def evaluatePredictionAUC(self):
run = 0
sumBaseAUC=0.0
sumCprAUC=0.0
for train, test in self.ttss:
print "Evaluating Run:{0}".format(run)
# get the indices for the training tensor
trainShape = list(self.X.shape)
trainShape[0] = len(train)
trainX = tensorSubset(self.X, train, trainShape)
trainY = self.Y[train]
## find the tensor factors for PTF-HT
klp = self.findFactors(trainX)
## Get the reduced features for the data points
ptfFeat = klp.projectSlice(self.X, 0)
## Evaluate the raw fit using logistic regression
baseAUC, basePred = predictionTools.getAUC(self.predModel, self.rawFeatures, self.Y, train, test)
cprAUC, cprPred = predictionTools.getAUC(self.predModel, ptfFeat, self.Y, train, test)
sumBaseAUC+=baseAUC
sumCprAUC+=cprAUC
run = run + 1
print sumBaseAUC/run
print('**************************************')
print sumCprAUC/run
return sumBaseAUC/run,sumCprAUC/run
def evaluatePredictionAUC_2(self,experCount,Demog):
run = 0
sumBaseAUC=0.0
sumCprAUC=0.0
lambda1=1
lambda4=1
DemoU=np.random.rand(self.R,Demog.shape[1])
MCPR, cpstats, mstats = cp_apr_demog.cp_apr(self.X, self.R,Demog,DemoU,lambda1,lambda4, maxiters=40, maxinner=self.innerIter)
MCPR.normalize_sort(1)
## scale by summing across the rows
totWeight = np.sum(MCPR.U[0], axis=1)
zeroIdx = np.where(totWeight < 1e-100)[0]
if len(zeroIdx) > 0:
# for the zero ones we're going to evenly distribute
evenDist = np.repeat(1.0 / self.R, len(zeroIdx)*self.R)
MCPR.U[0][zeroIdx, :] = evenDist.reshape((len(zeroIdx), self.R))
totWeight = np.sum(MCPR.U[0], axis=1)
twMat = np.repeat(totWeight, self.R).reshape(self.X.shape[0], self.R)
MCPR.U[0] = MCPR.U[0] / twMat
#print(MCPR.U[0])
#print(self.rawFeatures)
rawXfile=self.data_dir+'experimentDemo/rawdataX_'+str(experCount)+'.csv'
rawYfile=self.data_dir+'experimentDemo/rawdataY_'+str(experCount)+'.csv'
cprXfile=self.data_dir+'experimentDemo/cprdataX_'+str(experCount)+'.csv'
cprYfile=self.data_dir+'experimentDemo/cprdataY_'+str(experCount)+'.csv'
np.savetxt(rawXfile,self.rawFeatures)
np.savetxt(rawYfile,self.Y)
np.savetxt(cprXfile, MCPR.U[0])
np.savetxt(cprYfile,self.Y)
for train, test in self.ttss:
print "Evaluating Run:{0}".format(run)
# get the indices for the training tensor
trainShape = list(self.X.shape)
trainShape[0] = len(train)
trainX = tensorSubset(self.X, train, trainShape)
trainY = self.Y[train]
## Evaluate the raw fit using logistic regression
baseAUC, basePred = predictionTools.getAUC(self.predModel, self.rawFeatures, self.Y, train, test)
cprAUC, cprPred = predictionTools.getAUC(self.predModel, MCPR.U[0], self.Y, train, test)
sumBaseAUC+=baseAUC
sumCprAUC+=cprAUC
print('base:'+str(baseAUC))
print('apr:'+str(cprAUC))
run = run + 1
print('**************************************')
print sumBaseAUC/run
print sumCprAUC/run
return sumBaseAUC/run,sumCprAUC/run
import json
import numpy as np
from sklearn.cross_validation import StratifiedShuffleSplit
from sklearn.linear_model import LogisticRegression
import sys
sys.path.append("..")
