def testOnRealData(self):
dtype = np.float64 # DTYPE
rd.seed(0xBADB055)
data = DataSet.from_files(words_file=NipsWordsPath, links_file=NipsCitePath)
with open(NipsDictPath, "rb") as f:
d = pkl.load(f)
data.convert_to_dtype(dtype)
data.prune_and_shuffle(min_doc_len=50, min_link_count=0)
# IDF frequency for when we print out the vocab later
freq = np.squeeze(np.asarray(data.words.sum(axis=0)))
scale = np.reciprocal(1 + freq)
# Initialise the model
K = 10
model = lda.newModelAtRandom(data, K, dtype=dtype)
queryState = lda.newQueryState(data, model)
trainPlan = lda.newTrainPlan(iterations=30, logFrequency=2, debug=False, batchSize=50, rate_retardation=1, forgetting_rate=0.75)
# Train the model, and the immediately save the result to a file for subsequent inspection
model, query, (bndItrs, bndVals, bndLikes) = lda.train (data, model, queryState, trainPlan)
# with open(newModelFileFromModel(model), "wb") as f:
# pkl.dump ((model, query, (bndItrs, bndVals, bndLikes)), f)
# Plot the evolution of the bound during training.
fig, ax1 = plt.subplots()
ax1.plot(bndItrs, bndVals, 'b-')
ax1.set_xlabel('Iterations')
ax1.set_ylabel('Bound', color='b')
ax2 = ax1.twinx()
ax2.plot(bndItrs, bndLikes, 'r-')
ax2.set_ylabel('Likelihood', color='r')
fig.show()
plt.show()
vocab = lda.wordDists(model)
plt.imshow(vocab, interpolation="nearest", cmap=cm.Greys_r)
plt.show()
# Print out the most likely topic words
topWordCount = 100
kTopWordInds = [topWordIndices(vocab[k, :] * scale, topWordCount) \
for k in range(K)]
# Print out the most likely topic words
print("Prior %s" % (str(model.topicPrior)))
print("Perplexity: %f\n\n" % word_perplexity(lda.log_likelihood, model, query, data))
print("")
printWordDists(K, lda.wordDists(model), d)
def lda_vocab(ldaModel):
if ldaModel.name == lda_gibbs.MODEL_NAME:
return lda_gibbs.wordDists(ldaModel)
elif ldaModel.name == lda_vb.MODEL_NAME:
return lda_vb.wordDists(ldaModel)
else:
raise ValueError("Unknown LDA implementation")
def wordDists(model):
return lda.wordDists(model.ldaModel)
