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| author | Yanbo Liang <ybliang8@gmail.com> | 2015-11-13 08:43:05 -0800 |
|---|---|---|
| committer | Xiangrui Meng <meng@databricks.com> | 2015-11-13 08:43:05 -0800 |
| commit | 99693fef0a30432d94556154b81872356d921c64 (patch) | |
| tree | 09d76cc0ef6cae153718982a9a1ecc827ee12d5f /docs/ml-features.md | |
| parent | 61a28486ccbcdd37461419df958aea222c8b9f09 (diff) | |
| download | spark-99693fef0a30432d94556154b81872356d921c64.tar.gz spark-99693fef0a30432d94556154b81872356d921c64.tar.bz2 spark-99693fef0a30432d94556154b81872356d921c64.zip | |
[SPARK-11723][ML][DOC] Use LibSVM data source rather than MLUtils.loadLibSVMFile to load DataFrame
Use LibSVM data source rather than MLUtils.loadLibSVMFile to load DataFrame, include:
* Use libSVM data source for all example codes under examples/ml, and remove unused import.
* Use libSVM data source for user guides under ml-*** which were omitted by #8697.
* Fix bug: We should use ```sqlContext.read().format("libsvm").load(path)``` at Java side, but the API doc and user guides misuse as ```sqlContext.read.format("libsvm").load(path)```.
* Code cleanup.
mengxr
Author: Yanbo Liang <ybliang8@gmail.com>
Closes #9690 from yanboliang/spark-11723.
Diffstat (limited to 'docs/ml-features.md')
| -rw-r--r-- | docs/ml-features.md | 8 |
1 files changed, 4 insertions, 4 deletions
diff --git a/docs/ml-features.md b/docs/ml-features.md index 142afac2f3..cd1838d6d2 100644 --- a/docs/ml-features.md +++ b/docs/ml-features.md @@ -1109,7 +1109,7 @@ import org.apache.spark.ml.feature.VectorIndexer; import org.apache.spark.ml.feature.VectorIndexerModel; import org.apache.spark.sql.DataFrame; -DataFrame data = sqlContext.read.format("libsvm") +DataFrame data = sqlContext.read().format("libsvm") .load("data/mllib/sample_libsvm_data.txt"); VectorIndexer indexer = new VectorIndexer() .setInputCol("features") @@ -1187,7 +1187,7 @@ for more details on the API. import org.apache.spark.ml.feature.Normalizer; import org.apache.spark.sql.DataFrame; -DataFrame dataFrame = sqlContext.read.format("libsvm") +DataFrame dataFrame = sqlContext.read().format("libsvm") .load("data/mllib/sample_libsvm_data.txt"); // Normalize each Vector using $L^1$ norm. @@ -1273,7 +1273,7 @@ import org.apache.spark.ml.feature.StandardScaler; import org.apache.spark.ml.feature.StandardScalerModel; import org.apache.spark.sql.DataFrame; -DataFrame dataFrame = sqlContext.read.format("libsvm") +DataFrame dataFrame = sqlContext.read().format("libsvm") .load("data/mllib/sample_libsvm_data.txt"); StandardScaler scaler = new StandardScaler() .setInputCol("features") @@ -1366,7 +1366,7 @@ import org.apache.spark.ml.feature.MinMaxScaler; import org.apache.spark.ml.feature.MinMaxScalerModel; import org.apache.spark.sql.DataFrame; -DataFrame dataFrame = sqlContext.read.format("libsvm") +DataFrame dataFrame = sqlContext.read().format("libsvm") .load("data/mllib/sample_libsvm_data.txt"); MinMaxScaler scaler = new MinMaxScaler() .setInputCol("features") |