standardised = False train_config = { 'iterations' : 2000, 'init_UV' : 'exponential', 'expo_prior' : 0.1 } K_range = [6,8,10,12,14] no_folds = 10 output_file = "./results.txt" files_nested_performances = ["./fold_%s.txt" % fold for fold in range(1,no_folds+1)] # Construct the parameter search parameter_search = [{'K':K} for K in K_range] # Load in the Sanger dataset (_,X_min,M,_,_,_,_) = load_gdsc(standardised=standardised,sep=',') # Run the cross-validation framework random.seed(42) numpy.random.seed(9000) nested_crossval = MatrixNestedCrossValidation( method=NMF, X=X_min, M=M, K=no_folds, P=5, parameter_search=parameter_search, train_config=train_config, file_performance=output_file, files_nested_performances=files_nested_performances )
standardised = False #standardised Sanger or unstandardised repeats = 10 iterations = 500 init_UV = 'random' I, J, K = 622,138,25 alpha, beta = 1., 1. #1., 1. lambdaU = numpy.ones((I,K))/10. lambdaV = numpy.ones((J,K))/10. priors = { 'alpha':alpha, 'beta':beta, 'lambdaU':lambdaU, 'lambdaV':lambdaV } # Load in data (_,R,M,_,_,_,_) = load_gdsc(standardised=standardised) # Run the VB algorithm, <repeats> times times_repeats = [] performances_repeats = [] for i in range(0,repeats): # Set all the seeds numpy.random.seed(0) # Run the classifier BNMF = bnmf_vb_optimised(R,M,K,priors) BNMF.initialise(init_UV) BNMF.run(iterations) # Extract the performances and timestamps across all iterations
'init_FG': 'kmeans', 'init_S': 'exponential', 'expo_prior': 0.1 } P = 5 no_folds = 10 output_file = "./results.txt" files_nested_performances = [ "./fold_%s.txt" % fold for fold in range(1, no_folds + 1) ] # Construct the parameter search parameter_search = [{'K': K, 'L': L} for (K, L) in [(6, 6), (8, 8), (10, 10)]] # Load in the Sanger dataset (_, X_min, M, _, _, _, _) = load_gdsc(standardised=standardised) # Run the cross-validation framework #random.seed(42) #numpy.random.seed(9000) nested_crossval = MatrixNestedCrossValidation( method=NMTF, X=X_min, M=M, K=no_folds, P=5, parameter_search=parameter_search, train_config=train_config, file_performance=output_file, files_nested_performances=files_nested_performances) nested_crossval.run()