def lbd_actual_and_predicted_values(input_dict):
bterms=input_dict['bterms']
bow_model_constructor=input_dict['bow_model_constructor']
vocabulary=bow_model_constructor._vocab_to_idx()
actual_values=[0]*len(vocabulary)
for bterm in bterms:
if bterm in vocabulary:
actual_values[vocabulary[bterm]]=1
#print bterm
heuristics=flatten(input_dict['heuristics'])
return {'apv':[{'name': h.name,'predicted':list(h.scores),'actual':actual_values} for h in heuristics]}
def lbd_actual_and_predicted_values(input_dict):
bterms = input_dict['bterms']
bow_model_constructor = input_dict['bow_model_constructor']
vocabulary = bow_model_constructor._vocab_to_idx()
actual_values = [0] * len(vocabulary)
for bterm in bterms:
if bterm in vocabulary:
actual_values[vocabulary[bterm]] = 1
#print bterm
heuristics = flatten(input_dict['heuristics'])
return {
'apv': [{
'name': h.name,
'predicted': list(h.scores),
'actual': actual_values
} for h in heuristics]
}
def lbd_select_ensemble_heuristic_post(postdata, input_dict, output_dict):
widget_id = postdata.get('widget_id')[0]
from workflows.textflows_dot_net.serialization_utils import ToNetObj
import LatinoInterfaces
output_dict = {}
output_dict['serialized_adc'] = LatinoInterfaces.LatinoCF.Save(
ToNetObj(input_dict['adc']))
output_dict['vocabulary'] = input_dict[
'bow_model_constructor'].get_feature_names()
output_dict['heuristic_scores'] = [{
'name': hevr.name,
'scores': hevr.scores.tolist()
} for hevr in flatten(input_dict['heuristic_scores'])]
output_dict['bterms'] = input_dict['bterms']
output_dict['serialized_dataset'] = LatinoInterfaces.LatinoCF.Save(
ToNetObj(input_dict['dataset']))
#output_dict['primary_heuristic_index']=input_dict['primary_heuristic_index']
output_dict['primary_heuristic_index'] = int(
postdata.get('heuristic_index', [-1])[0])
return output_dict #{'heuristic_index': selected_heuristic}
def vipercharts_prepareCurveData(input_dict): #, subtype
import math
nPoints=4
performance = flatten(input_dict['predictions'])#chartdata
subtype = input_dict['subtype']
kenmax = 0.5
ratemax = 0.5
for curve in performance:
n = len(curve['actual'])
negs = curve['actual'].count(0)
poss = curve['actual'].count(1)
if poss == 0 or negs == 0:
raise Exception("Class Error, zero poss or zero negs, only one class or other type error.")
#return []
try:
ranks = curve['rank']
except:
ranks = range(n+1)[1:] # ranks from 1
paralel =[]
for i in range(n):
paralel.append([curve['actual'][i], float(curve['predicted'][i])])
if (subtype == '-score'):
ROCseries = [[0,0, '-Inf']]; PRseries = [[0,1, '-Inf']]; LIFTseries = [[0,0, '-Inf']]
ROChull = [[0,0,'-Inf']]; COSTseries = [[0,0,'-Inf']]; RATEseries = []; KENseries = [[0,0]]; KENup=[[0,1]]; KENdown=[[0,0]]
_oldrate = 0
_oldloss = 0
AUC = 0
AUPR = 0
ranked = sorted(paralel, key = lambda pair:pair[1], reverse=True)
print "ranked:"+curve['name']
print "by prediction: "+str(ranked)
print "by actual: "+str(sorted(paralel, key = lambda pair:pair[0], reverse=True))
k = 0
tp = 0; fp = 0; tp_old = 0; fp_old = 0; n1 = 0; concordant_pairs = 0; discordant_pairs = 0;
while k < len(ranked):
addedconc = 0; addeddisc = 0;
threshold = ranked[k][1];
group = [x[0] for x in ranked if x[1] >= threshold]
tp = group.count(1)
fp = group.count(0)
#next k is len(group).
