558e9c7e60
The Scala example under the "Example: Pipeline" heading in this document initializes the "test" variable to a DataFrame. Because test is already a DF, there is not need to call test.toDF as the example does in a subsequent line: model.transform(test.toDF). So, I removed the extraneous toDF invocation. Author: Matt Hagen <anonz3000@gmail.com> Closes #8875 from hagenhaus/SPARK-10663.
959 lines
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959 lines
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Markdown
---
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layout: global
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title: Spark ML Programming Guide
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---
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`\[
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\newcommand{\R}{\mathbb{R}}
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\newcommand{\E}{\mathbb{E}}
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\]`
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The `spark.ml` package aims to provide a uniform set of high-level APIs built on top of
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[DataFrames](sql-programming-guide.html#dataframes) that help users create and tune practical
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machine learning pipelines.
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See the [algorithm guides](#algorithm-guides) section below for guides on sub-packages of
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`spark.ml`, including feature transformers unique to the Pipelines API, ensembles, and more.
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**Table of contents**
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* This will become a table of contents (this text will be scraped).
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{:toc}
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# Algorithm guides
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We provide several algorithm guides specific to the Pipelines API.
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Several of these algorithms, such as certain feature transformers, are not in the `spark.mllib` API.
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Also, some algorithms have additional capabilities in the `spark.ml` API; e.g., random forests
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provide class probabilities, and linear models provide model summaries.
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* [Feature extraction, transformation, and selection](ml-features.html)
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* [Decision Trees for classification and regression](ml-decision-tree.html)
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* [Ensembles](ml-ensembles.html)
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* [Linear methods with elastic net regularization](ml-linear-methods.html)
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* [Multilayer perceptron classifier](ml-ann.html)
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# Main concepts in Pipelines
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Spark ML standardizes APIs for machine learning algorithms to make it easier to combine multiple
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algorithms into a single pipeline, or workflow.
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This section covers the key concepts introduced by the Spark ML API, where the pipeline concept is
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mostly inspired by the [scikit-learn](http://scikit-learn.org/) project.
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* **[`DataFrame`](ml-guide.html#dataframe)**: Spark ML uses `DataFrame` from Spark SQL as an ML
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dataset, which can hold a variety of data types.
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E.g., a `DataFrame` could have different columns storing text, feature vectors, true labels, and predictions.
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* **[`Transformer`](ml-guide.html#transformers)**: A `Transformer` is an algorithm which can transform one `DataFrame` into another `DataFrame`.
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E.g., an ML model is a `Transformer` which transforms `DataFrame` with features into a `DataFrame` with predictions.
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* **[`Estimator`](ml-guide.html#estimators)**: An `Estimator` is an algorithm which can be fit on a `DataFrame` to produce a `Transformer`.
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E.g., a learning algorithm is an `Estimator` which trains on a `DataFrame` and produces a model.
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* **[`Pipeline`](ml-guide.html#pipeline)**: A `Pipeline` chains multiple `Transformer`s and `Estimator`s together to specify an ML workflow.
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* **[`Parameter`](ml-guide.html#parameters)**: All `Transformer`s and `Estimator`s now share a common API for specifying parameters.
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## DataFrame
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Machine learning can be applied to a wide variety of data types, such as vectors, text, images, and structured data.
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Spark ML adopts the `DataFrame` from Spark SQL in order to support a variety of data types.
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`DataFrame` supports many basic and structured types; see the [Spark SQL datatype reference](sql-programming-guide.html#spark-sql-datatype-reference) for a list of supported types.
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In addition to the types listed in the Spark SQL guide, `DataFrame` can use ML [`Vector`](mllib-data-types.html#local-vector) types.
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A `DataFrame` can be created either implicitly or explicitly from a regular `RDD`. See the code examples below and the [Spark SQL programming guide](sql-programming-guide.html) for examples.
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Columns in a `DataFrame` are named. The code examples below use names such as "text," "features," and "label."
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## Pipeline components
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### Transformers
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A `Transformer` is an abstraction that includes feature transformers and learned models.
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Technically, a `Transformer` implements a method `transform()`, which converts one `DataFrame` into
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another, generally by appending one or more columns.
