def run_all_settings(all_K_alpha):
fout = open('results_varying_K_hmf_no_ARD_ge_gm_to_pm_std.txt', 'w')
all_average_performances = []
for K in all_K:
''' Compute the folds '''
n = len(X1)
n_folds = 10
shuffle, random_state = True, None
folds = KFold(n=n,
n_folds=n_folds,
shuffle=shuffle,
random_state=random_state)
''' Run HMF to predict Y from X '''
all_MSE, all_R2, all_Rp = numpy.zeros(n_folds), numpy.zeros(
n_folds), numpy.zeros(n_folds)
for i, (train_index, test_index) in enumerate(folds):
print "Training fold %s for HMF-MTF." % (i + 1)
''' Split into train and test '''
M_X1, M_X2, M_Y_train = numpy.ones(X1.shape), numpy.ones(
X2.shape), numpy.ones(Y.shape)
M_Y_train[test_index] = 0.
M_Y_test = 1. - M_Y_train
R = [(X1, M_X1, 'samples', 'genes', alpha[0]),
(X2, M_X2, 'samples', 'genes', alpha[1]),
(Y, M_Y_train, 'samples', 'genes', alpha[2])]
''' Train and predict '''
HMF = HMF_Gibbs(R, C, D, K, settings, hyperparameters)
HMF.initialise(init)
HMF.run(iterations)
''' Compute the performances '''
performances = HMF.predict_Rn(n=2,
M_pred=M_Y_test,
burn_in=burn_in,
thinning=thinning)
all_MSE[i], all_R2[i], all_Rp[i] = performances[
'MSE'], performances['R^2'], performances['Rp']
print "MSE: %s. R^2: %s. Rp: %s." % (
performances['MSE'], performances['R^2'], performances['Rp'])
print "Average MSE: %s +- %s. \nAverage R^2: %s +- %s. \nAverage Rp: %s +- %s." % \
(all_MSE.mean(),all_MSE.std(),all_R2.mean(),all_R2.std(),all_Rp.mean(),all_Rp.std())
fout.write('Tried MF on PM -> GE, with K = %s, alphan = %s.\n' %
(K, alpha))
fout.write('Average MSE: %s +- %s. \nAverage R^2: %s +- %s. \nAverage Rp: %s +- %s.\n' % \
(all_MSE.mean(),all_MSE.std(),all_R2.mean(),all_R2.std(),all_Rp.mean(),all_Rp.std()))
fout.write('All MSE: %s. \nAll R^2: %s. \nAll Rp: %s.\n\n' %
(list(all_MSE), list(all_R2), list(all_Rp)))
fout.flush()
all_average_performances.append(all_MSE.mean())
''' Print for plotting. '''
print "all_K = %s \nall_average_performances = %s" % (
all_K, all_average_performances)
def test_compute_statistics():
R0 = numpy.array([[1,2],[3,4]],dtype=float)
M0 = numpy.array([[1,1],[0,1]])
E = ['entity0','entity1']
K = {E[0]:3,E[1]:4}
R = [(R0,M0,E[0],E[1],1.)]
C, D = [], []
HMF = HMF_Gibbs(R,C,D,K,{},{})
R_pred = numpy.array([[500,550],[1220,1342]],dtype=float)
M_pred = numpy.array([[0,0],[1,1]])
MSE_pred = (1217**2 + 1338**2) / 2.0
R2_pred = 1. - (1217**2+1338**2)/(0.5**2+0.5**2) #mean=3.5
Rp_pred = 61. / ( math.sqrt(.5) * math.sqrt(7442.) ) #mean=3.5,var=0.5,mean_pred=1281,var_pred=7442,cov=61
assert MSE_pred == HMF.compute_MSE(M_pred,R0,R_pred)
assert R2_pred == HMF.compute_R2(M_pred,R0,R_pred)
assert Rp_pred == HMF.compute_Rp(M_pred,R0,R_pred)
for (fraction, Ms_train_test) in zip(fractions_unknown, all_Ms_train_test):
print "Trying fraction %s." % fraction
# Run the algorithm <repeats> times and store all the performances
for metric in metrics:
all_performances[metric].append([])
for repeat, (M_train, M_test) in zip(range(0, repeats), Ms_train_test):
print "Repeat %s of fraction %s." % (repeat + 1, fraction)
D = [(R_ctrp, M_train, 'Cell_lines', alpha_l[0]),
(R_gdsc, M_gdsc, 'Cell_lines', alpha_l[1]),
(R_ccle_ic, M_ccle_ic, 'Cell_lines', alpha_l[2]),
(R_ccle_ec, M_ccle_ec, 'Cell_lines', alpha_l[3])]
R, C = [], []
HMF = HMF_Gibbs(R, C, D, K, settings, hyperparameters)
HMF.initialise(init)
HMF.run(iterations)
# Measure the performances
performances = HMF.predict_Dl(l=0,
M_pred=M_test,
burn_in=burn_in,
thinning=thinning)
for metric in metrics:
# Add this metric's performance to the list of <repeat> performances for this fraction
all_performances[metric][-1].append(performances[metric])
# Compute the average across attempts
for metric in metrics:
average_performances[metric].append(
folds_test = mask.compute_folds_attempts(I=I,
J=J,
no_folds=no_folds,
attempts=1000,
M=M_gdsc)
folds_training = mask.compute_Ms(folds_test)
for i, (train, test) in enumerate(zip(folds_training, folds_test)):
print "Fold %s." % (i + 1)
''' Predict values. '''
R = [(R_gdsc, train, 'Cell_lines', 'Drugs', alpha[0]),
(R_ctrp, M_ctrp, 'Cell_lines', 'Drugs', alpha[1]),
(R_ccle_ic, M_ccle_ic, 'Cell_lines', 'Drugs', alpha[2]),
(R_ccle_ec, M_ccle_ec, 'Cell_lines', 'Drugs', alpha[3])]
HMF = HMF_Gibbs(R=R,
C=C,
D=D,
K=K,
settings=settings,
hyperparameters=hyperparameters)
HMF.initialise(init=init)
HMF.run(iterations=iterations)
R_pred = HMF.return_Rn(n=n, burn_in=burn_in, thinning=thinning)
''' Add predictions to list. '''
indices_test = [(i, j) for (i, j) in itertools.product(range(I), range(J))
if test[i, j]]
for i, j in indices_test:
