def testLikelihoodOnModelDerivedExample(self):
print("Cross-validated likelihoods on model-derived example")
rd.seed(0xBADB055) # Global init for repeatable test
modelState, _, _, _, X, W = sampleFromModel()
D, T, K, Q, F, P = X.shape[0], modelState.T, modelState.K, modelState.Q, modelState.F, modelState.P
# Create the cross-validation folds
folds = 5
foldSize = ceil(D / 5)
querySize = foldSize
trainSize = D - querySize
for fold in range(folds):
start = fold * foldSize
end = start + trainSize
trainSet = np.arange(start,end) % D
querySet = np.arange(end, end + querySize) % D
X_train, W_train = X[trainSet,:], W[trainSet,:]
X_query, W_query = X[querySet,:], W[querySet,:]
modelState = newVbModelState(K, Q, F, P, T)
modelState, queryState = train(modelState, X_train, W_train, iterations=100, logInterval=10, plotInterval=100)
trainSetLikely = log_likelihood(modelState, X_train, W_train, queryState)
queryState = query(modelState, X_query, W_query, iterations=50, epsilon=0.001, logInterval = 10, plotInterval = 100)
querySetLikely = log_likelihood(modelState, X_query, W_query, queryState)
print("Fold %d: Train-set Likelihood: %12f \t Query-set Likelihood: %12f" % (fold, trainSetLikely, querySetLikely))
print("End of Test")
def _testInferenceFromHandcraftedExampleWithKEqualingQ(self):
print ("Fully handcrafted example, K=Q")
rd.seed(0xC0FFEE) # Global init for repeatable test
T = 100 # Vocabulary size, the number of "terms". Must be a square number
Q = 6 # Topics: This cannot be changed without changing the code that generates the vocabulary
K = 6 # Observed topics
P = 8 # Features
F = 12 # Observed features
D = 200 # Sample documents (each with associated features)
avgWordsPerDoc = 500
# The vocabulary. Presented graphically there are two with horizontal bands
# (upper lower); two with vertical bands (left, right); and two with
# horizontal bands (inside, outside)
vocab = makeSixTopicVocab(T)
# Create our (sparse) features X, then our topic proportions ("tpcs")
# then our word counts W
lmda = np.zeros((D,K))
X = np.zeros((D,F))
for d in range(D):
for _ in range(3):
lmda[d,rd.randint(K)] += 1./3
for _ in range(int(F/3)):
X[d,rd.randint(F)] += 1
A = rd.random((K,F))
X = lmda.dot(la.pinv(A).T)
X = ssp.csr_matrix(X)
tpcs = lmda
docLens = rd.poisson(avgWordsPerDoc, (D,))
W = tpcs.dot(vocab)
W *= docLens[:, np.newaxis]
W = np.array(W, dtype=np.int32) # truncate word counts to integers
W = ssp.csr_matrix(W)
#
# Now finally try to train the model
#
modelState = newVbModelState(K, Q, F, P, T)
(trainedState, queryState) = train (modelState, X, W, logInterval=1, iterations=1)
tpcs_inf = rowwise_softmax(safe_log(queryState.expLmda))
W_inf = np.array(tpcs_inf.dot(trainedState.vocab) * queryState.docLen[:,np.newaxis], dtype=np.int32)
priorReconsError = np.sum(np.square(W - W_inf)) / D
(trainedState, queryState) = train (modelState, X, W, logInterval=1, plotInterval = 100, iterations=130)
tpcs_inf = rowwise_softmax(safe_log(queryState.expLmda))
W_inf = np.array(tpcs_inf.dot(trainedState.vocab) * queryState.docLen[:,np.newaxis], dtype=np.int32)
print ("Model Driven: Prior Reconstruction Error: %f" % (priorReconsError,))
print ("Model Driven: Final Reconstruction Error: %f" % (np.sum(np.square(W - W_inf)) / D,))
print("End of Test")
def _testInferenceFromHandcraftedExample(self):
print ("Partially hand-crafted example")
rd.seed(0xC0FFEE) # Global init for repeatable test
T = 100 # Vocabulary size, the number of "terms". Must be a square number
Q = 6 # Topics: This cannot be changed without changing the code that generates the vocabulary
K = 10 # Observed topics
P = 8 # Features
F = 12 # Observed features
D = 200 # Sample documents (each with associated features)
avgWordsPerDoc = 500
# Determine what A, U, Y and V should be
U = rd.random((K,Q))
Y = rd.random((Q,P))
V = rd.random((F,P))
A = U.dot(Y).dot(V.T)
