def run_experiment(path: Path, custom_settings: dict):
# 1. Initialize the experiment
exp = Experiment()
exp.load_config(config_file=path / 'config.json',
custom_settings=custom_settings)
if exp.config['model']['n_areas'] == "TBD":
exp.config['model']['n_areas'] = 2
# 2. Simulate contact areas
sim = Simulation(experiment=exp)
sim.run_simulation()
# 3. Define MCMC
mc = MCMC(data=sim, experiment=exp)
# 4. Warm-up sampler and sample from posterior
mc.warm_up()
mc.sample()
# 5. Evaluate ground truth
mc.eval_ground_truth()
# 6. Log sampling statistics and save samples to file
mc.save_samples(run=0)
def main(config=None, experiment_name=None):
if config is None:
parser = argparse.ArgumentParser(
description="An MCMC algorithm to identify contact zones")
parser.add_argument("config",
nargs="?",
type=Path,
help="The JSON configuration file")
parser.add_argument(
"name",
nargs="?",
type=str,
help=
"The experiment name used for logging and as the name of the results directory."
)
args = parser.parse_args()
config = args.config
experiment_name = args.name
# 0. Ask for config file via files-dialog, if not provided as argument.
if config is None:
tk.Tk().withdraw()
config = filedialog.askopenfilename(
title='Select a config file in JSON format.',
initialdir='..',
filetypes=(('json files', '*.json'), ('all files', '*.*')))
# Initialize the experiment
experiment = Experiment(experiment_name=experiment_name,
config_file=config,
log=True)
if experiment.is_simulation():
# The data is defined by a ´Simulation´ object
data = Simulation(experiment=experiment)
data.run_simulation()
data.log_simulation()
else:
# Experiment based on a specified (in config) data-set
data = Data(experiment=experiment)
data.load_features()
# Counts for priors
data.load_universal_counts()
data.load_inheritance_counts()
data.load_geo_cost_matrix()
# Log
data.log_loading()
# Rerun experiment to check for consistency
for run in range(experiment.config['mcmc']['n_runs']):
n_areas = experiment.config['model']['n_areas']
iterate_or_run(
x=n_areas,
config_setter=lambda x: experiment.config['model'].__setitem__(
'n_areas', x),
function=lambda x: run_experiment(experiment, data, run))
def load_and_run():
# Create Experiment object from config file
experiment = Experiment('prior_simulation', config_file='config.json', logging=True)
# Simulate data
data = Simulation(experiment=experiment)
data.run_simulation()
data.log_simulation()
# Run experiments
for run in range(experiment.config['mcmc']['N_RUNS']):
run_experiment(experiment, data, run)
from sbayes.experiment_setup import Experiment
from sbayes.simulation import Simulation
from sbayes.mcmc_setup import MCMC
if __name__ == '__main__':
# When performing the MCMC iterate over different setups (inheritance)
INHERITANCE = [False, True]
for IN in INHERITANCE:
# 1. Initialize the experiment
exp = Experiment()
exp.load_config(
config_file='experiments/simulation/sim_exp2/config.json',
custom_settings={'model': {
'INHERITANCE': IN
}})
exp.log_experiment()
# 2. Simulate contact areas
sim = Simulation(experiment=exp)
sim.run_simulation()
sim.log_simulation()
# 3. Define MCMC
mc = MCMC(data=sim, experiment=exp)
mc.log_setup()
# Rerun experiment to check for consistency
for run in range(exp.config['mcmc']['N_RUNS']):
SETUP = list(itertools.product(STRENGTH, AREA))
for S in SETUP:
# Update config information according to the current setup
custom_settings = {
'simulation': {
'i_contact': I_CONTACT[S[0]],
'e_contact': E_CONTACT[S[0]],
'STRENGTH': S[0],
'area': S[1]
}
}
# 1. Initialize the experiment
exp = Experiment()
exp.load_config(
config_file='experiments/simulation/sim_exp1/config.json',
custom_settings=custom_settings)
exp.log_experiment()
# 2. Simulate contact areas
sim = Simulation(experiment=exp)
sim.run_simulation()
sim.log_simulation()
# 3. Define MCMC
mc = MCMC(data=sim, experiment=exp)
mc.log_setup()
# Rerun experiment to check for consistency
from sbayes.experiment_setup import Experiment
from sbayes.simulation import Simulation
from sbayes.mcmc_setup import MCMC
if __name__ == '__main__':
import os
# 1. Initialize the experiment
exp = Experiment()
exp.load_config(config_file='experiments/simulation/sim_exp3/config.json')
exp.log_experiment()
# 2. Simulate contact areas
sim = Simulation(experiment=exp)
sim.run_simulation()
sim.log_simulation()
