def web_model_results(dir_path):
for noise in ['0.01', '0.05', '0.1']:
for bottleneck in ['1', '2', '3', '4']:
for exposures in ['1', '2', '3', '4']:
figure_path = dir_path + '%s_%s_%s.svg' % (noise, bottleneck,
exposures)
model_results_sim = il_results.load(
'../data/model_sim/1.0_%s_%s_%s.json' %
(noise, bottleneck, exposures),
start_gen=0,
end_gen=50,
method='lang')
model_results_inf = il_results.load(
'../data/model_inf/1.0_%s_%s_%s.json' %
(noise, bottleneck, exposures),
start_gen=0,
end_gen=50,
method='lang')
model_results_inf_500 = il_results.load(
'../data/model_inf/500.0_%s_%s_%s.json' %
(noise, bottleneck, exposures),
start_gen=0,
end_gen=50,
method='lang')
il_results.make_figure(
[(model_results_sim, 'Simplicity', colors.blue,
colors.light_blue, '-'),
(model_results_inf, 'Informativeness', colors.red,
colors.light_red, '-'),
(model_results_inf_500, 'Strong informativeness',
colors.red, colors.light_red, ':')],
figure_path=figure_path,
show_legend=True)
def make_model_results_figure_carstensen(figure_path):
model_results_sim_001 = il_results.load(
'../data/model_sim/1.0_0.01_4_2.json',
start_gen=0,
end_gen=100,
method='lang')
model_results_sim_005 = il_results.load(
'../data/model_sim/1.0_0.05_4_2.json',
start_gen=0,
end_gen=100,
method='lang')
model_results_sim_010 = il_results.load(
'../data/model_sim/1.0_0.1_4_2.json',
start_gen=0,
end_gen=100,
method='lang')
il_results.make_carstensen_figure([
(model_results_sim_001, 'ε = .01', colors.blue, colors.light_blue,
'-'),
(model_results_sim_005, 'ε = .05', colors.blue, colors.light_blue,
'--'),
(model_results_sim_010, 'ε = .1', colors.blue, colors.light_blue, ':')
],
figure_path=figure_path,
figsize=(3.46, 1.75))
def make_model_comparison_figure(figure_path):
experiment_results = il_results.load('../data/experiments/exp2_chains.json', start_gen=0, end_gen=10, method='prod')
experiment_results_est = il_results.load('../data/experiments/exp2_chains.json', start_gen=10, end_gen=50, method='prod')
model_results_sim = il_results.load('../data/model_sim/1.36_0.23_2_4.json', start_gen=0, end_gen=50, method='lang')
model_results_inf = il_results.load('../data/model_inf/243.3_0.37_2_4.json', start_gen=0, end_gen=50, method='lang')
il_results.make_figure([(experiment_results, 'Experimental results', colors.black, colors.light_black, '-'),
(experiment_results_est, '', colors.black, colors.light_black, ':'),
(model_results_sim, 'Simplicity prior (using best-fit parameters)', colors.blue, colors.light_blue, '-'),
(model_results_inf, 'Informativeness prior (using best-fit parameters)', colors.red, colors.light_red, '-')],
figure_path=figure_path, show_legend=True)
def make_model_results_figure(figure_path):
model_results_sim = il_results.load(
'../data/test_modelfit/model/results.json',
start_gen=0,
end_gen=10,
method='lang')
dataset = (model_results_sim, 'Simplicity', colors.blue, colors.light_blue,
'-')
opt = load_optimizer('../data/test_modelfit/modelfit/result')
make_figure(dataset, opt, figure_path=figure_path, n_levels=32)
def supplementary_model_results(dir_path):
il_results.figure_layout = [['expressivity', 'complexity'],
['cost', 'error']]
il_results.plt.rcParams.update({'font.size': 10})
for e, noise in enumerate(['0.01', '0.05', '0.1'], 1):
