisfied: scipy in ./opt/anaconda3/lib/python3.9/site-packages (from abess) (1.7.1)
Requirement already satisfied: scikit-learn>=0.24 in ./opt/anaconda3/lib/python3.9/site-packages (from abess) (0.24.2)
Requirement already satisfied: joblib>=0.11 in ./opt/anaconda3/lib/python3.9/site-packages (from scikit-learn>=0.24->abess) (1.1.0)
Requirement already satisfied: threadpoolctl>=2.0.0 in ./opt/anaconda3/lib/python3.9/site-packages (from scikit-learn>=0.24->abess) (2.2.0)
Building wheels for collected packages: abess
  Building wheel for abess (setup.py) ... error
  ERROR: Command errored out with exit status 1:
   command: /Users/jiaqihu/opt/anaconda3/bin/python -u -c 'import io, os, sys, setuptools, tokenize; sys.argv[0] = '"'"'/private/var/folders/pj/c4q3qfkx2119wyj2vbfr_yhr0000gn/T/pip-install-ofm5rwzp/abess_e1c5333de72248a2bdb93137c36fb890/setup.py'"'"'; __file__='"'"'/private/var/folders/pj/c4q3qfkx2119wyj2vbfr_yhr0000gn/T/pip-install-ofm5rwzp/abess_e1c5333de72248a2bdb93137c36fb890/setup.py'"'"';f = getattr(tokenize, '"'"'open'"'"', open)(__file__) if os.path.exists(__file__) else io.StringIO('"'"'from setuptools import setup; setup()'"'"');code = f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' bdist_wheel -d /private/var/folders/pj/c4q3qfkx2119wyj2vbfr_yhr0000gn/T/pip-wheel-j5ai4o5i
       cwd: /private/var/folders/pj/c4q3qfkx2119wyj2vbfr_yhr0000gn/T/pip-install-ofm5rwzp/abess_e1c5333de72248a2bdb93137c36fb890/
  Complete output (19 lines):
  bash: /private/var/folders/pj/c4q3qfkx2119wyj2vbfr_yhr0000gn/T/pip-install-ofm5rwzp/abess_e1c5333de72248a2bdb93137c36fb890/copy_src.sh: No such file or directory
  running bdist_wheel
  running build
  running build_py
  creating build
  creating build/lib.macosx-10.9-x86_64-3.9
  creating build/lib.macosx-10.9-x86_64-3.9/abess
  copying /private/var/folders/pj/c4q3qfkx2119wyj2vbfr_yhr0000gn/T/pip-install-ofm5rwzp/abess_e1c5333de72248a2bdb93137c36fb890/abess/metrics.py -> build/lib.macosx-10.9-x86_64-3.9/abess
  copying /private/var/folders/pj/c4q3qfkx2119wyj2vbfr_yhr0000gn/T/pip-install-ofm5rwzp/abess_e1c5333de72248a2bdb93137c36fb890/abess/linear.py -> build/lib.macosx-10.9-x86_64-3.9/abess
  copying /private/var/folders/pj/c4q3qfkx2119wyj2vbfr_yhr0000gn/T/pip-install-ofm5rwzp/abess_e1c5333de72248a2bdb93137c36fb890/abess/cabess.py -> build/lib.macosx-10.9-x86_64-3.9/abess
  copying /private/var/folders/pj/c4q3qfkx2119wyj2vbfr_yhr0000gn/T/pip-install-ofm5rwzp/abess_e1c5333de72248a2bdb93137c36fb890/abess/datasets.py -> build/lib.macosx-10.9-x86_64-3.9/abess
  copying /private/var/folders/pj/c4q3qfkx2119wyj2vbfr_yhr0000gn/T/pip-install-ofm5rwzp/abess_e1c5333de72248a2bdb93137c36fb890/abess/__init__.py -> build/lib.macosx-10.9-x86_64-3.9/abess