# Iterate over different setups (different number of areas)
NUMBER_AREAS = range(1, 8)
for N in NUMBER_AREAS:
# Update config information according to the current setup
exp.config['model']['n_areas'] = N
# 3. Define MCMC
mc = MCMC(data=sim, experiment=exp)
mc.log_setup()
# Rerun experiment to check for consistency
for run in range(exp.config['mcmc']['n_runs']):
from sbayes.experiment_setup import Experiment
from sbayes.load_data import Data
from sbayes.mcmc_setup import MCMC
if __name__ == '__main__':
# Initialize the experiment
exp = Experiment()
exp.load_config(config_file='experiments/south_america/config.json')
exp.log_experiment()
# Load Data
dat = Data(experiment=exp)
# Features
dat.load_features()
# Counts for priors
dat.load_universal_counts()
dat.load_inheritance_counts()
# Log
dat.log_loading()
NUMBER_AREAS = range(1, 8)
# Rerun experiment to check for consistency
for run in range(exp.config['mcmc']['N_RUNS']):
for N in NUMBER_AREAS:
# Update config information according to the current setup
exp.config['model']['N_AREAS'] = N
from sbayes.experiment_setup import Experiment
from sbayes.load_data import Data
from sbayes.mcmc_setup import MCMC
if __name__ == '__main__':
# Initialize the experiment
exp = Experiment()
exp.load_config(config_file='experiments/balkan/config.json')
exp.log_experiment()
# Load Data
dat = Data(experiment=exp)
# Features
dat.load_features()
# Counts for priors
dat.load_universal_counts()
dat.load_inheritance_counts()
# Log
dat.log_loading()
NUMBER_AREAS = range(1, 8)
# Rerun experiment to check for consistency
for run in range(exp.config['mcmc']['N_RUNS']):
for N in NUMBER_AREAS:
# Update config information according to the current setup
exp.config['model']['N_AREAS'] = N
# Number of samples
N_BACKGROUND = 20
N_AREA = 10
# Spatial distribution
BBOX = Rectangle(left=0, bottom=0, right=100, top=100)
AREA_MEAN = np.array([35, 60])
AREA_STD = 10
# Generate spatial locations
background_locations = generate_background_locations(N_BACKGROUND, BBOX)
area_locations = generate_area_locations(N_AREA, AREA_MEAN, AREA_STD)
plot_locations(background_locations, area_locations)
# 1. Initialize the experiment
exp = Experiment()
exp.load_config(config_file='../config.json')
exp.log_experiment()
# When simulating iterate over different setups (different areas and strengths of contact)
I_CONTACT = [1.5, 2, 2.5]
E_CONTACT = [1.25, 0.75, 0.25]
STRENGTH = range(len(E_CONTACT))
AREA = [4, 6, 3, 8]
SETUP = list(itertools.product(STRENGTH, AREA))
for S in SETUP:
# Update config information according to the current setup
exp.config['simulation']['I_CONTACT'] = I_CONTACT[S[0]]
exp.config['simulation']['E_CONTACT'] = E_CONTACT[S[0]]
def main(args=None):
if args is None:
parser = argparse.ArgumentParser(
description="An MCMC algorithm to identify contact zones")
parser.add_argument("config",
nargs="?",
type=Path,
help="The JSON configuration file")
args = parser.parse_args()
# 0. Ask for config file via files-dialog, if not provided as argument.
config = args.config
if config is None:
tk.Tk().withdraw()
config = filedialog.askopenfilename(
title='Select a config file in JSON format.',
initialdir='..',
filetypes=(('json files', '*.json'), ('all files', '*.*')))
# Initialize the experiment
experiment = Experiment(config_file=config, log=True)
if experiment.is_simulation():
# The data is defined by a ´Simulation´ object
data = Simulation(experiment=experiment)
data.run_simulation()
data.log_simulation()
else:
# Experiment based on a specified (in config) data-set
data = Data(experiment=experiment)
data.load_features()
# Counts for priors
data.load_universal_counts()
data.load_inheritance_counts()
# Log
data.log_loading()
initial_sample = None
# Rerun experiment to check for consistency
for run in range(experiment.config['mcmc']['N_RUNS']):
if isinstance(experiment.config['mcmc']['N_AREAS'], str):
assert experiment.config['mcmc']['N_AREAS'].lower() == 'tbd'
# Run the experiment multiple times to determine the number of areas.
for N in NUMBER_AREAS_GRID:
# Update config information according to the current setup
experiment.config['mcmc']['N_AREAS'] = N
# Run the experiment with the specified number of areas
initial_sample = run_experiment(experiment,
data,
run,
initial_sample=initial_sample)
else:
# Run the experiment once, with the specified settings
run_experiment(experiment, data, run)
initial_sample = None