i_j_real_pred.append((i, j, R_gdsc[i, j], R_pred[i, j]))
''' Store the performances. '''
with open(file_performance, 'w') as fout:
fout.write('%s' % i_j_real_pred)
},
]
R, C = [], []
main_dataset = 'D'
D, C = [], []
R = [(R_gdsc, M_gdsc, 'Cell_lines', 'Drugs', alpha_n[0]),
(R_ctrp, M_ctrp, 'Cell_lines', 'Drugs', alpha_n[1]),
(R_ccle_ic, M_ccle_ic, 'Cell_lines', 'Drugs', alpha_n[2]),
(R_ccle_ec, M_ccle_ec, 'Cell_lines', 'Drugs', alpha_n[3])]
index_main = 0 # GDSC
file_performances = './results/hmf_data_mtf_gdsc_ic.txt'
''' Run the methods with different inits and measure the convergence. '''
all_init_performances = []
for init in values_init:
all_performances = []
for n in range(n_repeats):
print "Repeat %s of initialisation experiment, with init %s." % (n,
init)
HMF = HMF_Gibbs(R, C, D, K, settings, hyperparameters)
HMF.initialise(init)
HMF.run(iterations)
all_performances.append(HMF.all_performances_Rn['MSE'][index_main])
average_performances = list(numpy.mean(all_performances, axis=0))
all_init_performances.append(average_performances)
''' Store performances in file. '''
with open(file_performances, 'w') as fout:
fout.write("%s" % all_init_performances)
E = ['genes', 'samples']
K = {'genes': 20, 'samples': 20}
alpha_n = [1., 1., 1.] # GE, PM, GM
''' Load in data '''
R_ge, R_pm, R_gm, genes, samples = filter_driver_genes_std()
M_ge, M_pm, M_gm = numpy.ones(R_ge.shape), numpy.ones(R_pm.shape), numpy.ones(
R_gm.shape)
R = [
(R_ge, M_ge, 'genes', 'samples', alpha_n[0]),
(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,
'lambdat': 'exp',
'lambdaS': 'exp',
'tau': 'exp',
}
alpha_n = [1., 1., 1., 1.] # GDSC, CTRP, CCLE IC, CCLE EC
alpha_m = []
K = {'Cell_lines': 5, 'Drugs': 5}
C, D = [], []
R = [(R_gdsc, M_gdsc, 'Cell_lines', 'Drugs', alpha_n[0]),
(R_ctrp, M_ctrp, 'Cell_lines', 'Drugs', alpha_n[1]),
(R_ccle_ic, M_ccle_ic, 'Cell_lines', 'Drugs', alpha_n[2]),
(R_ccle_ec, M_ccle_ec, 'Cell_lines', 'Drugs', alpha_n[3])]
''' Run the model. '''
HMF = HMF_Gibbs(R, C, D, K, settings, hyperparameters)
HMF.initialise(init)
HMF.run(iterations)
"""
''' Extract all factor matrices (F, S^n), and store in files - only store the burned-in and thinned out draws. '''
E_drugs, E_cell_lines = 'Drugs', 'Cell_lines'
n_gdsc, n_ctrp, n_ccle_ic, n_ccle_ec = 0, 1, 2, 3
folder = project_location+'HMF/drug_sensitivity/bicluster_analysis/matrices/'
thinned_F_drugs = numpy.array(HMF.iterations_all_Ft[E_drugs])[indices_thinning]
thinned_F_cell_lines = numpy.array(HMF.iterations_all_Ft[E_cell_lines])[indices_thinning]
thinned_S_gdsc = numpy.array(HMF.iterations_all_Sn[n_gdsc])[indices_thinning]
thinned_S_ctrp = numpy.array(HMF.iterations_all_Sn[n_ctrp])[indices_thinning]
thinned_S_ccle_ic = numpy.array(HMF.iterations_all_Sn[n_ccle_ic])[indices_thinning]
thinned_S_ccle_ec = numpy.array(HMF.iterations_all_Sn[n_ccle_ec])[indices_thinning]
def test_log_likelihood():
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_Ft['entity0'][2][0,0] = 24 #instead of 27 - to ensure we do not get 0 variance in our predictions
iterations_all_Sn = [[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_taum = [[m**2*2 for m in range(1,10+1)]]
iterations_all_Gl = [[numpy.ones((J[0],K[E[0]])) * 2*m**2 * 3 for m in range(1,10+1)]]
iterations_all_taul = [[m**2*3 for m in range(1,10+1)]]
R0 = numpy.array([[1,2,3],[4,5,6],[7,8,9],[10,11,12],[13,14,15]],dtype=float)
C0 = numpy.array([[1,2,3],[4,5,6],[7,8,9]],dtype=float)
D0 = numpy.array([[1,2,3,4,5,6],[7,8,9,10,11,12],[13,14,15,16,17,18],[19,20,21,22,23,24],[25,26,27,28,29,30]],dtype=float)
M0 = numpy.array([[0,0,1],[0,1,0],[0,0,0],[1,1,0],[0,0,0]]) #R->3,5,10,11, R_pred->3542112,3556224,3556224,3556224
M1 = numpy.array([[0,0,1],[0,1,0],[1,1,0]]) #C->3,7,8, C_pred->4741632,4741632,4741632 - entry 5 gets set to 0 since it is the diagonal
M2 = numpy.array([[0,0,1,0,0,1],[0,1,0,0,0,0],[1,1,0,0,0,0],[0,0,0,0,0,0],[1,0,0,0,0,0]]) #D->3,6,8,13,14,25, D_pred->63252,63252,63504,63504,63504,63504
R, C, D = [(R0,M0,E[0],E[1],1.)], [(C0,M1,E[1],1.)], [(D0,M2,E[0],1.)]
#expected_exp_F0 = numpy.array([[125.,126.],[126.,126.],[126.,126.],[126.,126.],[126.,126.]])
#expected_exp_F1 = numpy.array([[(9.+36.+81.)*(1./3.) for k in range(0,4)] for i in range(0,3)])
#expected_exp_Sn = 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.
#R_pred = numpy.array([[ 3542112., 3542112., 3542112.],[ 3556224., 3556224., 3556224.],[ 3556224., 3556224., 3556224.],[ 3556224., 3556224., 3556224.],[ 3556224., 3556224., 3556224.]])
#expected_exp_Sm = 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.
