PyAutoFit
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Published
20 hours ago •
rhayes777
rhayes777
Concise `model.info` / `model.results` for multi-analysis models
The following autolens example fits two datasets simultaneously:
https://github.com/Jammy2211/autolens_workspace/blob/release/scripts/multi/modeling/start_here.py
The model.info file is:
Total Free Parameters = 23
model Collection (N=23)
0 Collection (N=21)
galaxies Collection (N=21)
lens Galaxy (N=14)
bulge Sersic (N=7)
mass Isothermal (N=5)
shear ExternalShear (N=2)
source Galaxy (N=7)
bulge Sersic (N=7)
1 Collection (N=21)
galaxies Collection (N=21)
lens Galaxy (N=14)
bulge Sersic (N=7)
mass Isothermal (N=5)
shear ExternalShear (N=2)
source Galaxy (N=7)
bulge Sersic (N=7)
0
galaxies
lens
redshift 0.5
bulge
centre
centre_0 GaussianPrior [0], mean = 0.0, sigma = 0.3
centre_1 GaussianPrior [1], mean = 0.0, sigma = 0.3
ell_comps
ell_comps_0 GaussianPrior [2], mean = 0.0, sigma = 0.3
ell_comps_1 GaussianPrior [3], mean = 0.0, sigma = 0.3
intensity LogUniformPrior [21], lower_limit = 1e-06, upper_limit = 1000000.0
effective_radius UniformPrior [5], lower_limit = 0.0, upper_limit = 30.0
sersic_index UniformPrior [6], lower_limit = 0.8, upper_limit = 5.0
mass
centre
centre_0 GaussianPrior [7], mean = 0.0, sigma = 0.1
centre_1 GaussianPrior [8], mean = 0.0, sigma = 0.1
ell_comps
ell_comps_0 GaussianPrior [9], mean = 0.0, sigma = 0.3
ell_comps_1 GaussianPrior [10], mean = 0.0, sigma = 0.3
einstein_radius UniformPrior [11], lower_limit = 0.0, upper_limit = 8.0
shear
gamma_1 UniformPrior [12], lower_limit = -0.2, upper_limit = 0.2
gamma_2 UniformPrior [13], lower_limit = -0.2, upper_limit = 0.2
source
redshift 1.0
bulge
centre
centre_0 GaussianPrior [14], mean = 0.0, sigma = 0.3
centre_1 GaussianPrior [15], mean = 0.0, sigma = 0.3
ell_comps
ell_comps_0 GaussianPrior [16], mean = 0.0, sigma = 0.3
ell_comps_1 GaussianPrior [17], mean = 0.0, sigma = 0.3
intensity LogUniformPrior [22], lower_limit = 1e-06, upper_limit = 1000000.0
effective_radius UniformPrior [19], lower_limit = 0.0, upper_limit = 30.0
sersic_index UniformPrior [20], lower_limit = 0.8, upper_limit = 5.0
1
galaxies
lens
redshift 0.5
bulge
centre
centre_0 GaussianPrior [0], mean = 0.0, sigma = 0.3
centre_1 GaussianPrior [1], mean = 0.0, sigma = 0.3
ell_comps
ell_comps_0 GaussianPrior [2], mean = 0.0, sigma = 0.3
ell_comps_1 GaussianPrior [3], mean = 0.0, sigma = 0.3
intensity LogUniformPrior [23], lower_limit = 1e-06, upper_limit = 1000000.0
effective_radius UniformPrior [5], lower_limit = 0.0, upper_limit = 30.0
sersic_index UniformPrior [6], lower_limit = 0.8, upper_limit = 5.0
mass
centre
centre_0 GaussianPrior [7], mean = 0.0, sigma = 0.1
centre_1 GaussianPrior [8], mean = 0.0, sigma = 0.1
ell_comps
ell_comps_0 GaussianPrior [9], mean = 0.0, sigma = 0.3
ell_comps_1 GaussianPrior [10], mean = 0.0, sigma = 0.3
einstein_radius UniformPrior [11], lower_limit = 0.0, upper_limit = 8.0
shear
gamma_1 UniformPrior [12], lower_limit = -0.2, upper_limit = 0.2
gamma_2 UniformPrior [13], lower_limit = -0.2, upper_limit = 0.2
source
redshift 1.0
bulge
centre
centre_0 GaussianPrior [14], mean = 0.0, sigma = 0.3
centre_1 GaussianPrior [15], mean = 0.0, sigma = 0.3
ell_comps
ell_comps_0 GaussianPrior [16], mean = 0.0, sigma = 0.3
ell_comps_1 GaussianPrior [17], mean = 0.0, sigma = 0.3
