def LDA_spark():
data = sc.textFile("data/mllib/sample_lda_data.txt")
parsedData = data.map(lambda line: Vectors.dense([float(x) for x in line.strip().split(' ')]))
corpus = parsedData.zipWithIndex().map(lambda x: [x[1], x[0]]).cache()
# Cluster the documents into three topics using LDA
Model = LDA.train(corpus, k=3)
# Save and load model
Model.save(sc, "myModelPath")
sameModel = LDAModel.load(sc, "myModelPath")
def main():
for tn in tablenames:
data = spark.read.format("org.apache.spark.sql.cassandra")\
.options(table=tn, keyspace=keyspace).load().limit(1000)
data = data.sort('imdb_score', ascending=False)
desc = data.rdd.map(lambda x: x['description']).filter(
lambda x: x is not None)
StopWords = nltk.corpus.stopwords.words('english')
StopWords.extend([" ... See full summary"])
tokenized = desc.map( lambda y: y.strip().lower()).map( lambda x: re.split(" ", x))\
.map( lambda word: [x for x in word if x.isalpha()]).map( lambda word: [x for x in word if len(x) > 3] )\
.map( lambda word: [x for x in word if x not in StopWords]).zipWithIndex()
df_txts = spark.createDataFrame(tokenized, ["words", 'index'])
countVec = CountVectorizer(inputCol="words",
outputCol="raw_features",
vocabSize=5000,
minDF=10.0)
CountVectMod = countVec.fit(df_txts)
result = CountVectMod.transform(df_txts)
idf = IDF(inputCol="raw_features", outputCol="features")
idfModel = idf.fit(result)
resultTFIdf = idfModel.transform(result)
totalTopics = 10
totalItr = 100
LDAModel = MLlibLDA.train(resultTFIdf.select('index','features').rdd.mapValues(MLlibVectors.fromML).map(list),\
k=totalTopics, maxIterations=totalItr)
maxwordsTopic = 5
topicIndices = sc.parallelize(
LDAModel.describeTopics(maxTermsPerTopic=5))
VCarr = CountVectMod.vocabulary
def finalTopic(topic):
terms = topic[0]
result = []
for i in range(maxwordsTopic):
term = VCarr[terms[i]]
result.append(term)
return result
topics_final = topicIndices.map(
lambda topic: finalTopic(topic)).collect()
print(topics_final)
for topic in range(len(topics_final)):
print("Topic" + str(topic) + ":")
for term in topics_final[topic]:
print(term)
print('\n')
def test():
sc = SparkContext(master='local[4]', appName='lda')
sc.setLogLevel('ERROR')
def train():
data = sc.textFile(corpus_filename).map(lambda line: Vectors.dense(
[float(i) for i in line.strip().split()]))
corpus = data.zipWithIndex().map(lambda x: [x[1], x[0]]).cache()
# print(corpus.take(5))
lda_model = LDA.train(rdd=corpus,
maxIterations=max_iter,
seed=seed,
checkpointInterval=checkin_point_interval,
k=K,
optimizer=optimizer,
docConcentration=alpha,
topicConcentration=beta)
if os.path.exists('./ldamodel'):
__import__('shutil').rmtree('./ldamodel')
lda_model.save(sc, "./ldamodel")
# train()
lda_model = LDAModel.load(sc, "./ldamodel")
# topic-word分布(未归一化的dist，每列代表一个topic)
topics = lda_model.topicsMatrix()
# for tid in range(3):
# print('Topic' + str(tid) + ':')
# for wid in range(0, lda_model.vocabSize()):
# print(' ' + str(topics[wid, tid] / sum(topics[:, tid]))) # 加一个归一化
# # print(' ' + str(topics[wid, tid]))
# topic-word按词序排列分布([词id，按权重从大到小排列], [词在主题上的权重])
topics_dist = lda_model.describeTopics()
for tid, topic in enumerate(topics_dist):
print('Topic' + str(tid) + ':' + '\n', topic)
# 文档的主题分布(mllib不能，ml才可以)
# doc_topic = lda_model
sc.stop()
def test():
sc = SparkContext(master='local[4]', appName='lda')
sc.setLogLevel('ERROR')
def train():
data = sc.textFile(corpus_filename).map(lambda line: Vectors.dense([float(i) for i in line.strip().split()]))
corpus = data.zipWithIndex().map(lambda x: [x[1], x[0]]).cache()
# print(corpus.take(5))
lda_model = LDA.train(rdd=corpus, maxIterations=max_iter, seed=seed, checkpointInterval=checkin_point_interval,
k=K,
optimizer=optimizer, docConcentration=alpha, topicConcentration=beta)
if os.path.exists('./ldamodel'): __import__('shutil').rmtree('./ldamodel')
lda_model.save(sc, "./ldamodel")
# train()
lda_model = LDAModel.load(sc, "./ldamodel")
# topic-word分布(未归一化的dist，每列代表一个topic)
topics = lda_model.topicsMatrix()
# for tid in range(3):
# print('Topic' + str(tid) + ':')
# for wid in range(0, lda_model.vocabSize()):
# print(' ' + str(topics[wid, tid] / sum(topics[:, tid]))) # 加一个归一化
# # print(' ' + str(topics[wid, tid]))
# topic-word按词序排列分布([词id，按权重从大到小排列], [词在主题上的权重])
topics_dist = lda_model.describeTopics()
for tid, topic in enumerate(topics_dist):
print('Topic' + str(tid) + ':' + '\n', topic)
# 文档的主题分布(mllib不能，ml才可以)
# doc_topic = lda_model
sc.stop()
from pyspark import SparkContext