#C_pred = array([[4741632.,4741632.,4741632.],[4741632.,4741632.,4741632.],[4741632.,4741632.,4741632.]])
#expected_exp_Gl = numpy.array([[(27.+108.+243.)*(2./3.) for k in range(0,2)] for j in range(0,6)])
#expected_exp_taul = (27.+108.+243.)/3.
#D_pred = array([[63252.,63252.,63252.,63252.,63252.,63252.],[63504.,63504.,63504.,63504.,63504.,63504.],[63504.,63504.,63504.,63504.,63504.,63504.],[63504.,63504.,63504.,63504.,63504.,63504.],[63504.,63504.,63504.,63504.,63504.,63504.]])
MSE_R = ((3.-3542112.)**2 + (5.-3556224.)**2 + (10.-3556224.)**2 + (11.-3556224.)**2) / 4.
MSE_C = ((3.-4741632.)**2 + (7.-4741632.)**2 + (8.-4741632.)**2) / 3.
MSE_D = ((3.-63252.)**2 + (6.-63252.)**2 + (8.-63504.)**2 + (13.-63504.)**2 + (14.-63504.)**2 + (25.-63504.)**2) / 6.
HMF = HMF_Gibbs(R,C,D,K,{},{})
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_taun = iterations_all_taun
HMF.iterations_all_Sm = iterations_all_Sm
HMF.iterations_all_taum = iterations_all_taum
HMF.iterations_all_Gl = iterations_all_Gl
HMF.iterations_all_taul = iterations_all_taul
log_likelihood = 4./2. * (math.log(42.) - math.log(2*math.pi)) - 42./2.*(MSE_R*4.) + \
3./2. * (math.log(84.) - math.log(2*math.pi)) - 84./2.*(MSE_C*3.) + \
6./2. * (math.log(126.) - math.log(2*math.pi)) - 126./2.*(MSE_D*6.)
no_parameters = (5*2+4*3+2*4+4*4+2*6+2+4+3)
no_datapoints = 4+3+6
AIC = -2*log_likelihood + 2*no_parameters #F0,F1,Sn0,Sm0,G,lambda0,lambda1,tau
BIC = -2*log_likelihood + no_parameters*math.log(no_datapoints)
assert HMF.no_datapoints() == no_datapoints
assert HMF.no_parameters() == no_parameters
assert abs(log_likelihood - HMF.quality('loglikelihood',burn_in,thinning)) <= 1.
assert abs(AIC - HMF.quality('AIC',burn_in,thinning)) <= 1.
assert abs(BIC - HMF.quality('BIC',burn_in,thinning)) <= 1.
with pytest.raises(AssertionError) as error:
HMF.quality('FAIL',burn_in,thinning)
assert str(error.value) == "Unrecognised metric for model quality: FAIL."
def test_predict():
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_Ft['entity0'][2][0,0] = 24 #instead of 27 - to ensure we do not get 0 variance in our predictions
iterations_all_Sn = [[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_taum = [[m**2*2 for m in range(1,10+1)]]
iterations_all_Gl = [[numpy.ones((J[0],K[E[0]])) * 2*m**2 * 3 for m in range(1,10+1)]]
iterations_all_taul = [[m**2*3 for m in range(1,10+1)]]
R0 = numpy.array([[1,2,3],[4,5,6],[7,8,9],[10,11,12],[13,14,15]],dtype=float)
C0 = numpy.array([[1,2,3],[4,5,6],[7,8,9]],dtype=float)
D0 = numpy.array([[1,2,3,4,5,6],[7,8,9,10,11,12],[13,14,15,16,17,18],[19,20,21,22,23,24],[25,26,27,28,29,30]],dtype=float)
M0, M1, M2 = numpy.ones((5,3)), numpy.ones((3,3)), numpy.ones((5,6))
R, C, D = [(R0,M0,E[0],E[1],1.)], [(C0,M1,E[1],1.)], [(D0,M2,E[0],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',
'ARD' : True, 'orderF' : 'columns', 'orderG' : 'rows', 'orderSn' : 'rows', 'orderSm' : 'rows' }
#expected_exp_F0 = numpy.array([[125.,126.],[126.,126.],[126.,126.],[126.,126.],[126.,126.]])
#expected_exp_F1 = numpy.array([[(9.+36.+81.)*(1./3.) for k in range(0,4)] for i in range(0,3)])
#expected_exp_Sn = 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.
#R_pred = numpy.array([[ 3542112., 3542112., 3542112.],[ 3556224., 3556224., 3556224.],[ 3556224., 3556224., 3556224.],[ 3556224., 3556224., 3556224.],[ 3556224., 3556224., 3556224.]])
#expected_exp_Sm = 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.
#C_pred = array([[4741632.,4741632.,4741632.],[4741632.,4741632.,4741632.],[4741632.,4741632.,4741632.]])
#expected_exp_Gl = numpy.array([[(27.+108.+243.)*(2./3.) for k in range(0,2)] for j in range(0,6)])
#expected_exp_taul = (27.+108.+243.)/3.
#D_pred = array([[63252.,63252.,63252.,63252.,63252.,63252.],[63504.,63504.,63504.,63504.,63504.,63504.],[63504.,63504.,63504.,63504.,63504.,63504.],[63504.,63504.,63504.,63504.,63504.,63504.],[63504.,63504.,63504.,63504.,63504.,63504.]])
M_test_R = numpy.array([[0,0,1],[0,1,0],[0,0,0],[1,1,0],[0,0,0]]) #R->3,5,10,11, R_pred->3542112,3556224,3556224,3556224
MSE_R = ((3.-3542112.)**2 + (5.-3556224.)**2 + (10.-3556224.)**2 + (11.-3556224.)**2) / 4.
R2_R = 1. - ((3.-3542112.)**2 + (5.-3556224.)**2 + (10.-3556224.)**2 + (11.-3556224.)**2) / (4.25**2+2.25**2+2.75**2+3.75**2) #mean=7.25
Rp_R = 357. / ( math.sqrt(44.75) * math.sqrt(5292.) ) #mean=7.25,var=44.75, mean_pred=3552696,var_pred=5292, corr=(-4.25*-63 + -2.25*21 + 2.75*21 + 3.75*21)
M_test_C = numpy.array([[0,0,1],[0,1,0],[1,1,0]]) #C->3,5,7,8, C_pred->4741632,4741632,4741632,4741632
MSE_C = ((3.-4741632.)**2 + (5.-4741632.)**2 + (7.-4741632.)**2 + (8.-4741632.)**2) / 4.
