import sys
from collections import OrderedDict
sys.path.append("../..")

import ktensor
import tensorTools


def loadJSON(fn):
    with open(fn, 'rb') as outfile:
        jsonDict = json.load(outfile)
        outfile.close()
    return jsonDict


MBias = ktensor.loadTensor(
    "../results/pred-raw-bias-marble-{0}.dat".format(run))
M = ktensor.loadTensor("../results/pred-raw-marble-{0}.dat".format(run))
MCP = ktensor.loadTensor("../results/pred-raw-cpapr-{0}.dat".format(run))

X, axisDict, classDict = tensorTools.loadSingleTensor(
    "../data/cms-tensor-{0}.dat")

cptLevel = loadJSON("../data/cpt-level2.json")
icdLevel = loadJSON("../data/icd-level2.json")


## lookup values
def lookupDict(idx, n, axisDict, levelDict):
    ivAxis = {v: k for k, v in axisDict[n].items()}
    modeCat = [levelDict[str(ivAxis[k])] for k in idx]
    return modeCat
import json
import sys
from collections import OrderedDict
sys.path.append("../..")

import ktensor
import tensorTools

def loadJSON(fn):
	with open(fn, 'rb') as outfile:
		jsonDict = json.load(outfile)
		outfile.close()
	return jsonDict


MBias = ktensor.loadTensor("../results/pred-raw-bias-marble-{0}.dat".format(run))
M = ktensor.loadTensor("../results/pred-raw-marble-{0}.dat".format(run))
MCP = ktensor.loadTensor("../results/pred-raw-cpapr-{0}.dat".format(run))

X, axisDict, classDict = tensorTools.loadSingleTensor("../data/cms-tensor-{0}.dat")

cptLevel = loadJSON("../data/cpt-level2.json")
icdLevel = loadJSON("../data/icd-level2.json")

## lookup values
def lookupDict(idx, n, axisDict, levelDict):
	ivAxis = {v: k for k, v in axisDict[n].items()}
	modeCat = [levelDict[str(ivAxis[k])] for k in idx]
	return modeCat

## get the top k from MBias
Ejemplo n.º 3
0
import ktensor
import numpy
import decompTools

caseX = ktensor.loadTensor("results/apr-raw-1.dat")
controlX = ktensor.loadTensor("results/apr-raw-2.dat")
allX = ktensor.loadTensor("results/apr-raw-200.dat")

## since they don't share the same axis, remove one of the factors
caseX.U = [caseX.U[1], caseX.U[2]]
caseX = decompTools.zeroSmallFactors(caseX, 1e-2)
controlX.U = [controlX.U[1], controlX.U[2]]
controlX = decompTools.zeroSmallFactors(controlX, 1e-2)
allX.U = [allX.U[1], allX.U[2]]
allX = decompTools.zeroSmallFactors(allX, 1e-2)

fms = caseX.greedy_fms(controlX)
numpy.savetxt("plots/case-control.csv", fms, delimiter=",")
allFMS = caseX.greedy_fms(allX)
numpy.savetxt("plots/case-all.csv", allFMS, delimiter=",")
Ejemplo n.º 4
0
import ktensor
import numpy;
import decompTools

caseX = ktensor.loadTensor("results/apr-raw-1.dat")
controlX = ktensor.loadTensor("results/apr-raw-2.dat")
allX = ktensor.loadTensor("results/apr-raw-200.dat")

## since they don't share the same axis, remove one of the factors
caseX.U = [caseX.U[1], caseX.U[2]]
caseX = decompTools.zeroSmallFactors(caseX, 1e-2)
controlX.U = [controlX.U[1], controlX.U[2]]
controlX = decompTools.zeroSmallFactors(controlX, 1e-2)
allX.U = [allX.U[1], allX.U[2]]
allX = decompTools.zeroSmallFactors(allX, 1e-2)

fms = caseX.greedy_fms(controlX)
numpy.savetxt("plots/case-control.csv", fms, delimiter=",")
allFMS = caseX.greedy_fms(allX)
numpy.savetxt("plots/case-all.csv", allFMS, delimiter=",")
Ejemplo n.º 5
0
import numpy as np
import json
import sys

sys.path.append("../..")

import ktensor
import tensorTools

dataOut = []
bins = [None, None, None]
for run in range(400, 409):
    M = ktensor.loadTensor("../results/pred-raw-cpapr-{0}.dat".format(run))
    for n in range(M.ndims()):
        factVals = M.U[n].flatten()
        nnzIdx = np.nonzero(factVals)
        factVals = factVals[nnzIdx]
        if bins[n] == None:
            factHist = np.histogram(factVals, bins=10)
            bins[n] = factHist[1]
        else:
            factHist = np.histogram(factVals, bins=bins[n])
        dataOut.append({"expt": run, "mode": n, "count": factHist[0].tolist(), "bins": factHist[1].tolist()})

with open("cpapr-hist.json", "w") as outfile:
    json.dump(dataOut, outfile)
Ejemplo n.º 6
0
import numpy as np
import json
import sys
sys.path.append("../..")

import ktensor
import tensorTools

dataOut = []
bins = [None, None, None]
for run in range(400, 409):
    M = ktensor.loadTensor("../results/pred-raw-cpapr-{0}.dat".format(run))
    for n in range(M.ndims()):
        factVals = M.U[n].flatten()
        nnzIdx = np.nonzero(factVals)
        factVals = factVals[nnzIdx]
        if bins[n] == None:
            factHist = np.histogram(factVals, bins=10)
            bins[n] = factHist[1]
        else:
            factHist = np.histogram(factVals, bins=bins[n])
        dataOut.append({
            "expt": run,
            "mode": n,
            "count": factHist[0].tolist(),
            "bins": factHist[1].tolist()
        })

with open("cpapr-hist.json", 'w') as outfile:
    json.dump(dataOut, outfile)