parser.add_argument("iter", type=int, help="the number of iterations")
parser.add_argument("-t", "--testSize", type=float, help="test size", default=0.5)
args = parser.parse_args()
rank = args.rank
iter = args.iter
inputFile = args.inputFile
exptID = args.exptID
patientSet = args.patientSet
outsql = "results/pred-model-{0}-{1}.sql".format(exptID, rank)
print "Using Rank {0} and iterations {1} and test size {2}".format(rank, iter, args.testSize)
## Load information to run the tests
X = sptensor.loadTensor(inputFile.format("data"))
tensorInfo = shelve.open(inputFile.format("info"), "r")
Y = tensorInfo["class"]
XAxis = tensorInfo["axis"]
tensorInfo.close()
Y = np.array(Y, dtype=int)
pm = predictionModel.predictionModel(X, XAxis, Y, rank, testSize=args.testSize, outerIter=iter)
output = pm.evaluatePrediction()
output = np.column_stack((np.repeat(exptID, output.shape[0]), output))
outputFile = "results/pred-model-{0}-{1}.csv".format(exptID, rank)
np.savetxt(outputFile, output, delimiter=",")
sqlOut = file(outsql, "w")
sqlOut.write("load client from /home/joyceho/workspace/tensor/{0} of del modified by coldel, insert into joyceho.predictive_results;\n".format(outputFile))
sqlOut.write("insert into joyceho.predictive_models values({0}, {1}, \'{2}\',{3}, {4});\n".format(exptID, rank, patientSet, iter, 10))
sqlOut.close()
matrixSize = np.array([3412, 247, 816])
#print matrixValue
re = pd.read_csv(data_dir + 'vadationResult_3412.csv')
#print re
Y = np.array(re.ix[:, 'InHosLabel'])
#print Y
X = sptensor.sptensor(matrixIndex, matrixValue, matrixSize)
#print X.subs
#print X.subs.shape
'''
demoX=pd.read_csv(data_notebook+'demoF.csv')
demoX.index=demoX.ix[:,0]
demoX=np.array(demoX.ix[:,1:])
#demoX=demoX[:,1:]
print(demoX.shape)
#print demoX[:3]
'''
goNum = [10, 30, 50, 80, 100, 125, 150, 180, 200]
for i in range(len(goNum)):
phennum = goNum[i]
pm = predictionModel.predictionModel(X, X.subs, Y, phennum)
pm.evaluatePredictionAUC_1(str(phennum) + 'phens_Uapr_100outIter')
csvfile = file(data_dir + 'probablity', 'wb')
writer = csv.writer(csvfile)
csvfile.close()
import tensor import sptensor import numpy as np import ktensor import predictionModel subs = np.array([[0,3,1], [1,0,1], [1,2,1], [1,3,1], [2,4,0], [3,0,0],[4,4,1]]); vals = np.array([[1],[1],[1],[1],[4],[3],[2]]); siz = np.array([5,5,2]) # 5x5x2 tensor np.random.seed(0) X = sptensor.sptensor(subs, vals, siz) Y = np.random.randint(2, size=5) pm = predictionModel.predictionModel(X, Y, 2) pm.evaluatePrediction()
testXSize = np.array([1024, 247, 816])
testTensor = sptensor.sptensor(testXIndex, testXValue, testXSize)
trainRe = pd.read_csv(data_dir + 'TrainResult_2387.csv')
trainY = trainRe.ix[:, -1].as_matrix()
trainY[trainY == 0] = -1
print trainY.sum()
testRe = pd.read_csv(data_dir + 'TestResult_1024.csv')
testY = testRe.ix[:, -1].as_matrix()
testY[testY == 0] = -1
print testY.sum()
#goNum=[30,50,80,100,120,150,180,200]
goNum = [10, 30, 50, 80, 100, 125, 150, 180, 200]
for i in range(len(goNum)):
phennum = goNum[i]
pm = predictionModel.predictionModel(trainTensor, trainTensor.subs, trainY,
phennum)
if i == 0:
first = True
else:
first = False
pm.evaluatePredictionAUC_4(
str(phennum) + 'phens_ttapr_100outIter', trainTensor, trainY,
testTensor, testY)
csvfile = file(data_dir + 'probablity', 'wb')
writer = csv.writer(csvfile)
csvfile.close()
import tensor
import sptensor
import numpy as np
import ktensor
import predictionModel
subs = np.array([[0, 3, 1], [1, 0, 1], [1, 2, 1], [1, 3, 1], [2, 4, 0],
[3, 0, 0], [4, 4, 1]])
vals = np.array([[1], [1], [1], [1], [4], [3], [2]])
siz = np.array([5, 5, 2]) # 5x5x2 tensor
np.random.seed(0)
X = sptensor.sptensor(subs, vals, siz)
Y = np.random.randint(2, size=5)
pm = predictionModel.predictionModel(X, Y, 2)
pm.evaluatePrediction()
