def create_roc_report(file_prefix,
num_groups,
contrast_range,
num_trials,
reports_dir,
regenerate_plot=True):
num_extra_trials = 10
roc_report = Struct()
roc_report.auc = get_auc(file_prefix, contrast_range, num_trials,
num_extra_trials, num_groups)
roc_report.auc_single_option = []
roc_url = 'img/roc.png'
fname = os.path.join(reports_dir, roc_url)
roc_report.roc_url = roc_url
if regenerate_plot or not os.path.exists(fname):
fig = plt.figure()
for i in range(num_groups):
roc = get_roc_single_option(file_prefix, contrast_range,
num_trials, num_extra_trials, i)
plt.plot(roc[:, 0], roc[:, 1], 'x-', label='option %d' % i)
roc_report.auc_single_option.append(
get_auc_single_option(file_prefix, contrast_range, num_trials,
num_extra_trials, i))
plt.plot([0, 1], [0, 1], '--')
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
save_to_png(fig, fname)
save_to_eps(fig, os.path.join(reports_dir, 'img/roc.eps'))
plt.close()
return roc_report
def get_prob(x, output_dir):
num_groups = 2
trial_duration = 1 * second
input_sum = 40.0
num_trials = 5
num_extra_trials = 10
wta_params = default_params()
wta_params.p_b_e = 0.1
wta_params.p_x_e = 0.05
wta_params.p_e_e = x[0]
wta_params.p_e_i = x[1]
wta_params.p_i_i = x[2]
wta_params.p_i_e = x[3]
file_desc='wta.groups.%d.duration.%0.3f.p_b_e.%0.3f.p_x_e.%0.3f.p_e_e.%0.3f.p_e_i.%0.3f.p_i_i.%0.3f.p_i_e.%0.3f' % \
(num_groups, trial_duration, wta_params.p_b_e, wta_params.p_x_e, wta_params.p_e_e, wta_params.p_e_i,
wta_params.p_i_i, wta_params.p_i_e)
file_prefix = os.path.join(output_dir, file_desc)
num_example_trials = [0, 0]
for trial in range(num_trials):
inputs = np.zeros(2)
inputs[0] = np.random.random() * input_sum
inputs[1] = input_sum - inputs[0]
if inputs[0] > inputs[1]:
num_example_trials[0] += 1
else:
num_example_trials[1] += 1
if trial == num_trials - 1:
if num_example_trials[0] == 0:
inputs[
0] = input_sum * 0.5 + np.random.random() * input_sum * 0.5
inputs[1] = input_sum - inputs[0]
num_example_trials[0] += 1
elif num_example_trials[1] == 0:
inputs[
1] = input_sum * 0.5 + np.random.random() * input_sum * 0.5
inputs[0] = input_sum - inputs[1]
num_example_trials[1] += 1
output_file = '%s.trial.%d.h5' % (file_prefix, trial)
run_wta(wta_params,
num_groups,
inputs,
trial_duration,
output_file=output_file,
record_lfp=False,
record_voxel=False,
record_neuron_state=False,
record_spikes=False,
record_firing_rate=True,
record_inputs=True,
single_inh_pop=False)
auc = get_auc(file_prefix, num_trials, num_extra_trials, num_groups)
return auc
def get_prob(x, output_dir):
num_groups=2
trial_duration=1*second
input_sum=40.0
num_trials=5
num_extra_trials=10
wta_params=default_params()
wta_params.p_b_e=0.1
wta_params.p_x_e=0.05
wta_params.p_e_e=x[0]
wta_params.p_e_i=x[1]
wta_params.p_i_i=x[2]
wta_params.p_i_e=x[3]
file_desc='wta.groups.%d.duration.%0.3f.p_b_e.%0.3f.p_x_e.%0.3f.p_e_e.%0.3f.p_e_i.%0.3f.p_i_i.%0.3f.p_i_e.%0.3f' % \
(num_groups, trial_duration, wta_params.p_b_e, wta_params.p_x_e, wta_params.p_e_e, wta_params.p_e_i,
wta_params.p_i_i, wta_params.p_i_e)
file_prefix=os.path.join(output_dir,file_desc)
num_example_trials=[0,0]
for trial in range(num_trials):
inputs=np.zeros(2)
inputs[0]=np.random.random()*input_sum
inputs[1]=input_sum-inputs[0]
if inputs[0]>inputs[1]:
num_example_trials[0]+=1
else:
num_example_trials[1]+=1
if trial==num_trials-1:
if num_example_trials[0]==0:
inputs[0]=input_sum*0.5+np.random.random()*input_sum*0.5
inputs[1]=input_sum-inputs[0]
num_example_trials[0]+=1
elif num_example_trials[1]==0:
inputs[1]=input_sum*0.5+np.random.random()*input_sum*0.5
inputs[0]=input_sum-inputs[1]
num_example_trials[1]+=1
output_file='%s.trial.%d.h5' % (file_prefix,trial)
run_wta(wta_params, num_groups, inputs, trial_duration, output_file=output_file, record_lfp=False,
record_voxel=False, record_neuron_state=False, record_spikes=False, record_firing_rate=True,
record_inputs=True, single_inh_pop=False)
auc=get_auc(file_prefix, num_trials, num_extra_trials, num_groups)
return auc
def create_roc_report(file_prefix, num_groups, contrast_range, num_trials, reports_dir, regenerate_plot=True):
num_extra_trials=10
roc_report=Struct()
roc_report.auc=get_auc(file_prefix, contrast_range, num_trials, num_extra_trials, num_groups)
roc_report.auc_single_option=[]
roc_url = 'img/roc.png'
fname=os.path.join(reports_dir, roc_url)
roc_report.roc_url=roc_url
if regenerate_plot or not os.path.exists(fname):
fig=plt.figure()
for i in range(num_groups):
roc=get_roc_single_option(file_prefix, contrast_range, num_trials, num_extra_trials, i)
plt.plot(roc[:,0],roc[:,1],'x-',label='option %d' % i)
roc_report.auc_single_option.append(get_auc_single_option(file_prefix, contrast_range, num_trials, num_extra_trials, i))
plt.plot([0,1],[0,1],'--')
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
save_to_png(fig, fname)
save_to_eps(fig, os.path.join(reports_dir, 'img/roc.eps'))
plt.close()
return roc_report