R2_C = 1. - ((3.-4741632.)**2 + (5.-4741632.)**2 + (7.-4741632.)**2 + (8.-4741632.)**2) / (2.75**2+0.75**2+1.25**2+2.25**2) #mean=5.75
M_test_D = numpy.array([[0,0,1,0,0,1],[0,1,0,0,0,0],[1,1,0,0,0,0],[0,0,0,0,0,0],[0,0,0,0,0,0]]) #D->3,6,8,13,14, D_pred->63252,63252,63504,63504,63504
MSE_D = ((3.-63252.)**2 + (6.-63252.)**2 + (8.-63504.)**2 + (13.-63504.)**2 + (14.-63504.)**2) / 5.
R2_D = 1. - ((3.-63252.)**2 + (6.-63252.)**2 + (8.-63504.)**2 + (13.-63504.)**2 + (14.-63504.)**2) / (5.8**2+2.8**2+0.8**2+4.2**2+5.2**2) #mean=8.8
Rp_D = 0.84265143679484211
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_taun = iterations_all_taun
HMF.iterations_all_Sm = iterations_all_Sm
HMF.iterations_all_taum = iterations_all_taum
HMF.iterations_all_Gl = iterations_all_Gl
HMF.iterations_all_taul = iterations_all_taul
performances_R = HMF.predict_Rn(0,M_test_R,burn_in,thinning)
performances_C = HMF.predict_Cm(0,M_test_C,burn_in,thinning)
performances_D = HMF.predict_Dl(0,M_test_D,burn_in,thinning)
assert performances_R['MSE'] == MSE_R
assert performances_R['R^2'] == R2_R
assert performances_R['Rp'] == Rp_R
assert performances_C['MSE'] == MSE_C
assert performances_C['R^2'] == R2_C
assert numpy.isnan(performances_C['Rp'])
assert performances_D['MSE'] == MSE_D
assert performances_D['R^2'] == R2_D
assert abs(performances_D['Rp'] - Rp_D) < 0.00000000001
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
def test_run():
''' Settings '''
E0, E1, E2 = 'entity0','entity1',1337
I0, I1, I2 = 10,9,8
K0, K1, K2 = 3,2,1
J0 = 4
N, M, L, T = 3, 2, 1, 3
R0 = numpy.ones((I0,I1)) # relates E0, E1
R1 = numpy.ones((I0,I1)) # relates E0, E1
R2 = numpy.ones((I1,I2)) # relates E1, E2
C0 = numpy.ones((I0,I0)) # relates E0
C1 = numpy.ones((I2,I2)) # relates E2
D0 = numpy.ones((I2,J0)) # relates E2
Mn0 = numpy.ones((I0,I1))
Mn1 = numpy.ones((I0,I1))
Mn2 = numpy.ones((I1,I2))
Mm0 = numpy.ones((I0,I0))
Mm1 = numpy.ones((I2,I2))
Ml0 = numpy.ones((I2,J0))
#size_Omegan = [I0*I1,I0*I1,I1*I2]
#size_Omegam = [I0*(I0-1),I2*(I2-1)]
#size_Omegal = [I2*J0]
alphan = [11.,12.,13.]
alpham = [14.,15.]
alphal = [16.]
R = [(R0,Mn0,E0,E1,alphan[0]),(R1,Mn1,E0,E1,alphan[1]),(R2,Mn2,E1,E2,alphan[2])]
C = [(C0,Mm0,E0,alpham[0]),(C1,Mm1,E2,alpham[1])]
D = [(D0,Ml0,E2,alphal[0])]
E = [E0,E1,E2]
K = {E0:K0,E1:K1,E2:K2}
I = {E0:I0,E1:I1,E2:I2}
J = [J0]
#U1t = {'entity0':[0,1], 'entity1':[2], 1337:[] }
#U2t = {'entity0':[], 'entity1':[0,1], 1337:[2] }
#Vt = {'entity0':[0], 'entity1':[], 1337:[1] }
#Wt = {'entity0':[], 'entity1':[], 1337:[0]}
E_per_Rn = [(E0,E1),(E0,E1),(E1,E2)]
E_per_Cm = [E0,E2]
E_per_Dl = [E2]
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 }
init = { 'F': 'kmeans', 'G': 'least', 'Sn': 'least', 'Sm': 'least', 'lambdat': 'random', 'lambdaS': 'random', 'tau': 'random' }
iterations = 10
HMF = HMF_Gibbs(R,C,D,K,settings,priors)
HMF.initialise(init)
HMF.run(iterations)
''' Do size checks '''
for E0 in E:
assert len(HMF.iterations_all_Ft[E0]) == iterations
assert len(HMF.iterations_all_lambdat[E0]) == iterations
for n in range(0,N):
assert len(HMF.iterations_all_lambdan[n]) == iterations
assert len(HMF.iterations_all_Sn[n]) == iterations
assert len(HMF.iterations_all_taun[n]) == iterations
for m in range(0,M):
assert len(HMF.iterations_all_lambdam[m]) == iterations
assert len(HMF.iterations_all_Sm[m]) == iterations
assert len(HMF.iterations_all_taum[m]) == iterations
for l in range(0,L):
assert len(HMF.iterations_all_Gl[l]) == iterations
assert len(HMF.iterations_all_taul[l]) == iterations
''' Check whether values change each iteration '''
for iteration in range(1,iterations):
for E0 in E:
for k in range(0,K[E0]):
assert HMF.iterations_all_lambdat[E0][iteration][k] != HMF.iterations_all_lambdat[E0][iteration-1][k]
for i,k in itertools.product(xrange(0,I[E0]),xrange(0,K[E0])):
assert HMF.iterations_all_Ft[E0][iteration][i,k] != HMF.iterations_all_Ft[E0][iteration-1][i,k]
for n in range(0,N):
E0,E1 = E_per_Rn[n]
for k,l in itertools.product(xrange(0,K[E0]),xrange(0,K[E1])):
assert HMF.iterations_all_lambdan[n][iteration][k,l] != HMF.iterations_all_lambdan[n][iteration-1][k,l]
assert HMF.iterations_all_Sn[n][iteration][k,l] != HMF.iterations_all_Sn[n][iteration-1][k,l]
assert HMF.iterations_all_taun[n][iteration] != HMF.iterations_all_taun[n][iteration-1]
for m in range(0,M):
E0 = E_per_Cm[m]
for k,l in itertools.product(xrange(0,K[E0]),xrange(0,K[E0])):
assert HMF.iterations_all_lambdam[m][iteration][k,l] != HMF.iterations_all_lambdam[m][iteration-1][k,l]
assert HMF.iterations_all_Sm[m][iteration][k,l] != HMF.iterations_all_Sm[m][iteration-1][k,l]
assert HMF.iterations_all_taum[m][iteration] != HMF.iterations_all_taum[m][iteration-1]