# The vocabulary. Presented graphically there are two with horizontal bands
# (upper lower); two with vertical bands (left, right); and two with
# horizontal bands (inside, outside)
vocab = makeSixTopicVocab(T)
# Create our (sparse) features X, then our topic proportions ("tpcs")
# then our word counts W
X_low = np.array([1 if rd.random() < 0.3 else 0 for _ in range(D*P)]).reshape(D,P)
X = ssp.csr_matrix(X_low.dot(V.T))
lmda_low = X_low.dot(Y.T)
print ("lmda_low.mean() = %f" % (lmda_low.mean()))
tpcs = rowwise_softmax (lmda_low)
docLens = rd.poisson(avgWordsPerDoc, (D,))
W = tpcs.dot(vocab)
W *= docLens[:, np.newaxis]
W = np.array(W, dtype=np.int32) # truncate word counts to integers
W = ssp.csr_matrix(W)
#
# Now finally try to train the model
#
modelState = newVbModelState(K, Q, F, P, T)
(trainedState, queryState) = train (modelState, X, W, logInterval=1, plotInterval = 10, iterations=10)
tpcs_inf = rowwise_softmax(queryState.lmda)
W_inf = np.array(tpcs_inf.dot(trainedState.vocab) * queryState.docLen[:,np.newaxis], dtype=np.int32)
print("Handcrafted Test-Case")
print("=====================================================================")
print("Average, squared, per-element difference between true and estimated:")
print(" Topic Distribution: %f" % (np.sum(np.square(tpcs.dot(U.T) - tpcs_inf)) / len(tpcs),))
print(" Vocab Distribution: %f" % (np.sum(np.square(U.dot(vocab) - trainedState.vocab)) / len(vocab),))
print("Average absolute difference between true and reconstructed documents")
print(" Documents: %f" % (np.sum(np.abs(W.todense() - W_inf)) / np.sum(W.todense()),))
print("End of Test")
def testLikelihoodOnModelDerivedExample(self):
print("Cross-validated likelihoods on model-derived example")
rd.seed(0xBADB055) # Global init for repeatable test
modelState, _, _, _, X, W = sampleFromModel()
D, T, K, Q, F, P = X.shape[
0], modelState.T, modelState.K, modelState.Q, modelState.F, modelState.P
# Create the cross-validation folds
folds = 5
foldSize = ceil(D / 5)
querySize = foldSize
trainSize = D - querySize
for fold in range(folds):
start = fold * foldSize
end = start + trainSize
trainSet = np.arange(start, end) % D
querySet = np.arange(end, end + querySize) % D
X_train, W_train = X[trainSet, :], W[trainSet, :]
X_query, W_query = X[querySet, :], W[querySet, :]
modelState = newVbModelState(K, Q, F, P, T)
modelState, queryState = train(modelState,
X_train,
W_train,
iterations=100,
logInterval=10,
plotInterval=100)
trainSetLikely = log_likelihood(modelState, X_train, W_train,
queryState)
queryState = query(modelState,
X_query,
W_query,
iterations=50,
epsilon=0.001,
logInterval=10,
plotInterval=100)
querySetLikely = log_likelihood(modelState, X_query, W_query,
queryState)
print(
"Fold %d: Train-set Likelihood: %12f \t Query-set Likelihood: %12f"
% (fold, trainSetLikely, querySetLikely))
print("End of Test")
def _testInferenceFromHandcraftedExampleWithKEqualingQ(self):
print("Fully handcrafted example, K=Q")
rd.seed(0xC0FFEE) # Global init for repeatable test
T = 100 # Vocabulary size, the number of "terms". Must be a square number
Q = 6 # Topics: This cannot be changed without changing the code that generates the vocabulary
K = 6 # Observed topics
P = 8 # Features
F = 12 # Observed features
D = 200 # Sample documents (each with associated features)
avgWordsPerDoc = 500
# The vocabulary. Presented graphically there are two with horizontal bands
# (upper lower); two with vertical bands (left, right); and two with
# horizontal bands (inside, outside)
vocab = makeSixTopicVocab(T)
# Create our (sparse) features X, then our topic proportions ("tpcs")
# then our word counts W
lmda = np.zeros((D, K))
X = np.zeros((D, F))
for d in range(D):
for _ in range(3):
lmda[d, rd.randint(K)] += 1. / 3
for _ in range(int(F / 3)):
X[d, rd.randint(F)] += 1
A = rd.random((K, F))
X = lmda.dot(la.pinv(A).T)
X = ssp.csr_matrix(X)
tpcs = lmda
docLens = rd.poisson(avgWordsPerDoc, (D, ))
