[SPARK-10669] [DOCS] Link to each language's API in codetabs in ML docs: spark.mllib

In the Markdown docs for the spark.mllib Programming Guide, we have code examples with codetabs for each language. We should link to each language's API docs within the corresponding codetab, but we are inconsistent about this. For an example of what we want to do, see the "ChiSqSelector" section in https://github.com/apache/spark/blob/64743870f23bffb8d96dcc8a0181c1452782a151/docs/mllib-feature-extraction.md
This JIRA is just for spark.mllib, not spark.ml.

Please let me know if more work is needed, thanks a lot.

Author: Xin Ren <iamshrek@126.com>

Closes #8977 from keypointt/SPARK-10669.

1 files changed, 15 insertions, 0 deletions

diff --git a/docs/mllib-evaluation-metrics.md b/docs/mllib-evaluation-metrics.md
index 7066d5c974..2270f7a34b 100644
--- a/docs/mllib-evaluation-metrics.md
+++ b/docs/mllib-evaluation-metrics.md
@@ -102,6 +102,7 @@ The following code snippets illustrate how to load a sample dataset, train a bin
 data, and evaluate the performance of the algorithm by several binary evaluation metrics.
 
 <div data-lang="scala" markdown="1">
+Refer to the [`LogisticRegressionWithLBFGS` Scala docs](api/scala/index.html#org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS) and [`BinaryClassificationMetrics` Scala docs](api/scala/index.html#org.apache.spark.mllib.evaluation.BinaryClassificationMetrics) for details on the API.
 
 {% highlight scala %}
 import org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS
@@ -179,6 +180,7 @@ println("Area under ROC = " + auROC)
 </div>
 
 <div data-lang="java" markdown="1">
+Refer to the [`LogisticRegressionModel` Java docs](api/java/org/apache/spark/mllib/classification/LogisticRegressionModel.html) and [`LogisticRegressionWithLBFGS` Java docs](api/java/org/apache/spark/mllib/classification/LogisticRegressionWithLBFGS.html) for details on the API.
 
 {% highlight java %}
 import scala.Tuple2;
@@ -276,6 +278,7 @@ public class BinaryClassification {
 </div>
 
 <div data-lang="python" markdown="1">
+Refer to the [`BinaryClassificationMetrics` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.evaluation.BinaryClassificationMetrics) and [`LogisticRegressionWithLBFGS` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.classification.LogisticRegressionWithLBFGS) for more details on the API.
 
 {% highlight python %}
 from pyspark.mllib.classification import LogisticRegressionWithLBFGS
@@ -428,6 +431,7 @@ The following code snippets illustrate how to load a sample dataset, train a mul
 the data, and evaluate the performance of the algorithm by several multiclass classification evaluation metrics.
 
 <div data-lang="scala" markdown="1">
+Refer to the [`MulticlassMetrics` Scala docs](api/scala/index.html#org.apache.spark.mllib.evaluation.MulticlassMetrics) for details on the API.
 
 {% highlight scala %}
 import org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS
@@ -501,6 +505,7 @@ println(s"Weighted false positive rate: ${metrics.weightedFalsePositiveRate}")
 </div>
 
 <div data-lang="java" markdown="1">
+Refer to the [`MulticlassMetrics` Java docs](api/java/org/apache/spark/mllib/evaluation/MulticlassMetrics.html) for details on the API.
 
 {% highlight java %}
 import scala.Tuple2;
@@ -580,6 +585,7 @@ public class MulticlassClassification {
 </div>
 
 <div data-lang="python" markdown="1">
+Refer to the [`MulticlassMetrics` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.evaluation.MulticlassMetrics) for more details on the API.
 
 {% highlight python %}
 from pyspark.mllib.classification import LogisticRegressionWithLBFGS
@@ -758,6 +764,7 @@ True classes:
 <div class="codetabs">
 
 <div data-lang="scala" markdown="1">
+Refer to the [`MultilabelMetrics` Scala docs](api/scala/index.html#org.apache.spark.mllib.evaluation.MultilabelMetrics) for details on the API.
 
 {% highlight scala %}
 import org.apache.spark.mllib.evaluation.MultilabelMetrics
@@ -802,6 +809,7 @@ println(s"Subset accuracy = ${metrics.subsetAccuracy}")
 </div>
 
 <div data-lang="java" markdown="1">
+Refer to the [`MultilabelMetrics` Java docs](api/java/org/apache/spark/mllib/evaluation/MultilabelMetrics.html) for details on the API.
 
 {% highlight java %}
 import scala.Tuple2;
@@ -864,6 +872,7 @@ public class MultilabelClassification {
 </div>
 
 <div data-lang="python" markdown="1">
+Refer to the [`MultilabelMetrics` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.evaluation.MultilabelMetrics) for more details on the API.
 
 {% highlight python %}
 from pyspark.mllib.evaluation import MultilabelMetrics
@@ -1016,6 +1025,7 @@ expanded world of non-positive weights are "the same as never having interacted
 <div class="codetabs">
 
 <div data-lang="scala" markdown="1">
+Refer to the [`RegressionMetrics` Scala docs](api/scala/index.html#org.apache.spark.mllib.evaluation.RegressionMetrics) and [`RankingMetrics` Scala docs](api/scala/index.html#org.apache.spark.mllib.evaluation.RankingMetrics) for details on the API.
 
 {% highlight scala %}
 import org.apache.spark.mllib.evaluation.{RegressionMetrics, RankingMetrics}
@@ -1095,6 +1105,7 @@ println(s"R-squared = ${regressionMetrics.r2}")
 </div>
 
 <div data-lang="java" markdown="1">
+Refer to the [`RegressionMetrics` Java docs](api/java/org/apache/spark/mllib/evaluation/RegressionMetrics.html) and [`RankingMetrics` Java docs](api/java/org/apache/spark/mllib/evaluation/RankingMetrics.html) for details on the API.
 
 {% highlight java %}
 import scala.Tuple2;
@@ -1256,6 +1267,7 @@ public class Ranking {
 </div>
 
 <div data-lang="python" markdown="1">
+Refer to the [`RegressionMetrics` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.evaluation.RegressionMetrics) and [`RankingMetrics` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.evaluation.RankingMetrics) for more details on the API.
 
 {% highlight python %}
 from pyspark.mllib.recommendation import ALS, Rating
@@ -1336,6 +1348,7 @@ The following code snippets illustrate how to load a sample dataset, train a lin
 and evaluate the performance of the algorithm by several regression metrics.
 
 <div data-lang="scala" markdown="1">
+Refer to the [`RegressionMetrics` Scala docs](api/scala/index.html#org.apache.spark.mllib.evaluation.RegressionMetrics) for details on the API.
 
 {% highlight scala %}
 import org.apache.spark.mllib.regression.LabeledPoint
@@ -1379,6 +1392,7 @@ println(s"Explained variance = ${metrics.explainedVariance}")
 </div>
 
 <div data-lang="java" markdown="1">
+Refer to the [`RegressionMetrics` Java docs](api/java/org/apache/spark/mllib/evaluation/RegressionMetrics.html) for details on the API.
 
 {% highlight java %}
 import scala.Tuple2;
@@ -1455,6 +1469,7 @@ public class LinearRegression {
 </div>
 
 <div data-lang="python" markdown="1">
+Refer to the [`RegressionMetrics` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.evaluation.RegressionMetrics) for more details on the API.
 
 {% highlight python %}
 from pyspark.mllib.regression import LabeledPoint, LinearRegressionWithSGD