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| author | Xiangrui Meng <meng@databricks.com> | 2015-04-01 16:47:18 -0700 |
|---|---|---|
| committer | Xiangrui Meng <meng@databricks.com> | 2015-04-01 16:47:18 -0700 |
| commit | ccafd757eda478913f783f3127be715bf6413740 (patch) | |
| tree | 96459c20b8183b5742fb55fa31e030bf5daabf0f /python | |
| parent | f084c5de14eb10a6aba82a39e03e7877926ebb9e (diff) | |
| download | spark-ccafd757eda478913f783f3127be715bf6413740.tar.gz spark-ccafd757eda478913f783f3127be715bf6413740.tar.bz2 spark-ccafd757eda478913f783f3127be715bf6413740.zip | |
[SPARK-6642][MLLIB] use 1.2 lambda scaling and remove addImplicit from NormalEquation
This PR changes lambda scaling from number of users/items to number of explicit ratings. The latter is the behavior in 1.2. Slight refactor of NormalEquation to make it independent of ALS models. srowen codexiang
Author: Xiangrui Meng <meng@databricks.com>
Closes #5314 from mengxr/SPARK-6642 and squashes the following commits:
dc655a1 [Xiangrui Meng] relax python tests
f410df2 [Xiangrui Meng] use 1.2 scaling and remove addImplicit from NormalEquation
Diffstat (limited to 'python')
| -rw-r--r-- | python/pyspark/mllib/recommendation.py | 6 |
1 files changed, 3 insertions, 3 deletions
diff --git a/python/pyspark/mllib/recommendation.py b/python/pyspark/mllib/recommendation.py index b094e50856..c5c4c13dae 100644 --- a/python/pyspark/mllib/recommendation.py +++ b/python/pyspark/mllib/recommendation.py @@ -52,7 +52,7 @@ class MatrixFactorizationModel(JavaModelWrapper, JavaSaveable, JavaLoader): >>> ratings = sc.parallelize([r1, r2, r3]) >>> model = ALS.trainImplicit(ratings, 1, seed=10) >>> model.predict(2, 2) - 0.43... + 0.4... >>> testset = sc.parallelize([(1, 2), (1, 1)]) >>> model = ALS.train(ratings, 2, seed=0) @@ -82,14 +82,14 @@ class MatrixFactorizationModel(JavaModelWrapper, JavaSaveable, JavaLoader): >>> model = ALS.trainImplicit(ratings, 1, nonnegative=True, seed=10) >>> model.predict(2,2) - 0.43... + 0.4... >>> import os, tempfile >>> path = tempfile.mkdtemp() >>> model.save(sc, path) >>> sameModel = MatrixFactorizationModel.load(sc, path) >>> sameModel.predict(2,2) - 0.43... + 0.4... >>> sameModel.predictAll(testset).collect() [Rating(... >>> try: |