Benchopt
Dataset:
deconvolution[k=5,random_state=None,snr=10.0]
libsvm[dataset=bodyfat]
libsvm[dataset=housing]
ode[degree=3,dt=0.01,duration=1,noise_ratio=0.001,seed=None,system=Lorenz]
portfolio[instance=1,ratio=0.25]
portfolio[instance=1,ratio=0.5]
portfolio[instance=1,ratio=0.75]
portfolio[instance=2,ratio=0.25]
portfolio[instance=2,ratio=0.5]
portfolio[instance=2,ratio=0.75]
portfolio[instance=3,ratio=0.25]
portfolio[instance=3,ratio=0.5]
portfolio[instance=3,ratio=0.75]
portfolio[instance=4,ratio=0.25]
portfolio[instance=4,ratio=0.5]
portfolio[instance=4,ratio=0.75]
portfolio[instance=5,ratio=0.25]
portfolio[instance=5,ratio=0.5]
portfolio[instance=5,ratio=0.75]
simulated[density=0.1,n_features=50,n_samples=30,random_state=None,rho=0.9,snr=10.0]
Objective:
Sparse support recovery
Objective metrics
description
This benchmark compares the quality of different methods aiming to find a sparse vector solving the system y=Xw. It plots different statistics wrt a given amount of sparsity targeted in w.
objective_value
objective_n_nnz
objective_snr_y
objective_snr_y_dB
objective_snr_y_true
objective_snr_y_true_dB
objective_snr_w
objective_snr_w_dB
objective_tpr
objective_fpr
objective_tnr
objective_fnr
objective_f1score
objective_auc
objective_dist_to_supp
Chart type
objective_curve
suboptimality_curve
relative_suboptimality_curve
bar_chart
Scale
semilog-y
semilog-x
loglog
linear
X-axis
Quantiles
Save as view
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Dataset:
deconvolution[k=5,random_state=None,snr=10.0]
libsvm[dataset=bodyfat]
libsvm[dataset=housing]
ode[degree=3,dt=0.01,duration=1,noise_ratio=0.001,seed=None,system=Lorenz]
portfolio[instance=1,ratio=0.25]
portfolio[instance=1,ratio=0.5]
portfolio[instance=1,ratio=0.75]
portfolio[instance=2,ratio=0.25]
portfolio[instance=2,ratio=0.5]
portfolio[instance=2,ratio=0.75]
portfolio[instance=3,ratio=0.25]
portfolio[instance=3,ratio=0.5]
portfolio[instance=3,ratio=0.75]
portfolio[instance=4,ratio=0.25]
portfolio[instance=4,ratio=0.5]
portfolio[instance=4,ratio=0.75]
portfolio[instance=5,ratio=0.25]
portfolio[instance=5,ratio=0.5]
portfolio[instance=5,ratio=0.75]
simulated[density=0.1,n_features=50,n_samples=30,random_state=None,rho=0.9,snr=10.0]
Objective:
Sparse support recovery
Objective column
objective_value
objective_n_nnz
objective_snr_y
objective_snr_y_dB
objective_snr_y_true
objective_snr_y_true_dB
objective_snr_w
objective_snr_w_dB
objective_tpr
objective_fpr
objective_tnr
objective_fnr
objective_f1score
objective_auc
objective_dist_to_supp
Chart type
objective_curve
suboptimality_curve
relative_suboptimality_curve
bar_chart
Scale
semilog-y
semilog-x
loglog
linear
X-axis
Quantiles
Save as view
Result on sparse support recovery benchmark
CPU : 4
RAM (GB) : 4
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System information
CPU
: 4
RAM (GB)
: 4
platform
: Linux5.15.0-107-generic-x86_64
processor
: Intel(R) Core(TM) i5-2435M CPU @ 2.40GHz
numpy
: 1.26.4
scipy
: 1.13.0
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