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[SPARK-8531] [ML] Update ML user guide for MinMaxScaler #7211

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[SPARK-8531] [ML] Update ML user guide for MinMaxScaler #7211

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a0725a5
doc for MinMaxScaler
hhbyyh Jul 3, 2015
40942a7
small update
hhbyyh Aug 3, 2015
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Merge remote-tracking branch 'upstream/master' into minmaxdoc
hhbyyh Aug 13, 2015
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fix comments
hhbyyh Aug 13, 2015
1622f85
update document
Aug 24, 2015
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69 changes: 69 additions & 0 deletions docs/ml-features.md
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Expand Up @@ -865,6 +865,7 @@ val scaledData = scalerModel.transform(dataFrame)
{% highlight java %}
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.ml.feature.StandardScaler;
import org.apache.spark.ml.feature.StandardScalerModel;
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@jkbradley jkbradley Aug 11, 2015

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good catch!

import org.apache.spark.mllib.regression.LabeledPoint;
import org.apache.spark.mllib.util.MLUtils;
import org.apache.spark.sql.DataFrame;
Expand Down Expand Up @@ -905,6 +906,74 @@ scaledData = scalerModel.transform(dataFrame)
</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,

Rescaled(e_i) = \frac{e_i - E_{min}}{E_{max} - E_{min}} * (max - min) + min
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@jkbradley jkbradley Aug 11, 2015

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Could you please make this render as Latex? You can follow the examples in mllib-linear-methods.md

Same for other equations below.

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@hhbyyh hhbyyh Aug 13, 2015

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Sure, do you mean adding \begin{equation} ....\end{equation}, or I should further modify the 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.

More details can be found in the API docs for
[MinMaxScaler](api/scala/index.html#org.apache.spark.ml.feature.MinMaxScaler) and
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@jkbradley jkbradley Aug 11, 2015

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Could you please put this in the Scala code tab like this:

<div data-lang="scala">
(you can put it here)
{% highlight scala %}

And then could you also please add a reference to the other APIs under those code tabs? Thanks! (I'm trying to follow this pattern nowadays.)

[MinMaxScalerModel](api/scala/index.html#org.apache.spark.ml.feature.MinMaxScalerModel).

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">
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@jkbradley jkbradley Aug 18, 2015

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The links are not generated correctly, but you can fix it by modifying this line:

<div data-lang="scala" markdown="1">

{% 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 by fitting the StandardScaler
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@jkbradley jkbradley Aug 11, 2015

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Please update the inline comments.

val scalerModel = scaler.fit(dataFrame)

// Normalize each feature to have unit standard deviation.
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@jkbradley jkbradley Aug 11, 2015

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Please update the inline comments.

val scaledData = scalerModel.transform(dataFrame)
{% endhighlight %}
</div>

<div data-lang="java">
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@jkbradley jkbradley Aug 18, 2015

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same here (add markdown="1")

{% 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 by fitting the StandardScaler
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@jkbradley jkbradley Aug 11, 2015

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Please update the inline comments.

MinMaxScalerModel scalerModel = scaler.fit(dataFrame);

// Normalize each feature to have unit standard deviation.
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@jkbradley jkbradley Aug 11, 2015

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Please update the inline comments.

DataFrame scaledData = scalerModel.transform(dataFrame);
{% endhighlight %}
</div>
</div>

## 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:
Expand Down