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<h1 class="title">Spark ML Programming Guide</h1>
<p><code>spark.ml</code> is a new package introduced in Spark 1.2, 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.</p>
<p>Note that we will keep supporting and adding features to <code>spark.mllib</code> along with the
development of <code>spark.ml</code>.
Users should be comfortable using <code>spark.mllib</code> features and expect more features coming.
Developers should contribute new algorithms to <code>spark.mllib</code> and can optionally contribute
to <code>spark.ml</code>.</p>
<p><strong>Table of Contents</strong></p>
<ul id="markdown-toc">
<li><a href="#main-concepts">Main Concepts</a> <ul>
<li><a href="#ml-dataset">ML Dataset</a></li>
<li><a href="#ml-algorithms">ML Algorithms</a> <ul>
<li><a href="#transformers">Transformers</a></li>
<li><a href="#estimators">Estimators</a></li>
<li><a href="#properties-of-ml-algorithms">Properties of ML Algorithms</a></li>
</ul>
</li>
<li><a href="#pipeline">Pipeline</a> <ul>
<li><a href="#how-it-works">How It Works</a></li>
<li><a href="#details">Details</a></li>
</ul>
</li>
<li><a href="#parameters">Parameters</a></li>
</ul>
</li>
<li><a href="#code-examples">Code Examples</a> <ul>
<li><a href="#example-estimator-transformer-and-param">Example: Estimator, Transformer, and Param</a></li>
<li><a href="#example-pipeline">Example: Pipeline</a></li>
<li><a href="#example-model-selection-via-cross-validation">Example: Model Selection via Cross-Validation</a></li>
</ul>
</li>
<li><a href="#dependencies">Dependencies</a></li>
<li><a href="#migration-guide">Migration Guide</a> <ul>
<li><a href="#from-12-to-13">From 1.2 to 1.3</a></li>
</ul>
</li>
</ul>
<h1 id="main-concepts">Main Concepts</h1>
<p>Spark ML standardizes APIs for machine learning algorithms to make it easier to combine multiple algorithms into a single pipeline, or workflow. This section covers the key concepts introduced by the Spark ML API.</p>
<ul>
<li>
<p><strong><a href="ml-guide.html#ml-dataset">ML Dataset</a></strong>: Spark ML uses the <a href="api/scala/index.html#org.apache.spark.sql.DataFrame"><code>DataFrame</code></a> from Spark SQL as a dataset which can hold a variety of data types.
E.g., a dataset could have different columns storing text, feature vectors, true labels, and predictions.</p>
</li>
<li>
<p><strong><a href="ml-guide.html#transformers"><code>Transformer</code></a></strong>: A <code>Transformer</code> is an algorithm which can transform one <code>DataFrame</code> into another <code>DataFrame</code>.
E.g., an ML model is a <code>Transformer</code> which transforms an RDD with features into an RDD with predictions.</p>
</li>
<li>
<p><strong><a href="ml-guide.html#estimators"><code>Estimator</code></a></strong>: An <code>Estimator</code> is an algorithm which can be fit on a <code>DataFrame</code> to produce a <code>Transformer</code>.
E.g., a learning algorithm is an <code>Estimator</code> which trains on a dataset and produces a model.</p>
</li>
<li>
<p><strong><a href="ml-guide.html#pipeline"><code>Pipeline</code></a></strong>: A <code>Pipeline</code> chains multiple <code>Transformer</code>s and <code>Estimator</code>s together to specify an ML workflow.</p>
</li>
<li>
<p><strong><a href="ml-guide.html#parameters"><code>Param</code></a></strong>: All <code>Transformer</code>s and <code>Estimator</code>s now share a common API for specifying parameters.</p>
</li>
</ul>
<h2 id="ml-dataset">ML Dataset</h2>
<p>Machine learning can be applied to a wide variety of data types, such as vectors, text, images, and structured data.
Spark ML adopts the <a href="api/scala/index.html#org.apache.spark.sql.DataFrame"><code>DataFrame</code></a> from Spark SQL in order to support a variety of data types under a unified Dataset concept.</p>
<p><code>DataFrame</code> supports many basic and structured types; see the <a href="sql-programming-guide.html#spark-sql-datatype-reference">Spark SQL datatype reference</a> for a list of supported types.
In addition to the types listed in the Spark SQL guide, <code>DataFrame</code> can use ML <a href="api/scala/index.html#org.apache.spark.mllib.linalg.Vector"><code>Vector</code></a> types.</p>
<p>A <code>DataFrame</code> can be created either implicitly or explicitly from a regular <code>RDD</code>. See the code examples below and the <a href="sql-programming-guide.html">Spark SQL programming guide</a> for examples.</p>
<p>Columns in a <code>DataFrame</code> are named. The code examples below use names such as “text,” “features,” and “label.”</p>
<h2 id="ml-algorithms">ML Algorithms</h2>
<h3 id="transformers">Transformers</h3>
<p>A <a href="api/scala/index.html#org.apache.spark.ml.Transformer"><code>Transformer</code></a> is an abstraction which includes feature transformers and learned models. Technically, a <code>Transformer</code> implements a method <code>transform()</code> which converts one <code>DataFrame</code> into another, generally by appending one or more columns.
For example:</p>
<ul>
<li>A feature transformer might take a dataset, read a column (e.g., text), convert it into a new column (e.g., feature vectors), append the new column to the dataset, and output the updated dataset.</li>
<li>A learning model might take a dataset, read the column containing feature vectors, predict the label for each feature vector, append the labels as a new column, and output the updated dataset.</li>
</ul>
<h3 id="estimators">Estimators</h3>
<p>An <a href="api/scala/index.html#org.apache.spark.ml.Estimator"><code>Estimator</code></a> abstracts the concept of a learning algorithm or any algorithm which fits or trains on data. Technically, an <code>Estimator</code> implements a method <code>fit()</code> which accepts a <code>DataFrame</code> and produces a <code>Transformer</code>.
For example, a learning algorithm such as <code>LogisticRegression</code> is an <code>Estimator</code>, and calling <code>fit()</code> trains a <code>LogisticRegressionModel</code>, which is a <code>Transformer</code>.</p>
<h3 id="properties-of-ml-algorithms">Properties of ML Algorithms</h3>
<p><code>Transformer</code>s and <code>Estimator</code>s are both stateless. In the future, stateful algorithms may be supported via alternative concepts.</p>
<p>Each instance of a <code>Transformer</code> or <code>Estimator</code> has a unique ID, which is useful in specifying parameters (discussed below).</p>
<h2 id="pipeline">Pipeline</h2>
<p>In machine learning, it is common to run a sequence of algorithms to process and learn from data.
E.g., a simple text document processing workflow might include several stages:</p>
<ul>
<li>Split each document’s text into words.</li>
<li>Convert each document’s words into a numerical feature vector.</li>
<li>Learn a prediction model using the feature vectors and labels.</li>
</ul>
<p>Spark ML represents such a workflow as a <a href="api/scala/index.html#org.apache.spark.ml.Pipeline"><code>Pipeline</code></a>,
which consists of a sequence of <a href="api/scala/index.html#org.apache.spark.ml.PipelineStage"><code>PipelineStage</code>s</a> (<code>Transformer</code>s and <code>Estimator</code>s) to be run in a specific order. We will use this simple workflow as a running example in this section.</p>
<h3 id="how-it-works">How It Works</h3>
<p>A <code>Pipeline</code> is specified as a sequence of stages, and each stage is either a <code>Transformer</code> or an <code>Estimator</code>.
These stages are run in order, and the input dataset is modified as it passes through each stage.
For <code>Transformer</code> stages, the <code>transform()</code> method is called on the dataset.
For <code>Estimator</code> stages, the <code>fit()</code> method is called to produce a <code>Transformer</code> (which becomes part of the <code>PipelineModel</code>, or fitted <code>Pipeline</code>), and that <code>Transformer</code>’s <code>transform()</code> method is called on the dataset.</p>
<p>We illustrate this for the simple text document workflow. The figure below is for the <em>training time</em> usage of a <code>Pipeline</code>.</p>
<p style="text-align: center;">
<img src="img/ml-Pipeline.png" title="Spark ML Pipeline Example" alt="Spark ML Pipeline Example" width="80%" />
</p>
<p>Above, the top row represents a <code>Pipeline</code> with three stages.
The first two (<code>Tokenizer</code> and <code>HashingTF</code>) are <code>Transformer</code>s (blue), and the third (<code>LogisticRegression</code>) is an <code>Estimator</code> (red).
The bottom row represents data flowing through the pipeline, where cylinders indicate <code>DataFrame</code>s.
The <code>Pipeline.fit()</code> method is called on the original dataset which has raw text documents and labels.
The <code>Tokenizer.transform()</code> method splits the raw text documents into words, adding a new column with words into the dataset.
The <code>HashingTF.transform()</code> method converts the words column into feature vectors, adding a new column with those vectors to the dataset.
Now, since <code>LogisticRegression</code> is an <code>Estimator</code>, the <code>Pipeline</code> first calls <code>LogisticRegression.fit()</code> to produce a <code>LogisticRegressionModel</code>.
If the <code>Pipeline</code> had more stages, it would call the <code>LogisticRegressionModel</code>’s <code>transform()</code> method on the dataset before passing the dataset to the next stage.</p>
<p>A <code>Pipeline</code> is an <code>Estimator</code>.
Thus, after a <code>Pipeline</code>’s <code>fit()</code> method runs, it produces a <code>PipelineModel</code> which is a <code>Transformer</code>. This <code>PipelineModel</code> is used at <em>test time</em>; the figure below illustrates this usage.</p>
<p style="text-align: center;">
<img src="img/ml-PipelineModel.png" title="Spark ML PipelineModel Example" alt="Spark ML PipelineModel Example" width="80%" />
</p>
<p>In the figure above, the <code>PipelineModel</code> has the same number of stages as the original <code>Pipeline</code>, but all <code>Estimator</code>s in the original <code>Pipeline</code> have become <code>Transformer</code>s.
When the <code>PipelineModel</code>’s <code>transform()</code> method is called on a test dataset, the data are passed through the <code>Pipeline</code> in order.
Each stage’s <code>transform()</code> method updates the dataset and passes it to the next stage.</p>
<p><code>Pipeline</code>s and <code>PipelineModel</code>s help to ensure that training and test data go through identical feature processing steps.</p>
<h3 id="details">Details</h3>
<p><em>DAG <code>Pipeline</code>s</em>: A <code>Pipeline</code>’s stages are specified as an ordered array. The examples given here are all for linear <code>Pipeline</code>s, i.e., <code>Pipeline</code>s in which each stage uses data produced by the previous stage. It is possible to create non-linear <code>Pipeline</code>s as long as the data flow graph forms a Directed Acyclic Graph (DAG). This graph is currently specified implicitly based on the input and output column names of each stage (generally specified as parameters). If the <code>Pipeline</code> forms a DAG, then the stages must be specified in topological order.</p>
<p><em>Runtime checking</em>: Since <code>Pipeline</code>s can operate on datasets with varied types, they cannot use compile-time type checking. <code>Pipeline</code>s and <code>PipelineModel</code>s instead do runtime checking before actually running the <code>Pipeline</code>. This type checking is done using the dataset <em>schema</em>, a description of the data types of columns in the <code>DataFrame</code>.</p>
<h2 id="parameters">Parameters</h2>
<p>Spark ML <code>Estimator</code>s and <code>Transformer</code>s use a uniform API for specifying parameters.</p>
<p>A <a href="api/scala/index.html#org.apache.spark.ml.param.Param"><code>Param</code></a> is a named parameter with self-contained documentation.
A <a href="api/scala/index.html#org.apache.spark.ml.param.ParamMap"><code>ParamMap</code></a> is a set of (parameter, value) pairs.</p>
<p>There are two main ways to pass parameters to an algorithm:</p>
<ol>
<li>Set parameters for an instance. E.g., if <code>lr</code> is an instance of <code>LogisticRegression</code>, one could call <code>lr.setMaxIter(10)</code> to make <code>lr.fit()</code> use at most 10 iterations. This API resembles the API used in MLlib.</li>
<li>Pass a <code>ParamMap</code> to <code>fit()</code> or <code>transform()</code>. Any parameters in the <code>ParamMap</code> will override parameters previously specified via setter methods.</li>
</ol>
<p>Parameters belong to specific instances of <code>Estimator</code>s and <code>Transformer</code>s.
