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Dataset:
simulatedpoisson[X_density=0.1,n_features=500,n_samples=1000,random_state=27,rho=0]
simulatedpoisson[X_density=0.3,n_features=500,n_samples=1000,random_state=27,rho=0]
simulatedpoisson[X_density=0.4,n_features=500,n_samples=1000,random_state=27,rho=0]
simulatedpoisson[X_density=0.5,n_features=500,n_samples=1000,random_state=27,rho=0]
simulatedpoisson[X_density=0.1,n_features=500,n_samples=1000,random_state=27,rho=0.6]
simulatedpoisson[X_density=0.3,n_features=500,n_samples=1000,random_state=27,rho=0.6]
simulatedpoisson[X_density=0.4,n_features=500,n_samples=1000,random_state=27,rho=0.6]
simulatedpoisson[X_density=0.5,n_features=500,n_samples=1000,random_state=27,rho=0.6]
simulatedpoisson[X_density=0.1,n_features=200,n_samples=5000,random_state=27,rho=0]
simulatedpoisson[X_density=0.3,n_features=200,n_samples=5000,random_state=27,rho=0]
simulatedpoisson[X_density=0.4,n_features=200,n_samples=5000,random_state=27,rho=0]
simulatedpoisson[X_density=0.5,n_features=200,n_samples=5000,random_state=27,rho=0]
simulatedpoisson[X_density=0.1,n_features=200,n_samples=5000,random_state=27,rho=0.6]
simulatedpoisson[X_density=0.3,n_features=200,n_samples=5000,random_state=27,rho=0.6]
simulatedpoisson[X_density=0.4,n_features=200,n_samples=5000,random_state=27,rho=0.6]
simulatedpoisson[X_density=0.5,n_features=200,n_samples=5000,random_state=27,rho=0.6]
simulatedpoisson[X_density=0.1,n_features=500,n_samples=200,random_state=27,rho=0]
simulatedpoisson[X_density=0.3,n_features=500,n_samples=200,random_state=27,rho=0]
simulatedpoisson[X_density=0.4,n_features=500,n_samples=200,random_state=27,rho=0]
simulatedpoisson[X_density=0.5,n_features=500,n_samples=200,random_state=27,rho=0]
simulatedpoisson[X_density=0.1,n_features=500,n_samples=200,random_state=27,rho=0.6]
simulatedpoisson[X_density=0.3,n_features=500,n_samples=200,random_state=27,rho=0.6]
simulatedpoisson[X_density=0.4,n_features=500,n_samples=200,random_state=27,rho=0.6]
simulatedpoisson[X_density=0.5,n_features=500,n_samples=200,random_state=27,rho=0.6]
Objective:
GLM[model=poisson]
Objective metrics
objective_value
Chart type
objective_curve
suboptimality_curve
relative_suboptimality_curve
bar_chart
Scale
semilog-y
semilog-x
loglog
linear
X-axis
Time
Iteration
Quantiles
Save as view
Benchopt
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Dataset:
simulatedpoisson[X_density=0.1,n_features=500,n_samples=1000,random_state=27,rho=0]
simulatedpoisson[X_density=0.3,n_features=500,n_samples=1000,random_state=27,rho=0]
simulatedpoisson[X_density=0.4,n_features=500,n_samples=1000,random_state=27,rho=0]
simulatedpoisson[X_density=0.5,n_features=500,n_samples=1000,random_state=27,rho=0]
simulatedpoisson[X_density=0.1,n_features=500,n_samples=1000,random_state=27,rho=0.6]
simulatedpoisson[X_density=0.3,n_features=500,n_samples=1000,random_state=27,rho=0.6]
simulatedpoisson[X_density=0.4,n_features=500,n_samples=1000,random_state=27,rho=0.6]
simulatedpoisson[X_density=0.5,n_features=500,n_samples=1000,random_state=27,rho=0.6]
simulatedpoisson[X_density=0.1,n_features=200,n_samples=5000,random_state=27,rho=0]
simulatedpoisson[X_density=0.3,n_features=200,n_samples=5000,random_state=27,rho=0]
simulatedpoisson[X_density=0.4,n_features=200,n_samples=5000,random_state=27,rho=0]
simulatedpoisson[X_density=0.5,n_features=200,n_samples=5000,random_state=27,rho=0]
simulatedpoisson[X_density=0.1,n_features=200,n_samples=5000,random_state=27,rho=0.6]
simulatedpoisson[X_density=0.3,n_features=200,n_samples=5000,random_state=27,rho=0.6]
simulatedpoisson[X_density=0.4,n_features=200,n_samples=5000,random_state=27,rho=0.6]
simulatedpoisson[X_density=0.5,n_features=200,n_samples=5000,random_state=27,rho=0.6]
simulatedpoisson[X_density=0.1,n_features=500,n_samples=200,random_state=27,rho=0]
simulatedpoisson[X_density=0.3,n_features=500,n_samples=200,random_state=27,rho=0]
simulatedpoisson[X_density=0.4,n_features=500,n_samples=200,random_state=27,rho=0]
simulatedpoisson[X_density=0.5,n_features=500,n_samples=200,random_state=27,rho=0]
simulatedpoisson[X_density=0.1,n_features=500,n_samples=200,random_state=27,rho=0.6]
simulatedpoisson[X_density=0.3,n_features=500,n_samples=200,random_state=27,rho=0.6]
simulatedpoisson[X_density=0.4,n_features=500,n_samples=200,random_state=27,rho=0.6]
simulatedpoisson[X_density=0.5,n_features=500,n_samples=200,random_state=27,rho=0.6]
Objective:
GLM[model=poisson]
Objective column
objective_value
Chart type
objective_curve
suboptimality_curve
relative_suboptimality_curve
bar_chart
Scale
semilog-y
semilog-x
loglog
linear
X-axis
Time
Iteration
Quantiles
Save as view
Result on glm benchmark
CPU : 8
RAM (GB) : 15
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0.0e+0
1.0e+0
2.0e+0
3.0e+0
4.0e+0
5.0e+0
6.0e+0
7.0e+0
1.0e−1
2.0e−1
5.0e−1
1.0e+0
2.0e+0
5.0e+0
1.0e+1
2.0e+1
5.0e+1
1.0e+2
2.0e+2
5.0e+2
1.0e+3
2.0e+3
5.0e+3
1.0e+4
GLM[model=poisson]
Data: simulatedpoisson[X_density=0.1,n_features=500,n_samples=1000,random_state=27,rho=0]
Time [sec]
F(x)
plotly-logomark
Solvers
(Click on a solver to hide it or double click to hide all the others)
sklearn[lbfgs]
No description provided
sklearn[newton-lsmr]
No description provided
sklearn[newton-cholesky]
No description provided
System information
CPU
: 8
RAM (GB)
: 15
platform
: Linux6.8.0-35-generic-x86_64
processor
: Intel(R) Core(TM) i5-8350U CPU @ 1.70GHz
numpy
: 1.26.4
scipy
: 1.13.0
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