def test_initialise():
I,J,K = 5,3,2
R = numpy.ones((I,J))
M = numpy.ones((I,J))
lambdaU = 2*numpy.ones((I,K))
lambdaV = 3*numpy.ones((J,K))
alpha, beta = 3, 1
priors = { 'alpha':alpha, 'beta':beta, 'lambdaU':lambdaU, 'lambdaV':lambdaV }
# First do a random initialisation - we can then only check whether values are correctly initialised
init = 'random'
NMF = nmf_icm(R,M,K,priors)
NMF.initialise(init)
assert NMF.tau >= 0.0
for i,k in itertools.product(xrange(0,I),xrange(0,K)):
assert NMF.U[i,k] >= 0.0
for j,k in itertools.product(xrange(0,J),xrange(0,K)):
assert NMF.V[j,k] >= 0.0
#assert NMF.tau == 3./1.
# Then initialise with expectation values
init = 'exp'
NMF = nmf_icm(R,M,K,priors)
NMF.initialise(init)
assert NMF.tau >= 0.0
for i,k in itertools.product(xrange(0,I),xrange(0,K)):
assert NMF.U[i,k] == 1./2.
for j,k in itertools.product(xrange(0,J),xrange(0,K)):
assert NMF.V[j,k] == 1./3.
def test_log_likelihood():
R = numpy.array([[1,2],[3,4]],dtype=float)
M = numpy.array([[1,1],[0,1]])
I, J, K = 2, 2, 3
lambdaU = 2*numpy.ones((I,K))
lambdaV = 3*numpy.ones((J,K))
alpha, beta = 3, 1
priors = { 'alpha':alpha, 'beta':beta, 'lambdaU':lambdaU, 'lambdaV':lambdaV }
BNMF = nmf_icm(R,M,K,priors)
BNMF.U = numpy.ones((I,K))
BNMF.V = 2*numpy.ones((J,K))
BNMF.tau = 3.
# expU*expV.T = [[6.]]
log_likelihood = 3./2.*(math.log(3.)-math.log(2*math.pi)) - 3./2. * (5**2 + 4**2 + 2**2)
AIC = -2*log_likelihood + 2*(2*3+2*3)
BIC = -2*log_likelihood + (2*3+2*3)*math.log(3)
MSE = (5**2+4**2+2**2)/3.
assert log_likelihood == BNMF.quality('loglikelihood')
assert AIC == BNMF.quality('AIC')
assert BIC == BNMF.quality('BIC')
assert MSE == BNMF.quality('MSE')
with pytest.raises(AssertionError) as error:
BNMF.quality('FAIL')
assert str(error.value) == "Unrecognised metric for model quality: FAIL."
def test_predict():
(I,J,K) = (5,3,2)
U = numpy.array([[125.,126.],[126.,126.],[126.,126.],[126.,126.],[126.,126.]])
V = numpy.array([[84.,84.],[84.,84.],[84.,84.]])
taus = [m**2 for m in range(1,10+1)]
#R_pred = numpy.array([[21084.,21084.,21084.],[ 21168.,21168.,21168.],[21168.,21168.,21168.],[21168.,21168.,21168.],[21168.,21168.,21168.]])
R = numpy.array([[1,2,3],[4,5,6],[7,8,9],[10,11,12],[13,14,15]],dtype=float)
M = numpy.ones((I,J))
lambdaU = 2*numpy.ones((I,K))
lambdaV = 3*numpy.ones((J,K))
alpha, beta = 3, 1
priors = { 'alpha':alpha, 'beta':beta, 'lambdaU':lambdaU, 'lambdaV':lambdaV }
M_test = numpy.array([[0,0,1],[0,1,0],[0,0,0],[1,1,0],[0,0,0]]) #R->3,5,10,11, P_pred->21084,21168,21168,21168
MSE = (444408561. + 447872569. + 447660964. + 447618649) / 4.
