def count_scoring_binary(data, url, params):
"""Returns correct only if entire document correct"""
correct = sum(len(redactometer.censor_fill(url + i, **params)[1]) \
== data.total_censor[i] for i in data.index)
results = [(i, len(redactometer.censor_fill(url + i, **params)[1]), data.total_censor[i]) \
for i in data.index]
score = float(correct)/float(len(data.index))
return score, results
def eval(data, url, params):
""" return score and individual metrics"""
score = count_scoring(data, url, params)
metrics = [(i, len(redactometer.censor_fill(url + i, **params)[1]), data.total_censor[i], \
abs_error(len(redactometer.censor_fill(url + i, **params)[1]), \
data['total_censor'][i])) for i in data.index]
metrics = pandas.DataFrame(metrics, columns=['img', 'observed', 'actual', 'error'])
#scores.to_csv(outfile, sep="\t")
return score, metrics
def f_score(data, url, params):
predicted = float(sum([len(redactometer.censor_fill(url + i, **params)[1]) \
for i in data.index]))
truepos = float(sum([len(redactometer.censor_fill(url + i, **params)[1]) \
== data.total_censor[i] for i in data.index]))
#verify by coordinate! Above not correct. Truepos = relevant ^ retrieved
precision = truepos / predicted
recall = truepos / sum(data.total_censor)
F = 2 * (precision * recall) / (precision + recall)
return F
def count_scoring(data, url, params):
"""Count number of censors detected over real number"""
error = np.mean([abs_error(len(redactometer.censor_fill(url + i, **params)[1]), \
data.total_censor[i]) for i in data.index])
return 1 - error