(R_pm, M_pm, 'genes', 'samples', alpha_n[1]),
(R_gm, M_gm, 'genes', 'samples', alpha_n[1]),
]
C, D = [], []
''' Run the Gibbs sampler '''
HMF = HMF_Gibbs(R, C, D, K, settings, hyperparameters)
HMF.initialise(init)
HMF.run(iterations)
''' Store the mean of the matrices. '''
folder = project_location + 'HMF/methylation/bicluster_analysis/matrices/'
E_drugs, E_cell_lines = 'genes', 'samples'
n_ge, n_pm, n_gm = 0, 1, 2
exp_F_genes = HMF.approx_expectation_Ft(E=E_drugs,
burn_in=burn_in,
thinning=thinning)
exp_F_samples = HMF.approx_expectation_Ft(E=E_cell_lines,
burn_in=burn_in,
thinning=thinning)
exp_S_ge = HMF.approx_expectation_Sn(n=n_ge,
burn_in=burn_in,
thinning=thinning)
exp_S_pm = HMF.approx_expectation_Sn(n=n_pm,
burn_in=burn_in,
thinning=thinning)
exp_S_gm = HMF.approx_expectation_Sn(n=n_gm,
burn_in=burn_in,
thinning=thinning)
numpy.savetxt(fname=folder + 'F_genes', X=exp_F_genes)
def test_approx_expectation():
iterations = 10
burn_in = 2
thinning = 3 # so index 2,5,8 -> m=3,m=6,m=9
E = ['entity0','entity1']
I = {E[0]:5, E[1]:3}
K = {E[0]:2, E[1]:4}
J = [6]
iterations_all_Ft = {
E[0] : [numpy.ones((I[E[0]],K[E[0]])) * 3*m**2 for m in range(1,10+1)],
E[1] : [numpy.ones((I[E[1]],K[E[1]])) * 1*m**2 for m in range(1,10+1)]
}
iterations_all_lambdat = {
E[0] : [numpy.ones(K[E[0]]) * 3*m**2 for m in range(1,10+1)],
E[1] : [numpy.ones(K[E[1]]) * 1*m**2 for m in range(1,10+1)]
}
iterations_all_Sn = [[numpy.ones((K[E[0]],K[E[1]])) * 2*m**2 for m in range(1,10+1)]]
iterations_all_lambdan = [[numpy.ones((K[E[0]],K[E[1]])) * 2*m**2 for m in range(1,10+1)]]
iterations_all_taun = [[m**2 for m in range(1,10+1)]]
iterations_all_Sm = [[numpy.ones((K[E[1]],K[E[1]])) * 2*m**2 * 2 for m in range(1,10+1)]]
iterations_all_lambdam = [[numpy.ones((K[E[1]],K[E[1]])) * 2*m**2 * 2 for m in range(1,10+1)]]
iterations_all_taum = [[m**2*2 for m in range(1,10+1)]]
iterations_all_Gl = [[numpy.ones((J[0],K[E[1]])) * 2*m**2 * 3 for m in range(1,10+1)]]
iterations_all_taul = [[m**2*3 for m in range(1,10+1)]]
expected_exp_F0 = numpy.array([[9.+36.+81. for k in range(0,2)] for i in range(0,5)])
expected_exp_F1 = numpy.array([[(9.+36.+81.)*(1./3.) for k in range(0,4)] for i in range(0,3)])
expected_exp_lambda0 = numpy.array([9.+36.+81. for k in range(0,2)])
expected_exp_lambda1 = numpy.array([(9.+36.+81.)*(1./3.) for k in range(0,4)])
expected_exp_Sn = numpy.array([[(9.+36.+81.)*(2./3.) for l in range(0,4)] for k in range(0,2)])
expected_exp_lambdan = numpy.array([[(9.+36.+81.)*(2./3.) for l in range(0,4)] for k in range(0,2)])
expected_exp_taun = (9.+36.+81.)/3.
expected_exp_Sm = numpy.array([[(18.+72.+162.)*(2./3.) for l in range(0,4)] for k in range(0,4)])
expected_exp_lambdam = numpy.array([[(18.+72.+162.)*(2./3.) for l in range(0,4)] for k in range(0,4)])
expected_exp_taum = (18.+72.+162.)/3.
expected_exp_Gl = numpy.array([[(27.+108.+243.)*(2./3.) for k in range(0,4)] for j in range(0,6)])
expected_exp_taul = (27.+108.+243.)/3.