  copying /private/var/folders/pj/c4q3qfkx2119wyj2vbfr_yhr0000gn/T/pip-install-ofm5rwzp/abess_e1c5333de72248a2bdb93137c36fb890/abess/bess_base.py -> build/lib.macosx-10.9-x86_64-3.9/abess
  copying /private/var/folders/pj/c4q3qfkx2119wyj2vbfr_yhr0000gn/T/pip-install-ofm5rwzp/abess_e1c5333de72248a2bdb93137c36fb890/abess/pca.py -> build/lib.macosx-10.9-x86_64-3.9/abess
  running build_ext
  building 'abess._cabess' extension
  swigging /private/var/folders/pj/c4q3qfkx2119wyj2vbfr_yhr0000gn/T/pip-install-ofm5rwzp/abess_e1c5333de72248a2bdb93137c36fb890/src/pywrap.i to /private/var/folders/pj/c4q3qfkx2119wyj2vbfr_yhr0000gn/T/pip-install-ofm5rwzp/abess_e1c5333de72248a2bdb93137c36fb890/src/pywrap_wrap.cpp
  swig -python -c++ -o /private/var/folders/pj/c4q3qfkx2119wyj2vbfr_yhr0000gn/T/pip-install-ofm5rwzp/abess_e1c5333de72248a2bdb93137c36fb890/src/pywrap_wrap.cpp /private/var/folders/pj/c4q3qfkx2119wyj2vbfr_yhr0000gn/T/pip-install-ofm5rwzp/abess_e1c5333de72248a2bdb93137c36fb890/src/pywrap.i
  error: command 'swig' failed: No such file or directory
  ----------------------------------------
  ERROR: Failed building wheel for abess
  Running setup.py clean for abess
Failed to build abess
Installing collected packages: abess
    Running setup.py install for abess ... error
    ERROR: Command errored out with exit status 1:
     command: /Users/jiaqihu/opt/anaconda3/bin/python -u -c 'import io, os, sys, setuptools, tokenize; sys.argv[0] = '"'"'/private/var/folders/pj/c4q3qfkx2119wyj2vbfr_yhr0000gn/T/pip-install-ofm5rwzp/abess_e1c5333de72248a2bdb93137c36fb890/setup.py'"'"'; __file__='"'"'/private/var/folders/pj/c4q3qfkx2119wyj2vbfr_yhr0000gn/T/pip-install-ofm5rwzp/abess_e1c5333de72248a2bdb93137c36fb890/setup.py'"'"';f = getattr(tokenize, '"'"'open'"'"', open)(__file__) if os.path.exists(__file__) else io.StringIO('"'"'from setuptools import setup; setup()'"'"');code = f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' install --record /private/var/folders/pj/c4q3qfkx2119wyj2vbfr_yhr0000gn/T/pip-record-6eee5eyp/install-record.txt --single-version-externally-managed --compile --install-headers /Users/jiaqihu/opt/anaconda3/include/python3.9/abess
         cwd: /private/var/folders/pj/c4q3qfkx2119wyj2vbfr_yhr0000gn/T/pip-install-ofm5rwzp/abess_e1c5333de72248a2bdb93137c36fb890/
    Complete output (19 lines):
    bash: /private/var/folders/pj/c4q3qfkx2119wyj2vbfr_yhr0000gn/T/pip-install-ofm5rwzp/abess_e1c5333de72248a2bdb93137c36fb890/copy_src.sh: No such file or directory
    running install
    running build
    running build_py
    creating build
    creating build/lib.macosx-10.9-x86_64-3.9
    creating build/lib.macosx-10.9-x86_64-3.9/abess
    copying /private/var/folders/pj/c4q3qfkx2119wyj2vbfr_yhr0000gn/T/pip-install-ofm5rwzp/abess_e1c5333de72248a2bdb93137c36fb890/abess/metrics.py -> build/lib.macosx-10.9-x86_64-3.9/abess
