From a1894422ad6b3335c84c73ba9466da6677d893cb Mon Sep 17 00:00:00 2001
From: "Joseph K. Bradley" <joseph@databricks.com>
Date: Sun, 21 Jun 2015 16:25:25 -0700
Subject: [PATCH] [SPARK-7715] [MLLIB] [ML] [DOC] Updated MLlib programming
guide for release 1.4
Reorganized docs a bit. Added migration guides.
**Q**: Do we want to say more for the 1.3 -> 1.4 migration guide for ```spark.ml```? It would be a lot.
CC: mengxr
Author: Joseph K. Bradley <joseph@databricks.com>
Closes #6897 from jkbradley/ml-guide-1.4 and squashes the following commits:
4bf26d6 [Joseph K. Bradley] tiny fix
8085067 [Joseph K. Bradley] fixed spacing/layout issues in ml guide from previous commit in this PR
6cd5c78 [Joseph K. Bradley] Updated MLlib programming guide for release 1.4
---
docs/ml-guide.md | 32 +++++++++++++---------
docs/mllib-feature-extraction.md | 3 +-
docs/mllib-guide.md | 47 +++++++++++++++++++-------------
docs/mllib-migration-guides.md | 16 +++++++++++
4 files changed, 65 insertions(+), 33 deletions(-)
diff --git a/docs/ml-guide.md b/docs/ml-guide.md
index 4eb622d4b9..c74cb1f1ef 100644
--- a/docs/ml-guide.md
+++ b/docs/ml-guide.md
@@ -3,10 +3,10 @@ layout: global
title: Spark ML Programming Guide
---
-`spark.ml` is a new package introduced in Spark 1.2, 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.
+
+*Graduated from Alpha!* The Pipelines API is no longer an alpha component, although many elements of it are still `Experimental` or `DeveloperApi`.
Note that we will keep supporting and adding features to `spark.mllib` along with the
development of `spark.ml`.
@@ -14,6 +14,12 @@ Users should be comfortable using `spark.mllib` features and expect more feature
Developers should contribute new algorithms to `spark.mllib` and can optionally contribute
to `spark.ml`.
+Guides for sub-packages of `spark.ml` include:
+
+* [Feature Extraction, Transformation, and Selection](ml-features.html): Details on transformers supported in the Pipelines API, including a few not in the lower-level `spark.mllib` API
+* [Ensembles](ml-ensembles.html): Details on ensemble learning methods in the Pipelines API
+
+
**Table of Contents**
* This will become a table of contents (this text will be scraped).
@@ -148,16 +154,6 @@ Parameters belong to specific instances of `Estimator`s and `Transformer`s.
For example, if we have two `LogisticRegression` instances `lr1` and `lr2`, then we can build a `ParamMap` with both `maxIter` parameters specified: `ParamMap(lr1.maxIter -> 10, lr2.maxIter -> 20)`.
This is useful if there are two algorithms with the `maxIter` parameter in a `Pipeline`.
-# Algorithm Guides
-
-There are now several algorithms in the Pipelines API which are not in the lower-level MLlib API, so we link to documentation for them here. These algorithms are mostly feature transformers, which fit naturally into the `Transformer` abstraction in Pipelines, and ensembles, which fit naturally into the `Estimator` abstraction in the Pipelines.
-
-**Pipelines API Algorithm Guides**
-
-* [Feature Extraction, Transformation, and Selection](ml-features.html)
-* [Ensembles](ml-ensembles.html)
-
-
# Code Examples
This section gives code examples illustrating the functionality discussed above.
@@ -783,6 +779,16 @@ Spark ML also depends upon Spark SQL, but the relevant parts of Spark SQL do not
# Migration Guide
+## From 1.3 to 1.4
+
+Several major API changes occurred, including:
+* `Param` and other APIs for specifying parameters
+* `uid` unique IDs for Pipeline components
+* Reorganization of certain classes
+Since the `spark.ml` API was an Alpha Component in Spark 1.3, we do not list all changes here.
+
+However, now that `spark.ml` is no longer an Alpha Component, we will provide details on any API changes for future releases.
+
## From 1.2 to 1.3
The main API changes are from Spark SQL. We list the most important changes here:
diff --git a/docs/mllib-feature-extraction.md b/docs/mllib-feature-extraction.md
index 1197dbbb8d..83e937635a 100644
--- a/docs/mllib-feature-extraction.md
+++ b/docs/mllib-feature-extraction.md
@@ -576,8 +576,9 @@ parsedData = data.map(lambda x: [float(t) for t in x.split(" ")])
transformingVector = Vectors.dense([0.0, 1.0, 2.0])
transformer = ElementwiseProduct(transformingVector)
-# Batch transform.