W = tpcs.dot(vocab)
W *= docLens[:, np.newaxis]
W = np.array(W, dtype=np.int32) # truncate word counts to integers
W = ssp.csr_matrix(W)
#
# Now finally try to train the model
#
modelState = newVbModelState(K, Q, F, P, T)
(trainedState, queryState) = train(modelState,
X,
W,
logInterval=1,
iterations=1)
tpcs_inf = rowwise_softmax(safe_log(queryState.expLmda))
W_inf = np.array(tpcs_inf.dot(trainedState.vocab) *
queryState.docLen[:, np.newaxis],
dtype=np.int32)
priorReconsError = np.sum(np.square(W - W_inf)) / D
(trainedState, queryState) = train(modelState,
X,
W,
logInterval=1,
plotInterval=100,
iterations=130)
tpcs_inf = rowwise_softmax(safe_log(queryState.expLmda))
W_inf = np.array(tpcs_inf.dot(trainedState.vocab) *
queryState.docLen[:, np.newaxis],
dtype=np.int32)
print("Model Driven: Prior Reconstruction Error: %f" %
(priorReconsError, ))
print("Model Driven: Final Reconstruction Error: %f" %
(np.sum(np.square(W - W_inf)) / D, ))
print("End of Test")
def sampleFromModel(D=200, T=100, K=10, Q=6, F=12, P=8, avgWordsPerDoc=500):
'''
Create a test dataset according to the model
Params:
T - Vocabulary size, the number of "terms". Must be a square number
Q - Latent Topics:
K - Observed topics
P - Latent features
F - Observed features
D - Sample documents (each with associated features)
avgWordsPerDoc - average number of words per document generated (Poisson)
Returns:
modelState - a model state object configured for training
tpcs - the matrix of per-document topic distribution
vocab - the matrix of per-topic word distributions
docLens - the vector of document lengths
X - the DxF side information matrix
W - The DxW word matrix
'''
# Generate vocab
beta = 0.1
betaVec = np.ndarray((T, ))
betaVec.fill(beta)
vocab = np.zeros((K, T))
for k in range(K):
vocab[k, :] = rd.dirichlet(betaVec)
# Generate U, then V, then A
tau = 0.1
tsq = tau * tau
(vSdRow, vSdCol) = (5.0, 5.0)
(uSdRow, uSdCol) = (5.0, tau**2) # For the K-dimensions we use tsq
(ySdRow, ySdCol) = (5.0, 5.0)
(aSdRow, aSdCol) = (5.0, tau**2)
U = np.abs(
matrix_normal(np.zeros((K, Q)), uSdRow * np.eye(Q),
uSdCol * np.eye(K)))
Y = np.abs(
matrix_normal(np.zeros((Q, P)), ySdRow * np.eye(P),
ySdCol * np.eye(Q)))
V = np.abs(
matrix_normal(np.zeros((F, P)), vSdRow * np.eye(P),
vSdCol * np.eye(F)))
A = np.abs(
matrix_normal(
U.dot(Y).dot(V.T), aSdRow * np.eye(F), aSdCol * np.eye(K)))
# Generate the input features. Assume the features are multinomial and sparse
# (not quite a perfect match for the twitter example: twitter is binary, this
# may not be)
featuresDist = [1. / P] * P
maxNonZeroFeatures = 3
X_low = np.zeros((D, P), dtype=np.float32)
for d in range(D):
X_low[d, :] = rd.multinomial(maxNonZeroFeatures, featuresDist)
X = np.round(X_low.dot(V.T))
X = ssp.csr_matrix(X)
# Use the features and the matrix A to generate the topics and documents
tpcs = rowwise_softmax(X.dot(A.T))
docLens = rd.poisson(avgWordsPerDoc, (D, )).astype(np.float32)
W = tpcs.dot(vocab)
W *= docLens[:, np.newaxis]
W = np.array(W, dtype=np.int32) # truncate word counts to integers
W = ssp.csr_matrix(W)
# Initialise the model
modelState = newVbModelState(K, Q, F, P, T)
# Return the initialised model, the true parameter values, and the
# generated observations
return modelState, tpcs, vocab, docLens, X, W
def _testInferenceFromHandcraftedExample(self):
print("Partially hand-crafted example")
rd.seed(0xC0FFEE) # Global init for repeatable test
T = 100 # Vocabulary size, the number of "terms". Must be a square number
Q = 6 # Topics: This cannot be changed without changing the code that generates the vocabulary
K = 10 # Observed topics
P = 8 # Features
F = 12 # Observed features
D = 200 # Sample documents (each with associated features)
avgWordsPerDoc = 500
# Determine what A, U, Y and V should be
U = rd.random((K, Q))
Y = rd.random((Q, P))
V = rd.random((F, P))
A = U.dot(Y).dot(V.T)
