maxLocalAuc.numProcesses = multiprocessing.cpu_count() maxLocalAuc.numRecordAucSamples = 100 maxLocalAuc.numRowSamples = 30 maxLocalAuc.rate = "constant" maxLocalAuc.recordStep = 10 maxLocalAuc.rho = 1.0 maxLocalAuc.t0 = 1.0 maxLocalAuc.t0s = 2.0**-numpy.arange(7, 12, 1) maxLocalAuc.validationSize = 3 maxLocalAuc.validationUsers = 0 newM = X.shape[0]/4 modelSelectX, userInds = Sampling.sampleUsers(X, newM) if saveResults: meanObjs1, stdObjs1 = maxLocalAuc.modelSelect(X) meanObjs2, stdObjs2 = maxLocalAuc.modelSelect(trainX) meanObjs3, stdObjs3 = maxLocalAuc.modelSelect(modelSelectX) numpy.savez(outputFile, meanObjs1, meanObjs2, meanObjs3) else: data = numpy.load(outputFile) meanObjs1, meanObjs2, meanObjs3 = data["arr_0"], data["arr_1"], data["arr_2"] meanObjs1 = numpy.squeeze(meanObjs1) meanObjs2 = numpy.squeeze(meanObjs2) meanObjs3 = numpy.squeeze(meanObjs3) import matplotlib matplotlib.use("GTK3Agg")
X5, userInds = Sampling.sampleUsers2(X, 500000, prune=False) X6, userInds = Sampling.sampleUsers2(X, 200000, prune=False) X7, userInds = Sampling.sampleUsers2(X, 100000, prune=False) print(X.shape, X.nnz) print(X2.shape, X2.nnz) print(X3.shape, X3.nnz) print(X4.shape, X4.nnz) print(X5.shape, X5.nnz) print(X6.shape, X6.nnz) print(X7.shape, X7.nnz) meanF1s1, stdF1s1 = maxLocalAuc.modelSelect(X) meanF1s2, stdF1s2 = maxLocalAuc.modelSelect(X2) meanF1s3, stdF1s3 = maxLocalAuc.modelSelect(X3) meanF1s4, stdF1s4 = maxLocalAuc.modelSelect(X4) meanF1s5, stdF1s5 = maxLocalAuc.modelSelect(X5) meanF1s6, stdF1s6 = maxLocalAuc.modelSelect(X6) meanF1s7, stdF1s7 = maxLocalAuc.modelSelect(X7) numpy.savez(outputFile, meanF1s1, meanF1s2, meanF1s3, meanF1s4, meanF1s5, meanF1s6, meanF1s7) else: data = numpy.load(outputFile) meanF1s1, meanF1s2, meanF1s3, meanF1s4, meanF1s5, meanF1s6, meanF1s7 = data["arr_0"], data["arr_1"], data["arr_2"], data["arr_3"], data["arr_4"], data["arr_5"], data["arr_6"] meanF1s1 = numpy.squeeze(meanF1s1)