spark git commit: [MLlib] fix python example of ALS in guide

[me...@apache.org]

Repository: spark
Updated Branches:
  refs/heads/master ff356e2a2 -> fdaad4eb0


[MLlib] fix python example of ALS in guide

fix python example of ALS in guide, use Rating instead of np.array.

Author: Davies Liu <da...@databricks.com>

Closes #4226 from davies/fix_als_guide and squashes the following commits:

1433d76 [Davies Liu] fix python example of als in guide


Project: http://git-wip-us.apache.org/repos/asf/spark/repo
Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/fdaad4eb
Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/fdaad4eb
Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/fdaad4eb

Branch: refs/heads/master
Commit: fdaad4eb0388cfe43b5b6600927eb7b9182646f9
Parents: ff356e2
Author: Davies Liu <da...@databricks.com>
Authored: Tue Jan 27 15:33:01 2015 -0800
Committer: Xiangrui Meng <me...@databricks.com>
Committed: Tue Jan 27 15:33:01 2015 -0800

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 docs/mllib-collaborative-filtering.md | 11 +++++------
 1 file changed, 5 insertions(+), 6 deletions(-)
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http://git-wip-us.apache.org/repos/asf/spark/blob/fdaad4eb/docs/mllib-collaborative-filtering.md
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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>
 


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