def test_predict():
(I, J, K) = (5, 3, 2)
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))
K = 3
U = numpy.array([[125., 126.], [126., 126.], [126., 126.], [126., 126.],
[126., 126.]])
V = numpy.array([[84., 84.], [84., 84.], [84., 84.]])
M_test = numpy.array([[0, 0, 1], [0, 1, 0], [0, 0, 0], [1, 1, 0],
[0, 0,
0]]) #R->3,5,10,11, R_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(R, M, K)
nmf.U = U
nmf.V = V
performances = nmf.predict(M_test)
assert performances['MSE'] == MSE
assert performances['R^2'] == R2
assert performances['Rp'] == Rp
def test_run():
# Data generated from W = [[1,2],[3,4]], H = [[4,3],[2,1]]
R = [[8, 5], [20, 13]]
M = [[1, 1], [1, 0]]
K = 2
U = numpy.array([[10, 9], [8, 7]], dtype='f') #2x2
V = numpy.array([[6, 4], [5, 3]], dtype='f') #2x2
nmf = NMF(R, M, K)
# Check we get an Exception if W, H are undefined
with pytest.raises(AssertionError) as error:
nmf.run(0)
assert str(
error.value
) == "U and V have not been initialised - please run NMF.initialise() first."
# Then check for 1 iteration whether the updates work - heck just the first entry of U
nmf.U = U
nmf.V = V
nmf.run(1)
U_00 = 10 * (6 * 8 / 96.0 + 5 * 5 / 77.0) / (5.0 + 6.0) #0.74970484061
assert abs(U_00 - nmf.U[0][0]) < 0.000001
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
nmf = NMF(R,M,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 == nmf.compute_MSE(M_pred,R,R_pred)
assert R2_pred == nmf.compute_R2(M_pred,R,R_pred)
assert Rp_pred == nmf.compute_Rp(M_pred,R,R_pred)
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
nmf = NMF(R, M, 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 == nmf.compute_MSE(M_pred, R, R_pred)
assert R2_pred == nmf.compute_R2(M_pred, R, R_pred)
assert Rp_pred == nmf.compute_Rp(M_pred, R, R_pred)
def test_predict():
(I,J,K) = (5,3,2)
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))
K = 3
U = numpy.array([[125.,126.],[126.,126.],[126.,126.],[126.,126.],[126.,126.]])
V = numpy.array([[84.,84.],[84.,84.],[84.,84.]])
M_test = numpy.array([[0,0,1],[0,1,0],[0,0,0],[1,1,0],[0,0,0]]) #R->3,5,10,11, R_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(R,M,K)
nmf.U = U
nmf.V = V
performances = nmf.predict(M_test)
assert performances['MSE'] == MSE
assert performances['R^2'] == R2
assert performances['Rp'] == Rp
def test_compute_I_div():
R = [[1,2,0,4],[5,0,7,0]]
M = [[1,1,0,1],[1,0,1,0]]
K = 2
U = numpy.array([[1,2],[3,4]],dtype='f') #2x2
V = numpy.array([[5,7,9,11],[6,8,10,12]],dtype='f').T #4x2
#R_pred = [[17,23,29,35],[39,53,67,81]]
expected_I_div = sum([
1.0*math.log(1.0/17.0) - 1.0 + 17.0,
2.0*math.log(2.0/23.0) - 2.0 + 23.0,
4.0*math.log(4.0/35.0) - 4.0 + 35.0,
5.0*math.log(5.0/39.0) - 5.0 + 39.0,
7.0*math.log(7.0/67.0) - 7.0 + 67.0
])
nmf = NMF(R,M,K)
nmf.U = U
nmf.V = V
I_div = nmf.compute_I_div()
assert I_div == expected_I_div
def test_compute_I_div():
R = [[1, 2, 0, 4], [5, 0, 7, 0]]
M = [[1, 1, 0, 1], [1, 0, 1, 0]]
K = 2
U = numpy.array([[1, 2], [3, 4]], dtype='f') #2x2
V = numpy.array([[5, 7, 9, 11], [6, 8, 10, 12]], dtype='f').T #4x2
#R_pred = [[17,23,29,35],[39,53,67,81]]
expected_I_div = sum([
1.0 * math.log(1.0 / 17.0) - 1.0 + 17.0,
2.0 * math.log(2.0 / 23.0) - 2.0 + 23.0,
4.0 * math.log(4.0 / 35.0) - 4.0 + 35.0,
5.0 * math.log(5.0 / 39.0) - 5.0 + 39.0,
7.0 * math.log(7.0 / 67.0) - 7.0 + 67.0
])
nmf = NMF(R, M, K)
nmf.U = U
nmf.V = V
I_div = nmf.compute_I_div()
assert I_div == expected_I_div
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))
K = 0
with pytest.raises(AssertionError) as error:
NMF(R1, M, K)
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(R2, M, K)
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(R3, M, K)
assert str(
error.value
) == "Input matrix R is not of the same size as the indicator matrix M: (3, 2) and (2, 3) respectively."
