diff options
author | Davies Liu <davies@databricks.com> | 2015-01-27 15:33:01 -0800 |
---|---|---|
committer | Xiangrui Meng <meng@databricks.com> | 2015-01-27 15:33:01 -0800 |
commit | fdaad4eb0388cfe43b5b6600927eb7b9182646f9 (patch) | |
tree | f339161caa8277af8d8a5bce9c8f21c6e7332962 /docs | |
parent | ff356e2a21e31998cda3062e560a276a3bfaa7ab (diff) | |
download | spark-fdaad4eb0388cfe43b5b6600927eb7b9182646f9.tar.gz spark-fdaad4eb0388cfe43b5b6600927eb7b9182646f9.tar.bz2 spark-fdaad4eb0388cfe43b5b6600927eb7b9182646f9.zip |
[MLlib] fix python example of ALS in guide
fix python example of ALS in guide, use Rating instead of np.array.
Author: Davies Liu <davies@databricks.com>
Closes #4226 from davies/fix_als_guide and squashes the following commits:
1433d76 [Davies Liu] fix python example of als in guide
Diffstat (limited to 'docs')
-rw-r--r-- | docs/mllib-collaborative-filtering.md | 11 |
1 files changed, 5 insertions, 6 deletions
diff --git a/docs/mllib-collaborative-filtering.md b/docs/mllib-collaborative-filtering.md index 2094963392..ef18cec937 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> |