ties = len(group) - k
n1 += ties * (ties-1)/2
concordant_pairs += tp_old * (fp - fp_old)
discordant_pairs += fp_old * (tp - tp_old)
ROCpoint = [fp*1.0/negs,tp*1.0/poss, threshold]
ROCseries.append(ROCpoint)
AUC += (ROCpoint[1] + ROCseries[-2][1]) * (ROCpoint[0] - ROCseries[-2][0]) * 0.5
PRseries.append([tp*1.0/poss, tp*1.0/(tp+fp), threshold])
AUPR += (PRseries[-1][1] + PRseries[-2][1]) * (PRseries[-1][0] - PRseries[-2][0]) * 0.5
LIFTseries.append([len(group)*1.0/n, tp*1.0/poss, threshold])
#Convex hull and lower envelope:
while len(ROChull)>=2 and (ROChull[-1][0]==ROCpoint[0] or (ROChull[-2][0]!=ROChull[-1][0] and (ROChull[-1][1]-ROChull[-2][1])/(ROChull[-1][0]-ROChull[-2][0]) <= (ROCpoint[1]-ROChull[-1][1])/(ROCpoint[0]-ROChull[-1][0]))):
ROChull.pop()
COSTseries.pop()
ROChull.append(ROCpoint)
if(ROCpoint[0] != ROChull[-2][0]):
slope = (ROCpoint[1] - ROChull[-2][1]) / (ROCpoint[0] - ROChull[-2][0])
intercept = ROCpoint[1] - slope * ROCpoint[0]
COSTseries.append([1 / (slope + 1), (1 - intercept) / (1 + slope), threshold])
else:
if len(COSTseries) == 0:
COSTseries.append([0,0,threshold])
else:
COSTseries[0][2] = threshold
COSTend = 1 - ROCpoint[1]
#Rate driven curve:
#The Rate driven curve is a list of intervals. Each interval is a set of points on the appropriate parabola. There are nPoints number of points
RATEinterval = []
pi0 = poss * 1.0 / n
pi1 = 1 - pi0
_newrate = pi1*ROCpoint[0]+pi0*ROCpoint[1]
_newloss = 2*(_newrate*(pi0-_newrate) + pi1*ROCpoint[0])
RATEinterval.append([_oldrate, _oldloss, threshold, performance.index(curve)+1])
for i in range(1, nPoints):
alpha = i * 1.0/nPoints
rate = _oldrate + alpha * (_newrate - _oldrate)
loss = 2 * (rate * (pi0 - rate) + pi1 * (ROCseries[-2][0] + alpha * (ROCpoint[0] - ROCseries[-2][0])))
RATEinterval.append([rate, loss, 0])
RATEinterval.append([_newrate, _newloss, 0])
RATEseries.append(RATEinterval)
if _newloss > ratemax:
ratemax = _newloss
m = 0.5*(pi0+pi1*(ROCseries[-2][0]-ROCpoint[0])/(_newrate-_oldrate))
if m<_newrate and m>_oldrate:
mvalue=2*(m*(pi0-m)+pi1*((_newrate-m)*ROCseries[-2][0] + (m-_oldrate)*ROCpoint[0])/(_newrate - _oldrate))
if mvalue > ratemax:
ratemax = mvalue
#Kendall curve:
if _newrate <= pi0:
KENseries.append([_newrate, 2*pi1*ROCpoint[0], threshold])
else:
if _oldrate < pi0:
KENseries.append([pi0,(2*pi1*ROCpoint[0]-KENseries[-1][1])*(pi0-KENseries[-1][0])/(_newrate - KENseries[-1][0])+(KENseries[-1][1]), ''])
KENseries.append([_newrate, 2*pi0*(1-ROCpoint[1]), threshold])
if KENseries[-1][1] > kenmax:
kenmax = KENseries[-1][1]
_oldrate = _newrate
_oldloss = _newloss
k += len(group) - k
tp_old = tp
fp_old = fp
else:
ROCseries = [[0,0,0]]; PRseries = [[0,1,0]]; LIFTseries = [[0,0,0]]# x: y: rank:
ranked = sorted(paralel, key=lambda pair:pair[1])
print ranked
k = 0
while k < len(ranked):
tp = 0; fp = 0;
threshold = ranked[k][1];
group = [x[0] for x in ranked if x[1] <= threshold]
print group
tp = group.count('1')
fp = group.count('0')
ROCpoint = [fp*1.0/negs,tp*1.0/poss, threshold]
ROCseries.append([fp*1.0/negs, tp*1.0/poss, int(threshold)])
PRseries.append([tp*1.0/poss, tp*1.0/(tp+fp), int(threshold)])
LIFTseries.append([len(group)*1.0/n, tp*1.0/poss, int(threshold)])
while len(ROChull)>=2 and (ROChull[-1][0]==ROCpoint[0] or (ROChull[-2][0]!=ROChull[-1][0] and (ROChull[-1][1]-ROChull[-2][1])/(ROChull[-1][0]-ROChull[-2][0]) <= (ROCpoint[1]-ROChull[-1][1])/(ROCpoint[0]-ROChull[-1][0]))):
ROChull.pop()
COSTseries.pop()
ROChull.append(ROCpoint)
if(ROCpoint[0]!=ROChull[-2][0]):
slope=(ROCpoint[1]-ROChull[-2][1])/(ROCpoint[0]-ROChull[-2][0])
intercept=ROCpoint[1]-slope*ROCpoint[0]