for l in range(0,l):
E0 = E_per_Dl[l]
for j,k in itertools.product(xrange(0,J[l]),xrange(0,K[E0])):
assert HMF.iterations_all_Gl[l][iteration][j,k] != HMF.iterations_all_Dl[l][iteration-1][j,k]
assert HMF.iterations_all_taul[l][iteration] != HMF.iterations_all_taul[l][iteration-1]
def test_initialise():
E0, E1, E2 = 'entity0','entity1',1337
I0, I1, I2 = 10,9,8
K0, K1, K2 = 3,2,1
J0 = 4
N, M, L, T = 3, 2, 1, 3
R0 = numpy.ones((I0,I1)) # relates E0, E1
R1 = numpy.ones((I0,I1)) # relates E0, E1
R2 = numpy.ones((I1,I2)) # relates E1, E2
C0 = numpy.ones((I0,I0)) # relates E0
C1 = numpy.ones((I2,I2)) # relates E2
D0 = numpy.ones((I2,J0)) # relates E2
Mn0 = numpy.ones((I0,I1))
Mn1 = numpy.ones((I0,I1))
Mn2 = numpy.ones((I1,I2))
Mm0 = numpy.ones((I0,I0))
Mm1 = numpy.ones((I2,I2))
Ml0 = numpy.ones((I2,J0))
#size_Omegan = [I0*I1,I0*I1,I1*I2]
#size_Omegam = [I0*(I0-1),I2*(I2-1)]
#size_Omegal = [I2*J0]
alphan = [11.,12.,13.]
alpham = [14.,15.]
alphal = [16.]
R = [(R0,Mn0,E0,E1,alphan[0]),(R1,Mn1,E0,E1,alphan[1]),(R2,Mn2,E1,E2,alphan[2])]
C = [(C0,Mm0,E0,alpham[0]),(C1,Mm1,E2,alpham[1])]
D = [(D0,Ml0,E2,alphal[0])]
E = [E0,E1,E2]
K = {E0:K0,E1:K1,E2:K2}
I = {E0:I0,E1:I1,E2:I2}
J = [J0]
#U1t = {'entity0':[0,1], 'entity1':[2], 1337:[] }
#U2t = {'entity0':[], 'entity1':[0,1], 1337:[2] }
#Vt = {'entity0':[0], 'entity1':[], 1337:[1] }
#Wt = {'entity0':[], 'entity1':[], 1337:[0]}
E_per_Rn = [(E0,E1),(E0,E1),(E1,E2)]
E_per_Cm = [E0,E2]
E_per_Dl = [E2]
alphatau, betatau = 1., 2.
alpha0, beta0 = 6., 7.
alphaS, betaS = 9., 10.
lambdaF, lambdaG = 3., 8.
lambdaSn, lambdaSm = 4., 5.
priors = { 'alpha0':alpha0, 'beta0':beta0, 'alphaS':alphaS, 'betaS':betaS, 'alphatau':alphatau, 'betatau':betatau,
'lambdaF':lambdaF, 'lambdaG':lambdaG, 'lambdaSn':lambdaSn, 'lambdaSm':lambdaSm }
"""
We need to test the following cases:
- F ~ Exp or ~ N
- G ~ Exp or ~ N
- S ~ Exp or ~ N
- ARD or no ARD
- F init random, exp, kmeans
- G init random, exp, least
- S init random, exp, least
- lambdat init random, exp
- tau init random, exp
"""
''' F Exp, G Exp, S Exp, ARD, no element-wise sparsity. F exp, G exp, S exp, lambdat exp, tau exp. '''
settings = { 'priorF' : 'exponential', 'priorG' : 'exponential', 'priorSn' : 'exponential', 'priorSm' : 'exponential',
'orderF' : 'rows', 'orderG' : 'columns', 'orderSn' : 'individual', 'orderSm' : 'individual',
'ARD' : True, 'element_sparsity': True }
init = { 'F' : 'exp', 'G' : 'exp', 'Sn' : 'exp', 'Sm' : 'exp', 'lambdat' : 'exp', 'lambdaS': 'exp', 'tau' : 'exp'}
HMF = HMF_Gibbs(R,C,D,K,settings,priors)
HMF.initialise(init)
for E1 in E:
for k in range(0,K[E1]):
assert HMF.all_lambdat[E1][k] == alpha0 / float(beta0)
for i,k in itertools.product(xrange(0,I[E1]),xrange(0,K[E1])):
assert HMF.all_Ft[E1][i,k] == 1./HMF.all_lambdat[E1][k]
expected_all_taun = [0.015369654419961557,0.015367151516936775,0.2442062783472021]
for n in range(0,N):
E1,E2 = E_per_Rn[n]
for k,l in itertools.product(xrange(0,K[E1]),xrange(0,K[E2])):
expected_lambdan_kl = alphaS / float(betaS)
assert HMF.all_lambdan[n][k,l] == expected_lambdan_kl
assert HMF.all_Sn[n][k,l] == 1./expected_lambdan_kl
assert abs(HMF.all_taun[n] - expected_all_taun[n]) < 0.0000000001
expected_all_taum = [0.0062975762814580696,3.7505835292008993]
for m in range(0,M):
E1 = E_per_Cm[m]
for k,l in itertools.product(xrange(0,K[E1]),xrange(0,K[E1])):
expected_lambdam_kl = alphaS / float(betaS)
assert HMF.all_lambdam[m][k,l] == expected_lambdam_kl
assert HMF.all_Sm[m][k,l] == 1./expected_lambdam_kl
assert abs(HMF.all_taum[m] - expected_all_taum[m]) < 0.000000001
expected_all_taul = [7.2634333565945441]
for l in range(0,L):
E1 = E_per_Dl[l]
for j,k in itertools.product(xrange(0,J[l]),xrange(0,K[E1])):
assert HMF.all_Gl[l][j,k] == 1./HMF.all_lambdat[E1][k]
assert abs(HMF.all_taul[l] - expected_all_taul[l]) < 0.00000001
''' F Exp, G Exp, S N, no ARD, element-wise sparsity. F random, G exp, Sn exp, Sm random, tau random. '''
settings = { 'priorF' : 'exponential', 'priorG' : 'exponential', 'priorSn' : 'normal', 'priorSm' : 'normal',
'orderF' : 'columns', 'orderG' : 'rows', 'orderSn' : 'rows', 'orderSm' : 'rows',
'ARD' : False, 'element_sparsity' : False }
init = { 'F' : 'random', 'G' : 'exp', 'Sn' : 'exp', 'Sm' : 'random', 'lambdaS': 'exp', 'tau' : 'random' }
HMF = HMF_Gibbs(R,C,D,K,settings,priors)
HMF.initialise(init)
for E1 in E:
for i,k in itertools.product(xrange(0,I[E1]),xrange(0,K[E1])):
assert HMF.all_Ft[E1][i,k] != 1./lambdaF
for n in range(0,N):
E1,E2 = E_per_Rn[n]
for k,l in itertools.product(xrange(0,K[E1]),xrange(0,K[E2])):
assert HMF.all_Sn[n][k,l] == 0.01
assert HMF.all_taun[n] >= 0.
