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Dataset:
Drugs Potency[groups=4]
Molecular Biology Promoters[groups=4]
Simulated[groups=5,n_features=500,n_samples=200]
Simulated[groups=10,n_features=500,n_samples=200]
Simulated[groups=5,n_features=2000,n_samples=1000]
Simulated[groups=10,n_features=2000,n_samples=1000]
Objective:
Sparse Group Lasso[reg=1.0,tau=0]
Sparse Group Lasso[reg=0.1,tau=0]
Sparse Group Lasso[reg=0.01,tau=0]
Sparse Group Lasso[reg=1.0,tau=0.5]
Sparse Group Lasso[reg=0.1,tau=0.5]
Sparse Group Lasso[reg=0.01,tau=0.5]
Sparse Group Lasso[reg=1.0,tau=0.9]
Sparse Group Lasso[reg=0.1,tau=0.9]
Sparse Group Lasso[reg=0.01,tau=0.9]
Objective metrics
objective_value
Chart type
objective_curve
suboptimality_curve
relative_suboptimality_curve
bar_chart
boxplot
Scale
linear
semilog-y
semilog-x
loglog
log
X-axis
Time
Iteration
Solver
Y-axis
Objective metric
Time
Quantiles
Save as view
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Dataset:
Drugs Potency[groups=4]
Molecular Biology Promoters[groups=4]
Simulated[groups=5,n_features=500,n_samples=200]
Simulated[groups=10,n_features=500,n_samples=200]
Simulated[groups=5,n_features=2000,n_samples=1000]
Simulated[groups=10,n_features=2000,n_samples=1000]
Objective:
Sparse Group Lasso[reg=1.0,tau=0]
Sparse Group Lasso[reg=0.1,tau=0]
Sparse Group Lasso[reg=0.01,tau=0]
Sparse Group Lasso[reg=1.0,tau=0.5]
Sparse Group Lasso[reg=0.1,tau=0.5]
Sparse Group Lasso[reg=0.01,tau=0.5]
Sparse Group Lasso[reg=1.0,tau=0.9]
Sparse Group Lasso[reg=0.1,tau=0.9]
Sparse Group Lasso[reg=0.01,tau=0.9]
Objective column
objective_value
Chart type
objective_curve
suboptimality_curve
relative_suboptimality_curve
bar_chart
boxplot
Scale
linear
semilog-y
semilog-x
loglog
log
X-axis
Time
Iteration
Solver
Y-axis
Objective metric
Time
Quantiles
Save as view
Result on group lasso benchmark
CPU : 1
RAM (GB) : 15
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0.0e+0
1.0e+1
2.0e+1
3.0e+1
4.0e+1
5.0e+1
1.0e−10
1.0e−8
1.0e−6
1.0e−4
1.0e−2
1.0e+0
1.0e+2
Sparse Group Lasso[reg=0.01,tau=0.9]
Data: Simulated[groups=10,n_features=2000,n_samples=1000]
Time [sec]
F(x) - F(x*)
plotly-logomark
Solvers
(Click on a solver to hide it or double click to hide all the others)
gsroptim
E. Ndiaye, O. Fercoq, A. Gramfort, and J. Salmon, "GAP safe screening rules for Sparse-Group Lasso", NeurIPS 2016.
skglm
Q. Bertrand and Q. Klopfenstein and P.-A. Bannier and G. Gidel and M. Massias, "Beyond L1: Faster and Better Sparse Models with skglm", NeurIPS 2022.
System information
CPU
: 1
RAM (GB)
: 15
platform
: Linux6.8.0-41-generic-x86_64
processor
: Intel(R) Core(TM) i5-8350U CPU @ 1.70GHz
numpy
: 1.24.3 blas=mkl_rt lapack=mkl_rt
scipy
: 1.10.1
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