COSTseries.append([1/(1+slope), (1-intercept)/(1+slope)])
else:
COSTseries.append([0.0, ROCpoint[0]])
k += len(group) - k
if COSTseries[-1][0]<1:
#append final point with max threshold
COSTseries.append([1, COSTend, ranked[-1][1]])
curve['ROCpoints'] = ROCseries
curve['PRpoints'] = PRseries
curve['LIFTpoints'] = LIFTseries
curve['ROChull'] = ROChull
curve['COSTpoints'] = COSTseries
curve['RATEintervals'] = RATEseries
curve['KENpoints'] = KENseries
curve['AUC'] = AUC
curve['Gini'] = 2 * AUC - 1
n0=n*(n-1)/2
#curve['KENtau'] = (concordant_pairs - discordant_pairs) / math.sqrt((n0 - n1) * (n0 - (negs*(negs-1) + poss*(poss-1))/2))
curve['AUPR'] = AUPR
AUCH = 0
for i in range(1, len(ROChull)):
AUCH += (ROChull[i][1] + ROChull[i-1][1]) * (ROChull[i][0] - ROChull[i-1][0]) * 0.5
curve['AUCH'] = AUCH
performance[0]['KENmax'] = kenmax
performance[0]['RATEmax'] = ratemax
output_dict = {}
output_dict['performance'] = performance
return output_dict
def lbd_select_ensemble_heuristic(request, input_dict, output_dict, widget):
heuristics=[h.name for h in flatten(input_dict['heuristic_scores'])]
return render(request, 'interactions/lbd_select_ensemble_heuristic.html', {'heuristics': heuristics, 'widget':widget})
def lbd_heuristic_max(input_dict):
heuristic_names = flatten(input_dict.get('heuristics', []))
return {'heuristic': ('Max', heuristic_names)}
def lbd_ensemble_average_position(input_dict):
heuristic_names = flatten(input_dict['heuristics'])
return {'heuristic': ('AvgPos', heuristic_names)}
def lbd_ensemble_heuristic_vote(input_dict):
heuristic_names = flatten(input_dict['heuristics'])
return {'heuristic': ('Vote', heuristic_names)}
def lbd_heuristic_norm(input_dict):
heuristic_names = flatten(input_dict.get('heuristics', []))
return {
'norm_heuristics':
[('Norm', heuristic_name) for heuristic_name in heuristic_names]
}
def lbd_heuristic_sum(input_dict):
heuristic_names = flatten(input_dict.get('heuristics', []))
return {'heuristic': ('Sum', heuristic_names)}
def lbd_ensemble_average_position(input_dict):
heuristic_names=flatten(input_dict['heuristics'])
return {'heuristic': ('AvgPos',heuristic_names)}
def lbd_ensemble_heuristic_vote(input_dict):
heuristic_names=flatten(input_dict['heuristics'])
return {'heuristic': ('Vote',heuristic_names)}
def lbd_select_ensemble_heuristic_post(postdata, input_dict, output_dict):
widget_id = postdata.get('widget_id')[0]
from workflows.textflows_dot_net.serialization_utils import ToNetObj
import LatinoInterfaces
output_dict={}
output_dict['serialized_adc']=LatinoInterfaces.LatinoCF.Save(ToNetObj(input_dict['adc']))
output_dict['vocabulary']=input_dict['bow_model_constructor'].get_feature_names()
output_dict['heuristic_scores']=[{'name': hevr.name, 'scores': hevr.scores.tolist()} for hevr in flatten(input_dict['heuristic_scores'])]
output_dict['bterms']=input_dict['bterms']
output_dict['serialized_dataset']=LatinoInterfaces.LatinoCF.Save(ToNetObj(input_dict['dataset']))
#output_dict['primary_heuristic_index']=input_dict['primary_heuristic_index']
output_dict['primary_heuristic_index']=int(postdata.get('heuristic_index',[-1])[0])
return output_dict #{'heuristic_index': selected_heuristic}
def lbd_heuristic_norm(input_dict):
heuristic_names=flatten(input_dict.get('heuristics',[]))
return {'norm_heuristics': [('Norm',heuristic_name) for heuristic_name in heuristic_names]}
def lbd_heuristic_sum(input_dict):
heuristic_names=flatten(input_dict.get('heuristics',[]))
return {'heuristic': ('Sum',heuristic_names)}
def lbd_heuristic_max(input_dict):
heuristic_names=flatten(input_dict.get('heuristics',[]))
return {'heuristic': ('Max',heuristic_names)}
