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hybridmodel.py
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hybridmodel.py
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from pandas.core import base
from numpy import mean, std
from math import floor, ceil
from scipy.stats import median_abs_deviation
from numpy.lib.function_base import median
def itemsList(source):
linecount = 0
indcount = 0
line1 = True
lines = open(source)
ind = {}
perPerson = {}
baseline = ['BaselineRandom', 'CorrectReply','AlwaysReject']
models = ['CR&time', 'ClassicReas', 'FFT-Max', 'FFT-ZigZag(Z+)', 'HeurRecogn', 'HeurRecogn-lin.', 'S2MR', 'SentimentAnalysis','PartR', 'VanBavel',]
if '3' in source:
models = models + ['WMSupprByMood', 'WMImprByMood']
for line in lines:
listLine = line.replace('\r','').replace('\n','').split(',')
if line1:
line1 = False
for key in listLine:
ind[key] = indcount
indcount += 1
continue
linecount += 1
person = listLine[ind['id']]
if person not in perPerson.keys():
perPerson[person] = {}
for model in models:
if model not in perPerson[person].keys():
perPerson[person][model] = []
perPerson[person][model].append(1-abs(float((listLine[ind['binaryResponse']])) - float(listLine[ind[model]])))
maxModels = {}
maxPerfs = {}
for pers in perPerson.keys():
maxperf, maxmodel = 0, None
for model in perPerson[pers].keys():
if mean(perPerson[pers][model]) > maxperf:
maxperf, maxmodel = mean(perPerson[pers][model]), model
maxPerfs[pers] = maxperf
maxModels[pers] = maxmodel
numberOfModelAsMax = {}
for pers in maxModels.keys():
if maxModels[pers] not in numberOfModelAsMax.keys():
numberOfModelAsMax[maxModels[pers]] = 0
numberOfModelAsMax[maxModels[pers]] += 1
allPersPerfList = [maxPerfs[a] for a in maxPerfs.keys()]
#print(numberOfModelAsMax)
percOfModelsAsMax = {}
for a in numberOfModelAsMax.keys():
percOfModelsAsMax[a] = float(numberOfModelAsMax[a]) / sum(numberOfModelAsMax[a] for a in numberOfModelAsMax.keys())
print(percOfModelsAsMax)
print('mean', round(mean(allPersPerfList), 2), 'median', round(median(allPersPerfList), 2),'MAD', round(median_abs_deviation(allPersPerfList), 2))
def itemsList2models(source):
linecount = 0
indcount = 0
line1 = True
lines = open(source)
ind = {}
perPerson = {}
baseline = ['BaselineRandom', 'CorrectReply','AlwaysReject']
models = ['CR&time', 'ClassicReas', 'FFT-Max', 'FFT-ZigZag(Z+)', 'HeurRecogn', 'HeurRecogn-lin.', 'S2MR', 'SentimentAnalysis',]
if '3' in source:
models = models + ['WMSupprByMood', 'WMImprByMood']
for line in lines:
listLine = line.replace('\r','').replace('\n','').split(',')
if line1:
line1 = False
for key in listLine:
ind[key] = indcount
indcount += 1
continue
linecount += 1
person = listLine[ind['id']]
if person not in perPerson.keys():
perPerson[person] = {}
for model in models:# ind.keys():
if model not in perPerson[person].keys():
perPerson[person][model] = []
perPerson[person][model].append(1-abs(float(listLine[ind['binaryResponse']]) - float(listLine[ind[model]])))
for model in baseline:
if model not in perPerson[person].keys():
perPerson[person][model] = []
if model == 'BaselineRandom':
perPerson[person][model].append(1-abs(float((listLine[ind['binaryResponse']])) - float(0.5)))
if model == 'CorrectReply':
perPerson[person][model].append(1-abs(float((listLine[ind['binaryResponse']])) - float('T' in listLine[ind['truthful']])))
if model == 'AlwaysReject':
perPerson[person][model].append(1-abs(float((listLine[ind['binaryResponse']])) - float(0)))
pairs = []
pairdone = []
for model1 in models:
for model2 in models:
if model1 == model2:
continue
if model1 + model2 in pairdone or model2 + model1 in pairdone:
continue
pairdone.append(model1 + model2)
maxModels = {}
maxPerfs = {}
for pers in perPerson.keys():
maxperf, maxmodel = 0, None
for model in [model1,model2]:
if model not in perPerson[pers].keys():
continue
if mean(perPerson[pers][model]) > maxperf:
maxperf, maxmodel = mean(perPerson[pers][model]), model
maxPerfs[pers] = maxperf
maxModels[pers] = maxmodel
numberOfModelAsMax = {}
for pers in maxModels.keys():
if maxModels[pers] not in numberOfModelAsMax.keys():
numberOfModelAsMax[maxModels[pers]] = 0
numberOfModelAsMax[maxModels[pers]] += 1
allPersPerfList = [maxPerfs[a] for a in maxPerfs.keys()]
if len([a for a in numberOfModelAsMax.keys() if a != None]) < 2:
continue
pairs.append((numberOfModelAsMax,mean(allPersPerfList), std(allPersPerfList), median(allPersPerfList), median_abs_deviation(allPersPerfList)))
#pairs.append((numberOfModelAsMax,median(allPersPerfList), median_abs_deviation(allPersPerfList)))
pairs.sort(key=order)
print(pairs[:5])
for model in models + baseline:
meanresperpers = [mean(perPerson[pers][model]) for pers in perPerson.keys()]
print(model, ':', int(20-len(model))*' ', 'mean', round(mean(meanresperpers), 2), 'median', round(median(meanresperpers), 2),'MAD', round(median_abs_deviation(meanresperpers), 2))
def order(itemOfList):
dictionary, meanV, stdV, medainV, madV = itemOfList
return -meanV
for source in ['modeloutputs12.csv','modeloutputs3.csv']:
print(source, ':')
itemsList(source)
itemsList2models(source)