# Test getting an exception if a row or column is entirely unknown
R = numpy.ones((2, 3))
M1 = [[1, 1, 1], [0, 0, 0]]
M2 = [[1, 1, 0], [1, 0, 0]]
with pytest.raises(AssertionError) as error:
NMF(R, M1, K)
assert str(error.value) == "Fully unobserved row in R, row 1."
with pytest.raises(AssertionError) as error:
NMF(R, M2, K)
assert str(error.value) == "Fully unobserved column in R, column 2."
# Test whether we made a copy of R with 1's at unknown values
I, J = 2, 4
R = [[1, 2, 0, 4], [5, 0, 7, 0]]
M = [[1, 1, 0, 1], [1, 0, 1, 0]]
K = 2
R_excl_unknown = [[1, 2, 1, 4], [5, 1, 7, 1]]
nmf = NMF(R, M, K)
assert numpy.array_equal(R, nmf.R)
assert numpy.array_equal(M, nmf.M)
assert nmf.I == I
assert nmf.J == J
assert nmf.K == K
assert numpy.array_equal(R_excl_unknown, nmf.R_excl_unknown)
def test_initialisation():
I, J = 2, 3
R = numpy.ones((I, J))
M = numpy.ones((I, J))
K = 4
# Init ones
init_UV = 'ones'
nmf = NMF(R, M, K)
nmf.initialise(init_UV)
assert numpy.array_equal(numpy.ones((2, 4)), nmf.U)
assert numpy.array_equal(numpy.ones((3, 4)), nmf.V)
# Init random
init_UV = 'random'
nmf = NMF(R, M, K)
nmf.initialise(init_UV)
for (i, k) in itertools.product(range(0, I), range(0, K)):
assert nmf.U[i, k] > 0 and nmf.U[i, k] < 1
for (j, k) in itertools.product(range(0, J), range(0, K)):
assert nmf.V[j, k] > 0 and nmf.V[j, k] < 1
def test_run():
# Data generated from W = [[1,2],[3,4]], H = [[4,3],[2,1]]
R = [[8,5],[20,13]]
M = [[1,1],[1,0]]
K = 2
U = numpy.array([[10,9],[8,7]],dtype='f') #2x2
V = numpy.array([[6,4],[5,3]],dtype='f') #2x2
nmf = NMF(R,M,K)
# Check we get an Exception if W, H are undefined
with pytest.raises(AssertionError) as error:
nmf.run(0)
assert str(error.value) == "U and V have not been initialised - please run NMF.initialise() first."
