Repository: spark Updated Branches: refs/heads/branch-1.2 80904482e -> fea9b43ef
[MLlib] fix python example of ALS in guide fix python example of ALS in guide, use Rating instead of np.array. Author: Davies Liu <[email protected]> Closes #4226 from davies/fix_als_guide and squashes the following commits: 1433d76 [Davies Liu] fix python example of als in guide (cherry picked from commit fdaad4eb0388cfe43b5b6600927eb7b9182646f9) Signed-off-by: Xiangrui Meng <[email protected]> Project: http://git-wip-us.apache.org/repos/asf/spark/repo Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/fea9b43e Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/fea9b43e Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/fea9b43e Branch: refs/heads/branch-1.2 Commit: fea9b43ef93217ceb0efea536e0de4ae95e81b57 Parents: 8090448 Author: Davies Liu <[email protected]> Authored: Tue Jan 27 15:33:01 2015 -0800 Committer: Xiangrui Meng <[email protected]> Committed: Tue Jan 27 15:33:15 2015 -0800 ---------------------------------------------------------------------- docs/mllib-collaborative-filtering.md | 11 +++++------ 1 file changed, 5 insertions(+), 6 deletions(-) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/spark/blob/fea9b43e/docs/mllib-collaborative-filtering.md ---------------------------------------------------------------------- diff --git a/docs/mllib-collaborative-filtering.md b/docs/mllib-collaborative-filtering.md index 2094963..ef18cec 100644 --- a/docs/mllib-collaborative-filtering.md +++ b/docs/mllib-collaborative-filtering.md @@ -192,12 +192,11 @@ We use the default ALS.train() method which assumes ratings are explicit. We eva recommendation by measuring the Mean Squared Error of rating prediction. {% highlight python %} -from pyspark.mllib.recommendation import ALS -from numpy import array +from pyspark.mllib.recommendation import ALS, Rating # Load and parse the data data = sc.textFile("data/mllib/als/test.data") -ratings = data.map(lambda line: array([float(x) for x in line.split(',')])) +ratings = data.map(lambda l: l.split(',')).map(lambda l: Rating(int(l[0]), int(l[1]), float(l[2]))) # Build the recommendation model using Alternating Least Squares rank = 10 @@ -205,10 +204,10 @@ numIterations = 20 model = ALS.train(ratings, rank, numIterations) # Evaluate the model on training data -testdata = ratings.map(lambda p: (int(p[0]), int(p[1]))) +testdata = ratings.map(lambda p: (p[0], p[1])) predictions = model.predictAll(testdata).map(lambda r: ((r[0], r[1]), r[2])) ratesAndPreds = ratings.map(lambda r: ((r[0], r[1]), r[2])).join(predictions) -MSE = ratesAndPreds.map(lambda r: (r[1][0] - r[1][1])**2).reduce(lambda x, y: x + y)/ratesAndPreds.count() +MSE = ratesAndPreds.map(lambda r: (r[1][0] - r[1][1])**2).reduce(lambda x, y: x + y) / ratesAndPreds.count() print("Mean Squared Error = " + str(MSE)) {% endhighlight %} @@ -217,7 +216,7 @@ signals), you can use the trainImplicit method to get better results. {% highlight python %} # Build the recommendation model using Alternating Least Squares based on implicit ratings -model = ALS.trainImplicit(ratings, rank, numIterations, alpha = 0.01) +model = ALS.trainImplicit(ratings, rank, numIterations, alpha=0.01) {% endhighlight %} </div> --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
