baseConfig.ini

#TODO Updated list of models and datasets

[custom]
;directory for chains/output
;it is better if you set an absolute path
chainsdir = simplemc/chains/

;set model
;model options: LCDM, LCDMasslessnu, nuLCDM, NeffLCDM, noradLCDM, nuoLCDM,
;nuwLCDM, oLCDM, wCDM, waCDM, owCDM, owaCDM, JordiCDM, WeirdCDM, TLight, StepCDM,
;Spline, PolyCDM, fPolyCDM, Decay, Decay01, Decay05, EarlyDE, EarlyDE_rd_DE, SlowRDE, sline
;more options located in the RunBase.py
model = waCDM

;prefact options : [pre, phy]
prefact = phy

;varys8 True otherwise s8=0.8
varys8  = False

;set datasets used. Ex: UnionSN+BBAO+Planck
;data options: HD, BBAO, GBAO, GBAO_no6dF, CMASS, LBAO, LaBAO,
;LxBAO, MGS, Planck, WMAP, PlRd, WRd, PlDa, PlRdx10, CMBW, SN, SNx10, UnionSN,
;RiessH0, 6dFGS, dline, generic
datasets = DESI+DESY5

;following four lines is to use external datasets
;fn can be distance_mod, h, fs8
;datasets = generic
;path_to_data = /home/cosmocicatais/panth15.txt
;path_to_cov = /home/cosmocicatais/panth15cov.txt
;fn = distance_mod

;sampler can be {mcmc, nested, emcee}
;or analyzers {maxlike, genetic, ga_deap}
;
;mcmc -> metropolis-hastings
;nested -> nested sampling
;emcee
;maxlike -> Maximum Likelihood Analyzer
;ga_deap -> Genetic (genetic using deap library)
;pso -> Particle Swarm Optimization (pyswarms)
analyzername = nested

;add derived parameters (True/False) ,
;i.e. Omega_Lambda, H0, Age of the Universe
addDerived = False

;mcevidence = True to calculate Bayesian evidence wiht mcevidence
;Only valid to samplers (mcmc, emcee, nested).
;Nested sampling does not need it.
mcevidence = False
;mcevidence_k is the k number of nearest neighbours in mcevidence
mcevidence_k = 2

;overwrite = True -> overwrite output files with the same name
;overwrite = False -> if the outputname already exist
;it sends an error and ends the simplemc execution
overwrite = True

;options to triangle plots for mcmc, nested and emcee;
;if True any of the following options
;.png files will be saved in chainsdir
getdist = False
corner = False
simpleplot = False
;True to display figures; we recommended false
showfig = False

;use neural network to predict likelihoods (True/False),
;edit block neuralike to set options
useNeuralLike = False

;----------------------------------------------------------
[mcmc]
;Nsamples
nsamp   = 50000

;Burn-in
skip    = 500

;temperature at which to sample
temp    = 1

;Gelman-Rubin for convergence
GRstop  = 0.01

;every number of steps check the GR-criteria
checkGR = 500

;----------------------------------------------------------
[nested]
;type for dynesty -> {'single','multi', 'balls', 'cubes'}
nestedType = multi

;it is recommended around nlivepoints=50*ndim, recommended 1024
nlivepoints = 300

;recommended 0.001
accuracy = 0.0005

;u for flat(uniform) or g for gaussian prior
priortype = u

;when using gaussian prior
sigma = 2

;if nproc = 0 uses mp.cpu_count()//2 by default, 
;you can set with another positive integer
nproc = 4

;visualise the params array on the progress bar
print_params = False

;Produce output on the fly
showfiles = True

;dynamic option is only for dynesty engine
;dynamic and neuralNetwork can be False/True
dynamic = False

neuralNetwork = False
;if neuralNetwork = True, then you can set:

;----------------------------------------------------------
[neural]
;modified bambi
split = 0.8
; keras or nearestneighbour
learner = keras
;all the following options are only for keras learner
; number of neurons of the three hidden layers
numNeurons = 50
; epochs for training
epochs = 100
; number of training points
;ntrain = nlivepoints by default
;dlogz to start to train the neural net (we recommend dlogz_start <=10)
dlogz_start = 5
;number of nested (dynesty) iterations to start to train the neural net
it_to_start_net = 10000
;number of iterations to re-train the neural net. By default updInt = nlivepoints,
;choose updInt <= nlivepoints
;updInt = 500
;proxy_tolerance uncertainity of the net allowed.
proxy_tolerance = 0.3


[neuralike]
;neuralike contains options to use a neural network in likelihood evaluations over the parameter space
ndivsgrid = 4
epochs = 500
learning_rate = 1e-5
batch_size = 16
psplit = 0.8
;hidden_layers_neurons: number of nodes per layer separated by commas
hidden_layers_neurons = 100, 100, 100
;number of procesors to make the grid
nproc = 5


[emcee]
;walkers >= 2*dim
walkers = 10
nsamp = 200
burnin = 0
nproc = 4

;----------------------------------------------------------
[maxlike]
;DerivedParameters
compute_derived = False

;compute errror from Hessian matrix
;False/True
compute_errors = True

;if compute_erorrs is True
;plot Fisher matrix
show_contours = False

;if show_contours is True, then
;2D plot for the parameters:
plot_param1 = Om
plot_param2 = Ok

;----------------------------------------------------------
[ga_deap]
;genetic parameters

;number of processes to use for parallel evaluation
nproc = 3

;Population size
population = 100

;Crossover probability
crossover = 0.7

;Mutation probability
mutation = 0.3

;Max generation number
max_generation = 20

;Size of the Hall of Fame
hof_size = 1

;Crowding factor
crowding_factor = 1

;Plot Generation vs Fitness
plot_fitness = False

;compute errror from Hessian matrix
;False/True
compute_errors = True

;If compute_errors is True
;plot Fisher matrix
show_contours = True

;If show_contours is True, then
;2D plot for the parameters:
plot_param1 = Om
plot_param2 = h


;----------------------------------------------------------
[pso]
;particle swarm optimization

;number of processes to use for parallel evaluation
nproc = 4

;number of particles
nparticles = 40

;how many iterations
iterations = 50

;method to use to find the best: local or global
method = global

;cognitive parameter c1 favors 
;individual search 
opt_c1 = 0.5

;social parameter c2 controls the
;weight factor of the whole swarm search
opt_c2 = 0.3

;inertia weight parameter w, balances
;the global and local search abilities
opt_w = 0.8

;if method=local, k:neighbours 
opt_k = 2

;p:distance {1:Minkowski, 2:Euclidean}
opt_p = 1

;early termination
early_stop = False

;if estop=True, then
;it stops if new best does not improve tolerance
ftol = 1e-8

;it stops if condition is not met for ftol_iter 
;consecutive iterations.
ftol_iter = 30

;plot Generation vs Fitness
plot_fitness = True

;compute errror from Hessian matrix
;False/True
compute_errors = True

;If compute_errors is True
;plot Fisher matrix
show_contours = True

;If show_contours is True, then
;2D plot for the parameters:
plot_param1 = h
plot_param2 = Om

;save the full likelihood history
;only for testing as it doubles the time
save_like = False
;----------------------------------------------------------