Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/6504#discussion_r34312376
--- Diff: docs/ml-guide.md ---
@@ -157,6 +174,118 @@ There are now several algorithms in the Pipelines API
which are not in the lower
* [Feature Extraction, Transformation, and Selection](ml-features.html)
* [Ensembles](ml-ensembles.html)
+## Linear Methods with Elastic Net Regularization
+
+In MLlib, we implement popular linear methods such as logistic regression
and linear least squares with L1 or L2 regularization. Refer to [the linear
methods section](mllib-linear-methods.html) for details. In `spark.ml`, we also
include Pipelines API for [Elastic
net](http://en.wikipedia.org/wiki/Elastic_net_regularization), a hybrid of L1
and L2 regularization proposed in [this
paper](http://users.stat.umn.edu/~zouxx019/Papers/elasticnet.pdf).
Mathematically it is defined as a linear combination of the L1-norm and the
L2-norm:
+`\[
+\alpha \|\wv\|_1 + (1-\alpha) \frac{1}{2}\|\wv\|_2^2, \alpha \in [0, 1].
+\]`
+By setting $\alpha$ properly, it contains both L1 and L2 regularization as
special cases. For example, if a [linear
regression](/api/scala/index.html#org.apache.spark.ml.regression.LinearRegression)
model is trained with the elastic net parameter $\alpha$ set to $1$, it is
equivalent to a
[Lasso](http://en.wikipedia.org/wiki/Least_squares#Lasso_method) model. On the
other hand, if $\alpha$ is set to $0$, the trained model reduces to a [ridge
regression](http://en.wikipedia.org/wiki/Tikhonov_regularization) model. We
implement Pipelines API for both linear regression and logistic regression with
elastic net regularization.
+
+**Examples**
+
+<div class="codetabs">
+
+<div data-lang="scala" markdown="1">
+The following code illustrates how to load a sample dataset and use
logistic regression with elastic net regularization to fit a model.
+
+{% highlight scala %}
+
+import org.apache.spark.ml.classification.LogisticRegression
+import org.apache.spark.mllib.util.MLUtils
+
+// Load training data
+val training = MLUtils.loadLibSVMFile(sc,
"data/mllib/sample_libsvm_data.txt").toDF()
+
+val lr = new LogisticRegression()
+ .setRegParam(0.3)
+ .setElasticNetParam(0.8)
+ .setTol(1e-6)
+
+// Fit the model
+val lrModel = lr.fit(training)
+
+// Print the weights and intercept for logistic regression
+println(s"Weights: ${lrModel.weights} Intercept: ${lrModel.intercept}")
+
+{% endhighlight %}
+
+</div>
+
+<div data-lang="java" markdown="1">
+The following code illustrates how to load a sample dataset and use
logistic regression with elastic net regularization to fit a model.
+
+{% highlight java %}
+
+import org.apache.spark.ml.classification.LogisticRegression;
+import org.apache.spark.ml.classification.LogisticRegressionModel;
+import org.apache.spark.mllib.regression.LabeledPoint;
+import org.apache.spark.mllib.util.MLUtils;
+import org.apache.spark.SparkConf;
+import org.apache.spark.SparkContext;
+import org.apache.spark.sql.DataFrame;
+import org.apache.spark.sql.SQLContext;
+
+public class LogisticRegressionWithElasticNetExample {
+ public static void main(String[] args) {
+ SparkConf conf = new SparkConf()
+ .setAppName("Logistic Regression with Elastic Net Example");
+
+ SparkContext sc = new SparkContext(conf);
+ SQLContext sql = new SQLContext(sc);
+ String path = "sample_libsvm_data.txt";
+
+ // Load training data
+ DataFrame training = sql.createDataFrame(MLUtils.loadLibSVMFile(sc,
path).toJavaRDD(), LabeledPoint.class);
+
+ LogisticRegression lr = new LogisticRegression()
+ .setMaxIter(10)
+ .setRegParam(0.3)
+ .setElasticNetParam(0.8)
+ .setThreshold(0.6)
+ .setProbabilityCol("myProbability");
+
+ // Fit the model
+ LogisticRegressionModel lrModel = lr.fit(training);
+
+ // Print the weights and intercept for logistic regression
+ System.out.println("Weights: " + lrModel.weights() + " Intercept: " +
lrModel.intercept());
+ }
+}
+{% endhighlight %}
+</div>
+
+<div data-lang="python" markdown="1">
+The following code illustrates how to load a sample dataset and use
logistic regression with elastic net regularization to fit a model.
+
+{% highlight python %}
+
+from pyspark.ml.classification import LogisticRegression
+from pyspark.mllib.regression import LabeledPoint
+from pyspark.mllib.util import MLUtils
+
+# Load training data
+training = MLUtils.loadLibSVMFile(sc,
"data/mllib/sample_libsvm_data.txt").toDF()
+
+lr = LogisticRegression(maxIter=10, regParam=0.3)
--- End diff --
Let's be Pythonic and set the elasticNetParam via an argument, like the
other params.
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