    copying /private/var/folders/pj/c4q3qfkx2119wyj2vbfr_yhr0000gn/T/pip-install-ofm5rwzp/abess_e1c5333de72248a2bdb93137c36fb890/abess/linear.py -> build/lib.macosx-10.9-x86_64-3.9/abess
    copying /private/var/folders/pj/c4q3qfkx2119wyj2vbfr_yhr0000gn/T/pip-install-ofm5rwzp/abess_e1c5333de72248a2bdb93137c36fb890/abess/cabess.py -> build/lib.macosx-10.9-x86_64-3.9/abess
    copying /private/var/folders/pj/c4q3qfkx2119wyj2vbfr_yhr0000gn/T/pip-install-ofm5rwzp/abess_e1c5333de72248a2bdb93137c36fb890/abess/datasets.py -> build/lib.macosx-10.9-x86_64-3.9/abess
    copying /private/var/folders/pj/c4q3qfkx2119wyj2vbfr_yhr0000gn/T/pip-install-ofm5rwzp/abess_e1c5333de72248a2bdb93137c36fb890/abess/__init__.py -> build/lib.macosx-10.9-x86_64-3.9/abess
    copying /private/var/folders/pj/c4q3qfkx2119wyj2vbfr_yhr0000gn/T/pip-install-ofm5rwzp/abess_e1c5333de72248a2bdb93137c36fb890/abess/bess_base.py -> build/lib.macosx-10.9-x86_64-3.9/abess
    copying /private/var/folders/pj/c4q3qfkx2119wyj2vbfr_yhr0000gn/T/pip-install-ofm5rwzp/abess_e1c5333de72248a2bdb93137c36fb890/abess/pca.py -> build/lib.macosx-10.9-x86_64-3.9/abess
    running build_ext
    building 'abess._cabess' extension
    swigging /private/var/folders/pj/c4q3qfkx2119wyj2vbfr_yhr0000gn/T/pip-install-ofm5rwzp/abess_e1c5333de72248a2bdb93137c36fb890/src/pywrap.i to /private/var/folders/pj/c4q3qfkx2119wyj2vbfr_yhr0000gn/T/pip-install-ofm5rwzp/abess_e1c5333de72248a2bdb93137c36fb890/src/pywrap_wrap.cpp
    swig -python -c++ -o /private/var/folders/pj/c4q3qfkx2119wyj2vbfr_yhr0000gn/T/pip-install-ofm5rwzp/abess_e1c5333de72248a2bdb93137c36fb890/src/pywrap_wrap.cpp /private/var/folders/pj/c4q3qfkx2119wyj2vbfr_yhr0000gn/T/pip-install-ofm5rwzp/abess_e1c5333de72248a2bdb93137c36fb890/src/pywrap.i
    error: command 'swig' failed: No such file or directory
    ----------------------------------------
ERROR: Command errored out with exit status 1: /Users/jiaqihu/opt/anaconda3/bin/python -u -c 'import io, os, sys, setuptools, tokenize; sys.argv[0] = '"'"'/private/var/folders/pj/c4q3qfkx2119wyj2vbfr_yhr0000gn/T/pip-install-ofm5rwzp/abess_e1c5333de72248a2bdb93137c36fb890/setup.py'"'"'; __file__='"'"'/private/var/folders/pj/c4q3qfkx2119wyj2vbfr_yhr0000gn/T/pip-install-ofm5rwzp/abess_e1c5333de72248a2bdb93137c36fb890/setup.py'"'"';f = getattr(tokenize, '"'"'open'"'"', open)(__file__) if os.path.exists(__file__) else io.StringIO('"'"'from setuptools import setup; setup()'"'"');code = f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' install --record /private/var/folders/pj/c4q3qfkx2119wyj2vbfr_yhr0000gn/T/pip-record-6eee5eyp/install-record.txt --single-version-externally-managed --compile --install-headers /Users/jiaqihu/opt/anaconda3/include/python3.9/abess Check the logs for full command output.
    documentation 
    opened by JiaqiHu2021 4
  • Can't find cpp function