# Then check for 1 iteration whether the updates work - heck just the first entry of U
nmf.U = U
nmf.V = V
nmf.run(1)
U_00 = 10*(6*8/96.0+5*5/77.0)/(5.0+6.0) #0.74970484061
assert abs(U_00 - nmf.U[0][0]) < 0.000001
def test_initialisation():
I,J = 2,3
R = numpy.ones((I,J))
M = numpy.ones((I,J))
K = 4
# Init ones
init_UV = 'ones'
nmf = NMF(R,M,K)
nmf.initialise(init_UV)
assert numpy.array_equal(numpy.ones((2,4)),nmf.U)
assert numpy.array_equal(numpy.ones((3,4)),nmf.V)
# Init random
init_UV = 'random'
nmf = NMF(R,M,K)
nmf.initialise(init_UV)
for (i,k) in itertools.product(range(0,I),range(0,K)):
assert nmf.U[i,k] > 0 and nmf.U[i,k] < 1
for (j,k) in itertools.product(range(0,J),range(0,K)):
assert nmf.V[j,k] > 0 and nmf.V[j,k] < 1
input_folder = project_location + "BNMTF/experiments/generate_toy/bnmf/"
iterations = 200
I, J, K = 100, 80, 10
init_UV = "exponential"
expo_prior = 1 / 10.0
# Load in data
R = numpy.loadtxt(input_folder + "R.txt")
M = numpy.ones((I, J))
# M = numpy.loadtxt(input_folder+"M.txt")
M_test = calc_inverse_M(numpy.loadtxt(input_folder + "M.txt"))
# Run the VB algorithm
nmf = NMF(R, M, K)
nmf.initialise(init_UV, expo_prior)
nmf.run(iterations)
# Also measure the performances
performances = nmf.predict(M_test)
print "Performance on test set: %s." % performances
# Extract the performances across all iterations
print "np_all_performances = %s" % nmf.all_performances
"""
np_all_performances = {'R^2': [0.3032122365329063, 0.6133166405978905, 0.6431376044334463, 0.6593067085963278, 0.6717514727663727, 0.68228506665832, 0.6917125664190896, 0.7005339760645299, 0.7090723817749585, 0.7175252245809353, 0.7259934237903767, 0.7345036350654565, 0.7430317677039082, 0.75152937384811, 0.7599480851429183, 0.7682550228175197, 0.776436085576062, 0.784490285163333, 0.7924211663319218, 0.8002297289193113, 0.8079102319907545, 0.8154484610474781, 0.8228217510909723, 0.8300003544925733, 0.8369499438010377, 0.8436350246711801, 0.8500228818826858, 0.8560874492822713, 0.8618123094412313, 0.8671921091902559, 0.8722321105692831, 0.876946185389484, 0.8813539787340927, 0.8854780322176982, 0.8893414329509705, 0.8929662251700472, 0.896372547257493, 0.8995783054041361, 0.9025991611102144, 0.905448654072977, 0.9081383595637647, 0.9106780524041636, 0.9130758947401676, 0.9153386756498996, 0.9174721151698999, 0.9194812187586701, 0.9213706451927918, 0.923145040566608, 0.9248092957077478, 0.9263687005314527, 0.9278289900920716, 0.9291962964751085, 0.9304770333355508, 0.9316777441364161, 0.9328049422236668, 0.9338649637036158, 0.9348638457429038, 0.9358072355984199, 0.9367003304354385, 0.9375478448843411, 0.9383540018746, 0.9391225419794407, 0.9398567467937639, 0.9405594723975267, 0.941233189525269, 0.9418800275807032, 0.9425018200834387, 0.9431001495282554, 0.9436763900026273, 0.9442317462684834, 0.9447672883809836, 0.9452839812873185, 0.9457827092068222, 0.9462642949174859, 0.9467295143403786, 0.9471791070055592, 0.9476137830938077, 0.9480342277817988, 0.9484411035871358, 0.9488350513324426, 0.9492166902442043, 0.9495866175899556, 0.9499454081501066, 0.9502936137267446, 0.9506317628149454, 0.9509603605031547, 0.9512798886262251, 0.9515908061651354, 0.9518935498682876, 0.9521885350579884, 0.9524761565801545, 0.9527567898538667, 