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[SPARK-8531] [ML] Update ML user guide for MinMaxScaler

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jira: https://issues.apache.org/jira/browse/SPARK-8531

Update ML user guide for MinMaxScaler

Author: Yuhao Yang <hhbyyh@gmail.com>
Author: unknown <yuhaoyan@yuhaoyan-MOBL1.ccr.corp.intel.com>

Closes #7211 from hhbyyh/minmaxdoc.
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Yuhao Yang 2015-08-25 10:54:03 -07:00 committed by Joseph K. Bradley
parent 5c08c86bfa
commit b37f0cc1b4
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docs/ml-features.md
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@ -1133,6 +1133,7 @@ val scaledData = scalerModel.transform(dataFrame)
{% highlight java %} {% highlight java %}
import org.apache.spark.api.java.JavaRDD; import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.ml.feature.StandardScaler; import org.apache.spark.ml.feature.StandardScaler;
import org.apache.spark.ml.feature.StandardScalerModel;
import org.apache.spark.mllib.regression.LabeledPoint; import org.apache.spark.mllib.regression.LabeledPoint;
import org.apache.spark.mllib.util.MLUtils; import org.apache.spark.mllib.util.MLUtils;
import org.apache.spark.sql.DataFrame; import org.apache.spark.sql.DataFrame;
@ -1173,6 +1174,76 @@ scaledData = scalerModel.transform(dataFrame)
</div> </div>
</div> </div>
## MinMaxScaler
`MinMaxScaler` transforms a dataset of `Vector` rows, rescaling each feature to a specific range (often [0, 1]). It takes parameters:
* `min`: 0.0 by default. Lower bound after transformation, shared by all features.
* `max`: 1.0 by default. Upper bound after transformation, shared by all features.
`MinMaxScaler` computes summary statistics on a data set and produces a `MinMaxScalerModel`. The model can then transform each feature individually such that it is in the given range.
The rescaled value for a feature E is calculated as,
`\begin{equation}
Rescaled(e_i) = \frac{e_i - E_{min}}{E_{max} - E_{min}} * (max - min) + min
\end{equation}`
For the case `E_{max} == E_{min}`, `Rescaled(e_i) = 0.5 * (max + min)`
Note that since zero values will probably be transformed to non-zero values, output of the transformer will be DenseVector even for sparse input.
The following example demonstrates how to load a dataset in libsvm format and then rescale each feature to [0, 1].
<div class="codetabs">
<div data-lang="scala" markdown="1">
More details can be found in the API docs for
[MinMaxScaler](api/scala/index.html#org.apache.spark.ml.feature.MinMaxScaler) and
[MinMaxScalerModel](api/scala/index.html#org.apache.spark.ml.feature.MinMaxScalerModel).
{% highlight scala %}
import org.apache.spark.ml.feature.MinMaxScaler
import org.apache.spark.mllib.util.MLUtils
val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt")
val dataFrame = sqlContext.createDataFrame(data)
val scaler = new MinMaxScaler()
.setInputCol("features")
.setOutputCol("scaledFeatures")
// Compute summary statistics and generate MinMaxScalerModel
val scalerModel = scaler.fit(dataFrame)
// rescale each feature to range [min, max].
val scaledData = scalerModel.transform(dataFrame)
{% endhighlight %}
</div>
<div data-lang="java" markdown="1">
More details can be found in the API docs for
[MinMaxScaler](api/java/org/apache/spark/ml/feature/MinMaxScaler.html) and
[MinMaxScalerModel](api/java/org/apache/spark/ml/feature/MinMaxScalerModel.html).
{% highlight java %}
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.ml.feature.MinMaxScaler;
import org.apache.spark.ml.feature.MinMaxScalerModel;
import org.apache.spark.mllib.regression.LabeledPoint;
import org.apache.spark.mllib.util.MLUtils;
import org.apache.spark.sql.DataFrame;
JavaRDD<LabeledPoint> data =
MLUtils.loadLibSVMFile(jsc.sc(), "data/mllib/sample_libsvm_data.txt").toJavaRDD();
DataFrame dataFrame = jsql.createDataFrame(data, LabeledPoint.class);
MinMaxScaler scaler = new MinMaxScaler()
.setInputCol("features")
.setOutputCol("scaledFeatures");
// Compute summary statistics and generate MinMaxScalerModel
MinMaxScalerModel scalerModel = scaler.fit(dataFrame);
// rescale each feature to range [min, max].
DataFrame scaledData = scalerModel.transform(dataFrame);
{% endhighlight %}
</div>
</div>
## Bucketizer ## Bucketizer
`Bucketizer` transforms a column of continuous features to a column of feature buckets, where the buckets are specified by users. It takes a parameter: `Bucketizer` transforms a column of continuous features to a column of feature buckets, where the buckets are specified by users. It takes a parameter: