def pipelines(n_central, n_friends):
''' Includes all the pipelines and returns a pair of true label list and a
predicted label list. '''
# A list of standard distributions. Format is
# [(label, prob), (label, prob), ...]
#standards = serial.objFromFile('prob_groups.txt')
standards = gstat.generateEmpiricalDistributionFromSample(n_central, n_friends)
print standards
# Age and a set of names. Format is
# [(age, [names...]), (age, [names...])]
name_net = friends.getNameNetworkSampled(n_central, n_friends)
# Format becomes:
# [(age, prob, meta), (age, prob, meta), ...]
observed = gstat.convertNameNet2Observed(name_net)
# Format becomes:
# [(age, prediction, meta), ...]
prediction = convertObserved2Prediction(standards, observed)
# Format becomes:
# [(true1, true2,...), (predict1, predict2, ...), (meta1, meta2, ...)]
pairs = unzipGroupPrediction(prediction)
return pairs
def test1():
# A list of standard distributions. Format is
# [(age, prob), (age, prob), ...]
standards = serial.objFromFile('prob_groups.txt')
# Age and a set of names. Format is
# [(age, [names...]), (age, [names...])]
name_net = friends.getNameNetworkSampled(10, 100)
# Format is:
# [(age, prob, meta), (age, prob, meta), ...]
observed = convertNameNet2Observed(name_net)
def match(o): # a single observation, i.e., (age, [names...])
age = o[0]
p = o[1]
meta = o[2]
if sum(p) != 0:
matched = matchObserved(standards, p)
matched_ages = map(lambda x: x[0], matched)
return (age, p, matched_ages, meta)
else:
return (age, 0, meta)
matched_arr = map(match, observed)
print serial.toString(matched_arr)
def generateEmpiricalDistributionFromSample(n_central, n_friends):
# Age and a set of names. Format is
# [(age, [names...]), (age, [names...])]
name_net = friends.getNameNetworkSampled(n_central, n_friends)
# Format becomes:
# [(age, prob, meta), (age, prob, meta), ...]
observed = convertNameNet2Observed(name_net)
# Format becomes:
# [(label, prob), (label, prob), ...], labels are unique here
aggregated = aggregateObserved(observed)
return aggregated
