def _testPerplexityOnRealData(self):
dtype = np.float64 # DTYPE
rd.seed(0xBADB055)
data = DataSet.from_files(words_file=AclWordPath, links_file=AclCitePath)
with open(AclDictPath, "rb") as f:
dic = pkl.load(f)
data.convert_to_dtype(dtype)
data.convert_to_undirected_graph()
data.convert_to_binary_link_matrix()
data.prune_and_shuffle(min_doc_len=MinDocLen, min_link_count=MinLinkCount)
# 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 = TopicCount
model = rtm.newModelAtRandom(data, K, dtype=dtype)
queryState = rtm.newQueryState(data, model)
trainPlan = rtm.newTrainPlan(iterations=50, logFrequency=LogFreq, fastButInaccurate=False, debug=True)
# Train the model, and the immediately save the result to a file for subsequent inspection
model, query, (bndItrs, bndVals, bndLikes) = rtm.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()
# Print out the most likely topic words
# scale = np.reciprocal(1 + np.squeeze(np.array(data.words.sum(axis=0))))
vocab = rtm.wordDists(model)
topWordCount = 10
kTopWordInds = [self.topWordInds(vocab[k,:], topWordCount) for k in range(K)]
like = rtm.log_likelihood(data, model, query)
perp = perplexity_from_like(like, data.word_count)
print ("Prior %s" % (str(model.topicPrior)))
print ("Perplexity: %f\n\n" % perp)
for k in range(model.K):
print("\nTopic %d\n=============================" % k)
print("\n".join("%-20s\t%0.4f" % (dic[kTopWordInds[k][c]], vocab[k][kTopWordInds[k][c]]) for c in range(topWordCount)))
def testMapOnRealData(self):
dtype = np.float64 # DTYPE
rd.seed(0xBADB055)
data = DataSet.from_files(words_file=AclWordPath, links_file=AclCitePath)
with open(AclDictPath, "rb") as f:
dic = pkl.load(f)
data.convert_to_dtype(dtype)
data.convert_to_undirected_graph()
data.convert_to_binary_link_matrix()
data.prune_and_shuffle(min_doc_len=MinDocLen, min_link_count=MinLinkCount)
trainData, testData = data.doc_completion_split()
for pseudoNegCount in (5, 10, 25, 50, 100):
rd.seed(0xC0FFEE)
# Initialise the model
K = TopicCount
model = rtm.newModelAtRandom(trainData, K, dtype=dtype, pseudoNegCount=data.doc_count * pseudoNegCount)
queryState = rtm.newQueryState(trainData, model)
trainPlan = rtm.newTrainPlan(iterations=50, logFrequency=LogFreq, fastButInaccurate=False, debug=True)
# Train the model, and the immediately save the result to a file for subsequent inspection
model, topics, (bndItrs, bndVals, bndLikes) = rtm.train(trainData, 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()
# Print out the most likely topic words
# scale = np.reciprocal(1 + np.squeeze(np.array(data.words.sum(axis=0))))
vocab = rtm.wordDists(model)
topWordCount = 10
kTopWordInds = [self.topWordInds(vocab[k, :], topWordCount) for k in range(K)]
like = rtm.log_likelihood(trainData, model, topics)
perp = perplexity_from_like(like, trainData.word_count)
# print ("Prior %s" % (str(model.topicPrior)))
print ("Pseudo Neg-Count: %d " % pseudoNegCount)
print ("\tTrain Perplexity: %f\n\n" % perp)
# for k in range(model.K):
# print ("\nTopic %d\n=============================" % k)
# print ("\n".join("%-20s\t%0.4f" % (dic[kTopWordInds[k][c]], vocab[k][kTopWordInds[k][c]]) for c in range(topWordCount)))
min_probs = rtm.min_link_probs(model, topics, testData.links)
link_probs = rtm.link_probs(model, topics, min_probs)
try:
map = mean_average_prec(testData.links, link_probs)
except:
print ("Unexpected error")
print("\tThe Mean-Average-Precision is %.3f" % map)