0.9530307919790423, 0.953298502864411, 0.953560246343582, 0.9538163312538315, 0.954067052459742, 0.9543126918115871, 0.9545535190357457, 0.9547897925610067, 0.9550217602898616, 0.9552496603275022, 0.9554737216830019, 0.9556941649570642, 0.9559112030289433, 0.9561250417519456, 0.9563358806627454, 0.9565439137050391, 0.9567493299633228, 0.956952314398204, 0.9571530485710693, 0.9573517113433512, 0.9575484795342386, 0.9577435285205056, 0.9579370327630805, 0.9581291662469268, 0.9583201028234776, 0.9585100164480038, 0.9586990813076265, 0.9588874718388863, 0.9590753626367163, 0.9592629282590452, 0.9594503429330747, 0.9596377801704293, 0.9598254122988978, 0.9600134099184604, 0.9602019412888035, 0.9603911716547099, 0.9605812625147117, 0.9607723708373238, 0.960964648228191, 0.961158240050655, 0.961353284501683, 0.9615499116448762, 0.9617482424024153, 0.9619483875083532, 0.9621504464266579, 0.9623545062388184, 0.9625606405076733, 0.9627689081263755, 0.9629793521640283, 0.9631919987224604, 0.9634068558217966, 0.9636239123357836, 0.9638431370011578, 0.964064477528489, 0.9642878598447232, 0.9645131874998351, 0.9647403412713568, 0.9649691790007782, 0.9651995356947132, 0.9654312239210366, 0.9656640345257972, 0.9658977376905069, 0.9661320843414578, 0.9663668079131659, 0.9666016264572074, 0.9668362450760232, 0.9670703586492495, 0.9673036548084315, 0.9675358171052365, 0.9677665283091601, 0.9679954737637889, 0.9682223447264301, 0.9684468416146467, 0.9686686770850869, 0.9688875788749307, 0.969103292344028, 0.9693155826659883, 0.9695242366285453, 0.9697290640168207, 0.9699298985669633, 0.9701265984913543, 0.9703190465894936, 0.9705071499702334, 0.9706908394207924, 0.970870068465593, 0.9710448121632769, 0.9712150656932355, 0.9713808427837262, 0.9715421740323554, 0.9716991051667034, 0.9718516952884712, 0.9720000151391545, 0.9721441454192552, 0.9722841751867779, 0.9724202003545587, 0.972552322300062, 0.972680646595887, 0.9728052818644556, 0.9729263387563182, 0.9730439290482211, 0.9731581648545349, 0.9732691579438102, 0.9733770191510263, 0.9734818578754834, 0.9735837816541444, 0.9736828958005008, 0.9737793030996176, 0.973873103550837], 'MSE': [27.399257558274289, 15.20525691358168, 14.032629735542894, 13.396824297110738, 12.907468259842803, 12.493263724126439, 12.122553290305664, 11.775675679109096, 11.439926417335327, 11.107541680858105, 10.774553096156563, 10.439912502812888, 10.104567201268855, 9.7704222699410597, 9.4393796610303511, 9.1127322415429362, 8.7910345060569508, 8.4743253149837283, 8.162465280701575, 7.8554150873583133, 7.5534004813834965, 7.2569804087480039, 6.967046108024455, 6.6847673226874669, 6.411493885106732, 6.148621512538762, 5.8974366408173555, 5.6589642495486725, 5.4338499088490657, 5.2223041173414577, 5.0241199598142039, 4.8387519655414986, 4.6654276457921826, 4.503260613726126, 4.3513430323738893, 4.2088080729315367, 4.0748638490318934, 3.9488062490930589, 3.8300194278013406, 3.7179709738951754, 3.6122057217453727, 3.5123393034449846, 3.4180507651081431, 3.3290731449570705, 3.2451815183265595, 3.1661790593625017, 3.0918825745213767, 3.022109368953747, 2.9566671243484737, 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"""
# Plot the MSE values
check_empty_rows_columns(M, fraction)