R2 = 1. - (444408561. + 447872569. + 447660964. + 447618649) / (4.25**2+2.25**2+2.75**2+3.75**2) #mean=7.25
Rp = 357. / ( math.sqrt(44.75) * math.sqrt(5292.) ) #mean=7.25,var=44.75, mean_pred=21147,var_pred=5292, corr=(-4.25*-63 + -2.25*21 + 2.75*21 + 3.75*21)
NMF = nmf_icm(R,M,K,priors)
NMF.U = U
NMF.V = V
NMF.all_tau = taus
performances = NMF.predict(M_test)
assert performances['MSE'] == MSE
assert performances['R^2'] == R2
assert performances['Rp'] == Rp
def test_tauV():
NMF = nmf_icm(R,M,K,priors)
NMF.initialise(init)
NMF.tau = 3.
#U^2 = [[1/4,1/4],[1/4,1/4],[1/4,1/4],[1/4,1/4],[1/4,1/4]], sum_i U^2 = [1,1,1] (index=j)
tauV = 3.*numpy.array([[1.,1.],[1.,1.],[1.,1.]])
for j,k in itertools.product(xrange(0,J),xrange(0,K)):
assert NMF.tauV(k)[j] == tauV[j,k]
def test_tauU():
NMF = nmf_icm(R,M,K,priors)
NMF.initialise(init)
NMF.tau = 3.
#V^2 = [[1/9,1/9],[1/9,1/9],[1/9,1/9]], sum_j V^2 = [2/9,1/3,2/9,2/9,1/3] (index=i)
tauU = 3.*numpy.array([[2./9.,2./9.],[1./3.,1./3.],[2./9.,2./9.],[2./9.,2./9.],[1./3.,1./3.]])
for i,k in itertools.product(xrange(0,I),xrange(0,K)):
assert NMF.tauU(k)[i] == tauU[i,k]
def test_muV():
NMF = nmf_icm(R,M,K,priors)
NMF.initialise(init)
NMF.tau = 3.
#U*V^T - Uik*Vjk = [[1/6,..]], so Rij - Ui * Vj + Uik * Vjk = 5/6
tauV = 3.*numpy.array([[1.,1.],[1.,1.],[1.,1.]])
muV = 1./tauV * ( 3. * numpy.array([[4.*(5./6.)*(1./2.),4.*(5./6.)*(1./2.)],[4.*(5./6.)*(1./2.),4.*(5./6.)*(1./2.)],[4.*(5./6.)*(1./2.),4.*(5./6.)*(1./2.)]]) - lambdaV )
for j,k in itertools.product(xrange(0,J),xrange(0,K)):
assert NMF.muV(tauV[:,k],k)[j] == muV[j,k]
def test_muU():
NMF = nmf_icm(R,M,K,priors)
NMF.initialise(init)
NMF.tau = 3.
#U*V^T - Uik*Vjk = [[1/6,..]], so Rij - Ui * Vj + Uik * Vjk = 5/6
tauU = 3.*numpy.array([[2./9.,2./9.],[1./3.,1./3.],[2./9.,2./9.],[2./9.,2./9.],[1./3.,1./3.]])