R0, M0 = numpy.ones((I[E[0]],I[E[1]])), numpy.ones((I[E[0]],I[E[1]]))
C0, M1 = numpy.ones((I[E[1]],I[E[1]])), numpy.ones((I[E[1]],I[E[1]]))
D0, M2 = numpy.ones((I[E[1]],J[0])), numpy.ones((I[E[1]],J[0]))
R, C, D = [(R0,M0,E[0],E[1],1.)], [(C0,M1,E[1],1.)], [(D0,M2,E[1],1.)]
alphatau, betatau = 1., 2.
alpha0, beta0 = 6., 7.
lambdaF, lambdaG = 3., 8.
lambdaSn, lambdaSm = 4., 5.
priors = { 'alpha0':alpha0, 'beta0':beta0, 'alphatau':alphatau, 'betatau':betatau,
'lambdaF':lambdaF, 'lambdaG':lambdaG, 'lambdaSn':lambdaSn, 'lambdaSm':lambdaSm }
settings = { 'priorF' : 'exponential', 'priorG' : 'normal', 'priorSn' : 'normal', 'priorSm' : 'normal',
'orderF' : 'columns', 'orderG' : 'rows', 'orderSn' : 'rows', 'orderSm' : 'rows',
'ARD' : True, 'element_sparsity': True }
HMF = HMF_Gibbs(R,C,D,K,settings,priors)
HMF.iterations = iterations
HMF.iterations_all_Ft = iterations_all_Ft
HMF.iterations_all_lambdat = iterations_all_lambdat
HMF.iterations_all_Sn = iterations_all_Sn
HMF.iterations_all_lambdan = iterations_all_lambdan
HMF.iterations_all_taun = iterations_all_taun
HMF.iterations_all_Sm = iterations_all_Sm
HMF.iterations_all_lambdam = iterations_all_lambdam
HMF.iterations_all_taum = iterations_all_taum
HMF.iterations_all_Gl = iterations_all_Gl
HMF.iterations_all_taul = iterations_all_taul
exp_F0 = HMF.approx_expectation_Ft(E[0],burn_in,thinning)
exp_F1 = HMF.approx_expectation_Ft(E[1],burn_in,thinning)
exp_lambda0 = HMF.approx_expectation_lambdat(E[0],burn_in,thinning)
exp_lambda1 = HMF.approx_expectation_lambdat(E[1],burn_in,thinning)
exp_Sn = HMF.approx_expectation_Sn(0,burn_in,thinning)
exp_lambdan = HMF.approx_expectation_lambdan(0,burn_in,thinning)
exp_taun = HMF.approx_expectation_taun(0,burn_in,thinning)
exp_Sm = HMF.approx_expectation_Sm(0,burn_in,thinning)
exp_lambdam = HMF.approx_expectation_lambdam(0,burn_in,thinning)
exp_taum = HMF.approx_expectation_taum(0,burn_in,thinning)
exp_Gl = HMF.approx_expectation_Gl(0,burn_in,thinning)
exp_taul = HMF.approx_expectation_taul(0,burn_in,thinning)
assert numpy.array_equal(expected_exp_F0,exp_F0)
assert numpy.array_equal(expected_exp_F1,exp_F1)
assert numpy.array_equal(expected_exp_lambda0,exp_lambda0)
assert numpy.array_equal(expected_exp_lambda1,exp_lambda1)
assert numpy.array_equal(expected_exp_Sn,exp_Sn)
assert numpy.array_equal(expected_exp_lambdan,exp_lambdan)
assert expected_exp_taun == exp_taun
assert numpy.array_equal(expected_exp_Sm,exp_Sm)
assert numpy.array_equal(expected_exp_lambdam,exp_lambdam)
assert expected_exp_taum == exp_taum
assert numpy.array_equal(expected_exp_Gl,exp_Gl)
assert expected_exp_taul == exp_taul