for m in range(0,M):
E1 = E_per_Cm[m]
for k,l in itertools.product(xrange(0,K[E1]),xrange(0,K[E1])):
assert HMF.all_Sm[m][k,l] != 0.
assert HMF.all_taum[m] >= 0.
for l in range(0,L):
E1 = E_per_Dl[l]
for j,k in itertools.product(xrange(0,J[l]),xrange(0,K[E1])):
assert HMF.all_Gl[l][j,k] == 1./lambdaG
assert HMF.all_taul[l] >= 0.
''' F N, G N, Sn Exp, Sm N, ARD, no element-wise sparsity. F kmeans, G exp, S random, lambdat random, tau random. '''
settings = { 'priorF' : 'normal', 'priorG' : 'normal', 'priorSn' : 'exponential', 'priorSm' : 'normal',
'ARD' : True, 'orderF' : 'columns', 'orderG' : 'rows', 'orderSn' : 'rows', 'orderSm' : 'rows' }
init = { 'F' : 'kmeans', 'G' : 'exp', 'Sn' : 'random', 'Sm' : 'random', 'lambdat' : 'random', 'tau' : 'random' }
HMF = HMF_Gibbs(R,C,D,K,settings,priors)
HMF.initialise(init)
for E1 in E:
for k in range(0,K[E1]):
assert HMF.all_lambdat[E1][k] >= 0.
for i,k in itertools.product(xrange(0,I[E1]),xrange(0,K[E1])):
assert HMF.all_Ft[E1][i,k] == 0.2 or HMF.all_Ft[E1][i,k] == 1.2
for n in range(0,N):
E1,E2 = E_per_Rn[n]
for k,l in itertools.product(xrange(0,K[E1]),xrange(0,K[E2])):
assert HMF.all_Sn[n][k,l] >= 0.
assert HMF.all_taun[n] >= 0.
expected_all_taum = [0.47612886531245974,1.7230629295737439]
for m in range(0,M):
E1 = E_per_Cm[m]
for k,l in itertools.product(xrange(0,K[E1]),xrange(0,K[E1])):
assert HMF.all_Sm[m][k,l] != 0.
assert HMF.all_taum[m] >= 0.
expected_all_taul = [4.1601208459214458]
for l in range(0,L):
E1 = E_per_Dl[l]
for j,k in itertools.product(xrange(0,J[l]),xrange(0,K[E1])):
assert HMF.all_Gl[l][j,k] == 0.01
assert HMF.all_taul[l] >= 0.
''' F Exp, G N, S N, no ARD, no element-wise sparsity. F kmeans, G least, S least, lambdat random, tau random. '''
settings = { 'priorF' : 'exponential', 'priorG' : 'normal', 'priorSn' : 'normal', 'priorSm' : 'normal',
'orderF' : 'columns', 'orderG' : 'rows', 'orderSn' : 'rows', 'orderSm' : 'rows',
'ARD' : False, 'element_sparsity' : False }
init = { 'F': 'kmeans', 'G': 'least', 'Sn': 'least', 'Sm': 'least', 'lambdat': 'random', 'lambdaS': 'exp', 'tau': 'random' }
HMF = HMF_Gibbs(R,C,D,K,settings,priors)
HMF.initialise(init)
for E1 in E:
for i,k in itertools.product(xrange(0,I[E1]),xrange(0,K[E1])):
assert HMF.all_Ft[E1][i,k] == 0.2 or HMF.all_Ft[E1][i,k] == 1.2
for n in range(0,N):
E1,E2 = E_per_Rn[n]
for k,l in itertools.product(xrange(0,K[E1]),xrange(0,K[E2])):
assert HMF.all_Sn[n][k,l] != 0.
assert HMF.all_taun[n] >= 0.
expected_all_taum = [0.47612886531245974,1.7230629295737439]
for m in range(0,M):
E1 = E_per_Cm[m]
for k,l in itertools.product(xrange(0,K[E1]),xrange(0,K[E1])):
assert HMF.all_Sm[m][k,l] != 0.
assert HMF.all_taum[m] >= 0.
expected_all_taul = [4.1601208459214458]
for l in range(0,L):
E1 = E_per_Dl[l]
for j,k in itertools.product(xrange(0,J[l]),xrange(0,K[E1])):
assert HMF.all_Gl[l][j,k] != 1./lambdaG
assert HMF.all_taul[l] >= 0.
