[SPARK-5867] [SPARK-5892] [doc] [ml] [mllib] Doc cleanups for 1.3 release

For SPARK-5867:
* The spark.ml programming guide needs to be updated to use the new SQL DataFrame API instead of the old SchemaRDD API.
* It should also include Python examples now.

For SPARK-5892:
* Fix Python docs
* Various other cleanups

BTW, I accidentally merged this with master.  If you want to compile it on your own, use this branch which is based on spark/branch-1.3 and cherry-picks the commits from this PR: [https://github.com/jkbradley/spark/tree/doc-review-1.3-check]

CC: mengxr  (ML),  davies  (Python docs)

Author: Joseph K. Bradley <joseph@databricks.com>

Closes #4675 from jkbradley/doc-review-1.3 and squashes the following commits:

f191bb0 [Joseph K. Bradley] small cleanups
e786efa [Joseph K. Bradley] small doc corrections
6b1ab4a [Joseph K. Bradley] fixed python lint test
946affa [Joseph K. Bradley] Added sample data for ml.MovieLensALS example.  Changed spark.ml Java examples to use DataFrames API instead of sql()
da81558 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into doc-review-1.3
629dbf5 [Joseph K. Bradley] Updated based on code review: * made new page for old migration guides * small fixes * moved inherit_doc in python
b9df7c4 [Joseph K. Bradley] Small cleanups: toDF to toDF(), adding s for string interpolation
34b067f [Joseph K. Bradley] small doc correction
da16aef [Joseph K. Bradley] Fixed python mllib docs
8cce91c [Joseph K. Bradley] GMM: removed old imports, added some doc
695f3f6 [Joseph K. Bradley] partly done trying to fix inherit_doc for class hierarchies in python docs
a72c018 [Joseph K. Bradley] made ChiSqTestResult appear in python docs
b05a80d [Joseph K. Bradley] organize imports. doc cleanups
e572827 [Joseph K. Bradley] updated programming guide for ml and mllib

1 files changed, 15 insertions, 118 deletions

diff --git a/docs/mllib-guide.md b/docs/mllib-guide.md
index 0ca51f92d7..4c7a7d9115 100644
--- a/docs/mllib-guide.md
+++ b/docs/mllib-guide.md
@@ -46,7 +46,7 @@ and the migration guide below will explain all changes between releases.
 
 # spark.ml: high-level APIs for ML pipelines
 
-Spark 1.2 includes a new package called `spark.ml`, which aims to provide a uniform set of
+Spark 1.2 introduced a new package called `spark.ml`, which aims to provide a uniform set of
 high-level APIs that help users create and tune practical machine learning pipelines.
 It is currently an alpha component, and we would like to hear back from the community about
 how it fits real-world use cases and how it could be improved.
@@ -92,125 +92,22 @@ version 1.4 or newer.
 
 # Migration Guide
 
-## From 1.1 to 1.2
+For the `spark.ml` package, please see the [spark.ml Migration Guide](ml-guide.html#migration-guide).
 
-The only API changes in MLlib v1.2 are in
-[`DecisionTree`](api/scala/index.html#org.apache.spark.mllib.tree.DecisionTree),
-which continues to be an experimental API in MLlib 1.2:
+## From 1.2 to 1.3
 
-1. *(Breaking change)* The Scala API for classification takes a named argument specifying the number
-of classes.  In MLlib v1.1, this argument was called `numClasses` in Python and
-`numClassesForClassification` in Scala.  In MLlib v1.2, the names are both set to `numClasses`.
-This `numClasses` parameter is specified either via
-[`Strategy`](api/scala/index.html#org.apache.spark.mllib.tree.configuration.Strategy)
-or via [`DecisionTree`](api/scala/index.html#org.apache.spark.mllib.tree.DecisionTree)
-static `trainClassifier` and `trainRegressor` methods.
+In the `spark.mllib` package, there were several breaking changes.  The first change (in `ALS`) is the only one in a component not marked as Alpha or Experimental.
 
-2. *(Breaking change)* The API for
-[`Node`](api/scala/index.html#org.apache.spark.mllib.tree.model.Node) has changed.
-This should generally not affect user code, unless the user manually constructs decision trees
-(instead of using the `trainClassifier` or `trainRegressor` methods).
-The tree `Node` now includes more information, including the probability of the predicted label
-(for classification).
+* *(Breaking change)* In [`ALS`](api/scala/index.html#org.apache.spark.mllib.recommendation.ALS), the extraneous method `solveLeastSquares` has been removed.  The `DeveloperApi` method `analyzeBlocks` was also removed.
+* *(Breaking change)* [`StandardScalerModel`](api/scala/index.html#org.apache.spark.mllib.feature.StandardScalerModel) remains an Alpha component. In it, the `variance` method has been replaced with the `std` method.  To compute the column variance values returned by the original `variance` method, simply square the standard deviation values returned by `std`.
+* *(Breaking change)* [`StreamingLinearRegressionWithSGD`](api/scala/index.html#org.apache.spark.mllib.regression.StreamingLinearRegressionWithSGD) remains an Experimental component.  In it, there were two changes:
+    * The constructor taking arguments was removed in favor of a builder patten using the default constructor plus parameter setter methods.
+    * Variable `model` is no longer public.
+* *(Breaking change)* [`DecisionTree`](api/scala/index.html#org.apache.spark.mllib.tree.DecisionTree) remains an Experimental component.  In it and its associated classes, there were several changes:
+    * In `DecisionTree`, the deprecated class method `train` has been removed.  (The object/static `train` methods remain.)
+    * In `Strategy`, the `checkpointDir` parameter has been removed.  Checkpointing is still supported, but the checkpoint directory must be set before calling tree and tree ensemble training.
+* `PythonMLlibAPI` (the interface between Scala/Java and Python for MLlib) was a public API but is now private, declared `private[python]`.  This was never meant for external use.
 
