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| <h1 class="title">ML Pipelines</h1> |
| |
| |
| <p><code class="language-plaintext highlighter-rouge">\[ |
| \newcommand{\R}{\mathbb{R}} |
| \newcommand{\E}{\mathbb{E}} |
| \newcommand{\x}{\mathbf{x}} |
| \newcommand{\y}{\mathbf{y}} |
| \newcommand{\wv}{\mathbf{w}} |
| \newcommand{\av}{\mathbf{\alpha}} |
| \newcommand{\bv}{\mathbf{b}} |
| \newcommand{\N}{\mathbb{N}} |
| \newcommand{\id}{\mathbf{I}} |
| \newcommand{\ind}{\mathbf{1}} |
| \newcommand{\0}{\mathbf{0}} |
| \newcommand{\unit}{\mathbf{e}} |
| \newcommand{\one}{\mathbf{1}} |
| \newcommand{\zero}{\mathbf{0}} |
| \]</code></p> |
| |
| <p>In this section, we introduce the concept of <strong><em>ML Pipelines</em></strong>. |
| ML Pipelines provide a uniform set of high-level APIs built on top of |
| <a href="sql-programming-guide.html">DataFrames</a> that help users create and tune practical |
| machine learning pipelines.</p> |
| |
| <p><strong>Table of Contents</strong></p> |
| |
| <ul id="markdown-toc"> |
| <li><a href="#main-concepts-in-pipelines" id="markdown-toc-main-concepts-in-pipelines">Main concepts in Pipelines</a> <ul> |
| <li><a href="#dataframe" id="markdown-toc-dataframe">DataFrame</a></li> |
| <li><a href="#pipeline-components" id="markdown-toc-pipeline-components">Pipeline components</a> <ul> |
| <li><a href="#transformers" id="markdown-toc-transformers">Transformers</a></li> |
| <li><a href="#estimators" id="markdown-toc-estimators">Estimators</a></li> |
| <li><a href="#properties-of-pipeline-components" id="markdown-toc-properties-of-pipeline-components">Properties of pipeline components</a></li> |
| </ul> |
| </li> |
| <li><a href="#pipeline" id="markdown-toc-pipeline">Pipeline</a> <ul> |
| <li><a href="#how-it-works" id="markdown-toc-how-it-works">How it works</a></li> |
| <li><a href="#details" id="markdown-toc-details">Details</a></li> |
| </ul> |
| </li> |
| <li><a href="#parameters" id="markdown-toc-parameters">Parameters</a></li> |
| <li><a href="#ml-persistence-saving-and-loading-pipelines" id="markdown-toc-ml-persistence-saving-and-loading-pipelines">ML persistence: Saving and Loading Pipelines</a> <ul> |
| <li><a href="#backwards-compatibility-for-ml-persistence" id="markdown-toc-backwards-compatibility-for-ml-persistence">Backwards compatibility for ML persistence</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li><a href="#code-examples" id="markdown-toc-code-examples">Code examples</a> <ul> |
| <li><a href="#example-estimator-transformer-and-param" id="markdown-toc-example-estimator-transformer-and-param">Example: Estimator, Transformer, and Param</a></li> |
| <li><a href="#example-pipeline" id="markdown-toc-example-pipeline">Example: Pipeline</a></li> |
| <li><a href="#model-selection-hyperparameter-tuning" id="markdown-toc-model-selection-hyperparameter-tuning">Model selection (hyperparameter tuning)</a></li> |
| </ul> |
| </li> |
| </ul> |
| |
| <h1 id="main-concepts-in-pipelines">Main concepts in Pipelines</h1> |
| |
| <p>MLlib 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 Pipelines API, where the pipeline concept is |
| mostly inspired by the <a href="http://scikit-learn.org/">scikit-learn</a> project.</p> |
| |
| <ul> |
| <li> |
| <p><strong><a href="ml-pipeline.html#dataframe"><code class="language-plaintext highlighter-rouge">DataFrame</code></a></strong>: This ML API uses <code class="language-plaintext highlighter-rouge">DataFrame</code> from Spark SQL as an ML |
| dataset, which can hold a variety of data types. |
| E.g., a <code class="language-plaintext highlighter-rouge">DataFrame</code> could have different columns storing text, feature vectors, true labels, and predictions.</p> |
| </li> |
| <li> |
| <p><strong><a href="ml-pipeline.html#transformers"><code class="language-plaintext highlighter-rouge">Transformer</code></a></strong>: A <code class="language-plaintext highlighter-rouge">Transformer</code> is an algorithm which can transform one <code class="language-plaintext highlighter-rouge">DataFrame</code> into another <code class="language-plaintext highlighter-rouge">DataFrame</code>. |
| E.g., an ML model is a <code class="language-plaintext highlighter-rouge">Transformer</code> which transforms a <code class="language-plaintext highlighter-rouge">DataFrame</code> with features into a <code class="language-plaintext highlighter-rouge">DataFrame</code> with predictions.</p> |
| </li> |
| <li> |
| <p><strong><a href="ml-pipeline.html#estimators"><code class="language-plaintext highlighter-rouge">Estimator</code></a></strong>: An <code class="language-plaintext highlighter-rouge">Estimator</code> is an algorithm which can be fit on a <code class="language-plaintext highlighter-rouge">DataFrame</code> to produce a <code class="language-plaintext highlighter-rouge">Transformer</code>. |
| E.g., a learning algorithm is an <code class="language-plaintext highlighter-rouge">Estimator</code> which trains on a <code class="language-plaintext highlighter-rouge">DataFrame</code> and produces a model.</p> |
| </li> |
| <li> |
| <p><strong><a href="ml-pipeline.html#pipeline"><code class="language-plaintext highlighter-rouge">Pipeline</code></a></strong>: A <code class="language-plaintext highlighter-rouge">Pipeline</code> chains multiple <code class="language-plaintext highlighter-rouge">Transformer</code>s and <code class="language-plaintext highlighter-rouge">Estimator</code>s together to specify an ML workflow.</p> |
| </li> |
| <li> |
| <p><strong><a href="ml-pipeline.html#parameters"><code class="language-plaintext highlighter-rouge">Parameter</code></a></strong>: All <code class="language-plaintext highlighter-rouge">Transformer</code>s and <code class="language-plaintext highlighter-rouge">Estimator</code>s now share a common API for specifying parameters.</p> |
| </li> |
| </ul> |
| |
| <h2 id="dataframe">DataFrame</h2> |
| |
| <p>Machine learning can be applied to a wide variety of data types, such as vectors, text, images, and structured data. |
| This API adopts the <code class="language-plaintext highlighter-rouge">DataFrame</code> from Spark SQL in order to support a variety of data types.</p> |
| |
| <p><code class="language-plaintext highlighter-rouge">DataFrame</code> supports many basic and structured types; see the <a href="sql-ref-datatypes.html">Spark SQL datatype reference</a> for a list of supported types. |
| In addition to the types listed in the Spark SQL guide, <code class="language-plaintext highlighter-rouge">DataFrame</code> can use ML <a href="mllib-data-types.html#local-vector"><code class="language-plaintext highlighter-rouge">Vector</code></a> types.</p> |
| |
| <p>A <code class="language-plaintext highlighter-rouge">DataFrame</code> can be created either implicitly or explicitly from a regular <code class="language-plaintext highlighter-rouge">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 class="language-plaintext highlighter-rouge">DataFrame</code> are named. The code examples below use names such as “text”, “features”, and “label”.</p> |
| |
| <h2 id="pipeline-components">Pipeline components</h2> |
| |
| <h3 id="transformers">Transformers</h3> |
| |
| <p>A <code class="language-plaintext highlighter-rouge">Transformer</code> is an abstraction that includes feature transformers and learned models. |
| Technically, a <code class="language-plaintext highlighter-rouge">Transformer</code> implements a method <code class="language-plaintext highlighter-rouge">transform()</code>, which converts one <code class="language-plaintext highlighter-rouge">DataFrame</code> into |
| another, generally by appending one or more columns. |
| For example:</p> |
| |
| <ul> |
