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Diffstat (limited to 'examples/src/main/python/ml/als_example.py')
-rw-r--r-- | examples/src/main/python/ml/als_example.py | 57 |
1 files changed, 57 insertions, 0 deletions
diff --git a/examples/src/main/python/ml/als_example.py b/examples/src/main/python/ml/als_example.py new file mode 100644 index 0000000000..f61c8ab5d6 --- /dev/null +++ b/examples/src/main/python/ml/als_example.py @@ -0,0 +1,57 @@ +# +# 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 + +from pyspark import SparkContext +from pyspark.sql import SQLContext + +# $example on$ +import math + +from pyspark.ml.evaluation import RegressionEvaluator +from pyspark.ml.recommendation import ALS +from pyspark.sql import Row +# $example off$ + +if __name__ == "__main__": + sc = SparkContext(appName="ALSExample") + sqlContext = SQLContext(sc) + + # $example on$ + lines = sc.textFile("data/mllib/als/sample_movielens_ratings.txt") + parts = lines.map(lambda l: l.split("::")) + ratingsRDD = parts.map(lambda p: Row(userId=int(p[0]), movieId=int(p[1]), + rating=float(p[2]), timestamp=long(p[3]))) + ratings = sqlContext.createDataFrame(ratingsRDD) + (training, test) = ratings.randomSplit([0.8, 0.2]) + + # Build the recommendation model using ALS on the training data + als = ALS(maxIter=5, regParam=0.01, userCol="userId", itemCol="movieId", ratingCol="rating") + model = als.fit(training) + + # Evaluate the model by computing the RMSE on the test data + rawPredictions = model.transform(test) + predictions = rawPredictions\ + .withColumn("rating", rawPredictions.rating.cast("double"))\ + .withColumn("prediction", rawPredictions.prediction.cast("double")) + evaluator =\ + RegressionEvaluator(metricName="rmse", labelCol="rating", predictionCol="prediction") + rmse = evaluator.evaluate(predictions) + print("Root-mean-square error = " + str(rmse)) + # $example off$ + sc.stop() |