For example, if we have two <code>LogisticRegression</code> instances <code>lr1</code> and <code>lr2</code>, then we can build a <code>ParamMap</code> with both <code>maxIter</code> parameters specified: <code>ParamMap(lr1.maxIter -> 10, lr2.maxIter -> 20)</code>.
This is useful if there are two algorithms with the <code>maxIter</code> parameter in a <code>Pipeline</code>.</p>
<h1 id="code-examples">Code Examples</h1>
<p>This section gives code examples illustrating the functionality discussed above.
There is not yet documentation for specific algorithms in Spark ML. For more info, please refer to the <a href="api/scala/index.html#org.apache.spark.ml.package">API Documentation</a>. Spark ML algorithms are currently wrappers for MLlib algorithms, and the <a href="mllib-guide.html">MLlib programming guide</a> has details on specific algorithms.</p>
<h2 id="example-estimator-transformer-and-param">Example: Estimator, Transformer, and Param</h2>
<p>This example covers the concepts of <code>Estimator</code>, <code>Transformer</code>, and <code>Param</code>.</p>
<div class="codetabs">
<div data-lang="scala">
<div class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="k">import</span> <span class="nn">org.apache.spark.</span><span class="o">{</span><span class="nc">SparkConf</span><span class="o">,</span> <span class="nc">SparkContext</span><span class="o">}</span>
<span class="k">import</span> <span class="nn">org.apache.spark.ml.classification.LogisticRegression</span>
<span class="k">import</span> <span class="nn">org.apache.spark.ml.param.ParamMap</span>
<span class="k">import</span> <span class="nn">org.apache.spark.mllib.linalg.</span><span class="o">{</span><span class="nc">Vector</span><span class="o">,</span> <span class="nc">Vectors</span><span class="o">}</span>
<span class="k">import</span> <span class="nn">org.apache.spark.mllib.regression.LabeledPoint</span>
<span class="k">import</span> <span class="nn">org.apache.spark.sql.</span><span class="o">{</span><span class="nc">Row</span><span class="o">,</span> <span class="nc">SQLContext</span><span class="o">}</span>
<span class="k">val</span> <span class="n">conf</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">SparkConf</span><span class="o">().</span><span class="n">setAppName</span><span class="o">(</span><span class="s">"SimpleParamsExample"</span><span class="o">)</span>
<span class="k">val</span> <span class="n">sc</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">SparkContext</span><span class="o">(</span><span class="n">conf</span><span class="o">)</span>
<span class="k">val</span> <span class="n">sqlContext</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">SQLContext</span><span class="o">(</span><span class="n">sc</span><span class="o">)</span>
<span class="k">import</span> <span class="nn">sqlContext.implicits._</span>
<span class="c1">// Prepare training data.</span>
<span class="c1">// We use LabeledPoint, which is a case class. Spark SQL can convert RDDs of case classes</span>
<span class="c1">// into DataFrames, where it uses the case class metadata to infer the schema.</span>
<span class="k">val</span> <span class="n">training</span> <span class="k">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">parallelize</span><span class="o">(</span><span class="nc">Seq</span><span class="o">(</span>
<span class="nc">LabeledPoint</span><span class="o">(</span><span class="mf">1.0</span><span class="o">,</span> <span class="nc">Vectors</span><span class="o">.</span><span class="n">dense</span><span class="o">(</span><span class="mf">0.0</span><span class="o">,</span> <span class="mf">1.1</span><span class="o">,</span> <span class="mf">0.1</span><span class="o">)),</span>
<span class="nc">LabeledPoint</span><span class="o">(</span><span class="mf">0.0</span><span class="o">,</span> <span class="nc">Vectors</span><span class="o">.</span><span class="n">dense</span><span class="o">(</span><span class="mf">2.0</span><span class="o">,</span> <span class="mf">1.0</span><span class="o">,</span> <span class="o">-</span><span class="mf">1.0</span><span class="o">)),</span>
<span class="nc">LabeledPoint</span><span class="o">(</span><span class="mf">0.0</span><span class="o">,</span> <span class="nc">Vectors</span><span class="o">.</span><span class="n">dense</span><span class="o">(</span><span class="mf">2.0</span><span class="o">,</span> <span class="mf">1.3</span><span class="o">,</span> <span class="mf">1.0</span><span class="o">)),</span>
<span class="nc">LabeledPoint</span><span class="o">(</span><span class="mf">1.0</span><span class="o">,</span> <span class="nc">Vectors</span><span class="o">.</span><span class="n">dense</span><span class="o">(</span><span class="mf">0.0</span><span class="o">,</span> <span class="mf">1.2</span><span class="o">,</span> <span class="o">-</span><span class="mf">0.5</span><span class="o">))))</span>
<span class="c1">// Create a LogisticRegression instance. This instance is an Estimator.</span>
<span class="k">val</span> <span class="n">lr</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">LogisticRegression</span><span class="o">()</span>
<span class="c1">// Print out the parameters, documentation, and any default values.</span>
<span class="n">println</span><span class="o">(</span><span class="s">"LogisticRegression parameters:\n"</span> <span class="o">+</span> <span class="n">lr</span><span class="o">.</span><span class="n">explainParams</span><span class="o">()</span> <span class="o">+</span> <span class="s">"\n"</span><span class="o">)</span>
<span class="c1">// We may set parameters using setter methods.</span>
<span class="n">lr</span><span class="o">.</span><span class="n">setMaxIter</span><span class="o">(</span><span class="mi">10</span><span class="o">)</span>
<span class="o">.</span><span class="n">setRegParam</span><span class="o">(</span><span class="mf">0.01</span><span class="o">)</span>
<span class="c1">// Learn a LogisticRegression model. This uses the parameters stored in lr.</span>
<span class="k">val</span> <span class="n">model1</span> <span class="k">=</span> <span class="n">lr</span><span class="o">.</span><span class="n">fit</span><span class="o">(</span><span class="n">training</span><span class="o">.</span><span class="n">toDF</span><span class="o">)</span>
<span class="c1">// Since model1 is a Model (i.e., a Transformer produced by an Estimator),</span>
<span class="c1">// we can view the parameters it used during fit().</span>
<span class="c1">// This prints the parameter (name: value) pairs, where names are unique IDs for this</span>
<span class="c1">// LogisticRegression instance.</span>
<span class="n">println</span><span class="o">(</span><span class="s">"Model 1 was fit using parameters: "</span> <span class="o">+</span> <span class="n">model1</span><span class="o">.</span><span class="n">fittingParamMap</span><span class="o">)</span>
<span class="c1">// We may alternatively specify parameters using a ParamMap,</span>
<span class="c1">// which supports several methods for specifying parameters.</span>
<span class="k">val</span> <span class="n">paramMap</span> <span class="k">=</span> <span class="nc">ParamMap</span><span class="o">(</span><span class="n">lr</span><span class="o">.</span><span class="n">maxIter</span> <span class="o">-></span> <span class="mi">20</span><span class="o">)</span>
<span class="n">paramMap</span><span class="o">.</span><span class="n">put</span><span class="o">(</span><span class="n">lr</span><span class="o">.</span><span class="n">maxIter</span><span class="o">,</span> <span class="mi">30</span><span class="o">)</span> <span class="c1">// Specify 1 Param. This overwrites the original maxIter.</span>
<span class="n">paramMap</span><span class="o">.</span><span class="n">put</span><span class="o">(</span><span class="n">lr</span><span class="o">.</span><span class="n">regParam</span> <span class="o">-></span> <span class="mf">0.1</span><span class="o">,</span> <span class="n">lr</span><span class="o">.</span><span class="n">threshold</span> <span class="o">-></span> <span class="mf">0.55</span><span class="o">)</span> <span class="c1">// Specify multiple Params.</span>
<span class="c1">// One can also combine ParamMaps.</span>
<span class="k">val</span> <span class="n">paramMap2</span> <span class="k">=</span> <span class="nc">ParamMap</span><span class="o">(</span><span class="n">lr</span><span class="o">.</span><span class="n">probabilityCol</span> <span class="o">-></span> <span class="s">"myProbability"</span><span class="o">)</span> <span class="c1">// Change output column name</span>
<span class="k">val</span> <span class="n">paramMapCombined</span> <span class="k">=</span> <span class="n">paramMap</span> <span class="o">++</span> <span class="n">paramMap2</span>
<span class="c1">// Now learn a new model using the paramMapCombined parameters.</span>
<span class="c1">// paramMapCombined overrides all parameters set earlier via lr.set* methods.</span>
<span class="k">val</span> <span class="n">model2</span> <span class="k">=</span> <span class="n">lr</span><span class="o">.</span><span class="n">fit</span><span class="o">(</span><span class="n">training</span><span class="o">.</span><span class="n">toDF</span><span class="o">,</span> <span class="n">paramMapCombined</span><span class="o">)</span>
<span class="n">println</span><span class="o">(</span><span class="s">"Model 2 was fit using parameters: "</span> <span class="o">+</span> <span class="n">model2</span><span class="o">.</span><span class="n">fittingParamMap</span><span class="o">)</span>
<span class="c1">// Prepare test data.</span>
<span class="k">val</span> <span class="n">test</span> <span class="k">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">parallelize</span><span class="o">(</span><span class="nc">Seq</span><span class="o">(</span>
<span class="nc">LabeledPoint</span><span class="o">(</span><span class="mf">1.0</span><span class="o">,</span> <span class="nc">Vectors</span><span class="o">.</span><span class="n">dense</span><span class="o">(-</span><span class="mf">1.0</span><span class="o">,</span> <span class="mf">1.5</span><span class="o">,</span> <span class="mf">1.3</span><span class="o">)),</span>
<span class="nc">LabeledPoint</span><span class="o">(</span><span class="mf">0.0</span><span class="o">,</span> <span class="nc">Vectors</span><span class="o">.</span><span class="n">dense</span><span class="o">(</span><span class="mf">3.0</span><span class="o">,</span> <span class="mf">2.0</span><span class="o">,</span> <span class="o">-</span><span class="mf">0.1</span><span class="o">)),</span>
<span class="nc">LabeledPoint</span><span class="o">(</span><span class="mf">1.0</span><span class="o">,</span> <span class="nc">Vectors</span><span class="o">.</span><span class="n">dense</span><span class="o">(</span><span class="mf">0.0</span><span class="o">,</span> <span class="mf">2.2</span><span class="o">,</span> <span class="o">-</span><span class="mf">1.5</span><span class="o">))))</span>
<span class="c1">// Make predictions on test data using the Transformer.transform() method.</span>
<span class="c1">// LogisticRegression.transform will only use the 'features' column.</span>
<span class="c1">// Note that model2.transform() outputs a 'myProbability' column instead of the usual</span>
<span class="c1">// 'probability' column since we renamed the lr.probabilityCol parameter previously.</span>
<span class="n">model2</span><span class="o">.</span><span class="n">transform</span><span class="o">(</span><span class="n">test</span><span class="o">.</span><span class="n">toDF</span><span class="o">)</span>
<span class="o">.</span><span class="n">select</span><span class="o">(</span><span class="s">"features"</span><span class="o">,</span> <span class="s">"label"</span><span class="o">,</span> <span class="s">"myProbability"</span><span class="o">,</span> <span class="s">"prediction"</span><span class="o">)</span>
<span class="o">.</span><span class="n">collect</span><span class="o">()</span>
<span class="o">.</span><span class="n">foreach</span> <span class="o">{</span> <span class="k">case</span> <span class="nc">Row</span><span class="o">(</span><span class="n">features</span><span class="k">:</span> <span class="kt">Vector</span><span class="o">,</span> <span class="n">label</span><span class="k">:</span> <span class="kt">Double</span><span class="o">,</span> <span class="n">prob</span><span class="k">:</span> <span class="kt">Vector</span><span class="o">,</span> <span class="n">prediction</span><span class="k">:</span> <span class="kt">Double</span><span class="o">)</span> <span class="k">=></span>
<span class="n">println</span><span class="o">(</span><span class="s">"($features, $label) -> prob=$prob, prediction=$prediction"</span><span class="o">)</span>
<span class="o">}</span>
<span class="n">sc</span><span class="o">.</span><span class="n">stop</span><span class="o">()</span></code></pre></div>
</div>
<div data-lang="java">
<div class="highlight"><pre><code class="language-java" data-lang="java"><span class="kn">import</span> <span class="nn">java.util.List</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">com.google.common.collect.Lists</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.SparkConf</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.api.java.JavaSparkContext</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.ml.classification.LogisticRegressionModel</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.ml.param.ParamMap</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.ml.classification.LogisticRegression</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.mllib.linalg.Vectors</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.mllib.regression.LabeledPoint</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.sql.DataFrame</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.sql.SQLContext</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.sql.Row</span><span class="o">;</span>
<span class="n">SparkConf</span> <span class="n">conf</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">SparkConf</span><span class="o">().</span><span class="na">setAppName</span><span class="o">(</span><span class="s">"JavaSimpleParamsExample"</span><span class="o">);</span>