| <li>A feature transformer might take a <code class="language-plaintext highlighter-rouge">DataFrame</code>, read a column (e.g., text), map it into a new |
| column (e.g., feature vectors), and output a new <code class="language-plaintext highlighter-rouge">DataFrame</code> with the mapped column appended.</li> |
| <li>A learning model might take a <code class="language-plaintext highlighter-rouge">DataFrame</code>, read the column containing feature vectors, predict the |
| label for each feature vector, and output a new <code class="language-plaintext highlighter-rouge">DataFrame</code> with predicted labels appended as a |
| column.</li> |
| </ul> |
| |
| <h3 id="estimators">Estimators</h3> |
| |
| <p>An <code class="language-plaintext highlighter-rouge">Estimator</code> abstracts the concept of a learning algorithm or any algorithm that fits or trains on |
| data. |
| Technically, an <code class="language-plaintext highlighter-rouge">Estimator</code> implements a method <code class="language-plaintext highlighter-rouge">fit()</code>, which accepts a <code class="language-plaintext highlighter-rouge">DataFrame</code> and produces a |
| <code class="language-plaintext highlighter-rouge">Model</code>, which is a <code class="language-plaintext highlighter-rouge">Transformer</code>. |
| For example, a learning algorithm such as <code class="language-plaintext highlighter-rouge">LogisticRegression</code> is an <code class="language-plaintext highlighter-rouge">Estimator</code>, and calling |
| <code class="language-plaintext highlighter-rouge">fit()</code> trains a <code class="language-plaintext highlighter-rouge">LogisticRegressionModel</code>, which is a <code class="language-plaintext highlighter-rouge">Model</code> and hence a <code class="language-plaintext highlighter-rouge">Transformer</code>.</p> |
| |
| <h3 id="properties-of-pipeline-components">Properties of pipeline components</h3> |
| |
| <p><code class="language-plaintext highlighter-rouge">Transformer.transform()</code>s and <code class="language-plaintext highlighter-rouge">Estimator.fit()</code>s are both stateless. In the future, stateful algorithms may be supported via alternative concepts.</p> |
| |
| <p>Each instance of a <code class="language-plaintext highlighter-rouge">Transformer</code> or <code class="language-plaintext highlighter-rouge">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>MLlib represents such a workflow as a <code class="language-plaintext highlighter-rouge">Pipeline</code>, which consists of a sequence of |
| <code class="language-plaintext highlighter-rouge">PipelineStage</code>s (<code class="language-plaintext highlighter-rouge">Transformer</code>s and <code class="language-plaintext highlighter-rouge">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 class="language-plaintext highlighter-rouge">Pipeline</code> is specified as a sequence of stages, and each stage is either a <code class="language-plaintext highlighter-rouge">Transformer</code> or an <code class="language-plaintext highlighter-rouge">Estimator</code>. |
| These stages are run in order, and the input <code class="language-plaintext highlighter-rouge">DataFrame</code> is transformed as it passes through each stage. |
| For <code class="language-plaintext highlighter-rouge">Transformer</code> stages, the <code class="language-plaintext highlighter-rouge">transform()</code> method is called on the <code class="language-plaintext highlighter-rouge">DataFrame</code>. |
| For <code class="language-plaintext highlighter-rouge">Estimator</code> stages, the <code class="language-plaintext highlighter-rouge">fit()</code> method is called to produce a <code class="language-plaintext highlighter-rouge">Transformer</code> (which becomes part of the <code class="language-plaintext highlighter-rouge">PipelineModel</code>, or fitted <code class="language-plaintext highlighter-rouge">Pipeline</code>), and that <code class="language-plaintext highlighter-rouge">Transformer</code>’s <code class="language-plaintext highlighter-rouge">transform()</code> method is called on the <code class="language-plaintext highlighter-rouge">DataFrame</code>.</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 class="language-plaintext highlighter-rouge">Pipeline</code>.</p> |
| |
| <p style="text-align: center;"> |
| <img src="img/ml-Pipeline.png" title="ML Pipeline Example" alt="ML Pipeline Example" width="80%" /> |
| </p> |
| |
| <p>Above, the top row represents a <code class="language-plaintext highlighter-rouge">Pipeline</code> with three stages. |
| The first two (<code class="language-plaintext highlighter-rouge">Tokenizer</code> and <code class="language-plaintext highlighter-rouge">HashingTF</code>) are <code class="language-plaintext highlighter-rouge">Transformer</code>s (blue), and the third (<code class="language-plaintext highlighter-rouge">LogisticRegression</code>) is an <code class="language-plaintext highlighter-rouge">Estimator</code> (red). |
| The bottom row represents data flowing through the pipeline, where cylinders indicate <code class="language-plaintext highlighter-rouge">DataFrame</code>s. |
| The <code class="language-plaintext highlighter-rouge">Pipeline.fit()</code> method is called on the original <code class="language-plaintext highlighter-rouge">DataFrame</code>, which has raw text documents and labels. |
| The <code class="language-plaintext highlighter-rouge">Tokenizer.transform()</code> method splits the raw text documents into words, adding a new column with words to the <code class="language-plaintext highlighter-rouge">DataFrame</code>. |
| The <code class="language-plaintext highlighter-rouge">HashingTF.transform()</code> method converts the words column into feature vectors, adding a new column with those vectors to the <code class="language-plaintext highlighter-rouge">DataFrame</code>. |
| Now, since <code class="language-plaintext highlighter-rouge">LogisticRegression</code> is an <code class="language-plaintext highlighter-rouge">Estimator</code>, the <code class="language-plaintext highlighter-rouge">Pipeline</code> first calls <code class="language-plaintext highlighter-rouge">LogisticRegression.fit()</code> to produce a <code class="language-plaintext highlighter-rouge">LogisticRegressionModel</code>. |
| If the <code class="language-plaintext highlighter-rouge">Pipeline</code> had more <code class="language-plaintext highlighter-rouge">Estimator</code>s, it would call the <code class="language-plaintext highlighter-rouge">LogisticRegressionModel</code>’s <code class="language-plaintext highlighter-rouge">transform()</code> |
| method on the <code class="language-plaintext highlighter-rouge">DataFrame</code> before passing the <code class="language-plaintext highlighter-rouge">DataFrame</code> to the next stage.</p> |
| |
| <p>A <code class="language-plaintext highlighter-rouge">Pipeline</code> is an <code class="language-plaintext highlighter-rouge">Estimator</code>. |
| Thus, after a <code class="language-plaintext highlighter-rouge">Pipeline</code>’s <code class="language-plaintext highlighter-rouge">fit()</code> method runs, it produces a <code class="language-plaintext highlighter-rouge">PipelineModel</code>, which is a |
| <code class="language-plaintext highlighter-rouge">Transformer</code>. |
| This <code class="language-plaintext highlighter-rouge">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="ML PipelineModel Example" alt="ML PipelineModel Example" width="80%" /> |
| </p> |
| |
| <p>In the figure above, the <code class="language-plaintext highlighter-rouge">PipelineModel</code> has the same number of stages as the original <code class="language-plaintext highlighter-rouge">Pipeline</code>, but all <code class="language-plaintext highlighter-rouge">Estimator</code>s in the original <code class="language-plaintext highlighter-rouge">Pipeline</code> have become <code class="language-plaintext highlighter-rouge">Transformer</code>s. |
| When the <code class="language-plaintext highlighter-rouge">PipelineModel</code>’s <code class="language-plaintext highlighter-rouge">transform()</code> method is called on a test dataset, the data are passed |
| through the fitted pipeline in order. |
| Each stage’s <code class="language-plaintext highlighter-rouge">transform()</code> method updates the dataset and passes it to the next stage.</p> |
| |
| <p><code class="language-plaintext highlighter-rouge">Pipeline</code>s and <code class="language-plaintext highlighter-rouge">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 class="language-plaintext highlighter-rouge">Pipeline</code>s</em>: A <code class="language-plaintext highlighter-rouge">Pipeline</code>’s stages are specified as an ordered array. The examples given here are all for linear <code class="language-plaintext highlighter-rouge">Pipeline</code>s, i.e., <code class="language-plaintext highlighter-rouge">Pipeline</code>s in which each stage uses data produced by the previous stage. It is possible to create non-linear <code class="language-plaintext highlighter-rouge">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 class="language-plaintext highlighter-rouge">Pipeline</code> forms a DAG, then the stages must be specified in topological order.</p> |
| |
| <p><em>Runtime checking</em>: Since <code class="language-plaintext highlighter-rouge">Pipeline</code>s can operate on <code class="language-plaintext highlighter-rouge">DataFrame</code>s with varied types, they cannot use |
| compile-time type checking. |
| <code class="language-plaintext highlighter-rouge">Pipeline</code>s and <code class="language-plaintext highlighter-rouge">PipelineModel</code>s instead do runtime checking before actually running the <code class="language-plaintext highlighter-rouge">Pipeline</code>. |
| This type checking is done using the <code class="language-plaintext highlighter-rouge">DataFrame</code> <em>schema</em>, a description of the data types of columns in the <code class="language-plaintext highlighter-rouge">DataFrame</code>.</p> |
| |
| <p><em>Unique Pipeline stages</em>: A <code class="language-plaintext highlighter-rouge">Pipeline</code>’s stages should be unique instances. E.g., the same instance |
| <code class="language-plaintext highlighter-rouge">myHashingTF</code> should not be inserted into the <code class="language-plaintext highlighter-rouge">Pipeline</code> twice since <code class="language-plaintext highlighter-rouge">Pipeline</code> stages must have |
| unique IDs. However, different instances <code class="language-plaintext highlighter-rouge">myHashingTF1</code> and <code class="language-plaintext highlighter-rouge">myHashingTF2</code> (both of type <code class="language-plaintext highlighter-rouge">HashingTF</code>) |
| can be put into the same <code class="language-plaintext highlighter-rouge">Pipeline</code> since different instances will be created with different IDs.</p> |
| |
| <h2 id="parameters">Parameters</h2> |
| |
| <p>MLlib <code class="language-plaintext highlighter-rouge">Estimator</code>s and <code class="language-plaintext highlighter-rouge">Transformer</code>s use a uniform API for specifying parameters.</p> |
| |
| <p>A <code class="language-plaintext highlighter-rouge">Param</code> is a named parameter with self-contained documentation. |
| A <code class="language-plaintext highlighter-rouge">ParamMap</code> 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 class="language-plaintext highlighter-rouge">lr</code> is an instance of <code class="language-plaintext highlighter-rouge">LogisticRegression</code>, one could |
| call <code class="language-plaintext highlighter-rouge">lr.setMaxIter(10)</code> to make <code class="language-plaintext highlighter-rouge">lr.fit()</code> use at most 10 iterations. |
| This API resembles the API used in <code class="language-plaintext highlighter-rouge">spark.mllib</code> package.</li> |
| <li>Pass a <code class="language-plaintext highlighter-rouge">ParamMap</code> to <code class="language-plaintext highlighter-rouge">fit()</code> or <code class="language-plaintext highlighter-rouge">transform()</code>. Any parameters in the <code class="language-plaintext highlighter-rouge">ParamMap</code> will override parameters previously specified via setter methods.</li> |
| </ol> |
| |
| <p>Parameters belong to specific instances of <code class="language-plaintext highlighter-rouge">Estimator</code>s and <code class="language-plaintext highlighter-rouge">Transformer</code>s. |
| For example, if we have two <code class="language-plaintext highlighter-rouge">LogisticRegression</code> instances <code class="language-plaintext highlighter-rouge">lr1</code> and <code class="language-plaintext highlighter-rouge">lr2</code>, then we can build a <code class="language-plaintext highlighter-rouge">ParamMap</code> with both <code class="language-plaintext highlighter-rouge">maxIter</code> parameters specified: <code class="language-plaintext highlighter-rouge">ParamMap(lr1.maxIter -> 10, lr2.maxIter -> 20)</code>. |
| This is useful if there are two algorithms with the <code class="language-plaintext highlighter-rouge">maxIter</code> parameter in a <code class="language-plaintext highlighter-rouge">Pipeline</code>.</p> |
| |
| <h2 id="ml-persistence-saving-and-loading-pipelines">ML persistence: Saving and Loading Pipelines</h2> |
| |
| <p>Often times it is worth it to save a model or a pipeline to disk for later use. In Spark 1.6, a model import/export functionality was added to the Pipeline API. |
| As of Spark 2.3, the DataFrame-based API in <code class="language-plaintext highlighter-rouge">spark.ml</code> and <code class="language-plaintext highlighter-rouge">pyspark.ml</code> has complete coverage.</p> |
| |
| <p>ML persistence works across Scala, Java and Python. However, R currently uses a modified format, |
| so models saved in R can only be loaded back in R; this should be fixed in the future and is |
| tracked in <a href="https://issues.apache.org/jira/browse/SPARK-15572">SPARK-15572</a>.</p> |
| |
| <h3 id="backwards-compatibility-for-ml-persistence">Backwards compatibility for ML persistence</h3> |
| |
| <p>In general, MLlib maintains backwards compatibility for ML persistence. I.e., if you save an ML |
| model or Pipeline in one version of Spark, then you should be able to load it back and use it in a |
| future version of Spark. However, there are rare exceptions, described below.</p> |
| |
| <p>Model persistence: Is a model or Pipeline saved using Apache Spark ML persistence in Spark |
| version X loadable by Spark version Y?</p> |
| |
| <ul> |
| <li>Major versions: No guarantees, but best-effort.</li> |
| <li>Minor and patch versions: Yes; these are backwards compatible.</li> |
| <li>Note about the format: There are no guarantees for a stable persistence format, but model loading itself is designed to be backwards compatible.</li> |
| </ul> |
| |
| <p>Model behavior: Does a model or Pipeline in Spark version X behave identically in Spark version Y?</p> |
| |
| <ul> |
| <li>Major versions: No guarantees, but best-effort.</li> |
| <li>Minor and patch versions: Identical behavior, except for bug fixes.</li> |
| </ul> |
| |
| <p>For both model persistence and model behavior, any breaking changes across a minor version or patch |
| version are reported in the Spark version release notes. If a breakage is not reported in release |
| notes, then it should be treated as a bug to be fixed.</p> |