def test_init():
"""
We need to test the following cases:
1. Dataset R relates same two entity types
2. Rn and Cm are not 2-dimensional matrices
3. Rn and Mn are of different sizes
4. Cm and Mm are of different sizes
5. Cm is not a square matrix
6. R1 and R2 both relate E but have different no. of entities
7. R and C both relate E but have different no. of entities
8. An entity has no observed datapoints at all
9. K does not have an entry for each entity
10. Finally, we need to test whether all variables are correctly initialised
"""
E0, E1, E2 = 'entity0','entity1',1337
I0, I1, I2 = 10,9,8
K0, K1, K2 = 3,2,1
J0 = 4
N, M, L, T = 3, 2, 1, 3
R0 = numpy.ones((I0,I1)) # relates E0, E1
R1 = numpy.ones((I0,I1)) # relates E0, E1
R2 = numpy.ones((I1,I2)) # relates E1, E2
C0 = numpy.ones((I0,I0)) # relates E0
C1 = numpy.ones((I2,I2)) # relates E2
D0 = numpy.ones((I2,J0)) # relates E2
Mn0 = numpy.ones((I0,I1))
Mn1 = numpy.ones((I0,I1))
Mn2 = numpy.ones((I1,I2))
Mm0 = numpy.ones((I0,I0))
Mm1 = numpy.ones((I2,I2))
Ml0 = numpy.ones((I2,J0))
size_Omegan = [I0*I1,I0*I1,I1*I2]
size_Omegam = [I0*(I0-1),I2*(I2-1)]
size_Omegal = [I2*J0]
alphan = [11.,12.,13.]
alpham = [14.,15.]
alphal = [16.]
R = [(R0,Mn0,E0,E1,alphan[0]),(R1,Mn1,E0,E1,alphan[1]),(R2,Mn2,E1,E2,alphan[2])]
C = [(C0,Mm0,E0,alpham[0]),(C1,Mm1,E2,alpham[1])]
D = [(D0,Ml0,E2,alphal[0])]
E = [E0,E1,E2]
K = {E0:K0,E1:K1,E2:K2}
I = {E0:I0,E1:I1,E2:I2}
J = [J0]
U1t = {'entity0':[0,1], 'entity1':[2], 1337:[] }
U2t = {'entity0':[], 'entity1':[0,1], 1337:[2] }
Vt = {'entity0':[0], 'entity1':[], 1337:[1] }
Wt = {'entity0':[], 'entity1':[], 1337:[0]}
E_per_Rn = [(E0,E1),(E0,E1),(E1,E2)]
E_per_Cm = [E0,E2]
E_per_Dl = [E2]
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' : 'normal', 'priorG' : 'exponential', 'priorSn' : 'normal', 'priorSm' : 'exponential',
'orderF' : 'rows', 'orderG' : 'columns', 'orderSn' : 'individual', 'orderSm' : 'rows',
'ARD' : True, 'element_sparsity' : True, }
''' 1. Dataset R relates same two entity types '''
R = [(R0,Mn0,E0,E1,alphan[0]),(R1,Mn1,E1,E1,alphan[1]),(R2,Mn2,E1,E2,alphan[2])]
with pytest.raises(AssertionError) as error:
HMF_Gibbs(R,C,D,K,settings,priors)
assert str(error.value) == "Gave same entity type for R1: entity1."
''' 2. Rn and Cm are not 2-dimensional matrices '''
R1 = numpy.ones(I0)
R = [(R0,Mn0,E0,E1,alphan[0]),(R1,Mn1,E0,E1,alphan[1]),(R2,Mn2,E1,E2,alphan[2])]
with pytest.raises(AssertionError) as error:
HMF_Gibbs(R,C,D,K,settings,priors)
assert str(error.value) == "R1 is not 2-dimensional, but instead 1-dimensional."
R1 = numpy.ones((I0,I1))
R = [(R0,Mn0,E0,E1,alphan[0]),(R1,Mn1,E0,E1,alphan[1]),(R2,Mn2,E1,E2,alphan[2])]
C0 = numpy.ones((I0,I1,I2))
C = [(C0,Mm0,E0,alpham[0]),(C1,Mm1,E2,alpham[1])]
with pytest.raises(AssertionError) as error:
HMF_Gibbs(R,C,D,K,settings,priors)
assert str(error.value) == "C0 is not 2-dimensional, but instead 3-dimensional."
C0 = numpy.ones((I0,I0))
C = [(C0,Mm0,E0,alpham[0]),(C1,Mm1,E2,alpham[1])]
''' 3. Rn and Mn are of different sizes '''
R2 = numpy.ones((I1,I2))
Mn2 = numpy.ones((I0,I1))
R = [(R0,Mn0,E0,E1,alphan[0]),(R1,Mn1,E0,E1,alphan[1]),(R2,Mn2,E1,E2,alphan[1])]
with pytest.raises(AssertionError) as error:
HMF_Gibbs(R,C,D,K,settings,priors)
assert str(error.value) == "Different shapes for R2 and M2: (9, 8) and (10, 9)."
R2 = numpy.ones((I1,I2))
Mn2 = numpy.ones((I1,I2))
R = [(R0,Mn0,E0,E1,alphan[0]),(R1,Mn1,E0,E1,alphan[1]),(R2,Mn2,E1,E2,alphan[2])]
''' 4. Cm and Mm are of different sizes '''
C1 = numpy.ones((I2,I2))
Mm1 = numpy.ones((I1,I1))
C = [(C0,Mm0,E0,alpham[0]),(C1,Mm1,E2,alpham[1])]
with pytest.raises(AssertionError) as error:
HMF_Gibbs(R,C,D,K,settings,priors)
assert str(error.value) == "Different shapes for C1 and M1: (8, 8) and (9, 9)."
C1 = numpy.ones((I2,I2))
Mm1 = numpy.ones((I2,I2))
C = [(C0,Mm0,E0,alpham[0]),(C1,Mm1,E2,alpham[1])]
''' 5. Cm is not a square matrix '''
C0 = numpy.ones((I1,I2))
Mm0 = numpy.ones((I1,I2))
C = [(C0,Mm0,E0,alpham[0]),(C1,Mm1,E2,alpham[1])]
with pytest.raises(AssertionError) as error:
HMF_Gibbs(R,C,D,K,settings,priors)
assert str(error.value) == "C0 is not a square matrix: (9, 8)."
C0 = numpy.ones((I0,I0))
Mm0 = numpy.ones((I0,I0))
C = [(C0,Mm0,E0,alpham[0]),(C1,Mm1,E2,alpham[1])]
''' 6. R1 and R2 both relate E but have different no. of entities '''
R2 = numpy.ones((I1+1,I2))
Mn2 = numpy.ones((I1+1,I2))
R = [(R0,Mn0,E0,E1,alphan[0]),(R1,Mn1,E0,E1,alphan[1]),(R2,Mn2,E1,E2,alphan[2])]
with pytest.raises(AssertionError) as error:
HMF_Gibbs(R,C,D,K,settings,priors)
assert str(error.value) == "Different number of rows (10) in R2 for entity type entity1 than before (9)!"
