def var_bound(data, modelState, queryState):
'''
Determines the variational bounds. Values are mutated in place, but are
reset afterwards to their initial values. So it's safe to call in a serial
manner.
'''
# Unpack the the structs, for ease of access and efficiency
W = data.words
D,_ = W.shape
means, expMeans, varcs, docLens = queryState.means, queryState.expMeans, queryState.varcs, queryState.docLens
K, topicMean, sigT, vocab, vocabPrior, A = modelState.K, modelState.topicMean, modelState.sigT, modelState.vocab, modelState.vocabPrior, modelState.A
# Calculate some implicit variables
isigT = la.inv(sigT)
bound = 0
if USE_NIW_PRIOR:
pseudoObsMeans = K + NIW_PSEUDO_OBS_MEAN
pseudoObsVar = K + NIW_PSEUDO_OBS_VAR
# distribution over topic covariance
bound -= 0.5 * K * pseudoObsVar * log(NIW_PSI)
bound -= 0.5 * K * pseudoObsVar * log(2)
bound -= fns.multigammaln(pseudoObsVar / 2., K)
bound -= 0.5 * (pseudoObsVar + K - 1) * safe_log_det(sigT)
bound += 0.5 * NIW_PSI * np.trace(isigT)
# and its entropy
# is a constant which we skip
# distribution over means
bound -= 0.5 * K * log(1./pseudoObsMeans) * safe_log_det(sigT)
bound -= 0.5 / pseudoObsMeans * (topicMean).T.dot(isigT).dot(topicMean)
# and its entropy
bound += 0.5 * safe_log_det(sigT) # + a constant
# Distribution over document topics
bound -= (D*K)/2. * LN_OF_2_PI
bound -= D/2. * la.det(sigT)
diff = means - topicMean[np.newaxis,:]
bound -= 0.5 * np.sum (diff.dot(isigT) * diff)
bound -= 0.5 * np.sum(varcs * np.diag(isigT)[np.newaxis,:]) # = -0.5 * sum_d tr(V_d \Sigma^{-1}) when V_d is diagonal only.
# And its entropy
# bound += 0.5 * D * K * LN_OF_2_PI_E + 0.5 * np.sum(np.log(varcs))
# Distribution over word-topic assignments and words and the formers
# entropy. This is somewhat jumbled to avoid repeatedly taking the
# exp and log of the means
expMeans = np.exp(means - means.max(axis=1)[:,np.newaxis], out=expMeans)
R = sparseScalarQuotientOfDot(W, expMeans, vocab) # D x V [W / TB] is the quotient of the original over the reconstructed doc-term matrix
V = expMeans * (R.dot(vocab.T)) # D x K
bound += np.sum(docLens * np.log(np.sum(expMeans, axis=1)))
bound += np.sum(sparseScalarProductOfSafeLnDot(W, expMeans, vocab).data)
bound += np.sum(means * V)
bound += np.sum(2 * ssp.diags(docLens,0) * means.dot(A) * means)
bound -= 2. * scaledSelfSoftDot(means, docLens)
bound -= 0.5 * np.sum(docLens[:,np.newaxis] * V * (np.diag(A))[np.newaxis,:])
bound -= np.sum(means * V)
return bound
def var_bound(data, modelState, queryState, XTX=None):
'''
Determines the variational bounds. Values are mutated in place, but are
reset afterwards to their initial values. So it's safe to call in a serial
manner.
