iterations = 2000 init_FG = 'kmeans' init_S = 'random' I, J, K, L = 622,138,5,5 minimum_TN = 0.01 alpha, beta = 1., 1. lambdaF = numpy.ones((I,K))/10. lambdaS = numpy.ones((K,L))/10. lambdaG = numpy.ones((J,L))/10. priors = { 'alpha':alpha, 'beta':beta, 'lambdaF':lambdaF, 'lambdaS':lambdaS, 'lambdaG':lambdaG } # 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(3) # Run the classifier NMTF = nmtf_icm(R,M,K,L,priors) NMTF.initialise(init_S=init_S,init_FG=init_FG) NMTF.run(iterations,minimum_TN=minimum_TN) # 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()
'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) nested_crossval.run()