muU = 1./tauU * ( 3. * numpy.array([[2.*(5./6.)*(1./3.),10./18.],[15./18.,15./18.],[10./18.,10./18.],[10./18.,10./18.],[15./18.,15./18.]]) - lambdaU )
for i,k in itertools.product(xrange(0,I),xrange(0,K)):
assert abs(NMF.muU(tauU[:,k],k)[i] - muU[i,k]) < 0.000000000000001
def test_run():
I,J,K = 10,5,2
R = numpy.ones((I,J))
M = numpy.ones((I,J))
M[0,0], M[2,2], M[3,1] = 0, 0, 0
lambdaU = 2*numpy.ones((I,K))
lambdaV = 3*numpy.ones((J,K))
alpha, beta = 3, 1
priors = { 'alpha':alpha, 'beta':beta, 'lambdaU':lambdaU, 'lambdaV':lambdaV }
init = 'exp' #U=1/2,V=1/3
iterations = 15
NMF = nmf_icm(R,M,K,priors)
NMF.initialise(init)
NMF.run(iterations)
assert NMF.all_tau.shape == (iterations,)
assert NMF.all_tau[1] != alpha/float(beta)
def test_compute_statistics():
R = numpy.array([[1,2],[3,4]],dtype=float)
M = numpy.array([[1,1],[0,1]])
I, J, K = 2, 2, 3
lambdaU = 2*numpy.ones((I,K))
lambdaV = 3*numpy.ones((J,K))
alpha, beta = 3, 1
priors = { 'alpha':alpha, 'beta':beta, 'lambdaU':lambdaU, 'lambdaV':lambdaV }
BNMF = nmf_icm(R,M,K,priors)
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 == BNMF.compute_MSE(M_pred,R,R_pred)
assert R2_pred == BNMF.compute_R2(M_pred,R,R_pred)
assert Rp_pred == BNMF.compute_Rp(M_pred,R,R_pred)
all_R.append(R)
# We now run the VB algorithm on each of the M's for each noise ratio
all_performances = {metric: [] for metric in metrics}
average_performances = {metric: []
for metric in metrics} # averaged over repeats
for (noise, R, Ms, Ms_test) in zip(noise_ratios, all_R, all_Ms, all_Ms_test):
print "Trying noise ratio %s." % noise
# Run the algorithm <repeats> times and store all the performances
for metric in metrics:
all_performances[metric].append([])
for (repeat, M, M_test) in zip(range(0, repeats), Ms, Ms_test):
print "Repeat %s of noise ratio %s." % (repeat + 1, noise)
BNMF = nmf_icm(R, M, K, priors)
BNMF.initialise(init_UV)
BNMF.run(iterations, minimum_TN=minimum_TN)
# Measure the performances
performances = BNMF.predict(M_test)
for metric in metrics:
# Add this metric's performance to the list of <repeat> performances for this noise ratio
all_performances[metric][-1].append(performances[metric])
# Compute the average across attempts
for metric in metrics:
average_performances[metric].append(
sum(all_performances[metric][-1]) / repeats)
# Load in data
R = numpy.loadtxt(input_folder + "R.txt")
M = numpy.ones((I, J))
# Run the VB algorithm, <repeats> times
times_repeats = []
performances_repeats = []
for i in range(0, repeats):
# Set all the seeds
numpy.random.seed(0)
random.seed(0)
scipy.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
times_repeats.append(nmf.all_times)
performances_repeats.append(nmf.all_performances)
# Check whether seed worked: all performances should be the same
assert all([numpy.array_equal(performances, performances_repeats[0]) for performances in performances_repeats]), \
"Seed went wrong - performances not the same across repeats!"
# Print out the performances, and the average times
icm_all_times_average = list(numpy.average(times_repeats, axis=0))
icm_all_performances = performances_repeats[0]
print "icm_all_times_average = %s" % icm_all_times_average
from BNMTF.drug_sensitivity.experiments_gdsc.load_data import load_Sanger
import numpy, matplotlib.pyplot as plt
##########
standardised = False #standardised Sanger or unstandardised
iterations = 1000
init_UV = 'random'
I, J, K = 622,138,10
alpha, beta = 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
(_,X_min,M,_,_,_,_) = load_Sanger(standardised=standardised)
# Run the algorithm
NMF = nmf_icm(X_min,M,K,priors)
NMF.initialise(init_UV)
NMF.run(iterations)
# Plot the tau expectation values to check convergence
plt.plot(NMF.all_tau)
# Print the performances across iterations (MSE)
print "all_performances = %s" % NMF.all_performances['MSE']
def test_init():
# Test getting an exception when R and M are different sizes, and when R is not a 2D array.
R1 = numpy.ones(3)
M = numpy.ones((2,3))
I,J,K = 5,3,1
lambdaU = numpy.ones((I,K))
lambdaV = numpy.ones((J,K))
alpha, beta = 3, 1
priors = { 'alpha':alpha, 'beta':beta, 'lambdaU':lambdaU, 'lambdaV':lambdaV }
with pytest.raises(AssertionError) as error:
nmf_icm(R1,M,K,priors)
assert str(error.value) == "Input matrix R is not a two-dimensional array, but instead 1-dimensional."