    Can't find cpp function

    In my fork repo, the developing part in "bbayukari/abess/develop-ordinal" worked well before, but the new version which I want to PR to upstream in "bbayukari/abess/ordinal" didn't work(not only my part didn't work, but all models can't find the api). In the new version, I locate the cpp code at abess/src where isn't in R-package, that's the only difference between the two versions.

    After clone the code in "bbayukari/abess/ordinal", then "install and restart", and test simply like:

    dataset <- generate.data(150,100,3)
abess(dataset[["x"]],dataset[["y"]])

    then,

    Error in abessGLM_API(x = x, y = y, n = nobs, p = nvars, normalize_type = normalize,  : 
  could not find function "abessGLM_API"
Called from: abess.default(dataset[["x"]], dataset[["y"]])
    bug 
    opened by bbayukari 4
  • Have problem in abessMultinomial

    Have problem in abessMultinomial

    When I want to use abess in multi classfication,I use the abessMultinomial and run the example code

    from abess.linear import abessMultinomial
from abess.datasets import make_multivariate_glm_data
import numpy as np
np.random.seed(12345)
data = make_multivariate_glm_data(n = 100, p = 50, k = 10, M = 3, family = 'multinomial')
model = abessMultinomial(support_size = [10])
model.fit(data.x, data.y)
model.predict(data.x)

    And I get an int 47,but I think it should get a array of the class such as .

    array([[1., 0., 0.],
       [0., 0., 1.],
       [1., 0., 0.],
       [1., 0., 0.],
       [0., 0., 1.],
       [0., 1., 0.],
       [0., 1., 0.],
       [1., 0., 0.],
       [0., 1., 0.],
       [1., 0., 0.],
       [1., 0., 0.],
       [0., 1., 0.],
       [0., 0., 1.],
       [0., 1., 0.],

    I'm so sorry to bother you again.And thanks a lot for yours help.

    bug 
    opened by bored2020 4
  • Update something

    Update something

    • delete unused parameters
    • pack specific parameters (add function: initial_setting & clear_setting)
    • pack restore_for_normal
    • restructure gs_path & gs support CV results output
    • rewrite screening (now use algorithmXXX.fit() and unlink with model_fit.h)
    • API update
    • simplify algorithm pointer (use algorithm_list_xxx uniformly)
    • move beta_size to Algorithm class
    opened by oooo26 4
  • Unified GLM algorithm

    Unified GLM algorithm

    • Add _abessGLM in AlgorithmGLM.h as the base GLM class
    • Rewrite Logistic/Poisson/Gamma Regression on the basis of _abessGLM, which would be much simpler
    • Fix zero-weight sample bug #458
    enhancement 
    opened by oooo26 3
  • Use same keyword arguments for sample weight as sklearn

    Use same keyword arguments for sample weight as sklearn

    First of all: Thank you for the great package, it has been very helpful. Now to my suggestion:

    Abess uses weight https://abess.readthedocs.io/en/latest/Python-package/linear/Logistic.html?highlight=score#abess.linear.LogisticRegression.fit Sklearn uses sample_weight https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression.fit

    I'm using both in a project and it would be helpful if abess followed the sklearn convention.

    enhancement 
    opened by lindnemi 3
  • update R package Matrix API 'as()'

    update R package Matrix API 'as()'

    change methods::as(sparse.loading, "dgCMatrix") into as(as(as(sparse.loading, "dMatrix"), "generalMatrix"), "CsparseMatrix") in R-package/R/generate.spc.matrix.R

    opened by bbayukari 3
  • Cross Validation Principle of Cox model

    Cross Validation Principle of Cox model

    Hello, I 'm using the package to calculate some real survival data, I find the cross-validation can only choose deviance on the test cohort to determine the support size. could you guys add the c_index principle for cox model's cross validation?

    opened by EQUIWDH 0
  • Some problems about algorithm for cox model when the dimension is ultra-high