| |
| <h1 id="code-examples">Code examples</h1> |
| |
| <p>This section gives code examples illustrating the functionality discussed above. |
| For more info, please refer to the API documentation |
| (<a href="api/scala/org/apache/spark/ml/package.html">Scala</a>, |
| <a href="api/java/org/apache/spark/ml/package-summary.html">Java</a>, |
| and <a href="api/python/reference/pyspark.ml.html">Python</a>).</p> |
| |
| <h2 id="example-estimator-transformer-and-param">Example: Estimator, Transformer, and Param</h2> |
| |
| <p>This example covers the concepts of <code class="language-plaintext highlighter-rouge">Estimator</code>, <code class="language-plaintext highlighter-rouge">Transformer</code>, and <code class="language-plaintext highlighter-rouge">Param</code>.</p> |
| |
| <div class="codetabs"> |
| |
| <div data-lang="scala"> |
| |
| <p>Refer to the <a href="api/scala/org/apache/spark/ml/Estimator.html"><code class="language-plaintext highlighter-rouge">Estimator</code> Scala docs</a>, |
| the <a href="api/scala/org/apache/spark/ml/Transformer.html"><code class="language-plaintext highlighter-rouge">Transformer</code> Scala docs</a> and |
| the <a href="api/scala/org/apache/spark/ml/param/Params.html"><code class="language-plaintext highlighter-rouge">Params</code> Scala docs</a> for details on the API.</p> |
| |
| <div class="highlight"><pre class="codehilite"><code><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.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.ml.param.ParamMap</span> |
| <span class="k">import</span> <span class="nn">org.apache.spark.sql.Row</span> |
| |
| <span class="c1">// Prepare training data from a list of (label, features) tuples.</span> |
| <span class="k">val</span> <span class="nv">training</span> <span class="k">=</span> <span class="nv">spark</span><span class="o">.</span><span class="py">createDataFrame</span><span class="o">(</span><span class="nc">Seq</span><span class="o">(</span> |
| <span class="o">(</span><span class="mf">1.0</span><span class="o">,</span> <span class="nv">Vectors</span><span class="o">.</span><span class="py">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="o">(</span><span class="mf">0.0</span><span class="o">,</span> <span class="nv">Vectors</span><span class="o">.</span><span class="py">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="o">(</span><span class="mf">0.0</span><span class="o">,</span> <span class="nv">Vectors</span><span class="o">.</span><span class="py">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="o">(</span><span class="mf">1.0</span><span class="o">,</span> <span class="nv">Vectors</span><span class="o">.</span><span class="py">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="o">)).</span><span class="py">toDF</span><span class="o">(</span><span class="s">"label"</span><span class="o">,</span> <span class="s">"features"</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="nv">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="nf">println</span><span class="o">(</span><span class="n">s</span><span class="s">"LogisticRegression parameters:\n ${lr.explainParams()}\n"</span><span class="o">)</span> |
| |
| <span class="c1">// We may set parameters using setter methods.</span> |
| <span class="nv">lr</span><span class="o">.</span><span class="py">setMaxIter</span><span class="o">(</span><span class="mi">10</span><span class="o">)</span> |
| <span class="o">.</span><span class="py">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="nv">model1</span> <span class="k">=</span> <span class="nv">lr</span><span class="o">.</span><span class="py">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="nf">println</span><span class="o">(</span><span class="n">s</span><span class="s">"Model 1 was fit using parameters: ${model1.parent.extractParamMap}"</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="nv">paramMap</span> <span class="k">=</span> <span class="nc">ParamMap</span><span class="o">(</span><span class="nv">lr</span><span class="o">.</span><span class="py">maxIter</span> <span class="o">-></span> <span class="mi">20</span><span class="o">)</span> |
| <span class="o">.</span><span class="py">put</span><span class="o">(</span><span class="nv">lr</span><span class="o">.</span><span class="py">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="o">.</span><span class="py">put</span><span class="o">(</span><span class="nv">lr</span><span class="o">.</span><span class="py">regParam</span> <span class="o">-></span> <span class="mf">0.1</span><span class="o">,</span> <span class="nv">lr</span><span class="o">.</span><span class="py">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="nv">paramMap2</span> <span class="k">=</span> <span class="nc">ParamMap</span><span class="o">(</span><span class="nv">lr</span><span class="o">.</span><span class="py">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="nv">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="nv">model2</span> <span class="k">=</span> <span class="nv">lr</span><span class="o">.</span><span class="py">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="nf">println</span><span class="o">(</span><span class="n">s</span><span class="s">"Model 2 was fit using parameters: ${model2.parent.extractParamMap}"</span><span class="o">)</span> |
| |
| <span class="c1">// Prepare test data.</span> |
| <span class="k">val</span> <span class="nv">test</span> <span class="k">=</span> <span class="nv">spark</span><span class="o">.</span><span class="py">createDataFrame</span><span class="o">(</span><span class="nc">Seq</span><span class="o">(</span> |
| <span class="o">(</span><span class="mf">1.0</span><span class="o">,</span> <span class="nv">Vectors</span><span class="o">.</span><span class="py">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="o">(</span><span class="mf">0.0</span><span class="o">,</span> <span class="nv">Vectors</span><span class="o">.</span><span class="py">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="o">(</span><span class="mf">1.0</span><span class="o">,</span> <span class="nv">Vectors</span><span class="o">.</span><span class="py">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="o">)).</span><span class="py">toDF</span><span class="o">(</span><span class="s">"label"</span><span class="o">,</span> <span class="s">"features"</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="nv">model2</span><span class="o">.</span><span class="py">transform</span><span class="o">(</span><span class="n">test</span><span class="o">)</span> |
| <span class="o">.</span><span class="py">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="py">collect</span><span class="o">()</span> |
| <span class="o">.</span><span class="py">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="nf">println</span><span class="o">(</span><span class="n">s</span><span class="s">"($features, $label) -> prob=$prob, prediction=$prediction"</span><span class="o">)</span> |
| <span class="o">}</span></code></pre></div> |
| <div><small>Find full example code at "examples/src/main/scala/org/apache/spark/examples/ml/EstimatorTransformerParamExample.scala" in the Spark repo.</small></div> |
| </div> |
| |
| <div data-lang="java"> |
| |
| <p>Refer to the <a href="api/java/org/apache/spark/ml/Estimator.html"><code class="language-plaintext highlighter-rouge">Estimator</code> Java docs</a>, |
| the <a href="api/java/org/apache/spark/ml/Transformer.html"><code class="language-plaintext highlighter-rouge">Transformer</code> Java docs</a> and |
| the <a href="api/java/org/apache/spark/ml/param/Params.html"><code class="language-plaintext highlighter-rouge">Params</code> Java docs</a> for details on the API.</p> |