from pyspark.mllib.clustering import LDA, LDAModel
from pyspark.mllib.linalg import Vectors
# Load and parse the data
sc = SparkContext(appName="lda")
# data = sc.textFile("data/sample_lda_data.txt")
# parsedData = data.map(lambda line: Vectors.dense([float(x) for x in line.strip().split(' ')]))
# # Index documents with unique IDs
# corpus = parsedData.zipWithIndex().map(lambda x: [x[1], x[0]]).cache()
#
# # Cluster the documents into three topics using LDA
# ldaModel = LDA.train(corpus, k=3)
ldaModel = LDAModel.load(sc, "data/model/mymodel")
# Output topics. Each is a distribution over words (matching word count vectors)
print("Learned topics (as distributions over vocab of " + str(ldaModel.vocabSize()) + " words):")
topics = ldaModel.topicsMatrix()
for topic in range(3):
print("Topic " + str(topic) + ":")
for word in range(0, ldaModel.vocabSize()):
print(" " + str(topics[word][topic]))
# Save and load model
# ldaModel.save(sc, "data/model/mymodel")
# sameModel = LDAModel.load(sc, "data/model/mymodel")
corpus = sc.parallelize(data_features)
ldaModel = LDA.train(corpus, k=3)
# Output topics. Each is a distribution over words (matching word count vectors)
print("Learned topics (as distributions over vocab of " +
str(ldaModel.vocabSize()) + " words):")
topics = ldaModel.topicsMatrix()
for topic in range(3):
print("Topic " + str(topic) + ":")
for word in range(0, ldaModel.vocabSize()):
print(" " + str(topics[word][topic]))
# Save and load model
model.save(sc, "myModelPath")
sameModel = LDAModel.load(sc, "myModelPath")
ldaModel = LDA.train(corpus, k=3)
# Output topics. Each is a distribution over words (matching word count vectors)
print("Learned topics (as distributions over vocab of " +
str(ldaModel.vocabSize()) + " words):")
topics = ldaModel.topicsMatrix()
for topic in range(3):
print("Topic " + str(topic) + ":")
for word in range(0, ldaModel.vocabSize()):
print(" " + str(topics[word][topic]))
# Save and load model
model.save(sc, "myModelPath")
sameModel = LDAModel.load(sc, "myModelPath")
import sys
from pyspark.mllib.clustering import LDA, LDAModel
from pyspark.mllib.linalg import Vectors
from pyspark import SparkContext
sc = SparkContext()
for route, directories, files in os.walk('/media/deepak/data_words.csv'):
for file in files:
f_path = os.path.join(route, file)
f_name = os.path.join(route, file).split('/')
# Load and parse the data
data = sc.textFile(f_path)
dataParsed = data.map(lambda line: Vectors.dense(
[float(x) for x in line.strip().split(',')]))
# Index documents with unique IDs
corpus = dataParsed.zipWithIndex().map(lambda x: [x[1], x[0]]).cache()
# Cluster the documents into three topics using LDA
ldaModel = LDA.train(corpus, k=3)
# Output topics. Each is a distribution over words (matching word count vectors)
print(f_name[-1])
print("Learned topics (as distributions over vocab of " +
str(ldaModel.vocabSize()) + " words):")
topics = ldaModel.topicsMatrix()
for topic in range(3):
print("Topic " + str(topic) + ":")
for word in range(0, ldaModel.vocabSize()):
print(" " + str(topics[word][topic]))
# Save and load model
ldaModel.save(sc, "/media/deepak/lda_output/" + f_name[-1])
sameModel = LDAModel.load(sc, "/media/deepak/lda_output/" + f_name[-1])
sc =SparkContext()
corpus = sc.parallelize(data_features)
ldaModel = LDA.train(corpus, k=3)
# Output topics. Each is a distribution over words (matching word count vectors)
print("Learned topics (as distributions over vocab of " + str(ldaModel.vocabSize()) + " words):")
topics = ldaModel.topicsMatrix()
for topic in range(3):
print("Topic " + str(topic) + ":")
for word in range(0, ldaModel.vocabSize()):
print(" " + str(topics[word][topic]))
# Save and load model
model.save(sc, "myModelPath")
sameModel = LDAModel.load(sc, "myModelPath")
ldaModel = LDA.train(corpus, k=3)
# Output topics. Each is a distribution over words (matching word count vectors)
print("Learned topics (as distributions over vocab of " + str(ldaModel.vocabSize()) + " words):")
topics = ldaModel.topicsMatrix()
for topic in range(3):
print("Topic " + str(topic) + ":")
for word in range(0, ldaModel.vocabSize()):
print(" " + str(topics[word][topic]))
# Save and load model
model.save(sc, "myModelPath")
sameModel = LDAModel.load(sc, "myModelPath")
def load_model():
from pyspark.mllib.clustering import LDA, LDAModel
#sc = SparkContext(appName='lda_load', conf=conf)
path = "/user/rmusters/ldaModel2"
ldaModel = LDAModel.load(sc, path)
return ldaModel