<span class="n">JavaSparkContext</span> <span class="n">jsc</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">JavaSparkContext</span><span class="o">(</span><span class="n">conf</span><span class="o">);</span>
<span class="n">SQLContext</span> <span class="n">jsql</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">SQLContext</span><span class="o">(</span><span class="n">jsc</span><span class="o">);</span>
<span class="c1">// Prepare training data.</span>
<span class="c1">// We use LabeledPoint, which is a JavaBean. Spark SQL can convert RDDs of JavaBeans</span>
<span class="c1">// into DataFrames, where it uses the bean metadata to infer the schema.</span>
<span class="n">List</span><span class="o"><</span><span class="n">LabeledPoint</span><span class="o">></span> <span class="n">localTraining</span> <span class="o">=</span> <span class="n">Lists</span><span class="o">.</span><span class="na">newArrayList</span><span class="o">(</span>
<span class="k">new</span> <span class="nf">LabeledPoint</span><span class="o">(</span><span class="mf">1.0</span><span class="o">,</span> <span class="n">Vectors</span><span class="o">.</span><span class="na">dense</span><span class="o">(</span><span class="mf">0.0</span><span class="o">,</span> <span class="mf">1.1</span><span class="o">,</span> <span class="mf">0.1</span><span class="o">)),</span>
<span class="k">new</span> <span class="nf">LabeledPoint</span><span class="o">(</span><span class="mf">0.0</span><span class="o">,</span> <span class="n">Vectors</span><span class="o">.</span><span class="na">dense</span><span class="o">(</span><span class="mf">2.0</span><span class="o">,</span> <span class="mf">1.0</span><span class="o">,</span> <span class="o">-</span><span class="mf">1.0</span><span class="o">)),</span>
<span class="k">new</span> <span class="nf">LabeledPoint</span><span class="o">(</span><span class="mf">0.0</span><span class="o">,</span> <span class="n">Vectors</span><span class="o">.</span><span class="na">dense</span><span class="o">(</span><span class="mf">2.0</span><span class="o">,</span> <span class="mf">1.3</span><span class="o">,</span> <span class="mf">1.0</span><span class="o">)),</span>
<span class="k">new</span> <span class="nf">LabeledPoint</span><span class="o">(</span><span class="mf">1.0</span><span class="o">,</span> <span class="n">Vectors</span><span class="o">.</span><span class="na">dense</span><span class="o">(</span><span class="mf">0.0</span><span class="o">,</span> <span class="mf">1.2</span><span class="o">,</span> <span class="o">-</span><span class="mf">0.5</span><span class="o">)));</span>
<span class="n">DataFrame</span> <span class="n">training</span> <span class="o">=</span> <span class="n">jsql</span><span class="o">.</span><span class="na">createDataFrame</span><span class="o">(</span><span class="n">jsc</span><span class="o">.</span><span class="na">parallelize</span><span class="o">(</span><span class="n">localTraining</span><span class="o">),</span> <span class="n">LabeledPoint</span><span class="o">.</span><span class="na">class</span><span class="o">);</span>
<span class="c1">// Create a LogisticRegression instance. This instance is an Estimator.</span>
<span class="n">LogisticRegression</span> <span class="n">lr</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">LogisticRegression</span><span class="o">();</span>
<span class="c1">// Print out the parameters, documentation, and any default values.</span>
<span class="n">System</span><span class="o">.</span><span class="na">out</span><span class="o">.</span><span class="na">println</span><span class="o">(</span><span class="s">"LogisticRegression parameters:\n"</span> <span class="o">+</span> <span class="n">lr</span><span class="o">.</span><span class="na">explainParams</span><span class="o">()</span> <span class="o">+</span> <span class="s">"\n"</span><span class="o">);</span>
<span class="c1">// We may set parameters using setter methods.</span>
<span class="n">lr</span><span class="o">.</span><span class="na">setMaxIter</span><span class="o">(</span><span class="mi">10</span><span class="o">)</span>
<span class="o">.</span><span class="na">setRegParam</span><span class="o">(</span><span class="mf">0.01</span><span class="o">);</span>
<span class="c1">// Learn a LogisticRegression model. This uses the parameters stored in lr.</span>
<span class="n">LogisticRegressionModel</span> <span class="n">model1</span> <span class="o">=</span> <span class="n">lr</span><span class="o">.</span><span class="na">fit</span><span class="o">(</span><span class="n">training</span><span class="o">);</span>
<span class="c1">// Since model1 is a Model (i.e., a Transformer produced by an Estimator),</span>
<span class="c1">// we can view the parameters it used during fit().</span>
<span class="c1">// This prints the parameter (name: value) pairs, where names are unique IDs for this</span>
<span class="c1">// LogisticRegression instance.</span>
<span class="n">System</span><span class="o">.</span><span class="na">out</span><span class="o">.</span><span class="na">println</span><span class="o">(</span><span class="s">"Model 1 was fit using parameters: "</span> <span class="o">+</span> <span class="n">model1</span><span class="o">.</span><span class="na">fittingParamMap</span><span class="o">());</span>
<span class="c1">// We may alternatively specify parameters using a ParamMap.</span>
<span class="n">ParamMap</span> <span class="n">paramMap</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">ParamMap</span><span class="o">();</span>
<span class="n">paramMap</span><span class="o">.</span><span class="na">put</span><span class="o">(</span><span class="n">lr</span><span class="o">.</span><span class="na">maxIter</span><span class="o">().</span><span class="na">w</span><span class="o">(</span><span class="mi">20</span><span class="o">));</span> <span class="c1">// Specify 1 Param.</span>
<span class="n">paramMap</span><span class="o">.</span><span class="na">put</span><span class="o">(</span><span class="n">lr</span><span class="o">.</span><span class="na">maxIter</span><span class="o">(),</span> <span class="mi">30</span><span class="o">);</span> <span class="c1">// This overwrites the original maxIter.</span>
<span class="n">paramMap</span><span class="o">.</span><span class="na">put</span><span class="o">(</span><span class="n">lr</span><span class="o">.</span><span class="na">regParam</span><span class="o">().</span><span class="na">w</span><span class="o">(</span><span class="mf">0.1</span><span class="o">),</span> <span class="n">lr</span><span class="o">.</span><span class="na">threshold</span><span class="o">().</span><span class="na">w</span><span class="o">(</span><span class="mf">0.55</span><span class="o">));</span> <span class="c1">// Specify multiple Params.</span>
<span class="c1">// One can also combine ParamMaps.</span>
<span class="n">ParamMap</span> <span class="n">paramMap2</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">ParamMap</span><span class="o">();</span>
<span class="n">paramMap2</span><span class="o">.</span><span class="na">put</span><span class="o">(</span><span class="n">lr</span><span class="o">.</span><span class="na">probabilityCol</span><span class="o">().</span><span class="na">w</span><span class="o">(</span><span class="s">"myProbability"</span><span class="o">));</span> <span class="c1">// Change output column name</span>
<span class="n">ParamMap</span> <span class="n">paramMapCombined</span> <span class="o">=</span> <span class="n">paramMap</span><span class="o">.</span><span class="na">$plus$plus</span><span class="o">(</span><span class="n">paramMap2</span><span class="o">);</span>
<span class="c1">// Now learn a new model using the paramMapCombined parameters.</span>
<span class="c1">// paramMapCombined overrides all parameters set earlier via lr.set* methods.</span>
<span class="n">LogisticRegressionModel</span> <span class="n">model2</span> <span class="o">=</span> <span class="n">lr</span><span class="o">.</span><span class="na">fit</span><span class="o">(</span><span class="n">training</span><span class="o">,</span> <span class="n">paramMapCombined</span><span class="o">);</span>
<span class="n">System</span><span class="o">.</span><span class="na">out</span><span class="o">.</span><span class="na">println</span><span class="o">(</span><span class="s">"Model 2 was fit using parameters: "</span> <span class="o">+</span> <span class="n">model2</span><span class="o">.</span><span class="na">fittingParamMap</span><span class="o">());</span>
<span class="c1">// Prepare test documents.</span>
<span class="n">List</span><span class="o"><</span><span class="n">LabeledPoint</span><span class="o">></span> <span class="n">localTest</span> <span class="o">=</span> <span class="n">Lists</span><span class="o">.</span><span class="na">newArrayList</span><span class="o">(</span>
<span class="k">new</span> <span class="nf">LabeledPoint</span><span class="o">(</span><span class="mf">1.0</span><span class="o">,</span> <span class="n">Vectors</span><span class="o">.</span><span class="na">dense</span><span class="o">(-</span><span class="mf">1.0</span><span class="o">,</span> <span class="mf">1.5</span><span class="o">,</span> <span class="mf">1.3</span><span class="o">)),</span>
<span class="k">new</span> <span class="nf">LabeledPoint</span><span class="o">(</span><span class="mf">0.0</span><span class="o">,</span> <span class="n">Vectors</span><span class="o">.</span><span class="na">dense</span><span class="o">(</span><span class="mf">3.0</span><span class="o">,</span> <span class="mf">2.0</span><span class="o">,</span> <span class="o">-</span><span class="mf">0.1</span><span class="o">)),</span>
<span class="k">new</span> <span class="nf">LabeledPoint</span><span class="o">(</span><span class="mf">1.0</span><span class="o">,</span> <span class="n">Vectors</span><span class="o">.</span><span class="na">dense</span><span class="o">(</span><span class="mf">0.0</span><span class="o">,</span> <span class="mf">2.2</span><span class="o">,</span> <span class="o">-</span><span class="mf">1.5</span><span class="o">)));</span>
<span class="n">DataFrame</span> <span class="n">test</span> <span class="o">=</span> <span class="n">jsql</span><span class="o">.</span><span class="na">createDataFrame</span><span class="o">(</span><span class="n">jsc</span><span class="o">.</span><span class="na">parallelize</span><span class="o">(</span><span class="n">localTest</span><span class="o">),</span> <span class="n">LabeledPoint</span><span class="o">.</span><span class="na">class</span><span class="o">);</span>
<span class="c1">// Make predictions on test documents using the Transformer.transform() method.</span>
<span class="c1">// LogisticRegression.transform will only use the 'features' column.</span>
<span class="c1">// Note that model2.transform() outputs a 'myProbability' column instead of the usual</span>
<span class="c1">// 'probability' column since we renamed the lr.probabilityCol parameter previously.</span>
<span class="n">DataFrame</span> <span class="n">results</span> <span class="o">=</span> <span class="n">model2</span><span class="o">.</span><span class="na">transform</span><span class="o">(</span><span class="n">test</span><span class="o">);</span>
<span class="k">for</span> <span class="o">(</span><span class="n">Row</span> <span class="nl">r:</span> <span class="n">results</span><span class="o">.</span><span class="na">select</span><span class="o">(</span><span class="s">"features"</span><span class="o">,</span> <span class="s">"label"</span><span class="o">,</span> <span class="s">"myProbability"</span><span class="o">,</span> <span class="s">"prediction"</span><span class="o">).</span><span class="na">collect</span><span class="o">())</span> <span class="o">{</span>
<span class="n">System</span><span class="o">.</span><span class="na">out</span><span class="o">.</span><span class="na">println</span><span class="o">(</span><span class="s">"("</span> <span class="o">+</span> <span class="n">r</span><span class="o">.</span><span class="na">get</span><span class="o">(</span><span class="mi">0</span><span class="o">)</span> <span class="o">+</span> <span class="s">", "</span> <span class="o">+</span> <span class="n">r</span><span class="o">.</span><span class="na">get</span><span class="o">(</span><span class="mi">1</span><span class="o">)</span> <span class="o">+</span> <span class="s">") -> prob="</span> <span class="o">+</span> <span class="n">r</span><span class="o">.</span><span class="na">get</span><span class="o">(</span><span class="mi">2</span><span class="o">)</span>
<span class="o">+</span> <span class="s">", prediction="</span> <span class="o">+</span> <span class="n">r</span><span class="o">.</span><span class="na">get</span><span class="o">(</span><span class="mi">3</span><span class="o">));</span>
<span class="o">}</span>
<span class="n">jsc</span><span class="o">.</span><span class="na">stop</span><span class="o">();</span></code></pre></div>
</div>
</div>
<h2 id="example-pipeline">Example: Pipeline</h2>
<p>This example follows the simple text document <code>Pipeline</code> illustrated in the figures above.</p>
<div class="codetabs">
<div data-lang="scala">
<div class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="k">import</span> <span class="nn">org.apache.spark.</span><span class="o">{</span><span class="nc">SparkConf</span><span class="o">,</span> <span class="nc">SparkContext</span><span class="o">}</span>
<span class="k">import</span> <span class="nn">org.apache.spark.ml.Pipeline</span>
<span class="k">import</span> <span class="nn">org.apache.spark.ml.classification.LogisticRegression</span>
<span class="k">import</span> <span class="nn">org.apache.spark.ml.feature.</span><span class="o">{</span><span class="nc">HashingTF</span><span class="o">,</span> <span class="nc">Tokenizer</span><span class="o">}</span>
<span class="k">import</span> <span class="nn">org.apache.spark.mllib.linalg.Vector</span>
<span class="k">import</span> <span class="nn">org.apache.spark.sql.</span><span class="o">{</span><span class="nc">Row</span><span class="o">,</span> <span class="nc">SQLContext</span><span class="o">}</span>
<span class="c1">// Labeled and unlabeled instance types.</span>