| |
| <div class="highlight"><pre class="codehilite"><code><span class="kn">import</span> <span class="nn">java.util.Arrays</span><span class="o">;</span> |
| <span class="kn">import</span> <span class="nn">java.util.List</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.classification.LogisticRegressionModel</span><span class="o">;</span> |
| <span class="kn">import</span> <span class="nn">org.apache.spark.ml.linalg.VectorUDT</span><span class="o">;</span> |
| <span class="kn">import</span> <span class="nn">org.apache.spark.ml.linalg.Vectors</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.sql.Dataset</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.RowFactory</span><span class="o">;</span> |
| <span class="kn">import</span> <span class="nn">org.apache.spark.sql.types.DataTypes</span><span class="o">;</span> |
| <span class="kn">import</span> <span class="nn">org.apache.spark.sql.types.Metadata</span><span class="o">;</span> |
| <span class="kn">import</span> <span class="nn">org.apache.spark.sql.types.StructField</span><span class="o">;</span> |
| <span class="kn">import</span> <span class="nn">org.apache.spark.sql.types.StructType</span><span class="o">;</span> |
| |
| <span class="c1">// Prepare training data.</span> |
| <span class="nc">List</span><span class="o"><</span><span class="nc">Row</span><span class="o">></span> <span class="n">dataTraining</span> <span class="o">=</span> <span class="nc">Arrays</span><span class="o">.</span><span class="na">asList</span><span class="o">(</span> |
| <span class="nc">RowFactory</span><span class="o">.</span><span class="na">create</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="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="nc">RowFactory</span><span class="o">.</span><span class="na">create</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="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="nc">RowFactory</span><span class="o">.</span><span class="na">create</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="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="nc">RowFactory</span><span class="o">.</span><span class="na">create</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="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="o">);</span> |
| <span class="nc">StructType</span> <span class="n">schema</span> <span class="o">=</span> <span class="k">new</span> <span class="nc">StructType</span><span class="o">(</span><span class="k">new</span> <span class="nc">StructField</span><span class="o">[]{</span> |
| <span class="k">new</span> <span class="nf">StructField</span><span class="o">(</span><span class="s">"label"</span><span class="o">,</span> <span class="nc">DataTypes</span><span class="o">.</span><span class="na">DoubleType</span><span class="o">,</span> <span class="kc">false</span><span class="o">,</span> <span class="nc">Metadata</span><span class="o">.</span><span class="na">empty</span><span class="o">()),</span> |
| <span class="k">new</span> <span class="nf">StructField</span><span class="o">(</span><span class="s">"features"</span><span class="o">,</span> <span class="k">new</span> <span class="nc">VectorUDT</span><span class="o">(),</span> <span class="kc">false</span><span class="o">,</span> <span class="nc">Metadata</span><span class="o">.</span><span class="na">empty</span><span class="o">())</span> |
| <span class="o">});</span> |
| <span class="nc">Dataset</span><span class="o"><</span><span class="nc">Row</span><span class="o">></span> <span class="n">training</span> <span class="o">=</span> <span class="n">spark</span><span class="o">.</span><span class="na">createDataFrame</span><span class="o">(</span><span class="n">dataTraining</span><span class="o">,</span> <span class="n">schema</span><span class="o">);</span> |
| |
| <span class="c1">// Create a LogisticRegression instance. This instance is an Estimator.</span> |
| <span class="nc">LogisticRegression</span> <span class="n">lr</span> <span class="o">=</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="nc">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="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="nc">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="nc">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">parent</span><span class="o">().</span><span class="na">extractParamMap</span><span class="o">());</span> |
| |
| <span class="c1">// We may alternatively specify parameters using a ParamMap.</span> |
| <span class="nc">ParamMap</span> <span class="n">paramMap</span> <span class="o">=</span> <span class="k">new</span> <span class="nc">ParamMap</span><span class="o">()</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="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="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="nc">ParamMap</span> <span class="n">paramMap2</span> <span class="o">=</span> <span class="k">new</span> <span class="nc">ParamMap</span><span class="o">()</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="nc">ParamMap</span> <span class="n">paramMapCombined</span> <span class="o">=</span> <span class="n">paramMap</span><span class="o">.</span><span class="n">$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="nc">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="nc">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">parent</span><span class="o">().</span><span class="na">extractParamMap</span><span class="o">());</span> |
| |
| <span class="c1">// Prepare test documents.</span> |
| <span class="nc">List</span><span class="o"><</span><span class="nc">Row</span><span class="o">></span> <span class="n">dataTest</span> <span class="o">=</span> <span class="nc">Arrays</span><span class="o">.</span><span class="na">asList</span><span class="o">(</span> |
| <span class="nc">RowFactory</span><span class="o">.</span><span class="na">create</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="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="nc">RowFactory</span><span class="o">.</span><span class="na">create</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="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="nc">RowFactory</span><span class="o">.</span><span class="na">create</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="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="o">);</span> |
| <span class="nc">Dataset</span><span class="o"><</span><span class="nc">Row</span><span class="o">></span> <span class="n">test</span> <span class="o">=</span> <span class="n">spark</span><span class="o">.</span><span class="na">createDataFrame</span><span class="o">(</span><span class="n">dataTest</span><span class="o">,</span> <span class="n">schema</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="nc">Dataset</span><span class="o"><</span><span class="nc">Row</span><span class="o">></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="nc">Dataset</span><span class="o"><</span><span class="nc">Row</span><span class="o">></span> <span class="n">rows</span> <span class="o">=</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="k">for</span> <span class="o">(</span><span class="nc">Row</span> <span class="nl">r:</span> <span class="n">rows</span><span class="o">.</span><span class="na">collectAsList</span><span class="o">())</span> <span class="o">{</span> |
| <span class="nc">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></code></pre></div> |
| <div><small>Find full example code at "examples/src/main/java/org/apache/spark/examples/ml/JavaEstimatorTransformerParamExample.java" in the Spark repo.</small></div> |
| </div> |
| |
| <div data-lang="python"> |
| |
