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| author | Sean Owen <sowen@cloudera.com> | 2014-07-13 19:27:43 -0700 |
|---|---|---|
| committer | Xiangrui Meng <meng@databricks.com> | 2014-07-13 19:27:43 -0700 |
| commit | 635888cbed0e3f4127252fb84db449f0cc9ed659 (patch) | |
| tree | 43433e3393c889f25a8ef4898099664a1a5ce0a7 /docs/mllib-collaborative-filtering.md | |
| parent | 4c8be64e768fe71643b37f1e82f619c8aeac6eff (diff) | |
| download | spark-635888cbed0e3f4127252fb84db449f0cc9ed659.tar.gz spark-635888cbed0e3f4127252fb84db449f0cc9ed659.tar.bz2 spark-635888cbed0e3f4127252fb84db449f0cc9ed659.zip | |
SPARK-2363. Clean MLlib's sample data files
(Just made a PR for this, mengxr was the reporter of:)
MLlib has sample data under serveral folders:
1) data/mllib
2) data/
3) mllib/data/*
Per previous discussion with Matei Zaharia, we want to put them under `data/mllib` and clean outdated files.
Author: Sean Owen <sowen@cloudera.com>
Closes #1394 from srowen/SPARK-2363 and squashes the following commits:
54313dd [Sean Owen] Move ML example data from /mllib/data/ and /data/ into /data/mllib/
Diffstat (limited to 'docs/mllib-collaborative-filtering.md')
| -rw-r--r-- | docs/mllib-collaborative-filtering.md | 4 |
1 files changed, 2 insertions, 2 deletions
diff --git a/docs/mllib-collaborative-filtering.md b/docs/mllib-collaborative-filtering.md index d51002f015..5cd7173872 100644 --- a/docs/mllib-collaborative-filtering.md +++ b/docs/mllib-collaborative-filtering.md @@ -58,7 +58,7 @@ import org.apache.spark.mllib.recommendation.ALS import org.apache.spark.mllib.recommendation.Rating // Load and parse the data -val data = sc.textFile("mllib/data/als/test.data") +val data = sc.textFile("data/mllib/als/test.data") val ratings = data.map(_.split(',') match { case Array(user, item, rate) => Rating(user.toInt, item.toInt, rate.toDouble) }) @@ -112,7 +112,7 @@ from pyspark.mllib.recommendation import ALS from numpy import array # Load and parse the data -data = sc.textFile("mllib/data/als/test.data") +data = sc.textFile("data/mllib/als/test.data") ratings = data.map(lambda line: array([float(x) for x in line.split(',')])) # Build the recommendation model using Alternating Least Squares |