import sptenmat
import tensorIO
import predictionTools
X, axisDict, classDict = tensorIO.loadSingleTensor("data/cms-tensor-{0}.dat")
Y = np.array(classDict.values(), dtype='int')
predModel = LogisticRegression(C=990000, penalty='l1', tol=1e-6)
flatX = sptenmat.sptenmat(X, [0]).tocsrmat() # matricize along the first mode
testSize = 0.5
outfile = open("results/baseline-results.json", 'w')
for seed in range(0, 1000, 100):
ttss = StratifiedShuffleSplit(Y,
n_iter=1,
test_size=testSize,
random_state=seed)
for train, test in ttss:
trainY = Y[train]
baseAUC, basePred = predictionTools.getAUC(predModel, flatX, Y, train,
test)
output = {"type": "baseline", "seed": seed, "auc": baseAUC}
outfile.write(json.dumps(output) + '\n')
outfile.close()
## store off the raw file
MFact[0].writeRawFile("results/pred-raw-marble-{0}.dat".format(exptID))
MFact[1].writeRawFile(
"results/pred-raw-bias-marble-{0}.dat".format(exptID))
## compare to the traditional non-negative
startTime = time.time()
MCPR, cpstats, mstats = CP_APR.cp_apr(trainX,
R,
maxiters=outerIter,
maxinner=innerIter)
cpaprElapse = time.time() - startTime
MCPR.writeRawFile("results/pred-raw-cpapr-{0}.dat".format(exptID))
MCPR.normalize_sort(1)
klp = KLProjection.KLProjection(MCPR.U, MCPR.R)
cprFeat = klp.projectSlice(X, 0)
## prediction part
baseAUC, basePred = predictionTools.getAUC(predModel, rawFeatures, Y,
train, test)
marbleAUC, marblePred = predictionTools.getAUC(predModel, pftMat, Y, train,
test)
cprAUC, cprPred = predictionTools.getAUC(predModel, cprFeat, Y, train,
test)
output['time'] = [0, cpaprElapse, marbleElapse]
output['auc'] = [baseAUC, cprAUC, marbleAUC]
output['order'] = ['Baseline', 'CP-APR', 'Marble']
with open("results/pred-{0}.json".format(exptID), 'w') as outfile:
json.dump(output, outfile)
"""
Experiment to compute the baseline predictive model using flat features
"""
import json
import numpy as np
from sklearn.cross_validation import StratifiedShuffleSplit
from sklearn.linear_model import LogisticRegression
import sys
sys.path.append("..")
import sptenmat
import tensorIO
import predictionTools
X, axisDict, classDict = tensorIO.loadSingleTensor("data/cms-tensor-{0}.dat")
Y = np.array(classDict.values(), dtype='int')
predModel = LogisticRegression(C=990000, penalty='l1', tol=1e-6)
flatX = sptenmat.sptenmat(X, [0]).tocsrmat() # matricize along the first mode
testSize = 0.5
outfile = open("results/baseline-results.json", 'w')
for seed in range(0, 1000, 100):
ttss = StratifiedShuffleSplit(Y, n_iter=1, test_size=testSize, random_state=seed)
for train, test in ttss:
trainY = Y[train]
baseAUC, basePred = predictionTools.getAUC(predModel, flatX, Y, train, test)
output = {"type": "baseline", "seed": seed, "auc": baseAUC }
outfile.write(json.dumps(output) + '\n')
outfile.close()
## create the raw features
rawFeatures = predictionTools.createRawFeatures(X)
startTime = time.time()
MFact, Minfo = SP_NTF.sp_ntf(trainX, R=R, alpha=alpha, gamma=gamma, maxiters = outerIter, maxinner=innerIter)
marbleElapse = time.time() - startTime
pftMat, pftBias = SP_NTF.projectTensor(X, MFact, 0, maxinner=innerIter)
## store off the raw file
MFact[0].writeRawFile("results/pred-raw-marble-{0}.dat".format(exptID))
MFact[1].writeRawFile("results/pred-raw-bias-marble-{0}.dat".format(exptID))
## compare to the traditional non-negative
startTime = time.time()
MCPR, cpstats, mstats = CP_APR.cp_apr(trainX, R, maxiters=outerIter, maxinner=innerIter)
cpaprElapse = time.time() - startTime
MCPR.writeRawFile("results/pred-raw-cpapr-{0}.dat".format(exptID))
MCPR.normalize_sort(1)
klp = KLProjection.KLProjection(MCPR.U, MCPR.R)
cprFeat = klp.projectSlice(X, 0)
## prediction part
baseAUC, basePred = predictionTools.getAUC(predModel, rawFeatures, Y, train, test)
marbleAUC, marblePred = predictionTools.getAUC(predModel, pftMat, Y, train, test)
cprAUC, cprPred = predictionTools.getAUC(predModel, cprFeat, Y, train, test)
output['time'] = [0, cpaprElapse, marbleElapse]
output['auc'] = [baseAUC, cprAUC, marbleAUC]
output['order'] = ['Baseline', 'CP-APR', 'Marble']
with open("results/pred-{0}.json".format(exptID), 'w') as outfile:
json.dump(output, outfile)