| <p>Refer to the <a href="api/python/reference/api/pyspark.ml.Estimator.html"><code class="language-plaintext highlighter-rouge">Estimator</code> Python docs</a>, |
| the <a href="api/python/reference/api/pyspark.ml.Transformer.html"><code class="language-plaintext highlighter-rouge">Transformer</code> Python docs</a> and |
| the <a href="api/python/reference/api/pyspark.ml.param.Params.html"><code class="language-plaintext highlighter-rouge">Params</code> Python docs</a> for more details on the API.</p> |
| |
| <div class="highlight"><pre class="codehilite"><code><span class="kn">from</span> <span class="nn">pyspark.ml.linalg</span> <span class="kn">import</span> <span class="n">Vectors</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="c1"># Prepare training data from a list of (label, features) tuples. |
| </span><span class="n">training</span> <span class="o">=</span> <span class="n">spark</span><span class="p">.</span><span class="n">createDataFrame</span><span class="p">([</span> |
| <span class="p">(</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">Vectors</span><span class="p">.</span><span class="n">dense</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">])),</span> |
| <span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">Vectors</span><span class="p">.</span><span class="n">dense</span><span class="p">([</span><span class="mf">2.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.0</span><span class="p">])),</span> |
| <span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">Vectors</span><span class="p">.</span><span class="n">dense</span><span class="p">([</span><span class="mf">2.0</span><span class="p">,</span> <span class="mf">1.3</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">])),</span> |
| <span class="p">(</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">Vectors</span><span class="p">.</span><span class="n">dense</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.2</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.5</span><span class="p">]))],</span> <span class="p">[</span><span class="s">"label"</span><span class="p">,</span> <span class="s">"features"</span><span class="p">])</span> |
| |
| <span class="c1"># Create a LogisticRegression instance. This instance is an Estimator. |
| </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="c1"># Print out the parameters, documentation, and any default values. |
| </span><span class="k">print</span><span class="p">(</span><span class="s">"LogisticRegression parameters:</span><span class="se">\n</span><span class="s">"</span> <span class="o">+</span> <span class="n">lr</span><span class="p">.</span><span class="n">explainParams</span><span class="p">()</span> <span class="o">+</span> <span class="s">"</span><span class="se">\n</span><span class="s">"</span><span class="p">)</span> |
| |
| <span class="c1"># Learn a LogisticRegression model. This uses the parameters stored in lr. |
| </span><span class="n">model1</span> <span class="o">=</span> <span class="n">lr</span><span class="p">.</span><span class="n">fit</span><span class="p">(</span><span class="n">training</span><span class="p">)</span> |
| |
| <span class="c1"># Since model1 is a Model (i.e., a transformer produced by an Estimator), |
| # we can view the parameters it used during fit(). |
| # This prints the parameter (name: value) pairs, where names are unique IDs for this |
| # LogisticRegression instance. |
| </span><span class="k">print</span><span class="p">(</span><span class="s">"Model 1 was fit using parameters: "</span><span class="p">)</span> |
| <span class="k">print</span><span class="p">(</span><span class="n">model1</span><span class="p">.</span><span class="n">extractParamMap</span><span class="p">())</span> |
| |
| <span class="c1"># We may alternatively specify parameters using a Python dictionary as a paramMap |
| </span><span class="n">paramMap</span> <span class="o">=</span> <span class="p">{</span><span class="n">lr</span><span class="p">.</span><span class="n">maxIter</span><span class="p">:</span> <span class="mi">20</span><span class="p">}</span> |
| <span class="n">paramMap</span><span class="p">[</span><span class="n">lr</span><span class="p">.</span><span class="n">maxIter</span><span class="p">]</span> <span class="o">=</span> <span class="mi">30</span> <span class="c1"># Specify 1 Param, overwriting the original maxIter. |
| # Specify multiple Params. |
| </span><span class="n">paramMap</span><span class="p">.</span><span class="n">update</span><span class="p">({</span><span class="n">lr</span><span class="p">.</span><span class="n">regParam</span><span class="p">:</span> <span class="mf">0.1</span><span class="p">,</span> <span class="n">lr</span><span class="p">.</span><span class="n">threshold</span><span class="p">:</span> <span class="mf">0.55</span><span class="p">})</span> <span class="c1"># type: ignore |
| </span> |
| <span class="c1"># You can combine paramMaps, which are python dictionaries. |
| # Change output column name |
| </span><span class="n">paramMap2</span> <span class="o">=</span> <span class="p">{</span><span class="n">lr</span><span class="p">.</span><span class="n">probabilityCol</span><span class="p">:</span> <span class="s">"myProbability"</span><span class="p">}</span> |
| <span class="n">paramMapCombined</span> <span class="o">=</span> <span class="n">paramMap</span><span class="p">.</span><span class="n">copy</span><span class="p">()</span> |
| <span class="n">paramMapCombined</span><span class="p">.</span><span class="n">update</span><span class="p">(</span><span class="n">paramMap2</span><span class="p">)</span> <span class="c1"># type: ignore |
| </span> |
| <span class="c1"># Now learn a new model using the paramMapCombined parameters. |
| # paramMapCombined overrides all parameters set earlier via lr.set* methods. |
| </span><span class="n">model2</span> <span class="o">=</span> <span class="n">lr</span><span class="p">.</span><span class="n">fit</span><span class="p">(</span><span class="n">training</span><span class="p">,</span> <span class="n">paramMapCombined</span><span class="p">)</span> |
| <span class="k">print</span><span class="p">(</span><span class="s">"Model 2 was fit using parameters: "</span><span class="p">)</span> |
| <span class="k">print</span><span class="p">(</span><span class="n">model2</span><span class="p">.</span><span class="n">extractParamMap</span><span class="p">())</span> |
| |
| <span class="c1"># Prepare test data |
| </span><span class="n">test</span> <span class="o">=</span> <span class="n">spark</span><span class="p">.</span><span class="n">createDataFrame</span><span class="p">([</span> |
| <span class="p">(</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">Vectors</span><span class="p">.</span><span class="n">dense</span><span class="p">([</span><span class="o">-</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.5</span><span class="p">,</span> <span class="mf">1.3</span><span class="p">])),</span> |
| <span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">Vectors</span><span class="p">.</span><span class="n">dense</span><span class="p">([</span><span class="mf">3.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.1</span><span class="p">])),</span> |
| <span class="p">(</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">Vectors</span><span class="p">.</span><span class="n">dense</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">2.2</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.5</span><span class="p">]))],</span> <span class="p">[</span><span class="s">"label"</span><span class="p">,</span> <span class="s">"features"</span><span class="p">])</span> |
| |
