| # |
| # 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. |
| # |
| |
| """ |
| An example demonstrating LDA. |
| Run with: |
| bin/spark-submit examples/src/main/python/ml/lda_example.py |
| """ |
| # $example on$ |
| from pyspark.ml.clustering import LDA |
| # $example off$ |
| from pyspark.sql import SparkSession |
| |
| 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() |