# We now run the VB algorithm on each of the M's for each fraction.
all_performances = {metric: [] for metric in metrics}
average_performances = {metric: []
for metric in metrics} # averaged over repeats
for (fraction, Ms, Ms_test) in zip(fractions_unknown, all_Ms, all_Ms_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, M_test) in zip(range(0, repeats), Ms, Ms_test):
print "Repeat %s of fraction %s." % (repeat + 1, fraction)
nmf = NMF(R, M, K)
nmf.initialise(init_UV, expo_prior)
nmf.run(iterations)
# Measure the performances
performances = nmf.predict(M_test)
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(
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(R, M, K)
nmf.initialise(init_UV, expo_prior)
nmf.run(iterations)
# 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
all_times_average = list(numpy.average(times_repeats, axis=0))
all_performances = performances_repeats[0]
def test_update():
I, J = 2, 4
R = [[1, 2, 0, 4], [5, 0, 7, 0]]
M = [[1, 1, 0, 1], [1, 0, 1, 0]]
K = 2
U = numpy.array([[1, 2], [3, 4]], dtype='f') #2x2
V = numpy.array([[5, 6], [7, 8], [9, 10], [11, 12]], dtype='f') #4x2
new_U = [[
1 * (1 * 5 / 17.0 + 2 * 7 / 23.0 + 4 * 11 / 35.0) / float(5 + 7 + 11),
2 * (1 * 6 / 17.0 + 2 * 8 / 23.0 + 4 * 12 / 35.0) / float(6 + 8 + 12)
],
[
3 * (5 * 5 / 39.0 + 7 * 9 / 67.0) / float(5 + 9),
4 * (5 * 6 / 39.0 + 7 * 10 / 67.0) / float(6 + 10)
]]
new_V = [[
5 * (1 * 1 / 17.0 + 3 * 5 / 39.0) / float(1 + 3),
6 * (2 * 1 / 17.0 + 4 * 5 / 39.0) / float(2 + 4)
], [7 * (1 * 2 / 23.0) / float(1), 8 * (2 * 2 / 23.0) / float(2)],
[9 * (3 * 7 / 67.0) / float(3), 10 * (4 * 7 / 67.0) / float(4)],
[11 * (1 * 4 / 35.0) / float(1), 12 * (2 * 4 / 35.0) / float(2)]]
nmf = NMF(R, M, K)
def reset():
nmf.U = numpy.copy(U)
nmf.V = numpy.copy(V)
for k in range(0, K):
reset()
nmf.update_U(k)
for i in range(0, I):
assert abs(new_U[i][k] - nmf.U[i, k]) < 0.00001
for k in range(0, K):
reset()
nmf.update_V(k)
for j in range(0, J):
assert abs(new_V[j][k] - nmf.V[j, k]) < 0.00001
# Also if I = J
I, J, K = 2, 2, 3
R = [[1, 2], [3, 4]]
M = [[1, 1], [0, 1]]
U = numpy.array([[1, 2, 3], [4, 5, 6]], dtype='f') #2x3
V = numpy.array([[7, 8, 9], [10, 11, 12]], dtype='f') #2x3
R_pred = numpy.array([[50, 68], [122, 167]], dtype='f') #2x2
nmf = NMF(R, M, K)
def reset_2():
nmf.U = numpy.copy(U)
nmf.V = numpy.copy(V)
for k in range(0, K):
reset_2()
nmf.update_U(k)
for i in range(0, I):
new_Uik = U[i][k] * sum( [V[j][k] * R[i][j] / R_pred[i,j] for j in range(0,J) if M[i][j] ]) \