    Some problems about algorithm for cox model when the dimension is ultra-high

    Hello, I am doing some real data analysis about high-dimensional cox model. My real dataset's shape is like 240*7000, however, I try to use the abess.CoxPHSurvivalAnalysis() with cv and it can not choose any feature out. So, I must use screening before abess for Cox model. I also did simulation test for only screening method in abess package and found that the screening method can not contain all the real features spawn by make_glm_data. So, I doubt the algorithm of screening in this package, I hope you guys may adapt it, thank u!!!

    help wanted 
    opened by EQUIWDH 2
  • Illegal instruction with conda-forge package

    Illegal instruction with conda-forge package

    In the HPC environment (Intel, linux) even very simple script give Illegal Instruction errors. This suffices to generate the error

    model = abess.LinearRegression()
model.fit(np.random.rand(2,2), np.random.rand(2))

    However, this occurs only with the latest abess binary from conda-forge. If I use pip to install the package, everything runs smoothly.

    bug 
    opened by lindnemi 0
  • memory out when combining abess with auto-sklearn for classification.

    memory out when combining abess with auto-sklearn for classification.

    Describe the bug I'm doing some experiments about combining abess with auto-sklearn, when using MultinomialRegression for classification, the memory tends to increase very quickly and so much that it cannot be displayed on a web page, but for LinearRegression, there is no similar out-of-memory problem.

    Code for Reproduction

    My code is given as follows:

    from pprint import pprint

from ConfigSpace.configuration_space import ConfigurationSpace
from ConfigSpace.hyperparameters import CategoricalHyperparameter, \
    UniformIntegerHyperparameter, UniformFloatHyperparameter

import sklearn.metrics
import autosklearn.classification
import autosklearn.pipeline.components.classification
from autosklearn.pipeline.components.base \
    import AutoSklearnClassificationAlgorithm
from autosklearn.pipeline.constants import DENSE, SIGNED_DATA, UNSIGNED_DATA, \
    PREDICTIONS

from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
import openml
from abess import MultinomialRegression
from sklearn.ensemble import RandomForestClassifier
import numpy as np
import pandas as pd
from sklearn.datasets import fetch_openml
from sklearn import preprocessing
from sklearn.tree import DecisionTreeClassifier

import time
import matplotlib.pyplot as plt

import warnings
warnings.filterwarnings("ignore")

class AbessClassifier(AutoSklearnClassificationAlgorithm):

    def __init__(self, exchange_num, random_state=None):
        self.exchange_num = exchange_num
        self.random_state = random_state
        self.estimator = None

    def fit(self, X, y):
        from abess import MultinomialRegression
        self.estimator = MultinomialRegression()
        self.estimator.fit(X, y)
        return self

    def predict(self, X):
        if self.estimator is None:
            raise NotImplementedError
        return self.estimator.predict(X)
    
    def predict_proba(self, X):
        if self.estimator is None:
            raise NotImplementedError()
        return self.estimator.predict_proba(X)

    @staticmethod
    def get_properties(dataset_properties=None):
        return {
            'shortname': 'abess Classifier',
            'name': 'abess logistic Classifier',
            'handles_regression': False,
            'handles_classification': True,
            'handles_multiclass': True,
            'handles_multilabel': False,
            'handles_multioutput': False,
            'is_deterministic': False,
            # Both input and output must be tuple(iterable)
            'input': [DENSE, SIGNED_DATA, UNSIGNED_DATA],
            'output': [PREDICTIONS]
        }
    
    @staticmethod
    def get_hyperparameter_search_space(dataset_properties=None):
        cs = ConfigurationSpace() 
        exchange_num=UniformIntegerHyperparameter(
            name='exchange_num', lower=4, upper=6, default_value=5
        )
        cs.add_hyperparameters([exchange_num])
        return cs
    
