lambda p: Row(userId=int(p[0]), movieId=int(p[1]), rating=float(p[2])))
ratings = spark.createDataFrame(ratingsRDD)
(training, test) = ratings.randomSplit([0.8, 0.2])
# Build the recommendation model using ALS on the training data
# Note we set cold start strategy to 'drop' to ensure we don't get NaN evaluation metrics
als = ALS(rank=10,
maxIter=5,
regParam=0.01,
implicitPrefs=True,
alpha=40.0,
userCol="userId",
itemCol="movieId",
ratingCol="rating",
coldStartStrategy="drop")
print(
"\nALS training with implicitPrefs={}, rank={}, maxIter={}, regParam={}, alpha={}, seed={}\n"
.format(als.getImplicitPrefs(), als.getRank(), als.getMaxIter(),
als.getRegParam(), als.getAlpha(), als.getSeed()))
model = als.fit(training)
# Evaluate the model by computing the RMSE on the test data
predictions = model.transform(test)
evaluator = RegressionEvaluator(metricName="rmse",
labelCol="rating",
predictionCol="prediction")
rmse = evaluator.evaluate(predictions)
print("Root-mean-square error = " + str(rmse))
spark.stop()
