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',