| <span class="c1"># Make predictions on test data using the Transformer.transform() method. |
| # LogisticRegression.transform will only use the 'features' column. |
| # Note that model2.transform() outputs a "myProbability" column instead of the usual |
| # 'probability' column since we renamed the lr.probabilityCol parameter previously. |
| </span><span class="n">prediction</span> <span class="o">=</span> <span class="n">model2</span><span class="p">.</span><span class="n">transform</span><span class="p">(</span><span class="n">test</span><span class="p">)</span> |
| <span class="n">result</span> <span class="o">=</span> <span class="n">prediction</span><span class="p">.</span><span class="n">select</span><span class="p">(</span><span class="s">"features"</span><span class="p">,</span> <span class="s">"label"</span><span class="p">,</span> <span class="s">"myProbability"</span><span class="p">,</span> <span class="s">"prediction"</span><span class="p">)</span> \ |
| <span class="p">.</span><span class="n">collect</span><span class="p">()</span> |
| |
| <span class="k">for</span> <span class="n">row</span> <span class="ow">in</span> <span class="n">result</span><span class="p">:</span> |
| <span class="k">print</span><span class="p">(</span><span class="s">"features=%s, label=%s -> prob=%s, prediction=%s"</span> |
| <span class="o">%</span> <span class="p">(</span><span class="n">row</span><span class="p">.</span><span class="n">features</span><span class="p">,</span> <span class="n">row</span><span class="p">.</span><span class="n">label</span><span class="p">,</span> <span class="n">row</span><span class="p">.</span><span class="n">myProbability</span><span class="p">,</span> <span class="n">row</span><span class="p">.</span><span class="n">prediction</span><span class="p">))</span></code></pre></div> |
| <div><small>Find full example code at "examples/src/main/python/ml/estimator_transformer_param_example.py" in the Spark repo.</small></div> |
| </div> |
| |
| </div> |
| |
| <h2 id="example-pipeline">Example: Pipeline</h2> |
| |
| <p>This example follows the simple text document <code class="language-plaintext highlighter-rouge">Pipeline</code> illustrated in the figures above.</p> |
| |
| <div class="codetabs"> |
| |
| <div data-lang="scala"> |
| |
| <p>Refer to the <a href="api/scala/org/apache/spark/ml/Pipeline.html"><code class="language-plaintext highlighter-rouge">Pipeline</code> Scala docs</a> for details on the API.</p> |
| |
| <div class="highlight"><pre class="codehilite"><code><span class="k">import</span> <span class="nn">org.apache.spark.ml.</span><span class="o">{</span><span class="nc">Pipeline</span><span class="o">,</span> <span class="nc">PipelineModel</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.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.linalg.Vector</span> |
| <span class="k">import</span> <span class="nn">org.apache.spark.sql.Row</span> |
| |
| <span class="c1">// Prepare training documents from a list of (id, text, label) tuples.</span> |
| <span class="k">val</span> <span class="nv">training</span> <span class="k">=</span> <span class="nv">spark</span><span class="o">.</span><span class="py">createDataFrame</span><span class="o">(</span><span class="nc">Seq</span><span class="o">(</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="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="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="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="o">)).</span><span class="py">toDF</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">"label"</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="nv">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="py">setInputCol</span><span class="o">(</span><span class="s">"text"</span><span class="o">)</span> |
| <span class="o">.</span><span class="py">setOutputCol</span><span class="o">(</span><span class="s">"words"</span><span class="o">)</span> |
| <span class="k">val</span> <span class="nv">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="py">setNumFeatures</span><span class="o">(</span><span class="mi">1000</span><span class="o">)</span> |
| <span class="o">.</span><span class="py">setInputCol</span><span class="o">(</span><span class="nv">tokenizer</span><span class="o">.</span><span class="py">getOutputCol</span><span class="o">)</span> |
| <span class="o">.</span><span class="py">setOutputCol</span><span class="o">(</span><span class="s">"features"</span><span class="o">)</span> |
| <span class="k">val</span> <span class="nv">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="py">setMaxIter</span><span class="o">(</span><span class="mi">10</span><span class="o">)</span> |
| <span class="o">.</span><span class="py">setRegParam</span><span class="o">(</span><span class="mf">0.001</span><span class="o">)</span> |
| <span class="k">val</span> <span class="nv">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="py">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="nv">model</span> <span class="k">=</span> <span class="nv">pipeline</span><span class="o">.</span><span class="py">fit</span><span class="o">(</span><span class="n">training</span><span class="o">)</span> |
| |
| <span class="c1">// Now we can optionally save the fitted pipeline to disk</span> |
| <span class="nv">model</span><span class="o">.</span><span class="py">write</span><span class="o">.</span><span class="py">overwrite</span><span class="o">().</span><span class="py">save</span><span class="o">(</span><span class="s">"/tmp/spark-logistic-regression-model"</span><span class="o">)</span> |
| |
| <span class="c1">// We can also save this unfit pipeline to disk</span> |
| <span class="nv">pipeline</span><span class="o">.</span><span class="py">write</span><span class="o">.</span><span class="py">overwrite</span><span class="o">().</span><span class="py">save</span><span class="o">(</span><span class="s">"/tmp/unfit-lr-model"</span><span class="o">)</span> |
| |
| <span class="c1">// And load it back in during production</span> |
| <span class="k">val</span> <span class="nv">sameModel</span> <span class="k">=</span> <span class="nv">PipelineModel</span><span class="o">.</span><span class="py">load</span><span class="o">(</span><span class="s">"/tmp/spark-logistic-regression-model"</span><span class="o">)</span> |
| |
| <span class="c1">// Prepare test documents, which are unlabeled (id, text) tuples.</span> |
| <span class="k">val</span> <span class="nv">test</span> <span class="k">=</span> <span class="nv">spark</span><span class="o">.</span><span class="py">createDataFrame</span><span class="o">(</span><span class="nc">Seq</span><span class="o">(</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="o">(</span><span class="mi">5L</span><span class="o">,</span> <span class="s">"l m n"</span><span class="o">),</span> |
| <span class="o">(</span><span class="mi">6L</span><span class="o">,</span> <span class="s">"spark hadoop spark"</span><span class="o">),</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="o">)).</span><span class="py">toDF</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="c1">// Make predictions on test documents.</span> |
| <span class="nv">model</span><span class="o">.</span><span class="py">transform</span><span class="o">(</span><span class="n">test</span><span class="o">)</span> |
| <span class="o">.</span><span class="py">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="py">collect</span><span class="o">()</span> |
| <span class="o">.</span><span class="py">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="nf">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></code></pre></div> |