intensity LogUniformPrior [24], lower_limit = 1e-06, upper_limit = 1000000.0
effective_radius UniformPrior [19], lower_limit = 0.0, upper_limit = 30.0
sersic_index UniformPrior [20], lower_limit = 0.8, upper_limit = 5.0
As in https://github.com/rhayes777/PyAutoFit/issues/925, there is a lot of redundancy we should remove. Something like:
Total Free Parameters = 23
model Collection (N=23)
0-1 Collection (N=21)
galaxies Collection (N=21)
lens Galaxy (N=14)
bulge Sersic (N=7)
mass Isothermal (N=5)
shear ExternalShear (N=2)
source Galaxy (N=7)
bulge Sersic (N=7)
0-1
galaxies
lens
redshift 0.5
bulge
centre
centre_0 GaussianPrior [0], mean = 0.0, sigma = 0.3
centre_1 GaussianPrior [1], mean = 0.0, sigma = 0.3
ell_comps
ell_comps_0 GaussianPrior [2], mean = 0.0, sigma = 0.3
ell_comps_1 GaussianPrior [3], mean = 0.0, sigma = 0.3
intensity LogUniformPrior [21], lower_limit = 1e-06, upper_limit = 1000000.0
effective_radius UniformPrior [5], lower_limit = 0.0, upper_limit = 30.0
sersic_index UniformPrior [6], lower_limit = 0.8, upper_limit = 5.0
mass
centre
centre_0 GaussianPrior [7], mean = 0.0, sigma = 0.1
centre_1 GaussianPrior [8], mean = 0.0, sigma = 0.1
ell_comps
ell_comps_0 GaussianPrior [9], mean = 0.0, sigma = 0.3
ell_comps_1 GaussianPrior [10], mean = 0.0, sigma = 0.3
einstein_radius UniformPrior [11], lower_limit = 0.0, upper_limit = 8.0
shear
gamma_1 UniformPrior [12], lower_limit = -0.2, upper_limit = 0.2
gamma_2 UniformPrior [13], lower_limit = -0.2, upper_limit = 0.2
source
redshift 1.0
bulge
centre
centre_0 GaussianPrior [14], mean = 0.0, sigma = 0.3
centre_1 GaussianPrior [15], mean = 0.0, sigma = 0.3
ell_comps
ell_comps_0 GaussianPrior [16], mean = 0.0, sigma = 0.3
ell_comps_1 GaussianPrior [17], mean = 0.0, sigma = 0.3
effective_radius UniformPrior [19], lower_limit = 0.0, upper_limit = 30.0
sersic_index UniformPrior [20], lower_limit = 0.8, upper_limit = 5.0
0
galaxies
lens
bulge
intensity LogUniformPrior [21], lower_limit = 1e-06, upper_limit = 1000000.0
source
bulge
centre
intensity LogUniformPrior [22], lower_limit = 1e-06, upper_limit = 1000000.0
1
galaxies
lens
bulge
centre
intensity LogUniformPrior [23], lower_limit = 1e-06, upper_limit = 1000000.0
source
bulge
centre
intensity LogUniformPrior [24], lower_limit = 1e-06, upper_limit = 1000000.0
The model.results are already quite concise but the top could be reduced a bit:
Bayesian Evidence -275205.27009158
Maximum Log Likelihood -275196.85833552
Maximum Log Posterior 1107257.56762568
model Collection (N=23)
0 Collection (N=21)
galaxies Collection (N=21)
lens Galaxy (N=14)
bulge Sersic (N=7)
mass Isothermal (N=5)
shear ExternalShear (N=2)
source Galaxy (N=7)
bulge Sersic (N=7)
1 Collection (N=21)
galaxies Collection (N=21)
lens Galaxy (N=14)
bulge Sersic (N=7)
mass Isothermal (N=5)
shear ExternalShear (N=2)
source Galaxy (N=7)
bulge Sersic (N=7)
Maximum Log Likelihood Model:
1
galaxies
lens
bulge
centre
centre_0 -0.196
centre_1 0.160
ell_comps
ell_comps_0 0.011
ell_comps_1 0.577
effective_radius 18.604
sersic_index 4.781
intensity 0.003
mass
centre
centre_0 -0.060
centre_1 0.022
ell_comps
ell_comps_0 0.165
ell_comps_1 -0.172
einstein_radius 7.693
shear
gamma_1 -0.028
gamma_2 -0.014
source
bulge
centre
centre_0 -0.466
centre_1 0.097
ell_comps
ell_comps_0 -0.102
ell_comps_1 0.387
effective_radius 0.382
sersic_index 2.615
intensity 3.059
0
galaxies
lens
bulge
intensity 0.003
source
bulge
intensity 0.001