# Add abess logistic classifier component to auto-sklearn.
autosklearn.pipeline.components.classification.add_classifier(AbessClassifier)
cs = AbessClassifier.get_hyperparameter_search_space()
print(cs)

dataset = fetch_openml(data_id = int(29),as_frame=True)#507,183,44136
X=dataset.data
y=dataset.target
X.replace([np.inf,-np.inf],np.NaN,inplace=True)
## Remove rows with NaN or Inf values
inx=X[X.isna().values==True].index.unique()
X.drop(inx,inplace=True)
y.drop(inx,inplace=True)
##use dummy variables to replace classification variables:
X = pd.get_dummies(X)
## Keep only numeric columns
X = X.select_dtypes(np.number)
## Remove columns with NaN or Inf values
nan = np.isnan(X).any()[np.isnan(X).any() == True]
inf = np.isinf(X).any()[np.isinf(X).any() == True]
X = X.drop(columns = list(nan.index))
X = X.drop(columns = list(inf.index))
##Encode target labels with value between 0 and 1
le = preprocessing.LabelEncoder()
y = le.fit_transform(y)
X_train, X_test, y_train, y_test = train_test_split(X, y)
print(X_train.shape) #number of initial features
print(X_test.shape) #number of initial features

cls = autosklearn.classification.AutoSklearnClassifier(
    time_left_for_this_task=60,
    per_run_time_limit=10,
    include={
            'classifier': ['AbessClassifier'],
            'feature_preprocessor': ['polynomial']
        },
    memory_limit=6144,
    ensemble_size=1,
)
cls.fit(X_train, y_train, X_test, y_test)
predictions = cls.predict(X_test)
print("Accuracy score", sklearn.metrics.accuracy_score(y_test, predictions))

    After running this code , the memory gets to about 159MB, which is not friendly for users to open an .ipynb. Again, regression does not encounter the memory-out problem.

    bug 
    opened by belzheng 0
  • Cross validation is slower in version 0.4.5 than in 0.4.0

    Cross validation is slower in version 0.4.5 than in 0.4.0

    Describe the bug

    In my experiments, after updating abess from 0.4.0 to 0.4.5, I found the cv procedure get slower in some cases. The following code provides an example.

    Code for Reproduction

    library(microbenchmark)
library(abess)
n <- 3000
p <- 500
support.size <- 10

sim_once <- function(seed) {
  dataset <- generate.data(n, p, support.size, family = "binomial", seed = seed)

  time_cv <- microbenchmark(
    abess_fit <- abess(dataset[["x"]], dataset[["y"]], family = "binomial", tune.type = "cv", nfolds = 10),
    times = 1
  ) [["time"]] / 10^9

  time_cv
}

# average time
time <- sapply(1:5, sim_once)
mean(time)

    image

    invalid 
    opened by brtang63 1
Owner
Team developing polynomial algorithms for best subset selection
GitHub Repository https://abess.readthedocs.io/
render sprites into your desktop environment as shaped windows using GTK

spritegtk render static or animated sprites into your desktop environment as dynamic shaped windows using GTK requires pycairo and PYGobject: pip inst

hermit 20 Oct 27, 2022
Dynamic Neural Representational Decoders for High-Resolution Semantic Segmentation

Dynamic Neural Representational Decoders for High-Resolution Semantic Segmentation Requirements This repository needs mmsegmentation Training To train

20 May 28, 2022
GB-CosFace: Rethinking Softmax-based Face Recognition from the Perspective of Open Set Classification

GB-CosFace: Rethinking Softmax-based Face Recognition from the Perspective of Open Set Classification This is the official pytorch implementation of t

Alibaba Cloud 5 Nov 14, 2022
A simple and lightweight genetic algorithm for optimization of any machine learning model

geneticml This package contains a simple and lightweight genetic algorithm for optimization of any machine learning model. Installation Use pip to ins

Allan Barcelos 8 Aug 10, 2022
Hl classification bc - A Network-Based High-Level Data Classification Algorithm Using Betweenness Centrality

A Network-Based High-Level Data Classification Algorithm Using Betweenness Centr

Esteban Vilca 3 Dec 01, 2022
CowHerd is a partially-observed reinforcement learning environment

CowHerd is a partially-observed reinforcement learning environment, where the player walks around an area and is rewarded for milking cows. The cows try to escape and the player can place fences to h