<span class="c1">// Spark SQL can infer schema from case classes.</span>
<span class="k">case</span> <span class="k">class</span> <span class="nc">LabeledDocument</span><span class="o">(</span><span class="n">id</span><span class="k">:</span> <span class="kt">Long</span><span class="o">,</span> <span class="n">text</span><span class="k">:</span> <span class="kt">String</span><span class="o">,</span> <span class="n">label</span><span class="k">:</span> <span class="kt">Double</span><span class="o">)</span>
<span class="k">case</span> <span class="k">class</span> <span class="nc">Document</span><span class="o">(</span><span class="n">id</span><span class="k">:</span> <span class="kt">Long</span><span class="o">,</span> <span class="n">text</span><span class="k">:</span> <span class="kt">String</span><span class="o">)</span>
<span class="c1">// Set up contexts. Import implicit conversions to DataFrame from sqlContext.</span>
<span class="k">val</span> <span class="n">conf</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">SparkConf</span><span class="o">().</span><span class="n">setAppName</span><span class="o">(</span><span class="s">"SimpleTextClassificationPipeline"</span><span class="o">)</span>
<span class="k">val</span> <span class="n">sc</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">SparkContext</span><span class="o">(</span><span class="n">conf</span><span class="o">)</span>
<span class="k">val</span> <span class="n">sqlContext</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">SQLContext</span><span class="o">(</span><span class="n">sc</span><span class="o">)</span>
<span class="k">import</span> <span class="nn">sqlContext.implicits._</span>
<span class="c1">// Prepare training documents, which are labeled.</span>
<span class="k">val</span> <span class="n">training</span> <span class="k">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">parallelize</span><span class="o">(</span><span class="nc">Seq</span><span class="o">(</span>
<span class="nc">LabeledDocument</span><span class="o">(</span><span class="mi">0L</span><span class="o">,</span> <span class="s">"a b c d e spark"</span><span class="o">,</span> <span class="mf">1.0</span><span class="o">),</span>
<span class="nc">LabeledDocument</span><span class="o">(</span><span class="mi">1L</span><span class="o">,</span> <span class="s">"b d"</span><span class="o">,</span> <span class="mf">0.0</span><span class="o">),</span>
<span class="nc">LabeledDocument</span><span class="o">(</span><span class="mi">2L</span><span class="o">,</span> <span class="s">"spark f g h"</span><span class="o">,</span> <span class="mf">1.0</span><span class="o">),</span>
<span class="nc">LabeledDocument</span><span class="o">(</span><span class="mi">3L</span><span class="o">,</span> <span class="s">"hadoop mapreduce"</span><span class="o">,</span> <span class="mf">0.0</span><span class="o">)))</span>
<span class="c1">// Configure an ML pipeline, which consists of three stages: tokenizer, hashingTF, and lr.</span>
<span class="k">val</span> <span class="n">tokenizer</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">Tokenizer</span><span class="o">()</span>
<span class="o">.</span><span class="n">setInputCol</span><span class="o">(</span><span class="s">"text"</span><span class="o">)</span>
<span class="o">.</span><span class="n">setOutputCol</span><span class="o">(</span><span class="s">"words"</span><span class="o">)</span>
<span class="k">val</span> <span class="n">hashingTF</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">HashingTF</span><span class="o">()</span>
<span class="o">.</span><span class="n">setNumFeatures</span><span class="o">(</span><span class="mi">1000</span><span class="o">)</span>
<span class="o">.</span><span class="n">setInputCol</span><span class="o">(</span><span class="n">tokenizer</span><span class="o">.</span><span class="n">getOutputCol</span><span class="o">)</span>
<span class="o">.</span><span class="n">setOutputCol</span><span class="o">(</span><span class="s">"features"</span><span class="o">)</span>
<span class="k">val</span> <span class="n">lr</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">LogisticRegression</span><span class="o">()</span>
<span class="o">.</span><span class="n">setMaxIter</span><span class="o">(</span><span class="mi">10</span><span class="o">)</span>
<span class="o">.</span><span class="n">setRegParam</span><span class="o">(</span><span class="mf">0.01</span><span class="o">)</span>
<span class="k">val</span> <span class="n">pipeline</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">Pipeline</span><span class="o">()</span>
<span class="o">.</span><span class="n">setStages</span><span class="o">(</span><span class="nc">Array</span><span class="o">(</span><span class="n">tokenizer</span><span class="o">,</span> <span class="n">hashingTF</span><span class="o">,</span> <span class="n">lr</span><span class="o">))</span>
<span class="c1">// Fit the pipeline to training documents.</span>
<span class="k">val</span> <span class="n">model</span> <span class="k">=</span> <span class="n">pipeline</span><span class="o">.</span><span class="n">fit</span><span class="o">(</span><span class="n">training</span><span class="o">.</span><span class="n">toDF</span><span class="o">)</span>
<span class="c1">// Prepare test documents, which are unlabeled.</span>
<span class="k">val</span> <span class="n">test</span> <span class="k">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">parallelize</span><span class="o">(</span><span class="nc">Seq</span><span class="o">(</span>
<span class="nc">Document</span><span class="o">(</span><span class="mi">4L</span><span class="o">,</span> <span class="s">"spark i j k"</span><span class="o">),</span>
<span class="nc">Document</span><span class="o">(</span><span class="mi">5L</span><span class="o">,</span> <span class="s">"l m n"</span><span class="o">),</span>
<span class="nc">Document</span><span class="o">(</span><span class="mi">6L</span><span class="o">,</span> <span class="s">"mapreduce spark"</span><span class="o">),</span>
<span class="nc">Document</span><span class="o">(</span><span class="mi">7L</span><span class="o">,</span> <span class="s">"apache hadoop"</span><span class="o">)))</span>
<span class="c1">// Make predictions on test documents.</span>
<span class="n">model</span><span class="o">.</span><span class="n">transform</span><span class="o">(</span><span class="n">test</span><span class="o">.</span><span class="n">toDF</span><span class="o">)</span>
<span class="o">.</span><span class="n">select</span><span class="o">(</span><span class="s">"id"</span><span class="o">,</span> <span class="s">"text"</span><span class="o">,</span> <span class="s">"probability"</span><span class="o">,</span> <span class="s">"prediction"</span><span class="o">)</span>
<span class="o">.</span><span class="n">collect</span><span class="o">()</span>
<span class="o">.</span><span class="n">foreach</span> <span class="o">{</span> <span class="k">case</span> <span class="nc">Row</span><span class="o">(</span><span class="n">id</span><span class="k">:</span> <span class="kt">Long</span><span class="o">,</span> <span class="n">text</span><span class="k">:</span> <span class="kt">String</span><span class="o">,</span> <span class="n">prob</span><span class="k">:</span> <span class="kt">Vector</span><span class="o">,</span> <span class="n">prediction</span><span class="k">:</span> <span class="kt">Double</span><span class="o">)</span> <span class="k">=></span>
<span class="n">println</span><span class="o">(</span><span class="s">"($id, $text) --> prob=$prob, prediction=$prediction"</span><span class="o">)</span>
<span class="o">}</span>
<span class="n">sc</span><span class="o">.</span><span class="n">stop</span><span class="o">()</span></code></pre></div>
</div>
<div data-lang="java">
<div class="highlight"><pre><code class="language-java" data-lang="java"><span class="kn">import</span> <span class="nn">java.util.List</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">com.google.common.collect.Lists</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.SparkConf</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.api.java.JavaSparkContext</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.ml.Pipeline</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.ml.PipelineModel</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.ml.PipelineStage</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.ml.classification.LogisticRegression</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.ml.feature.HashingTF</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.ml.feature.Tokenizer</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.sql.DataFrame</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.sql.Row</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.sql.SQLContext</span><span class="o">;</span>
<span class="c1">// Labeled and unlabeled instance types.</span>
<span class="c1">// Spark SQL can infer schema from Java Beans.</span>
<span class="kd">public</span> <span class="kd">class</span> <span class="nc">Document</span> <span class="kd">implements</span> <span class="n">Serializable</span> <span class="o">{</span>
<span class="kd">private</span> <span class="kt">long</span> <span class="n">id</span><span class="o">;</span>
<span class="kd">private</span> <span class="n">String</span> <span class="n">text</span><span class="o">;</span>
<span class="kd">public</span> <span class="nf">Document</span><span class="o">(</span><span class="kt">long</span> <span class="n">id</span><span class="o">,</span> <span class="n">String</span> <span class="n">text</span><span class="o">)</span> <span class="o">{</span>
<span class="k">this</span><span class="o">.</span><span class="na">id</span> <span class="o">=</span> <span class="n">id</span><span class="o">;</span>
<span class="k">this</span><span class="o">.</span><span class="na">text</span> <span class="o">=</span> <span class="n">text</span><span class="o">;</span>
<span class="o">}</span>
<span class="kd">public</span> <span class="kt">long</span> <span class="nf">getId</span><span class="o">()</span> <span class="o">{</span> <span class="k">return</span> <span class="k">this</span><span class="o">.</span><span class="na">id</span><span class="o">;</span> <span class="o">}</span>
<span class="kd">public</span> <span class="kt">void</span> <span class="nf">setId</span><span class="o">(</span><span class="kt">long</span> <span class="n">id</span><span class="o">)</span> <span class="o">{</span> <span class="k">this</span><span class="o">.</span><span class="na">id</span> <span class="o">=</span> <span class="n">id</span><span class="o">;</span> <span class="o">}</span>
<span class="kd">public</span> <span class="n">String</span> <span class="nf">getText</span><span class="o">()</span> <span class="o">{</span> <span class="k">return</span> <span class="k">this</span><span class="o">.</span><span class="na">text</span><span class="o">;</span> <span class="o">}</span>
<span class="kd">public</span> <span class="kt">void</span> <span class="nf">setText</span><span class="o">(</span><span class="n">String</span> <span class="n">text</span><span class="o">)</span> <span class="o">{</span> <span class="k">this</span><span class="o">.</span><span class="na">text</span> <span class="o">=</span> <span class="n">text</span><span class="o">;</span> <span class="o">}</span>
<span class="o">}</span>
<span class="kd">public</span> <span class="kd">class</span> <span class="nc">LabeledDocument</span> <span class="kd">extends</span> <span class="n">Document</span> <span class="kd">implements</span> <span class="n">Serializable</span> <span class="o">{</span>
<span class="kd">private</span> <span class="kt">double</span> <span class="n">label</span><span class="o">;</span>
<span class="kd">public</span> <span class="nf">LabeledDocument</span><span class="o">(</span><span class="kt">long</span> <span class="n">id</span><span class="o">,</span> <span class="n">String</span> <span class="n">text</span><span class="o">,</span> <span class="kt">double</span> <span class="n">label</span><span class="o">)</span> <span class="o">{</span>
<span class="kd">super</span><span class="o">(</span><span class="n">id</span><span class="o">,</span> <span class="n">text</span><span class="o">);</span>
<span class="k">this</span><span class="o">.</span><span class="na">label</span> <span class="o">=</span> <span class="n">label</span><span class="o">;</span>
<span class="o">}</span>
<span class="kd">public</span> <span class="kt">double</span> <span class="nf">getLabel</span><span class="o">()</span> <span class="o">{</span> <span class="k">return</span> <span class="k">this</span><span class="o">.</span><span class="na">label</span><span class="o">;</span> <span class="o">}</span>
<span class="kd">public</span> <span class="kt">void</span> <span class="nf">setLabel</span><span class="o">(</span><span class="kt">double</span> <span class="n">label</span><span class="o">)</span> <span class="o">{</span> <span class="k">this</span><span class="o">.</span><span class="na">label</span> <span class="o">=</span> <span class="n">label</span><span class="o">;</span> <span class="o">}</span>
<span class="o">}</span>
<span class="c1">// Set up contexts.</span>
<span class="n">SparkConf</span> <span class="n">conf</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">SparkConf</span><span class="o">().</span><span class="na">setAppName</span><span class="o">(</span><span class="s">"JavaSimpleTextClassificationPipeline"</span><span class="o">);</span>
<span class="n">JavaSparkContext</span> <span class="n">jsc</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">JavaSparkContext</span><span class="o">(</span><span class="n">conf</span><span class="o">);</span>
<span class="n">SQLContext</span> <span class="n">jsql</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">SQLContext</span><span class="o">(</span><span class="n">jsc</span><span class="o">);</span>
<span class="c1">// Prepare training documents, which are labeled.</span>
<span class="n">List</span><span class="o"><</span><span class="n">LabeledDocument</span><span class="o">></span> <span class="n">localTraining</span> <span class="o">=</span> <span class="n">Lists</span><span class="o">.</span><span class="na">newArrayList</span><span class="o">(</span>