# The vocabulary. Presented graphically there are two with horizontal bands
# (upper lower); two with vertical bands (left, right); and two with
# horizontal bands (inside, outside)
vocab = makeSixTopicVocab(T)
# Create our (sparse) features X, then our topic proportions ("tpcs")
# then our word counts W
X_low = np.array([1 if rd.random() < 0.3 else 0
for _ in range(D * P)]).reshape(D, P)
X = ssp.csr_matrix(X_low.dot(V.T))
lmda_low = X_low.dot(Y.T)
print("lmda_low.mean() = %f" % (lmda_low.mean()))
tpcs = rowwise_softmax(lmda_low)
docLens = rd.poisson(avgWordsPerDoc, (D, ))
W = tpcs.dot(vocab)
W *= docLens[:, np.newaxis]
W = np.array(W, dtype=np.int32) # truncate word counts to integers
W = ssp.csr_matrix(W)
#
# Now finally try to train the model
#
modelState = newVbModelState(K, Q, F, P, T)
(trainedState, queryState) = train(modelState,
X,
W,
logInterval=1,
plotInterval=10,
iterations=10)
tpcs_inf = rowwise_softmax(queryState.lmda)
W_inf = np.array(tpcs_inf.dot(trainedState.vocab) *
queryState.docLen[:, np.newaxis],
dtype=np.int32)
print("Handcrafted Test-Case")
print(
"====================================================================="
)
print(
"Average, squared, per-element difference between true and estimated:"
)
print(" Topic Distribution: %f" %
(np.sum(np.square(tpcs.dot(U.T) - tpcs_inf)) / len(tpcs), ))
print(" Vocab Distribution: %f" %
(np.sum(np.square(U.dot(vocab) - trainedState.vocab)) /
len(vocab), ))
print(
"Average absolute difference between true and reconstructed documents"
)
print(" Documents: %f" %
(np.sum(np.abs(W.todense() - W_inf)) / np.sum(W.todense()), ))
print("End of Test")
def sampleFromModel(D=200, T=100, K=10, Q=6, F=12, P=8, avgWordsPerDoc = 500):
'''
Create a test dataset according to the model
Params:
T - Vocabulary size, the number of "terms". Must be a square number
Q - Latent Topics:
K - Observed topics
P - Latent features
F - Observed features
D - Sample documents (each with associated features)
avgWordsPerDoc - average number of words per document generated (Poisson)
Returns:
modelState - a model state object configured for training
tpcs - the matrix of per-document topic distribution
vocab - the matrix of per-topic word distributions
docLens - the vector of document lengths
X - the DxF side information matrix
W - The DxW word matrix
'''
# Generate vocab
beta = 0.1
betaVec = np.ndarray((T,))
betaVec.fill(beta)
vocab = np.zeros((K,T))
for k in range(K):
vocab[k,:] = rd.dirichlet(betaVec)
# Generate U, then V, then A
tau = 0.1
tsq = tau * tau
(vSdRow, vSdCol) = (5.0, 5.0)
(uSdRow, uSdCol) = (5.0, tau**2) # For the K-dimensions we use tsq
(ySdRow, ySdCol) = (5.0, 5.0)
(aSdRow, aSdCol) = (5.0, tau**2)
U = np.abs(matrix_normal(np.zeros((K,Q)), uSdRow * np.eye(Q), uSdCol * np.eye(K)))
Y = np.abs(matrix_normal(np.zeros((Q,P)), ySdRow * np.eye(P), ySdCol * np.eye(Q)))
V = np.abs(matrix_normal(np.zeros((F,P)), vSdRow * np.eye(P), vSdCol * np.eye(F)))
A = np.abs(matrix_normal(U.dot(Y).dot(V.T), aSdRow * np.eye(F), aSdCol * np.eye(K)))
# Generate the input features. Assume the features are multinomial and sparse
# (not quite a perfect match for the twitter example: twitter is binary, this
# may not be)
featuresDist = [1. / P] * P
maxNonZeroFeatures = 3
X_low = np.zeros((D,P), dtype=np.float32)
for d in range(D):
X_low[d,:] = rd.multinomial(maxNonZeroFeatures, featuresDist)
X = np.round(X_low.dot(V.T))
X = ssp.csr_matrix(X)
# Use the features and the matrix A to generate the topics and documents
tpcs = rowwise_softmax (X.dot(A.T))
docLens = rd.poisson(avgWordsPerDoc, (D,)).astype(np.float32)
W = tpcs.dot(vocab)
W *= docLens[:, np.newaxis]
W = np.array(W, dtype=np.int32) # truncate word counts to integers
W = ssp.csr_matrix(W)
# Initialise the model
modelState = newVbModelState(K, Q, F, P, T)
# Return the initialised model, the true parameter values, and the
# generated observations
return modelState, tpcs, vocab, docLens, X, W