for b, bottleneck in enumerate(['1', '2', '3', '4'], 1):
for x, exposures in enumerate(['1', '2', '3', '4'], 1):
figure_path = dir_path + '%i%i%i.pdf' % (b, x, e)
title = 'b = %s, ξ = %s, ε = %s' % (bottleneck, exposures,
noise)
model_results_sim = il_results.load(
'../data/model_sim/1.0_%s_%s_%s.json' %
(noise, bottleneck, exposures),
start_gen=0,
end_gen=50,
method='lang')
model_results_inf = il_results.load(
'../data/model_inf/1.0_%s_%s_%s.json' %
(noise, bottleneck, exposures),
start_gen=0,
end_gen=50,
method='lang')
model_results_inf_500 = il_results.load(
'../data/model_inf/500.0_%s_%s_%s.json' %
(noise, bottleneck, exposures),
start_gen=0,
end_gen=50,
method='lang')
il_results.make_figure(
[(model_results_sim, 'Simplicity prior (πsim, w = 1)',
colors.blue, colors.light_blue, '-'),
(model_results_inf, 'Informativeness prior (πinf, w = 1)',
colors.red, colors.light_red, '-'),
(model_results_inf_500,
'Strong informativeness prior (πinf, w = 500)',
colors.red, colors.light_red, ':')],
figure_path=figure_path,
title=title,
show_legend=True,
deep_legend=True)
def make_model_results_figure(figure_path):
model_results_sim = il_results.load('../data/model_sim/1.0_0.01_2_2.json',
start_gen=0,
end_gen=50,
method='lang')
model_results_inf = il_results.load('../data/model_inf/1.0_0.01_2_2.json',
start_gen=0,
end_gen=50,
method='lang')
model_results_inf_500 = il_results.load(
'../data/model_inf/500.0_0.01_2_2.json',
start_gen=0,
end_gen=50,
method='lang')
il_results.make_figure(
[(model_results_sim, 'Simplicity prior', colors.blue,
colors.light_blue, '-'),
(model_results_inf, 'Informativeness prior', colors.red,
colors.light_red, '-'),
(model_results_inf_500, 'Strong informativeness prior', colors.red,
colors.light_red, ':')],
figure_path=figure_path,
show_legend=True)
def thesis_appendix_model_results(dir_path):
for b, bottleneck in enumerate(['1', '2', '3', '4'], 1):
for x, exposures in enumerate(['1', '2', '3', '4'], 1):
for e, noise in enumerate(['0.01', '0.05', '0.1'], 1):
figure_path = dir_path + '%i%i%i.eps' % (b, x, e)
title = 'b = %s, ξ = %s, ε = %s' % (bottleneck, exposures,
noise)
model_results_sim = il_results.load(
'../data/model_sim/1.0_%s_%s_%s.json' %
(noise, bottleneck, exposures),
start_gen=0,
end_gen=50,
method='lang')
model_results_inf = il_results.load(
'../data/model_inf/1.0_%s_%s_%s.json' %
(noise, bottleneck, exposures),
start_gen=0,
end_gen=50,
method='lang')
model_results_inf_500 = il_results.load(
'../data/model_inf/500.0_%s_%s_%s.json' %
(noise, bottleneck, exposures),
start_gen=0,
end_gen=50,
method='lang')
il_results.make_figure(
[(model_results_sim, 'Simplicity prior (πsim, w = 1)',
colors.blue, colors.light_blue, '-'),
(model_results_inf, 'Informativeness prior (πinf, w = 1)',
colors.red, colors.light_red, '-'),
(model_results_inf_500,
'Strong informativeness prior (πinf, w = 500)',
colors.red, colors.light_red, ':')],
figure_path=figure_path,
title=title,
show_legend=False,
figsize=(5.1, 2.8))
def make_experiment_results_figure(figure_path):
experiment_results = il_results.load('../data/experiments/exp2_chains.json', start_gen=0, end_gen=10, method='prod')
il_results.make_figure([(experiment_results, 'Experiment', colors.black, colors.light_black, '-')],
figure_path=figure_path, show_legend=False)