/ sum( [V[j][k] for j in range(0,J) if M[i][j] ])
assert abs(new_Uik - nmf.U[i, k]) < 0.00001
for k in range(0, K):
reset_2()
nmf.update_V(k)
for j in range(0, J):
new_Vjk = V[j][k] * sum( [U[i][k] * R[i][j] / R_pred[i,j] for i in range(0,I) if M[i][j] ]) \
/ sum( [U[i][k] for i in range(0,I) if M[i][j] ])
assert abs(new_Vjk - nmf.V[j, k]) < 0.00001
def test_update():
I,J = 2,4
R = [[1,2,0,4],[5,0,7,0]]
M = [[1,1,0,1],[1,0,1,0]]
K = 2
U = numpy.array([[1,2],[3,4]],dtype='f') #2x2
V = numpy.array([[5,6],[7,8],[9,10],[11,12]],dtype='f') #4x2
new_U = [[
1 * ( 1*5/17.0 + 2*7/23.0 + 4*11/35.0 ) / float( 5+7+11 ),
2 * ( 1*6/17.0 + 2*8/23.0 + 4*12/35.0 ) / float( 6+8+12 )
],[
3 * ( 5*5/39.0 + 7*9/67.0 ) / float( 5+9 ),
4 * ( 5*6/39.0 + 7*10/67.0 ) / float( 6+10 )
]]
new_V = [[
5 * ( 1*1/17.0 + 3*5/39.0 ) / float( 1+3 ),
6 * ( 2*1/17.0 + 4*5/39.0 ) / float( 2+4 )
],[
7 * ( 1*2/23.0 ) / float( 1 ),
8 * ( 2*2/23.0 ) / float( 2 )
],[
9 * ( 3*7/67.0 ) / float( 3 ),
10 * ( 4*7/67.0 ) / float( 4 )
],[
11 * ( 1*4/35.0 ) / float( 1 ),
12 * ( 2*4/35.0 ) / float( 2 )
]]
nmf = NMF(R,M,K)
def reset():
nmf.U = numpy.copy(U)
nmf.V = numpy.copy(V)
for k in range(0,K):
reset()
nmf.update_U(k)
for i in range(0,I):
assert abs(new_U[i][k] - nmf.U[i,k]) < 0.00001
for k in range(0,K):
reset()
nmf.update_V(k)
for j in range(0,J):
assert abs(new_V[j][k] - nmf.V[j,k]) < 0.00001
# Also if I = J
I,J,K = 2,2,3
R = [[1,2],[3,4]]
M = [[1,1],[0,1]]
U = numpy.array([[1,2,3],[4,5,6]],dtype='f') #2x3
V = numpy.array([[7,8,9],[10,11,12]],dtype='f') #2x3
R_pred = numpy.array([[50,68],[122,167]],dtype='f') #2x2
nmf = NMF(R,M,K)
def reset_2():
nmf.U = numpy.copy(U)
nmf.V = numpy.copy(V)
for k in range(0,K):
reset_2()
nmf.update_U(k)
for i in range(0,I):
new_Uik = U[i][k] * sum( [V[j][k] * R[i][j] / R_pred[i,j] for j in range(0,J) if M[i][j] ]) \
/ sum( [V[j][k] for j in range(0,J) if M[i][j] ])
assert abs(new_Uik - nmf.U[i,k]) < 0.00001
for k in range(0,K):
reset_2()
nmf.update_V(k)
for j in range(0,J):
new_Vjk = V[j][k] * sum( [U[i][k] * R[i][j] / R_pred[i,j] for i in range(0,I) if M[i][j] ]) \
/ sum( [U[i][k] for i in range(0,I) if M[i][j] ])
assert abs(new_Vjk - nmf.V[j,k]) < 0.00001
# 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(R, M, K)
nmf.initialise(init_UV, expo_prior)
nmf.run(iterations)
# 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
all_times_average = list(numpy.average(times_repeats, axis=0))
all_performances = performances_repeats[0]
print "np_all_times_average = %s" % all_times_average
