repeats = 10 iterations = 1000 init_UV = 'random' I, J, K = 622,138,25 minimum_TN = 0.1 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 nmf = nmf_icm(R,M,K,priors) nmf.initialise(init_UV) nmf.run(iterations,minimum_TN=minimum_TN) # Extract the performances and timestamps across all iterations
sys.path.append(project_location) from BNMTF.code.nmtf_np import NMTF from BNMTF.drug_sensitivity.experiments_gdsc.load_data import load_gdsc import matplotlib.pyplot as plt ########## standardised = False #standardised Sanger or unstandardised iterations = 1000 I, J, K, L = 622,138,5, 5 init_S = 'exponential' init_FG = 'kmeans' expo_prior = 1/10. # Load in data (_,X_min,M,_,_,_,_) = load_gdsc(standardised=standardised) # Run the algorithm nmtf = NMTF(X_min,M,K,L) nmtf.initialise(init_S,init_FG,expo_prior) nmtf.run(iterations) # Print the performances across iterations (MSE) print "all_performances = %s" % nmtf.all_performances['MSE'] # Plot the performances (MSE) plt.plot(nmtf.all_performances['MSE'])
standardised = False train_config = { 'iterations' : 1000, 'init_UV' : 'exponential', 'expo_prior' : 0.1 } K_range = range(2,10+1,2) 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 = MatrixCrossValidation( method=NMF, X=X_min, M=M, K=no_folds, parameter_search=parameter_search, train_config=train_config, file_performance=output_file ) nested_crossval.run()
standardised = False train_config = { 'iterations': 1000, 'init_UV': 'exponential', 'expo_prior': 0.1 } K_range = range(2, 10 + 1, 2) 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 = MatrixCrossValidation(method=NMF, X=X_min, M=M, K=no_folds, parameter_search=parameter_search, train_config=train_config, file_performance=output_file) nested_crossval.run()