-3. Printing methods' output has changed.  The `toString` (Scala/Java) and `__repr__` (Python) methods used to print the full model; they now print a summary.  For the full model, use `toDebugString`.
+## Previous Spark Versions
 
-Examples in the Spark distribution and examples in the
-[Decision Trees Guide](mllib-decision-tree.html#examples) have been updated accordingly.
-
-## From 1.0 to 1.1
-
-The only API changes in MLlib v1.1 are in
-[`DecisionTree`](api/scala/index.html#org.apache.spark.mllib.tree.DecisionTree),
-which continues to be an experimental API in MLlib 1.1:
-
-1. *(Breaking change)* The meaning of tree depth has been changed by 1 in order to match
-the implementations of trees in
-[scikit-learn](http://scikit-learn.org/stable/modules/classes.html#module-sklearn.tree)
-and in [rpart](http://cran.r-project.org/web/packages/rpart/index.html).
-In MLlib v1.0, a depth-1 tree had 1 leaf node, and a depth-2 tree had 1 root node and 2 leaf nodes.
-In MLlib v1.1, a depth-0 tree has 1 leaf node, and a depth-1 tree has 1 root node and 2 leaf nodes.
-This depth is specified by the `maxDepth` parameter in
-[`Strategy`](api/scala/index.html#org.apache.spark.mllib.tree.configuration.Strategy)
-or via [`DecisionTree`](api/scala/index.html#org.apache.spark.mllib.tree.DecisionTree)
-static `trainClassifier` and `trainRegressor` methods.
-
-2. *(Non-breaking change)* We recommend using the newly added `trainClassifier` and `trainRegressor`
-methods to build a [`DecisionTree`](api/scala/index.html#org.apache.spark.mllib.tree.DecisionTree),
-rather than using the old parameter class `Strategy`.  These new training methods explicitly
-separate classification and regression, and they replace specialized parameter types with
-simple `String` types.
-
-Examples of the new, recommended `trainClassifier` and `trainRegressor` are given in the
-[Decision Trees Guide](mllib-decision-tree.html#examples).
-
-## From 0.9 to 1.0
-
-In MLlib v1.0, we support both dense and sparse input in a unified way, which introduces a few
-breaking changes.  If your data is sparse, please store it in a sparse format instead of dense to
-take advantage of sparsity in both storage and computation. Details are described below.
-
-<div class="codetabs">
-<div data-lang="scala" markdown="1">
-
-We used to represent a feature vector by `Array[Double]`, which is replaced by
-[`Vector`](api/scala/index.html#org.apache.spark.mllib.linalg.Vector) in v1.0. Algorithms that used
-to accept `RDD[Array[Double]]` now take
-`RDD[Vector]`. [`LabeledPoint`](api/scala/index.html#org.apache.spark.mllib.regression.LabeledPoint)
-is now a wrapper of `(Double, Vector)` instead of `(Double, Array[Double])`. Converting
-`Array[Double]` to `Vector` is straightforward:
-
-{% highlight scala %}
-import org.apache.spark.mllib.linalg.{Vector, Vectors}
-
-val array: Array[Double] = ... // a double array
-val vector: Vector = Vectors.dense(array) // a dense vector
-{% endhighlight %}
-
-[`Vectors`](api/scala/index.html#org.apache.spark.mllib.linalg.Vectors$) provides factory methods to create sparse vectors.
-
-*Note*: Scala imports `scala.collection.immutable.Vector` by default, so you have to import `org.apache.spark.mllib.linalg.Vector` explicitly to use MLlib's `Vector`.
-
-</div>
-
-<div data-lang="java" markdown="1">
-
-We used to represent a feature vector by `double[]`, which is replaced by
-[`Vector`](api/java/index.html?org/apache/spark/mllib/linalg/Vector.html) in v1.0. Algorithms that used
-to accept `RDD<double[]>` now take
-`RDD<Vector>`. [`LabeledPoint`](api/java/index.html?org/apache/spark/mllib/regression/LabeledPoint.html)
-is now a wrapper of `(double, Vector)` instead of `(double, double[])`. Converting `double[]` to
-`Vector` is straightforward:
-
-{% highlight java %}
-import org.apache.spark.mllib.linalg.Vector;
-import org.apache.spark.mllib.linalg.Vectors;
-
-double[] array = ... // a double array
-Vector vector = Vectors.dense(array); // a dense vector
-{% endhighlight %}
-
-[`Vectors`](api/scala/index.html#org.apache.spark.mllib.linalg.Vectors$) provides factory methods to
-create sparse vectors.
-
-</div>
-
-<div data-lang="python" markdown="1">
-
-We used to represent a labeled feature vector in a NumPy array, where the first entry corresponds to
-the label and the rest are features.  This representation is replaced by class
-[`LabeledPoint`](api/python/pyspark.mllib.regression.LabeledPoint-class.html), which takes both
-dense and sparse feature vectors.
-
-{% highlight python %}
-from pyspark.mllib.linalg import SparseVector
-from pyspark.mllib.regression import LabeledPoint
-
-# Create a labeled point with a positive label and a dense feature vector.
-pos = LabeledPoint(1.0, [1.0, 0.0, 3.0])
-
-# Create a labeled point with a negative label and a sparse feature vector.
-neg = LabeledPoint(0.0, SparseVector(3, [0, 2], [1.0, 3.0]))
-{% endhighlight %}
-</div>
-</div>
+Earlier migration guides are archived [on this page](mllib-migration-guides.html).