| <div><small>Find full example code at "examples/src/main/scala/org/apache/spark/examples/ml/PipelineExample.scala" in the Spark repo.</small></div> |
| </div> |
| |
| <div data-lang="java"> |
| |
| <p>Refer to the <a href="api/java/org/apache/spark/ml/Pipeline.html"><code class="language-plaintext highlighter-rouge">Pipeline</code> Java docs</a> for details on the API.</p> |
| |
| <div class="highlight"><pre class="codehilite"><code><span class="kn">import</span> <span class="nn">java.util.Arrays</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.Dataset</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="c1">// Prepare training documents, which are labeled.</span> |
| <span class="nc">Dataset</span><span class="o"><</span><span class="nc">Row</span><span class="o">></span> <span class="n">training</span> <span class="o">=</span> <span class="n">spark</span><span class="o">.</span><span class="na">createDataFrame</span><span class="o">(</span><span class="nc">Arrays</span><span class="o">.</span><span class="na">asList</span><span class="o">(</span> |
| <span class="k">new</span> <span class="nf">JavaLabeledDocument</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">JavaLabeledDocument</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">JavaLabeledDocument</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">JavaLabeledDocument</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="o">),</span> <span class="nc">JavaLabeledDocument</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="nc">Tokenizer</span> <span class="n">tokenizer</span> <span class="o">=</span> <span class="k">new</span> <span class="nc">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="nc">HashingTF</span> <span class="n">hashingTF</span> <span class="o">=</span> <span class="k">new</span> <span class="nc">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="nc">LogisticRegression</span> <span class="n">lr</span> <span class="o">=</span> <span class="k">new</span> <span class="nc">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">0.001</span><span class="o">);</span> |
| <span class="nc">Pipeline</span> <span class="n">pipeline</span> <span class="o">=</span> <span class="k">new</span> <span class="nc">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="nc">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="nc">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="nc">Dataset</span><span class="o"><</span><span class="nc">Row</span><span class="o">></span> <span class="n">test</span> <span class="o">=</span> <span class="n">spark</span><span class="o">.</span><span class="na">createDataFrame</span><span class="o">(</span><span class="nc">Arrays</span><span class="o">.</span><span class="na">asList</span><span class="o">(</span> |
| <span class="k">new</span> <span class="nf">JavaDocument</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">JavaDocument</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">JavaDocument</span><span class="o">(</span><span class="mi">6L</span><span class="o">,</span> <span class="s">"spark hadoop spark"</span><span class="o">),</span> |
| <span class="k">new</span> <span class="nf">JavaDocument</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="o">),</span> <span class="nc">JavaDocument</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="nc">Dataset</span><span class="o"><</span><span class="nc">Row</span><span class="o">></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="nc">Row</span> <span class="n">r</span> <span class="o">:</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">collectAsList</span><span class="o">())</span> <span class="o">{</span> |
| <span class="nc">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></code></pre></div> |
| <div><small>Find full example code at "examples/src/main/java/org/apache/spark/examples/ml/JavaPipelineExample.java" in the Spark repo.</small></div> |
| </div> |
| |
| <div data-lang="python"> |
| |
| <p>Refer to the <a href="api/python/reference/api/pyspark.ml.Pipeline.html"><code class="language-plaintext highlighter-rouge">Pipeline</code> Python docs</a> for more details on the API.</p> |
| |
| <div class="highlight"><pre class="codehilite"><code><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="c1"># Prepare training documents from a list of (id, text, label) tuples. |
| </span><span class="n">training</span> <span class="o">=</span> <span class="n">spark</span><span class="p">.</span><span class="n">createDataFrame</span><span class="p">([</span> |
| <span class="p">(</span><span class="mi">0</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="mi">1</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="mi">2</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="mi">3</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="p">],</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="c1"># Configure an ML pipeline, which consists of three 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="p">.</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">0.001</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="c1"># Fit the pipeline to training documents. |
| </span><span class="n">model</span> <span class="o">=</span> <span class="n">pipeline</span><span class="p">.</span><span class="n">fit</span><span class="p">(</span><span class="n">training</span><span class="p">)</span> |
| |
| <span class="c1"># Prepare test documents, which are unlabeled (id, text) tuples. |
| </span><span class="n">test</span> <span class="o">=</span> <span class="n">spark</span><span class="p">.</span><span class="n">createDataFrame</span><span class="p">([</span> |
| <span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="s">"spark i j k"</span><span class="p">),</span> |
| <span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="s">"l m n"</span><span class="p">),</span> |
| <span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="s">"spark hadoop spark"</span><span class="p">),</span> |
| <span class="p">(</span><span class="mi">7</span><span class="p">,</span> <span class="s">"apache hadoop"</span><span class="p">)</span> |
| <span class="p">],</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="c1"># 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="p">.</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="p">.</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">"probability"</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="p">.</span><span class="n">collect</span><span class="p">():</span> |
| <span class="n">rid</span><span class="p">,</span> <span class="n">text</span><span class="p">,</span> <span class="n">prob</span><span class="p">,</span> <span class="n">prediction</span> <span class="o">=</span> <span class="n">row</span> |
| <span class="k">print</span><span class="p">(</span> |
| <span class="s">"(%d, %s) --> prob=%s, prediction=%f"</span> <span class="o">%</span> <span class="p">(</span> |
| <span class="n">rid</span><span class="p">,</span> <span class="n">text</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="n">prob</span><span class="p">),</span> <span class="n">prediction</span> <span class="c1"># type: ignore |
| </span> <span class="p">)</span> |
| <span class="p">)</span></code></pre></div> |
| <div><small>Find full example code at "examples/src/main/python/ml/pipeline_example.py" in the Spark repo.</small></div> |
| </div> |
| |
| </div> |
| |
| <h2 id="model-selection-hyperparameter-tuning">Model selection (hyperparameter tuning)</h2> |
| |
| <p>A big benefit of using ML Pipelines is hyperparameter optimization. See the <a href="ml-tuning.html">ML Tuning Guide</a> for more information on automatic model selection.</p> |
| |
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