Danijar Hafner 6 Mar 06, 2022
1st Solution For ICDAR 2021 Competition on Mathematical Formula Detection

This project releases our 1st place solution on ICDAR 2021 Competition on Mathematical Formula Detection. We implement our solution based on MMDetection, which is an open source object detection tool

yuxzho 94 Dec 25, 2022
This is the official released code for our paper, The Emergence of Objectness: Learning Zero-Shot Segmentation from Videos

The-Emergence-of-Objectness This is the official released code for our paper, The Emergence of Objectness: Learning Zero-Shot Segmentation from Videos

44 Oct 08, 2022
Code for the paper "Spatio-temporal Self-Supervised Representation Learning for 3D Point Clouds" (ICCV 2021)

Spatio-temporal Self-Supervised Representation Learning for 3D Point Clouds This is the official code implementation for the paper "Spatio-temporal Se

Hesper 63 Jan 05, 2023
Web mining module for Python, with tools for scraping, natural language processing, machine learning, network analysis and visualization.

Pattern Pattern is a web mining module for Python. It has tools for: Data Mining: web services (Google, Twitter, Wikipedia), web crawler, HTML DOM par

Computational Linguistics Research Group 8.4k Jan 03, 2023
Negative Sample Matters: A Renaissance of Metric Learning for Temporal Grounding

2D-TAN (Optimized) Introduction This is an optimized re-implementation repository for AAAI'2020 paper: Learning 2D Temporal Localization Networks for

Joya Chen 112 Dec 31, 2022
FishNet: One Stage to Detect, Segmentation and Pose Estimation

FishNet FishNet: One Stage to Detect, Segmentation and Pose Estimation Introduction In this project, we combine target detection, instance segmentatio

1 Oct 05, 2022
IPATool-py: download ipa easily

IPATool-py Python version of IPATool! Installation pip3 install -r requirements.txt Usage Quickstart: download app with specific bundleId into DIR: p

159 Dec 30, 2022
Code for "Offline Meta-Reinforcement Learning with Advantage Weighting" [ICML 2021]

Offline Meta-Reinforcement Learning with Advantage Weighting (MACAW) MACAW code used for the experiments in the ICML 2021 paper. Installing the enviro

Eric Mitchell 28 Jan 01, 2023
Code for Multinomial Diffusion

Code for Multinomial Diffusion Abstract Generative flows and diffusion models have been predominantly trained on ordinal data, for example natural ima

104 Jan 04, 2023
Rule Based Classification Project For Python

Rule-Based-Classification-Project (ENG) Business Problem: A game company wants to create new level-based customer definitions (personas) by using some

Deniz Can OĞUZ 4 Oct 29, 2022
K Closest Points and Maximum Clique Pruning for Efficient and Effective 3D Laser Scan Matching (To appear in RA-L 2022)

KCP The official implementation of KCP: k Closest Points and Maximum Clique Pruning for Efficient and Effective 3D Laser Scan Matching, accepted for p

Yu-Kai Lin 109 Dec 14, 2022
Código de um painel de auto atendimento feito em Python.

Painel de Auto-Atendimento O intuito desse projeto era fazer em Python um programa que simulasse um painel de auto atendimento, no maior estilo Mac Do

Calebe Alves Evangelista 2 Nov 09, 2022
The software associated with a paper accepted at EMNLP 2021 titled "Open Knowledge Graphs Canonicalization using Variational Autoencoders".

Open-KG-canonicalization The software associated with a paper accepted at EMNLP 2021 titled "Open Knowledge Graphs Canonicalization using Variational

International Business Machines 13 Nov 11, 2022
Official code for: A Probabilistic Hard Attention Model For Sequentially Observed Scenes

"A Probabilistic Hard Attention Model For Sequentially Observed Scenes" Authors: Samrudhdhi Rangrej, James Clark Accepted to: BMVC'21 A recurrent atte

5 Nov 19, 2022