<span class="k">new</span> <span class="nf">LabeledDocument</span><span class="o">(</span><span class="mi">0L</span><span class="o">,</span> <span class="s">"a b c d e spark"</span><span class="o">,</span> <span class="mf">1.0</span><span class="o">),</span>
<span class="k">new</span> <span class="nf">LabeledDocument</span><span class="o">(</span><span class="mi">1L</span><span class="o">,</span> <span class="s">"b d"</span><span class="o">,</span> <span class="mf">0.0</span><span class="o">),</span>
<span class="k">new</span> <span class="nf">LabeledDocument</span><span class="o">(</span><span class="mi">2L</span><span class="o">,</span> <span class="s">"spark f g h"</span><span class="o">,</span> <span class="mf">1.0</span><span class="o">),</span>
<span class="k">new</span> <span class="nf">LabeledDocument</span><span class="o">(</span><span class="mi">3L</span><span class="o">,</span> <span class="s">"hadoop mapreduce"</span><span class="o">,</span> <span class="mf">0.0</span><span class="o">));</span>
<span class="n">DataFrame</span> <span class="n">training</span> <span class="o">=</span> <span class="n">jsql</span><span class="o">.</span><span class="na">createDataFrame</span><span class="o">(</span><span class="n">jsc</span><span class="o">.</span><span class="na">parallelize</span><span class="o">(</span><span class="n">localTraining</span><span class="o">),</span> <span class="n">LabeledDocument</span><span class="o">.</span><span class="na">class</span><span class="o">);</span>
<span class="c1">// Configure an ML pipeline, which consists of three stages: tokenizer, hashingTF, and lr.</span>
<span class="n">Tokenizer</span> <span class="n">tokenizer</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">Tokenizer</span><span class="o">()</span>
<span class="o">.</span><span class="na">setInputCol</span><span class="o">(</span><span class="s">"text"</span><span class="o">)</span>
<span class="o">.</span><span class="na">setOutputCol</span><span class="o">(</span><span class="s">"words"</span><span class="o">);</span>
<span class="n">HashingTF</span> <span class="n">hashingTF</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">HashingTF</span><span class="o">()</span>
<span class="o">.</span><span class="na">setNumFeatures</span><span class="o">(</span><span class="mi">1000</span><span class="o">)</span>
<span class="o">.</span><span class="na">setInputCol</span><span class="o">(</span><span class="n">tokenizer</span><span class="o">.</span><span class="na">getOutputCol</span><span class="o">())</span>
<span class="o">.</span><span class="na">setOutputCol</span><span class="o">(</span><span class="s">"features"</span><span class="o">);</span>
<span class="n">LogisticRegression</span> <span class="n">lr</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">LogisticRegression</span><span class="o">()</span>
<span class="o">.</span><span class="na">setMaxIter</span><span class="o">(</span><span class="mi">10</span><span class="o">)</span>
<span class="o">.</span><span class="na">setRegParam</span><span class="o">(</span><span class="mf">0.01</span><span class="o">);</span>
<span class="n">Pipeline</span> <span class="n">pipeline</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">Pipeline</span><span class="o">()</span>
<span class="o">.</span><span class="na">setStages</span><span class="o">(</span><span class="k">new</span> <span class="n">PipelineStage</span><span class="o">[]</span> <span class="o">{</span><span class="n">tokenizer</span><span class="o">,</span> <span class="n">hashingTF</span><span class="o">,</span> <span class="n">lr</span><span class="o">});</span>
<span class="c1">// Fit the pipeline to training documents.</span>
<span class="n">PipelineModel</span> <span class="n">model</span> <span class="o">=</span> <span class="n">pipeline</span><span class="o">.</span><span class="na">fit</span><span class="o">(</span><span class="n">training</span><span class="o">);</span>
<span class="c1">// Prepare test documents, which are unlabeled.</span>
<span class="n">List</span><span class="o"><</span><span class="n">Document</span><span class="o">></span> <span class="n">localTest</span> <span class="o">=</span> <span class="n">Lists</span><span class="o">.</span><span class="na">newArrayList</span><span class="o">(</span>
<span class="k">new</span> <span class="nf">Document</span><span class="o">(</span><span class="mi">4L</span><span class="o">,</span> <span class="s">"spark i j k"</span><span class="o">),</span>
<span class="k">new</span> <span class="nf">Document</span><span class="o">(</span><span class="mi">5L</span><span class="o">,</span> <span class="s">"l m n"</span><span class="o">),</span>
<span class="k">new</span> <span class="nf">Document</span><span class="o">(</span><span class="mi">6L</span><span class="o">,</span> <span class="s">"mapreduce spark"</span><span class="o">),</span>
<span class="k">new</span> <span class="nf">Document</span><span class="o">(</span><span class="mi">7L</span><span class="o">,</span> <span class="s">"apache hadoop"</span><span class="o">));</span>
<span class="n">DataFrame</span> <span class="n">test</span> <span class="o">=</span> <span class="n">jsql</span><span class="o">.</span><span class="na">createDataFrame</span><span class="o">(</span><span class="n">jsc</span><span class="o">.</span><span class="na">parallelize</span><span class="o">(</span><span class="n">localTest</span><span class="o">),</span> <span class="n">Document</span><span class="o">.</span><span class="na">class</span><span class="o">);</span>
<span class="c1">// Make predictions on test documents.</span>
<span class="n">DataFrame</span> <span class="n">predictions</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="na">transform</span><span class="o">(</span><span class="n">test</span><span class="o">);</span>
<span class="k">for</span> <span class="o">(</span><span class="n">Row</span> <span class="nl">r:</span> <span class="n">predictions</span><span class="o">.</span><span class="na">select</span><span class="o">(</span><span class="s">"id"</span><span class="o">,</span> <span class="s">"text"</span><span class="o">,</span> <span class="s">"probability"</span><span class="o">,</span> <span class="s">"prediction"</span><span class="o">).</span><span class="na">collect</span><span class="o">())</span> <span class="o">{</span>
<span class="n">System</span><span class="o">.</span><span class="na">out</span><span class="o">.</span><span class="na">println</span><span class="o">(</span><span class="s">"("</span> <span class="o">+</span> <span class="n">r</span><span class="o">.</span><span class="na">get</span><span class="o">(</span><span class="mi">0</span><span class="o">)</span> <span class="o">+</span> <span class="s">", "</span> <span class="o">+</span> <span class="n">r</span><span class="o">.</span><span class="na">get</span><span class="o">(</span><span class="mi">1</span><span class="o">)</span> <span class="o">+</span> <span class="s">") --> prob="</span> <span class="o">+</span> <span class="n">r</span><span class="o">.</span><span class="na">get</span><span class="o">(</span><span class="mi">2</span><span class="o">)</span>
<span class="o">+</span> <span class="s">", prediction="</span> <span class="o">+</span> <span class="n">r</span><span class="o">.</span><span class="na">get</span><span class="o">(</span><span class="mi">3</span><span class="o">));</span>
<span class="o">}</span>
<span class="n">jsc</span><span class="o">.</span><span class="na">stop</span><span class="o">();</span></code></pre></div>
</div>
<div data-lang="python">
<div class="highlight"><pre><code class="language-python" data-lang="python"><span class="kn">from</span> <span class="nn">pyspark</span> <span class="kn">import</span> <span class="n">SparkContext</span>
<span class="kn">from</span> <span class="nn">pyspark.ml</span> <span class="kn">import</span> <span class="n">Pipeline</span>
<span class="kn">from</span> <span class="nn">pyspark.ml.classification</span> <span class="kn">import</span> <span class="n">LogisticRegression</span>
<span class="kn">from</span> <span class="nn">pyspark.ml.feature</span> <span class="kn">import</span> <span class="n">HashingTF</span><span class="p">,</span> <span class="n">Tokenizer</span>
<span class="kn">from</span> <span class="nn">pyspark.sql</span> <span class="kn">import</span> <span class="n">Row</span><span class="p">,</span> <span class="n">SQLContext</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="p">(</span><span class="n">appName</span><span class="o">=</span><span class="s">"SimpleTextClassificationPipeline"</span><span class="p">)</span>
<span class="n">sqlContext</span> <span class="o">=</span> <span class="n">SQLContext</span><span class="p">(</span><span class="n">sc</span><span class="p">)</span>
<span class="c"># Prepare training documents, which are labeled.</span>
<span class="n">LabeledDocument</span> <span class="o">=</span> <span class="n">Row</span><span class="p">(</span><span class="s">"id"</span><span class="p">,</span> <span class="s">"text"</span><span class="p">,</span> <span class="s">"label"</span><span class="p">)</span>
<span class="n">training</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">parallelize</span><span class="p">([(</span><span class="il">0L</span><span class="p">,</span> <span class="s">"a b c d e spark"</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">),</span>
<span class="p">(</span><span class="il">1L</span><span class="p">,</span> <span class="s">"b d"</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">),</span>
<span class="p">(</span><span class="il">2L</span><span class="p">,</span> <span class="s">"spark f g h"</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">),</span>
<span class="p">(</span><span class="il">3L</span><span class="p">,</span> <span class="s">"hadoop mapreduce"</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">)])</span> \
<span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">LabeledDocument</span><span class="p">(</span><span class="o">*</span><span class="n">x</span><span class="p">))</span><span class="o">.</span><span class="n">toDF</span><span class="p">()</span>
<span class="c"># Configure an ML pipeline, which consists of tree stages: tokenizer, hashingTF, and lr.</span>
<span class="n">tokenizer</span> <span class="o">=</span> <span class="n">Tokenizer</span><span class="p">(</span><span class="n">inputCol</span><span class="o">=</span><span class="s">"text"</span><span class="p">,</span> <span class="n">outputCol</span><span class="o">=</span><span class="s">"words"</span><span class="p">)</span>
<span class="n">hashingTF</span> <span class="o">=</span> <span class="n">HashingTF</span><span class="p">(</span><span class="n">inputCol</span><span class="o">=</span><span class="n">tokenizer</span><span class="o">.</span><span class="n">getOutputCol</span><span class="p">(),</span> <span class="n">outputCol</span><span class="o">=</span><span class="s">"features"</span><span class="p">)</span>
<span class="n">lr</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">(</span><span class="n">maxIter</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">regParam</span><span class="o">=</span><span class="mf">0.01</span><span class="p">)</span>
<span class="n">pipeline</span> <span class="o">=</span> <span class="n">Pipeline</span><span class="p">(</span><span class="n">stages</span><span class="o">=</span><span class="p">[</span><span class="n">tokenizer</span><span class="p">,</span> <span class="n">hashingTF</span><span class="p">,</span> <span class="n">lr</span><span class="p">])</span>
<span class="c"># Fit the pipeline to training documents.</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">pipeline</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">training</span><span class="p">)</span>
<span class="c"># Prepare test documents, which are unlabeled.</span>
<span class="n">Document</span> <span class="o">=</span> <span class="n">Row</span><span class="p">(</span><span class="s">"id"</span><span class="p">,</span> <span class="s">"text"</span><span class="p">)</span>
<span class="n">test</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">parallelize</span><span class="p">([(</span><span class="il">4L</span><span class="p">,</span> <span class="s">"spark i j k"</span><span class="p">),</span>
<span class="p">(</span><span class="il">5L</span><span class="p">,</span> <span class="s">"l m n"</span><span class="p">),</span>
<span class="p">(</span><span class="il">6L</span><span class="p">,</span> <span class="s">"mapreduce spark"</span><span class="p">),</span>
<span class="p">(</span><span class="il">7L</span><span class="p">,</span> <span class="s">"apache hadoop"</span><span class="p">)])</span> \
<span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">Document</span><span class="p">(</span><span class="o">*</span><span class="n">x</span><span class="p">))</span><span class="o">.</span><span class="n">toDF</span><span class="p">()</span>
<span class="c"># Make predictions on test documents and print columns of interest.</span>
<span class="n">prediction</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">test</span><span class="p">)</span>
<span class="n">selected</span> <span class="o">=</span> <span class="n">prediction</span><span class="o">.</span><span class="n">select</span><span class="p">(</span><span class="s">"id"</span><span class="p">,</span> <span class="s">"text"</span><span class="p">,</span> <span class="s">"prediction"</span><span class="p">)</span>
<span class="k">for</span> <span class="n">row</span> <span class="ow">in</span> <span class="n">selected</span><span class="o">.</span><span class="n">collect</span><span class="p">():</span>
<span class="k">print</span> <span class="n">row</span>
<span class="n">sc</span><span class="o">.</span><span class="n">stop</span><span class="p">()</span></code></pre></div>
</div>
</div>
<h2 id="example-model-selection-via-cross-validation">Example: Model Selection via Cross-Validation</h2>
<p>An important task in ML is <em>model selection</em>, or using data to find the best model or parameters for a given task. This is also called <em>tuning</em>.
