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:
d = pkl.load(f)
data.convert_to_dtype(dtype)
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 = 50
model = mtm.newModelAtRandom(data, K, K - 1, dtype=dtype)
queryState = mtm.newQueryState(data, model)
trainPlan = mtm.newTrainPlan(iterations=200, logFrequency=10, fastButInaccurate=False, debug=True)
# Train the model, and the immediately save the result to a file for subsequent inspection
model, query, (bndItrs, bndVals, bndLikes) = mtm.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()
fig, ax1 = plt.subplots()
ax1.imshow(model.topicCov, interpolation="nearest", cmap=cm.Greys_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 = mtm.wordDists(model)
topWordCount = 10
kTopWordInds = [self.topWordInds(vocab[k,:], topWordCount) for k in range(K)]
like = mtm.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" % (d[kTopWordInds[k][c]], vocab[k][kTopWordInds[k][c]]) for c in range(topWordCount)))
def testPerplexityOnRealDataWithLdaInc(self):
dtype = np.float64 # DTYPE
rd.seed(0xBADB055)
data = DataSet.from_files(words_file=AclWordPath, links_file=AclCitePath)
with open(AclDictPath, "rb") as f:
d = pkl.load(f)
data.convert_to_dtype(dtype)
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
topicCounts = [5, 10, 15, 20, 25, 30, 35, 40, 45, 50]
perps = []
for K in topicCounts:
model = lda.newModelAtRandom(data, K, dtype=dtype)
queryState = lda.newQueryState(data, model)
trainPlan = lda.newTrainPlan(iterations=800, logFrequency=10, fastButInaccurate=False, debug=False)
# 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)
# Print out the most likely topic words
# scale = np.reciprocal(1 + np.squeeze(np.array(data.words.sum(axis=0))))
# vocab = lda.wordDists(model)
# topWordCount = 10
# kTopWordInds = [self.topWordInds(vocab[k,:], topWordCount) for k in range(K)]
like = lda.log_likelihood(data, model, query)
perp = perplexity_from_like(like, data.word_count)
perps.append(perp)
print ("K = %2d : Perplexity = %f\n\n" % (K, perp))
#
# for k in range(model.K):
# print("\nTopic %d\n=============================" % k)
# print("\n".join("%-20s\t%0.4f" % (d[kTopWordInds[k][c]], vocab[k][kTopWordInds[k][c]]) for c in range(topWordCount)))
# Plot the evolution of the bound during training.
fig, ax1 = plt.subplots()
ax1.plot(topicCounts, perps, 'b-')
ax1.set_xlabel('Topic Count')
ax1.set_ylabel('Perplexity', color='b')
fig.show()
plt.show()
def testCrossValPerplexityOnRealDataWithLdaGibbsInc(self):
ActiveFolds = 3
dtype = np.float64 # DTYPE
rd.seed(0xBADB055)
data = DataSet.from_files(words_file=AclWordPath, links_file=AclCitePath)
data.convert_to_dtype(np.int32) # Gibbs expects integers as input, regardless of model dtype
data.prune_and_shuffle(min_doc_len=MinDocLen, min_link_count=MinLinkCount)
# Training setup
TrainSamplesPerTopic = 10
QuerySamplesPerTopic = 2
Thin = 2
Debug = False
# Start running experiments
topicCounts = [5, 10, 15, 20, 25, 30, 35, 40, 45, 50]
for K in topicCounts:
trainPlan = lda_gibbs.newTrainPlan(K * TrainSamplesPerTopic, thin=Thin, debug=Debug)
queryPlan = lda_gibbs.newTrainPlan(K * QuerySamplesPerTopic, thin=Thin, debug=Debug)
trainPerps = []
queryPerps = []
for fold in range(ActiveFolds): # range(NumFolds):
trainData, queryData = data.cross_valid_split(fold, NumFolds)
estData, evalData = queryData.doc_completion_split()
model = lda_gibbs.newModelAtRandom(trainData, K, dtype=dtype)
query = lda_gibbs.newQueryState(trainData, model)
# Train the model, and the immediately save the result to a file for subsequent inspection
model, trainResult, (_, _, _) = lda_gibbs.train (trainData, model, query, trainPlan)
like = lda_gibbs.log_likelihood(trainData, model, trainResult)
perp = perplexity_from_like(like, trainData.word_count)
trainPerps.append(perp)
query = lda_gibbs.newQueryState(estData, model)
_, queryResult = lda_gibbs.query(estData, model, query, queryPlan)
like = lda_gibbs.log_likelihood(evalData, model, queryResult)
perp = perplexity_from_like(like, evalData.word_count)
queryPerps.append(perp)
trainPerps.append(sum(trainPerps) / ActiveFolds)
queryPerps.append(sum(queryPerps) / ActiveFolds)
print("K=%d,Segment=Train,%s" % (K, ",".join([str(p) for p in trainPerps])))
print("K=%d,Segment=Query,%s" % (K, ",".join([str(p) for p in queryPerps])))
def testOnRealData(self):
dtype = np.float64 # DTYPE
rd.seed(0xBADB055)
data = DataSet.from_files(words_file=TNewsWordsPath)
with open(TNewsDictPath, "rb") as f:
