ARD = False lambdaF, lambdaS, lambdaG = 0.1, 0.1, 0.1 alphatau, betatau = 1., 1. alpha0, beta0 = 1., 1. hyperparams = { 'alphatau': alphatau, 'betatau': betatau, 'alpha0': alpha0, 'beta0': beta0, 'lambdaF': lambdaF, 'lambdaS': lambdaS, 'lambdaG': lambdaG } ''' Load in data. ''' R, M = load_ccle_ec50() I, J = M.shape ''' Generate matrices M - one list of M's for each fraction. ''' M_attempts = 1000 all_Ms = [[ try_generate_M(I=I, J=J, fraction=fraction, attempts=M_attempts, M=M)[0] for r in range(repeats) ] for fraction in fractions_unknown] all_Ms_test = [[calc_inverse_M(M_train, M_combined=M) for M_train in Ms] for Ms in all_Ms] ''' Make sure each M has no empty rows or columns. ''' def check_empty_rows_columns(matrix, fraction): sums_columns = matrix.sum(axis=0) sums_rows = matrix.sum(axis=1)
project_location = "/home/tab43/Documents/Projects/libraries/" # "/Users/thomasbrouwer/Documents/Projects/libraries/" import sys sys.path.append(project_location) from BNMTF_ARD.data.drug_sensitivity.load_data import load_gdsc_ic50 from BNMTF_ARD.data.drug_sensitivity.load_data import load_ctrp_ec50 from BNMTF_ARD.data.drug_sensitivity.load_data import load_ccle_ic50 from BNMTF_ARD.data.drug_sensitivity.load_data import load_ccle_ec50 import itertools import matplotlib.pyplot as plt ''' Load in the data. ''' R_gdsc, M_gdsc = load_gdsc_ic50() R_ctrp, M_ctrp = load_ctrp_ec50() R_ccle_ic, M_ccle_ic = load_ccle_ic50() R_ccle_ec, M_ccle_ec = load_ccle_ec50() def extract_values(R, M): I, J = R.shape return [ R[i, j] for i, j in itertools.product(range(I), range(J)) if M[i, j] ] values_plotnames_bins = [ (extract_values(R_gdsc, M_gdsc), 'distribution_gdsc_ic50.pdf', [v - 0.5 for v in range(0, 100 + 10, 5)]), (extract_values(R_ctrp, M_ctrp), 'distribution_ctrp_ec50.pdf', [v - 0.5 for v in range(0, 100 + 10, 5)]), (extract_values(R_ccle_ic, M_ccle_ic), 'distribution_ccle_ic50.pdf',