<code>Pipeline</code>s facilitate model selection by making it easy to tune an entire <code>Pipeline</code> at once, rather than tuning each element in the <code>Pipeline</code> separately.</p>
<p>Currently, <code>spark.ml</code> supports model selection using the <a href="api/scala/index.html#org.apache.spark.ml.tuning.CrossValidator"><code>CrossValidator</code></a> class, which takes an <code>Estimator</code>, a set of <code>ParamMap</code>s, and an <a href="api/scala/index.html#org.apache.spark.ml.Evaluator"><code>Evaluator</code></a>.
<code>CrossValidator</code> begins by splitting the dataset into a set of <em>folds</em> which are used as separate training and test datasets; e.g., with <code>$k=3$</code> folds, <code>CrossValidator</code> will generate 3 (training, test) dataset pairs, each of which uses 2/3 of the data for training and 1/3 for testing.
<code>CrossValidator</code> iterates through the set of <code>ParamMap</code>s. For each <code>ParamMap</code>, it trains the given <code>Estimator</code> and evaluates it using the given <code>Evaluator</code>.
The <code>ParamMap</code> which produces the best evaluation metric (averaged over the <code>$k$</code> folds) is selected as the best model.
<code>CrossValidator</code> finally fits the <code>Estimator</code> using the best <code>ParamMap</code> and the entire dataset.</p>
<p>The following example demonstrates using <code>CrossValidator</code> to select from a grid of parameters.
To help construct the parameter grid, we use the <a href="api/scala/index.html#org.apache.spark.ml.tuning.ParamGridBuilder"><code>ParamGridBuilder</code></a> utility.</p>
<p>Note that cross-validation over a grid of parameters is expensive.
E.g., in the example below, the parameter grid has 3 values for <code>hashingTF.numFeatures</code> and 2 values for <code>lr.regParam</code>, and <code>CrossValidator</code> uses 2 folds. This multiplies out to <code>$(3 \times 2) \times 2 = 12$</code> different models being trained.
In realistic settings, it can be common to try many more parameters and use more folds (<code>$k=3$</code> and <code>$k=10$</code> are common).
In other words, using <code>CrossValidator</code> can be very expensive.
However, it is also a well-established method for choosing parameters which is more statistically sound than heuristic hand-tuning.</p>
<div class="codetabs">
<div data-lang="scala">
<div class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="k">import</span> <span class="nn">org.apache.spark.</span><span class="o">{</span><span class="nc">SparkConf</span><span class="o">,</span> <span class="nc">SparkContext</span><span class="o">}</span>
<span class="k">import</span> <span class="nn">org.apache.spark.ml.Pipeline</span>
<span class="k">import</span> <span class="nn">org.apache.spark.ml.classification.LogisticRegression</span>
<span class="k">import</span> <span class="nn">org.apache.spark.ml.evaluation.BinaryClassificationEvaluator</span>
<span class="k">import</span> <span class="nn">org.apache.spark.ml.feature.</span><span class="o">{</span><span class="nc">HashingTF</span><span class="o">,</span> <span class="nc">Tokenizer</span><span class="o">}</span>
<span class="k">import</span> <span class="nn">org.apache.spark.ml.tuning.</span><span class="o">{</span><span class="nc">ParamGridBuilder</span><span class="o">,</span> <span class="nc">CrossValidator</span><span class="o">}</span>
<span class="k">import</span> <span class="nn">org.apache.spark.mllib.linalg.Vector</span>
<span class="k">import</span> <span class="nn">org.apache.spark.sql.</span><span class="o">{</span><span class="nc">Row</span><span class="o">,</span> <span class="nc">SQLContext</span><span class="o">}</span>
<span class="c1">// Labeled and unlabeled instance types.</span>
<span class="c1">// Spark SQL can infer schema from case classes.</span>
<span class="k">case</span> <span class="k">class</span> <span class="nc">LabeledDocument</span><span class="o">(</span><span class="n">id</span><span class="k">:</span> <span class="kt">Long</span><span class="o">,</span> <span class="n">text</span><span class="k">:</span> <span class="kt">String</span><span class="o">,</span> <span class="n">label</span><span class="k">:</span> <span class="kt">Double</span><span class="o">)</span>
<span class="k">case</span> <span class="k">class</span> <span class="nc">Document</span><span class="o">(</span><span class="n">id</span><span class="k">:</span> <span class="kt">Long</span><span class="o">,</span> <span class="n">text</span><span class="k">:</span> <span class="kt">String</span><span class="o">)</span>
<span class="k">val</span> <span class="n">conf</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">SparkConf</span><span class="o">().</span><span class="n">setAppName</span><span class="o">(</span><span class="s">"CrossValidatorExample"</span><span class="o">)</span>
<span class="k">val</span> <span class="n">sc</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">SparkContext</span><span class="o">(</span><span class="n">conf</span><span class="o">)</span>
<span class="k">val</span> <span class="n">sqlContext</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">SQLContext</span><span class="o">(</span><span class="n">sc</span><span class="o">)</span>
<span class="k">import</span> <span class="nn">sqlContext.implicits._</span>
<span class="c1">// Prepare training documents, which are labeled.</span>
<span class="k">val</span> <span class="n">training</span> <span class="k">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">parallelize</span><span class="o">(</span><span class="nc">Seq</span><span class="o">(</span>
<span class="nc">LabeledDocument</span><span class="o">(</span><span class="mi">0L</span><span class="o">,</span> <span class="s">"a b c d e spark"</span><span class="o">,</span> <span class="mf">1.0</span><span class="o">),</span>
<span class="nc">LabeledDocument</span><span class="o">(</span><span class="mi">1L</span><span class="o">,</span> <span class="s">"b d"</span><span class="o">,</span> <span class="mf">0.0</span><span class="o">),</span>
<span class="nc">LabeledDocument</span><span class="o">(</span><span class="mi">2L</span><span class="o">,</span> <span class="s">"spark f g h"</span><span class="o">,</span> <span class="mf">1.0</span><span class="o">),</span>
<span class="nc">LabeledDocument</span><span class="o">(</span><span class="mi">3L</span><span class="o">,</span> <span class="s">"hadoop mapreduce"</span><span class="o">,</span> <span class="mf">0.0</span><span class="o">),</span>
<span class="nc">LabeledDocument</span><span class="o">(</span><span class="mi">4L</span><span class="o">,</span> <span class="s">"b spark who"</span><span class="o">,</span> <span class="mf">1.0</span><span class="o">),</span>
<span class="nc">LabeledDocument</span><span class="o">(</span><span class="mi">5L</span><span class="o">,</span> <span class="s">"g d a y"</span><span class="o">,</span> <span class="mf">0.0</span><span class="o">),</span>
<span class="nc">LabeledDocument</span><span class="o">(</span><span class="mi">6L</span><span class="o">,</span> <span class="s">"spark fly"</span><span class="o">,</span> <span class="mf">1.0</span><span class="o">),</span>
<span class="nc">LabeledDocument</span><span class="o">(</span><span class="mi">7L</span><span class="o">,</span> <span class="s">"was mapreduce"</span><span class="o">,</span> <span class="mf">0.0</span><span class="o">),</span>
<span class="nc">LabeledDocument</span><span class="o">(</span><span class="mi">8L</span><span class="o">,</span> <span class="s">"e spark program"</span><span class="o">,</span> <span class="mf">1.0</span><span class="o">),</span>
<span class="nc">LabeledDocument</span><span class="o">(</span><span class="mi">9L</span><span class="o">,</span> <span class="s">"a e c l"</span><span class="o">,</span> <span class="mf">0.0</span><span class="o">),</span>
<span class="nc">LabeledDocument</span><span class="o">(</span><span class="mi">10L</span><span class="o">,</span> <span class="s">"spark compile"</span><span class="o">,</span> <span class="mf">1.0</span><span class="o">),</span>
<span class="nc">LabeledDocument</span><span class="o">(</span><span class="mi">11L</span><span class="o">,</span> <span class="s">"hadoop software"</span><span class="o">,</span> <span class="mf">0.0</span><span class="o">)))</span>
<span class="c1">// Configure an ML pipeline, which consists of three stages: tokenizer, hashingTF, and lr.</span>
<span class="k">val</span> <span class="n">tokenizer</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">Tokenizer</span><span class="o">()</span>
<span class="o">.</span><span class="n">setInputCol</span><span class="o">(</span><span class="s">"text"</span><span class="o">)</span>
<span class="o">.</span><span class="n">setOutputCol</span><span class="o">(</span><span class="s">"words"</span><span class="o">)</span>
<span class="k">val</span> <span class="n">hashingTF</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">HashingTF</span><span class="o">()</span>
<span class="o">.</span><span class="n">setInputCol</span><span class="o">(</span><span class="n">tokenizer</span><span class="o">.</span><span class="n">getOutputCol</span><span class="o">)</span>
<span class="o">.</span><span class="n">setOutputCol</span><span class="o">(</span><span class="s">"features"</span><span class="o">)</span>
<span class="k">val</span> <span class="n">lr</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">LogisticRegression</span><span class="o">()</span>
<span class="o">.</span><span class="n">setMaxIter</span><span class="o">(</span><span class="mi">10</span><span class="o">)</span>
<span class="k">val</span> <span class="n">pipeline</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">Pipeline</span><span class="o">()</span>
<span class="o">.</span><span class="n">setStages</span><span class="o">(</span><span class="nc">Array</span><span class="o">(</span><span class="n">tokenizer</span><span class="o">,</span> <span class="n">hashingTF</span><span class="o">,</span> <span class="n">lr</span><span class="o">))</span>
<span class="c1">// We now treat the Pipeline as an Estimator, wrapping it in a CrossValidator instance.</span>
<span class="c1">// This will allow us to jointly choose parameters for all Pipeline stages.</span>
<span class="c1">// A CrossValidator requires an Estimator, a set of Estimator ParamMaps, and an Evaluator.</span>
<span class="k">val</span> <span class="n">crossval</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">CrossValidator</span><span class="o">()</span>
<span class="o">.</span><span class="n">setEstimator</span><span class="o">(</span><span class="n">pipeline</span><span class="o">)</span>
<span class="o">.</span><span class="n">setEvaluator</span><span class="o">(</span><span class="k">new</span> <span class="nc">BinaryClassificationEvaluator</span><span class="o">)</span>
<span class="c1">// We use a ParamGridBuilder to construct a grid of parameters to search over.</span>
<span class="c1">// With 3 values for hashingTF.numFeatures and 2 values for lr.regParam,</span>
<span class="c1">// this grid will have 3 x 2 = 6 parameter settings for CrossValidator to choose from.</span>
<span class="k">val</span> <span class="n">paramGrid</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">ParamGridBuilder</span><span class="o">()</span>
<span class="o">.</span><span class="n">addGrid</span><span class="o">(</span><span class="n">hashingTF</span><span class="o">.</span><span class="n">numFeatures</span><span class="o">,</span> <span class="nc">Array</span><span class="o">(</span><span class="mi">10</span><span class="o">,</span> <span class="mi">100</span><span class="o">,</span> <span class="mi">1000</span><span class="o">))</span>
<span class="o">.</span><span class="n">addGrid</span><span class="o">(</span><span class="n">lr</span><span class="o">.</span><span class="n">regParam</span><span class="o">,</span> <span class="nc">Array</span><span class="o">(</span><span class="mf">0.1</span><span class="o">,</span> <span class="mf">0.01</span><span class="o">))</span>
<span class="o">.</span><span class="n">build</span><span class="o">()</span>
<span class="n">crossval</span><span class="o">.</span><span class="n">setEstimatorParamMaps</span><span class="o">(</span><span class="n">paramGrid</span><span class="o">)</span>
