ratingsRDD = parts.map(lambda p: Row(userId=int(p[0]),
movieId=int(p[1]),
rating=float(p[2]),
timestamp=long(p[3])))
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(maxIter=10,
regParam=0.1,
userCol="userId",
itemCol="movieId",
ratingCol="rating",
coldStartStrategy="drop")
als.rank = 4
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))
#print(np.asarray([x[1] for x in model.userFactors.collect()]))
userfactorinoinnp = np.asarray([x[1] for x in model.userFactors.collect()])
itemfactorinoinnp = np.asarray([x[1] for x in model.itemFactors.collect()])
filename = 'ALSItem'
outfile = open(filename, 'wb')