'''
# Unpack the the structs, for ease of access and efficiency
W, X = data.words, data.feats
D, _ = W.shape
means, expMeans, varcs, docLens = queryState.means, queryState.expMeans, queryState.varcs, queryState.docLens
F, P, K, A, R_A, fv, Y, R_Y, lfv, V, sigT, vocab, Ab, dtype = modelState.F, modelState.P, modelState.K, modelState.A, modelState.R_A, modelState.fv, modelState.Y, modelState.R_Y, modelState.lfv, modelState.V, modelState.sigT, modelState.vocab, modelState.Ab, modelState.dtype
# Calculate some implicit variables
isigT = la.inv(sigT)
lnDetSigT = lnDetOfDiagMat(sigT)
verifyProper(lnDetSigT, "lnDetSigT")
if XTX is None:
XTX = X.T.dot(X)
bound = 0
# Distribution over latent space
bound -= (P * K) / 2. * LN_OF_2_PI
bound -= P * lnDetSigT
bound -= K * P * log(lfv)
bound -= 0.5 * np.sum(1. / lfv * isigT.dot(Y) * Y)
bound -= 0.5 * K * np.trace(R_Y)
# And its entropy
detR_Y = safeDet(R_Y, "R_Y")
bound += 0.5 * LN_OF_2_PI_E + P / 2. * lnDetSigT + K / 2. * log(detR_Y)
# Distribution over mapping from features to topics
diff = (A - Y.dot(V))
bound -= (F * K) / 2. * LN_OF_2_PI
bound -= F * lnDetSigT
bound -= K * P * log(fv)
bound -= 0.5 * np.sum(1. / lfv * isigT.dot(diff) * diff)
bound -= 0.5 * K * np.trace(R_A)
# And its entropy
detR_A = safeDet(R_A, "R_A")
bound += 0.5 * LN_OF_2_PI_E + F / 2. * lnDetSigT + K / 2. * log(detR_A)
# Distribution over document topics
bound -= (D * K) / 2. * LN_OF_2_PI
bound -= D / 2. * lnDetSigT
diff = means - X.dot(A.T)
bound -= 0.5 * np.sum(diff.dot(isigT) * diff)
bound -= 0.5 * np.sum(
varcs * np.diag(isigT)[np.newaxis, :]
) # = -0.5 * sum_d tr(V_d \Sigma^{-1}) when V_d is diagonal only.
bound -= 0.5 * K * np.trace(XTX.dot(R_A))
# And its entropy
bound += 0.5 * D * K * LN_OF_2_PI_E + 0.5 * np.sum(np.log(varcs))
# Distribution over word-topic assignments, and their entropy
# and distribution over words. This is re-arranged as we need
# means for some parts, and exp(means) for other parts
expMeans = np.exp(means - means.max(axis=1)[:, np.newaxis], out=expMeans)
R = sparseScalarQuotientOfDot(
W, expMeans, vocab
) # D x V [W / TB] is the quotient of the original over the reconstructed doc-term matrix
S = expMeans * (R.dot(vocab.T)) # D x K
bound += np.sum(docLens * np.log(np.sum(expMeans, axis=1)))
bound += np.sum(sparseScalarProductOfSafeLnDot(W, expMeans, vocab).data)
bound += np.sum(means * S)
bound += np.sum(2 * ssp.diags(docLens, 0) * means.dot(Ab) * means)
bound -= 2. * scaledSelfSoftDot(means, docLens)
bound -= 0.5 * np.sum(docLens[:, np.newaxis] * S *
(np.diag(Ab))[np.newaxis, :])
bound -= np.sum(means * S)
return bound
def var_bound(data, modelState, queryState):
'''
Determines the variational bounds. Values are mutated in place, but are
reset afterwards to their initial values. So it's safe to call in repeatedly.
