for b in stats.filter(k).get('beta'):
plot(b)
for b in stats.filter(k).get('boot_betas'):
for bb in b:
plot(bb,'--')
stats.save(path + identifier +E3+ '_' + str(cell) + "_" + str(m.name).replace("/","_") + '_stats.stat')
job "E3.4. Saving Data":
require previous
stats.load_glob(path + identifier +E3+ '_*_Model*_stats.stat')
stats.save(path + identifier +E3+ '_all_models.stat')
job "Results 1. Plotting" for Ei in range(len(Es)):
require "E1.2. Saving Data"
require "E1b.2. Saving Data"
require "E2.4. Saving Data"
require "E3.4. Saving Data"
E = Es[Ei]
stats = StatCollector()
titles = results_titles
invert = results_invert
with View(job_path + '_results_' + E + 'plots.html') as view:
stats.load(path + identifier + E + '_all_models.stat')
for k in stats.keys():
stats.stats[k]['BIC'] = stats.stats[k]['statistics']['bic'] # get bic from 'statistics' into the statcollector
statsr = stats.rename_value_to_tree()
for i in range(model_cells):
for dim in ['EIC','AIC','BIC','EICE2','llf_test_model','llf_train_model']:
with view.figure('Cells/tabs/'+str(i)+'/tree/'+dim):
statsr.filter('Model 0').plotTree(dim)
view.render(path+'results_'+E+identifier+'.html')
job "E3.4. Saving Data":
require previous
stats.load_glob(path + identifier +E3+ '_*_Model*_stats.stat')
stats.save(path + identifier +E3+ '_all_models.stat')
job "Results 1. Plotting" for Ei in range(len(Es)):
require "E1.2. Saving Data"
require "E1b.2. Saving Data"
require "E2.4. Saving Data"
require "E3.4. Saving Data"
E = Es[Ei]
stats = StatCollector()
titles = results_titles
invert = results_invert
with View(job_path + '_results.html') as view:
stats.load(path + identifier + E + '_all_models.stat')
for k in stats.keys():
stats.stats[k]['BIC'] = stats.stats[k]['statistics']['bic'] # get bic from 'statistics' into the statcollector
all_l = []
all_model_points = []
for cell in model_cells:
for dim in ['llf_test_model','EIC','AIC','llf_boot','llf_train','llf_train_model','llf_test','EICE_bias','EICE2','BIC']:
print dim
with view.figure("/tabs/"+E+"/Cells/tabs/Cell "+str(cell)+"/tabs/Trees From 2nd Level/tabs/"+dim,figsize=(7,10)):
stats.filter('Model '+str(cell)).plotTree(dim)
pylab.title(titles[dim])
with view.figure("/tabs/"+E+"/Cells/tabs/Cell "+str(cell)+"/tabs/Trees From 2nd Level Inverse/tabs/"+dim,figsize=(7,10)):
stats.filter('Model '+str(cell)).plotTree(dim,right_to_left=True)
pylab.title(titles[dim])
for dim in ['EICE','llf_boot','llf_train','llf_test','EICE_bias','EICE2','EICE_bias_uncorrected']:
with view.figure("/tabs/"+E+"/Cumulative Mean/tabs/Cell "+str(cell)+"/tabs/Cumulative Mean/tabs/"+dim,figsize=(4,4)):