d = pkl.load(f)
data.convert_to_dtype(dtype)
data.prune_and_shuffle(min_doc_len=50, min_link_count=0)
# Initialise the model
K = 6
model = mom.newModelAtRandom(data, K, dtype=dtype)
queryState = mom.newQueryState(data, model)
# trainPlan = mom.newTrainPlan(iterations=1000, logFrequency=10, debug=False, burnIn=1000, thinning=10)
trainPlan = mom.newTrainPlan(iterations=200,
logFrequency=10,
debug=False)
# Train the model, and the immediately save the result to a file for subsequent inspection
model, query, (bndItrs, bndVals,
bndLikes) = mom.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
print("Prior %s" % (str(model.topicPrior)))
print("Perplexity: %f\n\n" %
word_perplexity(mom.log_likelihood, model, query, data))
print("")
printWordDists(K, mom.wordDists(model), d)
def testCrossValPerplexityOnRealDataWithLdaInc(self):
ActiveFolds = 3
dtype = np.float64 # DTYPE
rd.seed(0xBADB055)
data = DataSet.from_files(words_file=AclWordPath, links_file=AclCitePath)
data.convert_to_dtype(dtype)
data.prune_and_shuffle(min_doc_len=MinDocLen, min_link_count=MinLinkCount)
# Initialise the model
trainPlan = lda.newTrainPlan(iterations=800, logFrequency=10, fastButInaccurate=False, debug=False)
queryPlan = lda.newTrainPlan(iterations=50, logFrequency=5, fastButInaccurate=False, debug=False)
topicCounts = [30, 35, 40, 45, 50] # [5, 10, 15, 20, 25, 30, 35, 40, 45, 50]
for K in topicCounts:
trainPerps = []
queryPerps = []
for fold in range(ActiveFolds): # range(NumFolds):
trainData, queryData = data.cross_valid_split(fold, NumFolds)
model = lda.newModelAtRandom(trainData, K, dtype=dtype)
query = lda.newQueryState(trainData, model)
# Train the model, and the immediately save the result to a file for subsequent inspection
model, trainResult, (_, _, _) = lda.train (trainData, model, query, trainPlan)
like = lda.log_likelihood(trainData, model, trainResult)
perp = perplexity_from_like(like, trainData.word_count)
trainPerps.append(perp)
estData, evalData = queryData.doc_completion_split()
query = lda.newQueryState(estData, model)
model, queryResult = lda.query(estData, model, query, queryPlan)
like = lda.log_likelihood(evalData, model, queryResult)
perp = perplexity_from_like(like, evalData.word_count)
queryPerps.append(perp)
trainPerps.append(sum(trainPerps) / ActiveFolds)
queryPerps.append(sum(queryPerps) / ActiveFolds)
print("K=%d,Segment=Train,%s" % (K, ",".join([str(p) for p in trainPerps])))
print("K=%d,Segment=Query,%s" % (K, ",".join([str(p) for p in queryPerps])))
def testPerplexityOnRealDataWithMtm2(self):
dtype = np.float64 # DTYPE
rd.seed(0xBADB055)
data = DataSet.from_files(words_file=AclWordPath, links_file=AclCitePath)
with open(AclDictPath, "rb") as f:
d = pkl.load(f)
data.convert_to_dtype(dtype)
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 = 30 # TopicCount
model = mtm2.newModelAtRandom(data, K, dtype=dtype)
queryState = mtm2.newQueryState(data, model)
trainPlan = mtm2.newTrainPlan(iterations=200, logFrequency=10, fastButInaccurate=False, debug=False)
# Train the model, and the immediately save the result to a file for subsequent inspection
model, query, (bndItrs, bndVals, bndLikes) = mtm2.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()
fig, ax1 = plt.subplots()
ax1.imshow(model.topicCov, interpolation="nearest", cmap=cm.Greys_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 = mtm2.wordDists(model)
topWordCount = 10
kTopWordInds = [self.topWordInds(vocab[k,:], topWordCount) for k in range(K)]
like = mtm2.log_likelihood(data, model, query)
perp = perplexity_from_like(like, data.word_count)
print("Perplexity: %f\n\n" % perp)
for k in range(model.K):
print("\nTopic %d\n=============================" % k)
print("\n".join("%-20s\t%0.4f" % (d[kTopWordInds[k][c]], vocab[k][kTopWordInds[k][c]]) for c in range(topWordCount)))
print ("Most likely documents for each topic")
print ("====================================")
with open ("/Users/bryanfeeney/iCloud/Datasets/ACL/ACL.100/doc_ids.pkl", 'rb') as f:
fileIds = pkl.load (f)
docs_dict = [fileIds[fi] for fi in data.order]
for k in range(model.K):
arg_max_prob = np.argmax(query.means[:, k])
print("K=%2d Document ID = %s (found at %d)" % (k, docs_dict[arg_max_prob], arg_max_prob))
print ("Done")
with open ("/Users/bryanfeeney/Desktop/mtm2-" + str(K) + ".pkl", "wb") as f:
pkl.dump((model, query), f)
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 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)