'''
# Unpack the the structs, for ease of access and efficiency
W, X = data.words, data.feats
D, T, F = W.shape[0], W.shape[1], X.shape[1]
means, docLens = queryState.means, queryState.docLens
K, A, U, Y, V, covA, tv, ltv, fv, lfv, vocab, vocabPrior, dtype = \
modelState.K, modelState.A, modelState.U, modelState.Y, modelState.V, modelState.covA, modelState.tv, modelState.ltv, modelState.fv, modelState.lfv, modelState.vocab, modelState.vocabPrior, modelState.dtype
H = 0.5 * (np.eye(K) - np.ones((K, K), dtype=dtype) / K)
Log2Pi = log(2 * pi)
bound = 0
# U and V are parameters with no distribution
#
# Y has a normal distribution, it's covariance is unfortunately an expensive computation
#
P, Q = U.shape[1], V.shape[1]
covY = np.eye(P * Q) * (lfv * ltv)
covY += np.kron(V.T.dot(V), U.T.dot(U))
covY = la.inv(covY, overwrite_a=True)
# The expected likelihood of Y
bound -= 0.5 * P * Q * Log2Pi
bound -= 0.5 * P * Q * log(ltv * lfv)
bound -= 0.5 / (lfv * ltv) * np.sum(
Y * Y) # 5x faster than np.trace(Y.dot(Y.T))
bound -= 0.5 * np.trace(covY) * (lfv * ltv)
# the traces of the posterior+prior covariance products cancel out across likelihoods
# The entropy of Y
bound += 0.5 * P * Q * (Log2Pi + 1) + 0.5 * safe_log_det(covY)
#
# A has a normal distribution/
#
F, K = A.shape[0], A.shape[1]
diff = A - U.dot(Y).dot(V.T)
diff *= diff
# The expected likelihood of A
bound -= 0.5 * K * F * Log2Pi
bound -= 0.5 * K * F * log(tv * fv)
bound -= 0.5 / (fv * tv) * np.sum(diff)
# The entropy of A
bound += 0.5 * F * K * (Log2Pi + 1) + 0.5 * K * safe_log_det(covA)
#
# Theta, the matrix of means, has a normal distribution. Its row-covarince is diagonal
# (i.e. it's several independent multi-var normal distros). The posterior is made
# up of D K-dimensional normals with diagonal covariances
#
# We iterate through the topics in batches, to control memory use
batchSize = min(BatchSize, D)
batchCount = ceil(D / batchSize)
feats = np.ndarray(shape=(batchSize, F), dtype=dtype)
tops = np.ndarray(shape=(batchSize, K), dtype=dtype)
trace = 0
for b in range(0, batchCount):
start = b * batchSize
end = min(start + batchSize, D)
batchSize = min(batchSize, end - start)
feats[:batchSize, :] = X[start:end, :].toarray()
np.dot(feats[:batchSize, :], A, out=tops[:batchSize, :])
tops[:batchSize, :] -= means[start:end, :]
tops[:batchSize, :] *= tops[:batchSize, :]
trace += np.sum(tops[:batchSize, :])
feats = None
# The expected likelihood of the topic-assignments
bound -= 0.5 * D * K * Log2Pi
bound -= 0.5 * D * K * log(tv)
bound -= 0.5 / tv * trace
bound -= 0.5 * tv * np.sum(covA) # this trace doesn't cancel as we
# don't have a posterior on tv
# The entropy of the topic-assignments
bound += 0.5 * D * K * (Log2Pi + 1) + 0.5 * np.sum(covA)
# Distribution over word-topic assignments and words and the formers
# entropy. This is somewhat jumbled to avoid repeatedly taking the
# exp and log of the means
# Again we batch this for safety
batchSize = min(BatchSize, D)
batchCount = ceil(D / batchSize)
V = np.ndarray(shape=(batchSize, K), dtype=dtype)
for b in range(0, batchCount):
start = b * batchSize
end = min(start + batchSize, D)
batchSize = min(batchSize, end - start)
meansBatch = means[start:end, :]
docLensBatch = docLens[start:end]
np.exp(meansBatch - meansBatch.max(axis=1)[:, np.newaxis],
out=tops[:batchSize, :])
expMeansBatch = tops[:batchSize, :]
R = sparseScalarQuotientOfDot(
W, expMeansBatch, vocab, start=start, end=end
) # BatchSize x V: [W / TB] is the quotient of the original over the reconstructed doc-term matrix
V[:batchSize, :] = expMeansBatch * (R[:batchSize, :].dot(vocab.T)
) # BatchSize x K
VBatch = V[:batchSize, :]
bound += np.sum(docLensBatch * np.log(np.sum(expMeansBatch, axis=1)))
bound += np.sum(
sparseScalarProductOfSafeLnDot(W,
expMeansBatch,
vocab,
start=start,
end=end).data)
bound += np.sum(meansBatch * VBatch)
bound += np.sum(2 * ssp.diags(docLensBatch, 0) * meansBatch.dot(H) *
meansBatch)
bound -= 2. * scaledSelfSoftDot(meansBatch, docLensBatch)
bound -= 0.5 * np.sum(docLensBatch[:, np.newaxis] * VBatch *
(np.diag(H))[np.newaxis, :])
bound -= np.sum(meansBatch * VBatch)
return bound
def var_bound(data, modelState, queryState):
'''
Determines the variational bounds. Values are mutated in place, but are
reset afterwards to their initial values. So it's safe to call in a serial
manner.
