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author | Yanbo Liang <ybliang8@gmail.com> | 2015-08-17 17:25:41 -0700 |
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committer | Joseph K. Bradley <joseph@databricks.com> | 2015-08-17 17:25:41 -0700 |
commit | 0076e8212334c613599dcbc2ac23f49e9e50cc44 (patch) | |
tree | 80b13441324071ab6bc4978c017a072c0decc686 /docs/ml-features.md | |
parent | 52ae952574f5d641a398dd185e09e5a79318c8a9 (diff) | |
download | spark-0076e8212334c613599dcbc2ac23f49e9e50cc44.tar.gz spark-0076e8212334c613599dcbc2ac23f49e9e50cc44.tar.bz2 spark-0076e8212334c613599dcbc2ac23f49e9e50cc44.zip |
[SPARK-9768] [PYSPARK] [ML] Add Python API and user guide for ml.feature.ElementwiseProduct
Add Python API, user guide and example for ml.feature.ElementwiseProduct.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes #8061 from yanboliang/SPARK-9768.
Diffstat (limited to 'docs/ml-features.md')
-rw-r--r-- | docs/ml-features.md | 23 |
1 files changed, 19 insertions, 4 deletions
diff --git a/docs/ml-features.md b/docs/ml-features.md index cec2cbe673..6b2e36b353 100644 --- a/docs/ml-features.md +++ b/docs/ml-features.md @@ -1212,7 +1212,7 @@ v_N This example below demonstrates how to transform vectors using a transforming vector value. <div class="codetabs"> -<div data-lang="scala"> +<div data-lang="scala" markdown="1"> {% highlight scala %} import org.apache.spark.ml.feature.ElementwiseProduct import org.apache.spark.mllib.linalg.Vectors @@ -1229,12 +1229,12 @@ val transformer = new ElementwiseProduct() .setOutputCol("transformedVector") // Batch transform the vectors to create new column: -val transformedData = transformer.transform(dataFrame) +transformer.transform(dataFrame).show() {% endhighlight %} </div> -<div data-lang="java"> +<div data-lang="java" markdown="1"> {% highlight java %} import com.google.common.collect.Lists; @@ -1267,10 +1267,25 @@ ElementwiseProduct transformer = new ElementwiseProduct() .setInputCol("vector") .setOutputCol("transformedVector"); // Batch transform the vectors to create new column: -DataFrame transformedData = transformer.transform(dataFrame); +transformer.transform(dataFrame).show(); {% endhighlight %} </div> + +<div data-lang="python" markdown="1"> +{% highlight python %} +from pyspark.ml.feature import ElementwiseProduct +from pyspark.mllib.linalg import Vectors + +data = [(Vectors.dense([1.0, 2.0, 3.0]),), (Vectors.dense([4.0, 5.0, 6.0]),)] +df = sqlContext.createDataFrame(data, ["vector"]) +transformer = ElementwiseProduct(scalingVec=Vectors.dense([0.0, 1.0, 2.0]), + inputCol="vector", outputCol="transformedVector") +transformer.transform(df).show() + +{% endhighlight %} +</div> + </div> ## VectorAssembler |