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#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from __future__ import print_function
# $example on$
from pyspark.ml.clustering import LDA
# $example off$
from pyspark.sql import SparkSession
"""
An example demonstrating LDA.
Run with:
bin/spark-submit examples/src/main/python/ml/lda_example.py
"""
if __name__ == "__main__":
spark = SparkSession \
.builder \
.appName("LDAExample") \
.getOrCreate()
# $example on$
# Loads data.
dataset = spark.read.format("libsvm").load("data/mllib/sample_lda_libsvm_data.txt")
# Trains a LDA model.
lda = LDA(k=10, maxIter=10)
model = lda.fit(dataset)
ll = model.logLikelihood(dataset)
lp = model.logPerplexity(dataset)
print("The lower bound on the log likelihood of the entire corpus: " + str(ll))
print("The upper bound on perplexity: " + str(lp))
# Describe topics.
topics = model.describeTopics(3)
print("The topics described by their top-weighted terms:")
topics.show(truncate=False)
# Shows the result
transformed = model.transform(dataset)
transformed.show(truncate=False)
# $example off$
spark.stop()
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