<span class="n">crossval</span><span class="o">.</span><span class="n">setNumFolds</span><span class="o">(</span><span class="mi">2</span><span class="o">)</span> <span class="c1">// Use 3+ in practice</span>
<span class="c1">// Run cross-validation, and choose the best set of parameters.</span>
<span class="k">val</span> <span class="n">cvModel</span> <span class="k">=</span> <span class="n">crossval</span><span class="o">.</span><span class="n">fit</span><span class="o">(</span><span class="n">training</span><span class="o">.</span><span class="n">toDF</span><span class="o">)</span>
<span class="c1">// Prepare test documents, which are unlabeled.</span>
<span class="k">val</span> <span class="n">test</span> <span class="k">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">parallelize</span><span class="o">(</span><span class="nc">Seq</span><span class="o">(</span>
<span class="nc">Document</span><span class="o">(</span><span class="mi">4L</span><span class="o">,</span> <span class="s">"spark i j k"</span><span class="o">),</span>
<span class="nc">Document</span><span class="o">(</span><span class="mi">5L</span><span class="o">,</span> <span class="s">"l m n"</span><span class="o">),</span>
<span class="nc">Document</span><span class="o">(</span><span class="mi">6L</span><span class="o">,</span> <span class="s">"mapreduce spark"</span><span class="o">),</span>
<span class="nc">Document</span><span class="o">(</span><span class="mi">7L</span><span class="o">,</span> <span class="s">"apache hadoop"</span><span class="o">)))</span>
<span class="c1">// Make predictions on test documents. cvModel uses the best model found (lrModel).</span>
<span class="n">cvModel</span><span class="o">.</span><span class="n">transform</span><span class="o">(</span><span class="n">test</span><span class="o">.</span><span class="n">toDF</span><span class="o">)</span>
<span class="o">.</span><span class="n">select</span><span class="o">(</span><span class="s">"id"</span><span class="o">,</span> <span class="s">"text"</span><span class="o">,</span> <span class="s">"probability"</span><span class="o">,</span> <span class="s">"prediction"</span><span class="o">)</span>
<span class="o">.</span><span class="n">collect</span><span class="o">()</span>
<span class="o">.</span><span class="n">foreach</span> <span class="o">{</span> <span class="k">case</span> <span class="nc">Row</span><span class="o">(</span><span class="n">id</span><span class="k">:</span> <span class="kt">Long</span><span class="o">,</span> <span class="n">text</span><span class="k">:</span> <span class="kt">String</span><span class="o">,</span> <span class="n">prob</span><span class="k">:</span> <span class="kt">Vector</span><span class="o">,</span> <span class="n">prediction</span><span class="k">:</span> <span class="kt">Double</span><span class="o">)</span> <span class="k">=></span>
<span class="n">println</span><span class="o">(</span><span class="n">s</span><span class="s">"($id, $text) --> prob=$prob, prediction=$prediction"</span><span class="o">)</span>
<span class="o">}</span>
<span class="n">sc</span><span class="o">.</span><span class="n">stop</span><span class="o">()</span></code></pre></div>
</div>
<div data-lang="java">
<div class="highlight"><pre><code class="language-java" data-lang="java"><span class="kn">import</span> <span class="nn">java.util.List</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">com.google.common.collect.Lists</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.SparkConf</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.api.java.JavaSparkContext</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.ml.Pipeline</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.ml.PipelineStage</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.ml.classification.LogisticRegression</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.ml.evaluation.BinaryClassificationEvaluator</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.ml.feature.HashingTF</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.ml.feature.Tokenizer</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.ml.param.ParamMap</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.ml.tuning.CrossValidator</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.ml.tuning.CrossValidatorModel</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.ml.tuning.ParamGridBuilder</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.sql.DataFrame</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.sql.Row</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.sql.SQLContext</span><span class="o">;</span>
<span class="c1">// Labeled and unlabeled instance types.</span>
<span class="c1">// Spark SQL can infer schema from Java Beans.</span>
<span class="kd">public</span> <span class="kd">class</span> <span class="nc">Document</span> <span class="kd">implements</span> <span class="n">Serializable</span> <span class="o">{</span>
<span class="kd">private</span> <span class="kt">long</span> <span class="n">id</span><span class="o">;</span>
<span class="kd">private</span> <span class="n">String</span> <span class="n">text</span><span class="o">;</span>
<span class="kd">public</span> <span class="nf">Document</span><span class="o">(</span><span class="kt">long</span> <span class="n">id</span><span class="o">,</span> <span class="n">String</span> <span class="n">text</span><span class="o">)</span> <span class="o">{</span>
<span class="k">this</span><span class="o">.</span><span class="na">id</span> <span class="o">=</span> <span class="n">id</span><span class="o">;</span>
<span class="k">this</span><span class="o">.</span><span class="na">text</span> <span class="o">=</span> <span class="n">text</span><span class="o">;</span>
<span class="o">}</span>
<span class="kd">public</span> <span class="kt">long</span> <span class="nf">getId</span><span class="o">()</span> <span class="o">{</span> <span class="k">return</span> <span class="k">this</span><span class="o">.</span><span class="na">id</span><span class="o">;</span> <span class="o">}</span>
<span class="kd">public</span> <span class="kt">void</span> <span class="nf">setId</span><span class="o">(</span><span class="kt">long</span> <span class="n">id</span><span class="o">)</span> <span class="o">{</span> <span class="k">this</span><span class="o">.</span><span class="na">id</span> <span class="o">=</span> <span class="n">id</span><span class="o">;</span> <span class="o">}</span>
<span class="kd">public</span> <span class="n">String</span> <span class="nf">getText</span><span class="o">()</span> <span class="o">{</span> <span class="k">return</span> <span class="k">this</span><span class="o">.</span><span class="na">text</span><span class="o">;</span> <span class="o">}</span>
<span class="kd">public</span> <span class="kt">void</span> <span class="nf">setText</span><span class="o">(</span><span class="n">String</span> <span class="n">text</span><span class="o">)</span> <span class="o">{</span> <span class="k">this</span><span class="o">.</span><span class="na">text</span> <span class="o">=</span> <span class="n">text</span><span class="o">;</span> <span class="o">}</span>
<span class="o">}</span>
<span class="kd">public</span> <span class="kd">class</span> <span class="nc">LabeledDocument</span> <span class="kd">extends</span> <span class="n">Document</span> <span class="kd">implements</span> <span class="n">Serializable</span> <span class="o">{</span>
<span class="kd">private</span> <span class="kt">double</span> <span class="n">label</span><span class="o">;</span>
<span class="kd">public</span> <span class="nf">LabeledDocument</span><span class="o">(</span><span class="kt">long</span> <span class="n">id</span><span class="o">,</span> <span class="n">String</span> <span class="n">text</span><span class="o">,</span> <span class="kt">double</span> <span class="n">label</span><span class="o">)</span> <span class="o">{</span>
<span class="kd">super</span><span class="o">(</span><span class="n">id</span><span class="o">,</span> <span class="n">text</span><span class="o">);</span>
<span class="k">this</span><span class="o">.</span><span class="na">label</span> <span class="o">=</span> <span class="n">label</span><span class="o">;</span>
<span class="o">}</span>
<span class="kd">public</span> <span class="kt">double</span> <span class="nf">getLabel</span><span class="o">()</span> <span class="o">{</span> <span class="k">return</span> <span class="k">this</span><span class="o">.</span><span class="na">label</span><span class="o">;</span> <span class="o">}</span>
<span class="kd">public</span> <span class="kt">void</span> <span class="nf">setLabel</span><span class="o">(</span><span class="kt">double</span> <span class="n">label</span><span class="o">)</span> <span class="o">{</span> <span class="k">this</span><span class="o">.</span><span class="na">label</span> <span class="o">=</span> <span class="n">label</span><span class="o">;</span> <span class="o">}</span>
<span class="o">}</span>
<span class="n">SparkConf</span> <span class="n">conf</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">SparkConf</span><span class="o">().</span><span class="na">setAppName</span><span class="o">(</span><span class="s">"JavaCrossValidatorExample"</span><span class="o">);</span>
<span class="n">JavaSparkContext</span> <span class="n">jsc</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">JavaSparkContext</span><span class="o">(</span><span class="n">conf</span><span class="o">);</span>
<span class="n">SQLContext</span> <span class="n">jsql</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">SQLContext</span><span class="o">(</span><span class="n">jsc</span><span class="o">);</span>
<span class="c1">// Prepare training documents, which are labeled.</span>
<span class="n">List</span><span class="o"><</span><span class="n">LabeledDocument</span><span class="o">></span> <span class="n">localTraining</span> <span class="o">=</span> <span class="n">Lists</span><span class="o">.</span><span class="na">newArrayList</span><span class="o">(</span>
<span class="k">new</span> <span class="nf">LabeledDocument</span><span class="o">(</span><span class="mi">0L</span><span class="o">,</span> <span class="s">"a b c d e spark"</span><span class="o">,</span> <span class="mf">1.0</span><span class="o">),</span>
<span class="k">new</span> <span class="nf">LabeledDocument</span><span class="o">(</span><span class="mi">1L</span><span class="o">,</span> <span class="s">"b d"</span><span class="o">,</span> <span class="mf">0.0</span><span class="o">),</span>
<span class="k">new</span> <span class="nf">LabeledDocument</span><span class="o">(</span><span class="mi">2L</span><span class="o">,</span> <span class="s">"spark f g h"</span><span class="o">,</span> <span class="mf">1.0</span><span class="o">),</span>
<span class="k">new</span> <span class="nf">LabeledDocument</span><span class="o">(</span><span class="mi">3L</span><span class="o">,</span> <span class="s">"hadoop mapreduce"</span><span class="o">,</span> <span class="mf">0.0</span><span class="o">),</span>
<span class="k">new</span> <span class="nf">LabeledDocument</span><span class="o">(</span><span class="mi">4L</span><span class="o">,</span> <span class="s">"b spark who"</span><span class="o">,</span> <span class="mf">1.0</span><span class="o">),</span>
<span class="k">new</span> <span class="nf">LabeledDocument</span><span class="o">(</span><span class="mi">5L</span><span class="o">,</span> <span class="s">"g d a y"</span><span class="o">,</span> <span class="mf">0.0</span><span class="o">),</span>
<span class="k">new</span> <span class="nf">LabeledDocument</span><span class="o">(</span><span class="mi">6L</span><span class="o">,</span> <span class="s">"spark fly"</span><span class="o">,</span> <span class="mf">1.0</span><span class="o">),</span>
<span class="k">new</span> <span class="nf">LabeledDocument</span><span class="o">(</span><span class="mi">7L</span><span class="o">,</span> <span class="s">"was mapreduce"</span><span class="o">,</span> <span class="mf">0.0</span><span class="o">),</span>
<span class="k">new</span> <span class="nf">LabeledDocument</span><span class="o">(</span><span class="mi">8L</span><span class="o">,</span> <span class="s">"e spark program"</span><span class="o">,</span> <span class="mf">1.0</span><span class="o">),</span>