R2 = numpy.ones((4,3,2))
with pytest.raises(AssertionError) as error:
nmf_icm(R2,M,K,priors)
assert str(error.value) == "Input matrix R is not a two-dimensional array, but instead 3-dimensional."
R3 = numpy.ones((3,2))
with pytest.raises(AssertionError) as error:
nmf_icm(R3,M,K,priors)
assert str(error.value) == "Input matrix R is not of the same size as the indicator matrix M: (3, 2) and (2, 3) respectively."
# Similarly for lambdaU, lambdaV
R4 = numpy.ones((2,3))
lambdaU = numpy.ones((2+1,1))
priors = { 'alpha':alpha, 'beta':beta, 'lambdaU':lambdaU, 'lambdaV':lambdaV }
with pytest.raises(AssertionError) as error:
nmf_icm(R4,M,K,priors)
assert str(error.value) == "Prior matrix lambdaU has the wrong shape: (3, 1) instead of (2, 1)."
lambdaU = numpy.ones((2,1))
lambdaV = numpy.ones((3+1,1))
priors = { 'alpha':alpha, 'beta':beta, 'lambdaU':lambdaU, 'lambdaV':lambdaV }
with pytest.raises(AssertionError) as error:
nmf_icm(R4,M,K,priors)
assert str(error.value) == "Prior matrix lambdaV has the wrong shape: (4, 1) instead of (3, 1)."
# Test getting an exception if a row or column is entirely unknown
lambdaU = numpy.ones((2,1))
lambdaV = numpy.ones((3,1))
M1 = [[1,1,1],[0,0,0]]
M2 = [[1,1,0],[1,0,0]]
priors = { 'alpha':alpha, 'beta':beta, 'lambdaU':lambdaU, 'lambdaV':lambdaV }
with pytest.raises(AssertionError) as error:
nmf_icm(R4,M1,K,priors)
assert str(error.value) == "Fully unobserved row in R, row 1."
with pytest.raises(AssertionError) as error:
nmf_icm(R4,M2,K,priors)
assert str(error.value) == "Fully unobserved column in R, column 2."
# Finally, a successful case
I,J,K = 3,2,2
R5 = 2*numpy.ones((I,J))
lambdaU = numpy.ones((I,K))
lambdaV = numpy.ones((J,K))
M = numpy.ones((I,J))
priors = { 'alpha':alpha, 'beta':beta, 'lambdaU':lambdaU, 'lambdaV':lambdaV }
NMF = nmf_icm(R5,M,K,priors)
assert numpy.array_equal(NMF.R,R5)
assert numpy.array_equal(NMF.M,M)
assert NMF.I == I
assert NMF.J == J
assert NMF.K == K
assert NMF.size_Omega == I*J
assert NMF.alpha == alpha
assert NMF.beta == beta
assert numpy.array_equal(NMF.lambdaU,lambdaU)
assert numpy.array_equal(NMF.lambdaV,lambdaV)
# And when lambdaU and lambdaV are integers
I,J,K = 3,2,2
R5 = 2*numpy.ones((I,J))
lambdaU = 3.
lambdaV = 4.
M = numpy.ones((I,J))
priors = { 'alpha':alpha, 'beta':beta, 'lambdaU':lambdaU, 'lambdaV':lambdaV }
NMF = nmf_icm(R5,M,K,priors)
assert numpy.array_equal(NMF.R,R5)
assert numpy.array_equal(NMF.M,M)
assert NMF.I == I
assert NMF.J == J
assert NMF.K == K
assert NMF.size_Omega == I*J
assert NMF.alpha == alpha
assert NMF.beta == beta
assert numpy.array_equal(NMF.lambdaU,lambdaU*numpy.ones((I,K)))
assert numpy.array_equal(NMF.lambdaV,lambdaV*numpy.ones((J,K)))
def test_beta_s():
NMF = nmf_icm(R,M,K,priors)
NMF.initialise(init)
beta_s = beta + .5*(12*(2./3.)**2) #U*V.T = [[1/6+1/6,..]]
