negative_count = sum([1 for x in X_negative if x >= alpha])
negative_rate = negative_count/float(len(X_negative))
roc_X.append(negative_rate)
roc_Y.append(positive_rate)
#print roc_X
plt.plot(roc_X,roc_Y,color="green")
classify = IterativeEM()
classify.__classify__(vote_list,2)
estimates = classify.__getEstimates__()
X_positive = []
X_negative = []
for subject_index,zooniverse_id in enumerate(big_subjectList):
probability = estimates[subject_index]
wreness_condor = gold_condor[subject_index]
if wreness_condor == 0:
X_negative.append(probability)
else:
X_positive.append(probability)
alpha_list = X_negative[:]
alpha_list.extend(X_positive)
alpha_list.sort()
positive_count = sum([1 for x in X_positive if x >= alpha])
positive_rate = positive_count / float(len(X_positive))
negative_count = sum([1 for x in X_negative if x >= alpha])
negative_rate = negative_count / float(len(X_negative))
roc_X.append(negative_rate)
roc_Y.append(positive_rate)
#print roc_X
plt.plot(roc_X, roc_Y, color="green")
classify = IterativeEM()
classify.__classify__(vote_list, 2)
estimates = classify.__getEstimates__()
X_positive = []
X_negative = []
for subject_index, zooniverse_id in enumerate(big_subjectList):
probability = estimates[subject_index]
wreness_condor = gold_condor[subject_index]
if wreness_condor == 0:
X_negative.append(probability)
else:
X_positive.append(probability)
alpha_list = X_negative[:]
alpha_list.extend(X_positive)
alpha_list.sort()