<span class="k">new</span> <span class="nf">LabeledDocument</span><span class="o">(</span><span class="mi">9L</span><span class="o">,</span> <span class="s">"a e c l"</span><span class="o">,</span> <span class="mf">0.0</span><span class="o">),</span>
<span class="k">new</span> <span class="nf">LabeledDocument</span><span class="o">(</span><span class="mi">10L</span><span class="o">,</span> <span class="s">"spark compile"</span><span class="o">,</span> <span class="mf">1.0</span><span class="o">),</span>
<span class="k">new</span> <span class="nf">LabeledDocument</span><span class="o">(</span><span class="mi">11L</span><span class="o">,</span> <span class="s">"hadoop software"</span><span class="o">,</span> <span class="mf">0.0</span><span class="o">));</span>
<span class="n">DataFrame</span> <span class="n">training</span> <span class="o">=</span> <span class="n">jsql</span><span class="o">.</span><span class="na">createDataFrame</span><span class="o">(</span><span class="n">jsc</span><span class="o">.</span><span class="na">parallelize</span><span class="o">(</span><span class="n">localTraining</span><span class="o">),</span> <span class="n">LabeledDocument</span><span class="o">.</span><span class="na">class</span><span class="o">);</span>
<span class="c1">// Configure an ML pipeline, which consists of three stages: tokenizer, hashingTF, and lr.</span>
<span class="n">Tokenizer</span> <span class="n">tokenizer</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">Tokenizer</span><span class="o">()</span>
<span class="o">.</span><span class="na">setInputCol</span><span class="o">(</span><span class="s">"text"</span><span class="o">)</span>
<span class="o">.</span><span class="na">setOutputCol</span><span class="o">(</span><span class="s">"words"</span><span class="o">);</span>
<span class="n">HashingTF</span> <span class="n">hashingTF</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">HashingTF</span><span class="o">()</span>
<span class="o">.</span><span class="na">setNumFeatures</span><span class="o">(</span><span class="mi">1000</span><span class="o">)</span>
<span class="o">.</span><span class="na">setInputCol</span><span class="o">(</span><span class="n">tokenizer</span><span class="o">.</span><span class="na">getOutputCol</span><span class="o">())</span>
<span class="o">.</span><span class="na">setOutputCol</span><span class="o">(</span><span class="s">"features"</span><span class="o">);</span>
<span class="n">LogisticRegression</span> <span class="n">lr</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">LogisticRegression</span><span class="o">()</span>
<span class="o">.</span><span class="na">setMaxIter</span><span class="o">(</span><span class="mi">10</span><span class="o">)</span>
<span class="o">.</span><span class="na">setRegParam</span><span class="o">(</span><span class="mf">0.01</span><span class="o">);</span>
<span class="n">Pipeline</span> <span class="n">pipeline</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">Pipeline</span><span class="o">()</span>
<span class="o">.</span><span class="na">setStages</span><span class="o">(</span><span class="k">new</span> <span class="n">PipelineStage</span><span class="o">[]</span> <span class="o">{</span><span class="n">tokenizer</span><span class="o">,</span> <span class="n">hashingTF</span><span class="o">,</span> <span class="n">lr</span><span class="o">});</span>
<span class="c1">// We now treat the Pipeline as an Estimator, wrapping it in a CrossValidator instance.</span>
<span class="c1">// This will allow us to jointly choose parameters for all Pipeline stages.</span>
<span class="c1">// A CrossValidator requires an Estimator, a set of Estimator ParamMaps, and an Evaluator.</span>
<span class="n">CrossValidator</span> <span class="n">crossval</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">CrossValidator</span><span class="o">()</span>
<span class="o">.</span><span class="na">setEstimator</span><span class="o">(</span><span class="n">pipeline</span><span class="o">)</span>
<span class="o">.</span><span class="na">setEvaluator</span><span class="o">(</span><span class="k">new</span> <span class="nf">BinaryClassificationEvaluator</span><span class="o">());</span>
<span class="c1">// We use a ParamGridBuilder to construct a grid of parameters to search over.</span>
<span class="c1">// With 3 values for hashingTF.numFeatures and 2 values for lr.regParam,</span>
<span class="c1">// this grid will have 3 x 2 = 6 parameter settings for CrossValidator to choose from.</span>
<span class="n">ParamMap</span><span class="o">[]</span> <span class="n">paramGrid</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">ParamGridBuilder</span><span class="o">()</span>
<span class="o">.</span><span class="na">addGrid</span><span class="o">(</span><span class="n">hashingTF</span><span class="o">.</span><span class="na">numFeatures</span><span class="o">(),</span> <span class="k">new</span> <span class="kt">int</span><span class="o">[]{</span><span class="mi">10</span><span class="o">,</span> <span class="mi">100</span><span class="o">,</span> <span class="mi">1000</span><span class="o">})</span>
<span class="o">.</span><span class="na">addGrid</span><span class="o">(</span><span class="n">lr</span><span class="o">.</span><span class="na">regParam</span><span class="o">(),</span> <span class="k">new</span> <span class="kt">double</span><span class="o">[]{</span><span class="mf">0.1</span><span class="o">,</span> <span class="mf">0.01</span><span class="o">})</span>
<span class="o">.</span><span class="na">build</span><span class="o">();</span>
<span class="n">crossval</span><span class="o">.</span><span class="na">setEstimatorParamMaps</span><span class="o">(</span><span class="n">paramGrid</span><span class="o">);</span>
<span class="n">crossval</span><span class="o">.</span><span class="na">setNumFolds</span><span class="o">(</span><span class="mi">2</span><span class="o">);</span> <span class="c1">// Use 3+ in practice</span>
<span class="c1">// Run cross-validation, and choose the best set of parameters.</span>
<span class="n">CrossValidatorModel</span> <span class="n">cvModel</span> <span class="o">=</span> <span class="n">crossval</span><span class="o">.</span><span class="na">fit</span><span class="o">(</span><span class="n">training</span><span class="o">);</span>
<span class="c1">// Prepare test documents, which are unlabeled.</span>
<span class="n">List</span><span class="o"><</span><span class="n">Document</span><span class="o">></span> <span class="n">localTest</span> <span class="o">=</span> <span class="n">Lists</span><span class="o">.</span><span class="na">newArrayList</span><span class="o">(</span>
<span class="k">new</span> <span class="nf">Document</span><span class="o">(</span><span class="mi">4L</span><span class="o">,</span> <span class="s">"spark i j k"</span><span class="o">),</span>
<span class="k">new</span> <span class="nf">Document</span><span class="o">(</span><span class="mi">5L</span><span class="o">,</span> <span class="s">"l m n"</span><span class="o">),</span>
<span class="k">new</span> <span class="nf">Document</span><span class="o">(</span><span class="mi">6L</span><span class="o">,</span> <span class="s">"mapreduce spark"</span><span class="o">),</span>
<span class="k">new</span> <span class="nf">Document</span><span class="o">(</span><span class="mi">7L</span><span class="o">,</span> <span class="s">"apache hadoop"</span><span class="o">));</span>
<span class="n">DataFrame</span> <span class="n">test</span> <span class="o">=</span> <span class="n">jsql</span><span class="o">.</span><span class="na">createDataFrame</span><span class="o">(</span><span class="n">jsc</span><span class="o">.</span><span class="na">parallelize</span><span class="o">(</span><span class="n">localTest</span><span class="o">),</span> <span class="n">Document</span><span class="o">.</span><span class="na">class</span><span class="o">);</span>
<span class="c1">// Make predictions on test documents. cvModel uses the best model found (lrModel).</span>
<span class="n">DataFrame</span> <span class="n">predictions</span> <span class="o">=</span> <span class="n">cvModel</span><span class="o">.</span><span class="na">transform</span><span class="o">(</span><span class="n">test</span><span class="o">);</span>
<span class="k">for</span> <span class="o">(</span><span class="n">Row</span> <span class="nl">r:</span> <span class="n">predictions</span><span class="o">.</span><span class="na">select</span><span class="o">(</span><span class="s">"id"</span><span class="o">,</span> <span class="s">"text"</span><span class="o">,</span> <span class="s">"probability"</span><span class="o">,</span> <span class="s">"prediction"</span><span class="o">).</span><span class="na">collect</span><span class="o">())</span> <span class="o">{</span>
<span class="n">System</span><span class="o">.</span><span class="na">out</span><span class="o">.</span><span class="na">println</span><span class="o">(</span><span class="s">"("</span> <span class="o">+</span> <span class="n">r</span><span class="o">.</span><span class="na">get</span><span class="o">(</span><span class="mi">0</span><span class="o">)</span> <span class="o">+</span> <span class="s">", "</span> <span class="o">+</span> <span class="n">r</span><span class="o">.</span><span class="na">get</span><span class="o">(</span><span class="mi">1</span><span class="o">)</span> <span class="o">+</span> <span class="s">") --> prob="</span> <span class="o">+</span> <span class="n">r</span><span class="o">.</span><span class="na">get</span><span class="o">(</span><span class="mi">2</span><span class="o">)</span>
<span class="o">+</span> <span class="s">", prediction="</span> <span class="o">+</span> <span class="n">r</span><span class="o">.</span><span class="na">get</span><span class="o">(</span><span class="mi">3</span><span class="o">));</span>
<span class="o">}</span>
<span class="n">jsc</span><span class="o">.</span><span class="na">stop</span><span class="o">();</span></code></pre></div>
</div>
</div>
<h1 id="dependencies">Dependencies</h1>
<p>Spark ML currently depends on MLlib and has the same dependencies.
Please see the <a href="mllib-guide.html#dependencies">MLlib Dependencies guide</a> for more info.</p>
<p>Spark ML also depends upon Spark SQL, but the relevant parts of Spark SQL do not bring additional dependencies.</p>
<h1 id="migration-guide">Migration Guide</h1>
<h2 id="from-12-to-13">From 1.2 to 1.3</h2>
<p>The main API changes are from Spark SQL. We list the most important changes here:</p>
<ul>
<li>The old <a href="http://spark.apache.org/docs/1.2.1/api/scala/index.html#org.apache.spark.sql.SchemaRDD">SchemaRDD</a> has been replaced with <a href="api/scala/index.html#org.apache.spark.sql.DataFrame">DataFrame</a> with a somewhat modified API. All algorithms in Spark ML which used to use SchemaRDD now use DataFrame.</li>
<li>In Spark 1.2, we used implicit conversions from <code>RDD</code>s of <code>LabeledPoint</code> into <code>SchemaRDD</code>s by calling <code>import sqlContext._</code> where <code>sqlContext</code> was an instance of <code>SQLContext</code>. These implicits have been moved, so we now call <code>import sqlContext.implicits._</code>.</li>
<li>Java APIs for SQL have also changed accordingly. Please see the examples above and the <a href="sql-programming-guide.html">Spark SQL Programming Guide</a> for details.</li>
</ul>
<p>Other changes were in <code>LogisticRegression</code>:</p>
<ul>
<li>The <code>scoreCol</code> output column (with default value “score”) was renamed to be <code>probabilityCol</code> (with default value “probability”). The type was originally <code>Double</code> (for the probability of class 1.0), but it is now <code>Vector</code> (for the probability of each class, to support multiclass classification in the future).</li>
<li>In Spark 1.2, <code>LogisticRegressionModel</code> did not include an intercept. In Spark 1.3, it includes an intercept; however, it will always be 0.0 since it uses the default settings for <a href="api/scala/index.html#org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS">spark.mllib.LogisticRegressionWithLBFGS</a>. The option to use an intercept will be added in the future.</li>
</ul>
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