R2 = numpy.ones((I1,I2))
Mn2 = numpy.ones((I1,I2))
R = [(R0,Mn0,E0,E1,alphan[0]),(R1,Mn1,E0,E1,alphan[1]),(R2,Mn2,E1,E2,alphan[2])]
''' 7. R and C both relate E but have different no. of entities '''
R2 = numpy.ones((I1,I2+1))
Mn2 = numpy.ones((I1,I2+1))
R = [(R0,Mn0,E0,E1,alphan[0]),(R1,Mn1,E0,E1,alphan[1]),(R2,Mn2,E1,E2,alphan[2])]
with pytest.raises(AssertionError) as error:
HMF_Gibbs(R,C,D,K,settings,priors)
assert str(error.value) == "Different number of rows (8) in C1 for entity type 1337 than before (9)!"
R2 = numpy.ones((I1,I2))
Mn2 = numpy.ones((I1,I2))
R = [(R0,Mn0,E0,E1,alphan[0]),(R1,Mn1,E0,E1,alphan[1]),(R2,Mn2,E1,E2,alphan[2])]
''' 8. An entity has no observed datapoints at all '''
Mn0[:,1] = numpy.zeros(I0)
Mn1[:,1] = numpy.zeros(I0)
Mn2[1,:] = numpy.zeros(I2)
''' Concurrently also test not getting an error for entity0 '''
Mn0[2,:] = numpy.zeros(I1)
Mn1[2,:] = numpy.zeros(I1)
Mm0[2,:] = numpy.zeros(I0)
R = [(R0,Mn0,E0,E1,alphan[0]),(R1,Mn1,E0,E1,alphan[1]),(R2,Mn2,E1,E2,alphan[2])]
C = [(C0,Mm0,E0,alpham[0]),(C1,Mm1,E2,alpham[0])]
with pytest.raises(AssertionError) as error:
HMF_Gibbs(R,C,D,K,settings,priors)
assert str(error.value) == "No observed datapoints in any dataset for entity 1 of type entity1."
Mn0 = numpy.ones((I0,I1))
Mn1 = numpy.ones((I0,I1))
Mn2 = numpy.ones((I1,I2))
Mm0 = numpy.ones((I0,I0))
R = [(R0,Mn0,E0,E1,alphan[0]),(R1,Mn1,E0,E1,alphan[1]),(R2,Mn2,E1,E2,alphan[2])]
C = [(C0,Mm0,E0,alpham[0]),(C1,Mm1,E2,alpham[1])]
''' 9. K does not have an entry for each entity '''
K = {E0:K0,E2:K2}
with pytest.raises(AssertionError) as error:
HMF_Gibbs(R,C,D,K,settings,priors)
assert str(error.value) == "Did not get an entry for entity entity1 in K = {1337: 1, 'entity0': 3}."
K = {E0:K0,E1:K1,E2:K2}
''' 10. Finally, we need to test whether all variables are correctly initialised '''
HMF = HMF_Gibbs(R,C,D,K,settings,priors)
assert numpy.array_equal(HMF.all_E,E)
for R,Rtrue in zip(HMF.all_Rn,[R0,R1,R2]):
assert numpy.array_equal(R,Rtrue)
for Mn,Mntrue in zip(HMF.all_Mn,[Mn0,Mn1,Mn2]):
assert numpy.array_equal(Mn,Mntrue)
for C,Ctrue in zip(HMF.all_Cm,[C0,C1]):
assert numpy.array_equal(C,Ctrue)
for Mm,Mmtrue in zip(HMF.all_Mm,[Mm0,Mm1]):
assert numpy.array_equal(Mm,Mmtrue)
for Dl,Dltrue in zip(HMF.all_Dl,[D0]):
assert numpy.array_equal(Dl,Dltrue)
assert HMF.size_Omegan == size_Omegan
assert HMF.size_Omegam == size_Omegam
assert HMF.size_Omegal == size_Omegal
assert HMF.E_per_Rn == E_per_Rn
assert HMF.E_per_Cm == E_per_Cm
assert HMF.E_per_Dl == E_per_Dl
assert HMF.all_alphan == alphan
assert HMF.all_alpham == alpham
assert HMF.all_alphal == alphal
assert numpy.array_equal(HMF.K,K)
assert HMF.I == I
assert HMF.J == J
assert HMF.N == N
assert HMF.M == M
assert HMF.L == L
assert HMF.T == T
assert HMF.U1t == U1t
assert HMF.U2t == U2t
assert HMF.Vt == Vt
assert HMF.Wt == Wt
assert HMF.all_Ft == { 'entity0':[], 'entity1':[], 1337:[] }
assert HMF.all_Sn == []
assert HMF.all_Sm == []
assert HMF.all_Gl == []
assert HMF.all_taun == []
assert HMF.all_taum == []
assert HMF.all_taul == []
assert HMF.alpha0 == alpha0
assert HMF.beta0 == beta0
assert HMF.alphatau == alphatau
assert HMF.betatau == betatau
assert HMF.lambdaF == lambdaF
assert HMF.lambdaG == lambdaG
assert HMF.lambdaSn == lambdaSn
assert HMF.lambdaSm == lambdaSm
# { 'priorF' : 'normal', 'priorG' : 'exponential', 'priorSn' : 'normal', 'priorSm' : 'exponential',
# 'orderF' : 'rows', 'orderG' : 'columns', 'orderSn' : 'individual', 'orderSm' : 'rows', 'ARD' : True }
assert HMF.prior_F == 'normal'
assert HMF.prior_G == ['exponential']
assert HMF.prior_Sn == ['normal','normal','normal']
assert HMF.prior_Sm == ['exponential','exponential']
assert HMF.order_F == 'rows'
assert HMF.order_G == ['columns']
assert HMF.order_Sn == ['individual','individual','individual']
assert HMF.order_Sm == ['rows','rows']
assert HMF.ARD == True
assert HMF.element_sparsity == True
assert HMF.rows_F == True
assert HMF.rows_G == [False]
assert HMF.rows_Sn == [False,False,False]
assert HMF.rows_Sm == [True,True]
assert HMF.nonnegative_F == False
assert HMF.nonnegative_G == [True]
assert HMF.nonnegative_Sn == [False,False,False]
assert HMF.nonnegative_Sm == [True,True]