assert abs(NMF.beta_s() - beta_s) < 0.000000000000001
def test_alpha_s():
NMF = nmf_icm(R,M,K,priors)
NMF.initialise(init)
alpha_s = alpha + 6.
assert NMF.alpha_s() == alpha_s
all_R.append(R)
# We now run the VB algorithm on each of the M's for each noise ratio
all_performances = {metric: [] for metric in metrics}
average_performances = {metric: [] for metric in metrics} # averaged over repeats
for (noise, R, Ms, Ms_test) in zip(noise_ratios, all_R, all_Ms, all_Ms_test):
print "Trying noise ratio %s." % noise
# Run the algorithm <repeats> times and store all the performances
for metric in metrics:
all_performances[metric].append([])
for (repeat, M, M_test) in zip(range(0, repeats), Ms, Ms_test):
print "Repeat %s of noise ratio %s." % (repeat + 1, noise)
BNMF = nmf_icm(R, M, K, priors)
BNMF.initialise(init_UV)
BNMF.run(iterations, minimum_TN=minimum_TN)
# Measure the performances
performances = BNMF.predict(M_test)
for metric in metrics:
# Add this metric's performance to the list of <repeat> performances for this noise ratio
all_performances[metric][-1].append(performances[metric])
# Compute the average across attempts
for metric in metrics:
average_performances[metric].append(sum(all_performances[metric][-1]) / repeats)
print "repeats=%s \nnoise_ratios = %s \nall_performances = %s \naverage_performances = %s" % (
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
times_repeats.append(nmf.all_times)
performances_repeats.append(nmf.all_performances)
# Check whether seed worked: all performances should be the same
assert all(numpy.array_equal(performances, performances_repeats[0]) for performances in performances_repeats), \
"Seed went wrong - performances not the same across repeats!"
# Print out the performances, and the average times
icm_all_times_average = list(numpy.average(times_repeats, axis=0))
icm_all_performances = performances_repeats[0]
print "icm_all_times_average = %s" % icm_all_times_average
##########
standardised = False #standardised Sanger or unstandardised
no_folds = 5
iterations = 1000
init_UV = 'random'
I, J, K = 622, 139, 10
alpha, beta = 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
(_, X_min, M, _, _, _, _) = load_Sanger(standardised=standardised)
# Run the algorithm
NMF = nmf_icm(X_min, M, K, priors)
NMF.initialise(init_UV)
NMF.run(iterations)
# Plot the tau expectation values to check convergence
plt.plot(NMF.all_tau)
# Print the performances across iterations (MSE)
print "all_performances = %s" % NMF.all_performances['MSE']
'''
all_performances = [68.185592823781633, 3.7406955878671249, 3.0952956497536146, 3.0093196335883539, 2.9825243533200183, 2.9643577550979545, 2.9459850054262682, 2.9241393181988635, 2.8970426441581938, 2.8647979550112606, 2.8287634364681966, 2.7912521958298973, 2.7539589370082029, 2.7186618043440998, 2.6869029979300709, 2.6594967226303559, 2.6369317554566618, 2.6184327951545603, 2.6031048058598536, 2.5902861012156722, 2.5791468430768449, 2.5689608242742055, 2.5594562573764947, 2.5501146959110974, 2.5406938709182296, 2.5310858259397389, 2.5212931254248749, 2.5114342295654812, 2.501580454868308, 2.4918258599447105, 2.4823868778575466, 2.473389145201657, 2.4648198771053091, 2.4566954595658497, 2.4490573255748709, 2.4419128535976755, 2.4352541036106987, 2.4290595079091366, 2.423309384201072, 2.4179947009310823, 2.413125243804942, 2.4086708937924723, 2.4045824571856476, 2.4008310567294608, 2.3973987473688405, 2.3942619257426667, 2.3913947844606285, 2.3887730250321679, 2.3863758273338123, 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