+# Batch transform
transformedData = transformer.transform(parsedData)
+# Single-row transform
transformedData2 = transformer.transform(parsedData.first())
{% endhighlight %}
diff --git a/docs/mllib-guide.md b/docs/mllib-guide.md
index de7d66fb2d..d2d1cc93fe 100644
--- a/docs/mllib-guide.md
+++ b/docs/mllib-guide.md
@@ -7,7 +7,19 @@ description: MLlib machine learning library overview for Spark SPARK_VERSION_SHO
MLlib is Spark's scalable machine learning library consisting of common learning algorithms and utilities,
including classification, regression, clustering, collaborative
-filtering, dimensionality reduction, as well as underlying optimization primitives, as outlined below:
+filtering, dimensionality reduction, as well as underlying optimization primitives.
+Guides for individual algorithms are listed below.
+
+The API is divided into 2 parts:
+
+* [The original `spark.mllib` API](mllib-guide.html#mllib-types-algorithms-and-utilities) is the primary API.
+* [The "Pipelines" `spark.ml` API](mllib-guide.html#sparkml-high-level-apis-for-ml-pipelines) is a higher-level API for constructing ML workflows.
+
+We list major functionality from both below, with links to detailed guides.
+
+# MLlib types, algorithms and utilities
+
+This lists functionality included in `spark.mllib`, the main MLlib API.
* [Data types](mllib-data-types.html)
* [Basic statistics](mllib-statistics.html)
@@ -49,8 +61,8 @@ and the migration guide below will explain all changes between releases.
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.
+
+*Graduated from Alpha!* The Pipelines API is no longer an alpha component, although many elements of it are still `Experimental` or `DeveloperApi`.
Note that we will keep supporting and adding features to `spark.mllib` along with the
development of `spark.ml`.
@@ -58,7 +70,11 @@ Users should be comfortable using `spark.mllib` features and expect more feature
Developers should contribute new algorithms to `spark.mllib` and can optionally contribute
to `spark.ml`.
-See the **[spark.ml programming guide](ml-guide.html)** for more information on this package.
+More detailed guides for `spark.ml` include:
+
+* **[spark.ml programming guide](ml-guide.html)**: overview of the Pipelines API and major concepts
+* [Feature transformers](ml-features.html): Details on transformers supported in the Pipelines API, including a few not in the lower-level `spark.mllib` API
+* [Ensembles](ml-ensembles.html): Details on ensemble learning methods in the Pipelines API
# Dependencies
@@ -90,21 +106,14 @@ version 1.4 or newer.
For the `spark.ml` package, please see the [spark.ml Migration Guide](ml-guide.html#migration-guide).
-## From 1.2 to 1.3
-
-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.
-
-* *(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.
-* In linear regression (including Lasso and ridge regression), the squared loss is now divided by 2.
- So in order to produce the same result as in 1.2, the regularization parameter needs to be divided by 2 and the step size needs to be multiplied by 2.
+## From 1.3 to 1.4
+
+In the `spark.mllib` package, there were several breaking changes, but all in `DeveloperApi` or `Experimental` APIs:
+
+* Gradient-Boosted Trees
+ * *(Breaking change)* The signature of the [`Loss.gradient`](api/scala/index.html#org.apache.spark.mllib.tree.loss.Loss) method was changed. This is only an issues for users who wrote their own losses for GBTs.
+ * *(Breaking change)* The `apply` and `copy` methods for the case class [`BoostingStrategy`](api/scala/index.html#org.apache.spark.mllib.tree.configuration.BoostingStrategy) have been changed because of a modification to the case class fields. This could be an issue for users who use `BoostingStrategy` to set GBT parameters.
+* *(Breaking change)* The return value of [`LDA.run`](api/scala/index.html#org.apache.spark.mllib.clustering.LDA) has changed. It now returns an abstract class `LDAModel` instead of the concrete class `DistributedLDAModel`. The object of type `LDAModel` can still be cast to the appropriate concrete type, which depends on the optimization algorithm.
## Previous Spark Versions
diff --git a/docs/mllib-migration-guides.md b/docs/mllib-migration-guides.md
index 4de2d9491a..8df68d81f3 100644
--- a/docs/mllib-migration-guides.md
+++ b/docs/mllib-migration-guides.md
@@ -7,6 +7,22 @@ description: MLlib migration guides from before Spark SPARK_VERSION_SHORT
The migration guide for the current Spark version is kept on the [MLlib Programming Guide main page](mllib-guide.html#migration-guide).
+## From 1.2 to 1.3
+
+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.
+
+* *(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 pattern 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.
+* In linear regression (including Lasso and ridge regression), the squared loss is now divided by 2.
+ So in order to produce the same result as in 1.2, the regularization parameter needs to be divided by 2 and the step size needs to be multiplied by 2.
+
## From 1.1 to 1.2
The only API changes in MLlib v1.2 are in
--
GitLab