def get_combained_values(truth_list, result_list):
combained_values = []
for truth, result in zip(truth_list, result_list):
t_truth = [datetime.datetime.strptime(truth_point, '%Y-%m-%d %H:%M:%S') for truth_point in truth[0]]
dict_item = {'GT': [t_truth, truth[1]],
'LS': result}
combained_values.append(dict_item)
combained_values = cook_raw_value(combained_values)
return combained_values
delta = timedelta(seconds=0)
while delta <= delta_max:
f_init.update({delta: f})
f += f_item
delta += t_item
for s in observed_keys:
cef = [c, e, f_init]
cef_measures.update({s: cef})
return cef_measures
if __name__ == '__main__':
ground_truth = ['Wisc', 'MSR']
observed_cases = cook_raw_value(get_observed_cases())
for case_number, observed in enumerate(observed_cases):
observed_keys = sorted(observed.keys())
cef_measures = cef_initialization(c=0.5, e=0.5, observed=observed)
iter_quantity = 0
sources_number = len(observed_keys)
life_span = get_life_span(observed=observed, cef_measures=cef_measures)
# print initial info
print 'CASE NUMBER: {}'.format(case_number)
print 'Ground truth: {}'.format(ground_truth)
for key in observed_keys:
print '{}: {}'.format(key, observed.get(key)[1])
print 'Initial life span: {}'.format(life_span)
"S2.C",
"S2.E",
"S2.F, days",
"S3.C",
"S3.E",
"S3.F, days",
"S4.C",
"S4.E",
"S4.F, days",
"S5.C",
"S5.E",
"S5.F, days",
]
# ground_truth = ['Wisc', 'MSR']
raw_cases = get_observed_cases()
observed_cases = cook_raw_value(raw_cases)
observed_keys = sorted(observed_cases[0].keys())
cef_measures = cef_initialization(c=0.99, e=0.95, observed=observed_cases[0])
set_of_life_spans = []
sources_number = len(observed_keys)
cases_number = len(observed_cases)
iter_quantity = 0
data_for_csv = {}
for case_number, observed in enumerate(observed_cases):
life_span = get_life_span(observed=observed, cef_measures=cef_measures)
set_of_life_spans.append(life_span)
# print initial info
print "OBJECT NUMBER: {}".format(case_number)
# print 'Ground truth: {}'.format(ground_truth)
delta = timedelta(seconds=0)
while delta <= delta_max:
f_init.update({delta: f})
f += f_item
delta += t_item
for s in observed_keys:
cef = [c, e, f_init]
cef_measures.update({s: cef})
return cef_measures
if __name__ == '__main__':
ground_truth = ['Wisc', 'MSR']
observed_cases = cook_raw_value(get_observed_cases())
for case_number, observed in enumerate(observed_cases):
observed_keys = sorted(observed.keys())
cef_measures = cef_initialization(c=0.5, e=0.5, observed=observed)
iter_quantity = 0
sources_number = len(observed_keys)
life_span = get_life_span(observed=observed, cef_measures=cef_measures)
# print initial info
print 'CASE NUMBER: {}'.format(case_number)
print 'Ground truth: {}'.format(ground_truth)
for key in observed_keys:
print '{}: {}'.format(key, observed.get(key)[1])
print 'Initial life span: {}'.format(life_span)
def get_CEF(life_span_set, sources_data):
"""
:param O:
:param S:
:return:
cl(S,O): capturable
c(S,O): captured
ml(S,O): mis-capturable
m(S,O): mis-captured
covg: coverage
exac: exactness
fresh: freshness
"""
cl = c = ml = m = 0
data_for_freshness = []
for s_life_span, s_data in zip(life_span_set, sources_data):
synchronised_values = cook_raw_value([{'O': s_life_span, 'S': s_data}])[0]
O = synchronised_values.get('O')[1]
S = synchronised_values.get('S')[1]
s_time_points = synchronised_values.get('S')[0]
N = len(s_time_points)
for i in range(N-1):
if O[i] == S[i] and i == 0:
c += 1
cl += 1
ml += 1
data_for_freshness.append(timedelta(seconds=0))
elif O[i] == S[i] and O[i] != O[i-1] and S[i] != S[i-1]:
c += 1
cl += 1
ml += 1
data_for_freshness.append(timedelta(seconds=0))
ml += 1
if O[i] != S[i]:
cl += 1
if O[i+1] == S[i+1]:
c += 1
data_for_freshness.append(s_time_points[i+1]-s_time_points[i])
elif O[i+1] != S[i+1]:
m += 1
if O[i] == S[i] and O[i+1] != S[i+1]:
m += 1
if O[0] != S[0]:
cl += 1
ml += 1
m += 1
if O[N-1] == S[N-1] and O[N-1] != O[N-2] and S[N-1] != S[N-2]:
c += 1
cl += 1
ml += 1
data_for_freshness.append(timedelta(seconds=0))
ml += 1
if O[N-1] != S[N-1]:
cl += 1
c = float(c)
exac = 1 - float(m)/ml
covg = float(c)/cl
delta_for_freshness = sorted(list(set(data_for_freshness)))
fresh = {timedelta(seconds=0): 0.0}
for delta in delta_for_freshness:
c_delta = len([k for k in data_for_freshness if delta >= k])
fresh_delta = c_delta/c
fresh.update({delta: fresh_delta})
return [covg, exac, fresh]