'''
# Unpack the the structs, for ease of access and efficiency
W, L, X = data.words, data.links, data.feats
D, _ = W.shape
means, varcs, docLens = queryState.means, queryState.varcs, queryState.docLens
K, topicMean, topicCov, vocab, A = modelState.K, modelState.topicMean, modelState.topicCov, modelState.vocab, modelState.A
# Calculate some implicit variables
itopicCov = la.inv(topicCov)
bound = 0
expMeansOut = np.exp(means - means.max(axis=1)[:, np.newaxis])
expMeansIn = np.exp(means - means.max(axis=0)[np.newaxis, :])
lse_at_k = expMeansIn.sum(axis=0)
if USE_NIW_PRIOR:
pseudoObsMeans = K + NIW_PSEUDO_OBS_MEAN
pseudoObsVar = K + NIW_PSEUDO_OBS_VAR
# distribution over topic covariance
bound -= 0.5 * K * pseudoObsVar * log(NIW_PSI)
bound -= 0.5 * K * pseudoObsVar * log(2)
bound -= fns.multigammaln(pseudoObsVar / 2., K)
bound -= 0.5 * (pseudoObsVar + K - 1) * safe_log_det(topicCov)
bound += 0.5 * NIW_PSI * np.trace(itopicCov)
# and its entropy
# is a constant which we skip
# distribution over means
bound -= 0.5 * K * log(1. / pseudoObsMeans) * safe_log_det(topicCov)
bound -= 0.5 / pseudoObsMeans * (
topicMean).T.dot(itopicCov).dot(topicMean)
# and its entropy
bound += 0.5 * safe_log_det(topicCov) # + a constant
# Distribution over document topics
bound -= (D * K) / 2. * LN_OF_2_PI
bound -= D / 2. * la.det(topicCov)
diff = means - topicMean[np.newaxis, :]
bound -= 0.5 * np.sum(diff.dot(itopicCov) * diff)
bound -= 0.5 * np.sum(
varcs * np.diag(itopicCov)[np.newaxis, :]
) # = -0.5 * sum_d tr(V_d \Sigma^{-1}) when V_d is diagonal only.
# And its entropy
# bound += 0.5 * D * K * LN_OF_2_PI_E + 0.5 * np.sum(np.log(varcs))
# Distribution over word-topic assignments and words and the formers
# entropy, and similaarly for out-links. This is somewhat jumbled to
# avoid repeatedly taking the exp and log of the means
W_weights = sparseScalarQuotientOfDot(
W, expMeansOut, vocab
) # D x V [W / TB] is the quotient of the original over the reconstructed doc-term matrix
w_top_sums = expMeansOut * (W_weights.dot(vocab.T)) # D x K
L_weights = sparseScalarQuotientOfNormedDot(L, expMeansOut, expMeansIn,
lse_at_k)
l_top_sums = L_weights.dot(expMeansIn) / lse_at_k[
np.newaxis, :] * expMeansOut
bound += np.sum(docLens * np.log(np.sum(expMeansOut, axis=1)))
bound += np.sum(sparseScalarProductOfSafeLnDot(W, expMeansOut, vocab).data)
# means = np.log(expMeans, out=expMeans)
#means = safe_log(expMeansOut, out=means)
bound += np.sum(means * w_top_sums)
bound += np.sum(2 * ssp.diags(docLens, 0) * means.dot(A) * means)
bound -= 2. * scaledSelfSoftDot(means, docLens)
bound -= 0.5 * np.sum(docLens[:, np.newaxis] * w_top_sums *
(np.diag(A))[np.newaxis, :])
bound -= np.sum(means * w_top_sums)
return bound