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For example:
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* A feature transformer might take a `DataFrame`, read a column (e.g., text), map it into a new
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column (e.g., feature vectors), and output a new `DataFrame` with the mapped column appended.
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* A learning model might take a `DataFrame`, read the column containing feature vectors, predict the
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label for each feature vector, and output a new `DataFrame` with predicted labels appended as a
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column.
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### Estimators
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An `Estimator` abstracts the concept of a learning algorithm or any algorithm that fits or trains on
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data.
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Technically, an `Estimator` implements a method `fit()`, which accepts a `DataFrame` and produces a
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`Model`, which is a `Transformer`.
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For example, a learning algorithm such as `LogisticRegression` is an `Estimator`, and calling
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`fit()` trains a `LogisticRegressionModel`, which is a `Model` and hence a `Transformer`.
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### Properties of pipeline components
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`Transformer.transform()`s and `Estimator.fit()`s are both stateless. In the future, stateful algorithms may be supported via alternative concepts.
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Each instance of a `Transformer` or `Estimator` has a unique ID, which is useful in specifying parameters (discussed below).
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## Pipeline
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In machine learning, it is common to run a sequence of algorithms to process and learn from data.
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E.g., a simple text document processing workflow might include several stages:
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* Split each document's text into words.
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* Convert each document's words into a numerical feature vector.
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* Learn a prediction model using the feature vectors and labels.
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Spark ML represents such a workflow as a `Pipeline`, which consists of a sequence of
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`PipelineStage`s (`Transformer`s and `Estimator`s) to be run in a specific order.
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We will use this simple workflow as a running example in this section.
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### How it works
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A `Pipeline` is specified as a sequence of stages, and each stage is either a `Transformer` or an `Estimator`.
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These stages are run in order, and the input `DataFrame` is transformed as it passes through each stage.
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For `Transformer` stages, the `transform()` method is called on the `DataFrame`.
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For `Estimator` stages, the `fit()` method is called to produce a `Transformer` (which becomes part of the `PipelineModel`, or fitted `Pipeline`), and that `Transformer`'s `transform()` method is called on the `DataFrame`.
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We illustrate this for the simple text document workflow. The figure below is for the *training time* usage of a `Pipeline`.
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<p style="text-align: center;">
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<img
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src="img/ml-Pipeline.png"
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title="Spark ML Pipeline Example"
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alt="Spark ML Pipeline Example"
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width="80%"
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/>
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</p>
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Above, the top row represents a `Pipeline` with three stages.
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The first two (`Tokenizer` and `HashingTF`) are `Transformer`s (blue), and the third (`LogisticRegression`) is an `Estimator` (red).
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The bottom row represents data flowing through the pipeline, where cylinders indicate `DataFrame`s.
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The `Pipeline.fit()` method is called on the original `DataFrame`, which has raw text documents and labels.
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The `Tokenizer.transform()` method splits the raw text documents into words, adding a new column with words to the `DataFrame`.
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The `HashingTF.transform()` method converts the words column into feature vectors, adding a new column with those vectors to the `DataFrame`.
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Now, since `LogisticRegression` is an `Estimator`, the `Pipeline` first calls `LogisticRegression.fit()` to produce a `LogisticRegressionModel`.
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If the `Pipeline` had more stages, it would call the `LogisticRegressionModel`'s `transform()`
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method on the `DataFrame` before passing the `DataFrame` to the next stage.
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A `Pipeline` is an `Estimator`.
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Thus, after a `Pipeline`'s `fit()` method runs, it produces a `PipelineModel`, which is a
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`Transformer`.
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This `PipelineModel` is used at *test time*; the figure below illustrates this usage.
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<p style="text-align: center;">
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<img
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src="img/ml-PipelineModel.png"
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title="Spark ML PipelineModel Example"
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alt="Spark ML PipelineModel Example"
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width="80%"
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/>
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</p>
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In the figure above, the `PipelineModel` has the same number of stages as the original `Pipeline`, but all `Estimator`s in the original `Pipeline` have become `Transformer`s.
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When the `PipelineModel`'s `transform()` method is called on a test dataset, the data are passed
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through the fitted pipeline in order.
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Each stage's `transform()` method updates the dataset and passes it to the next stage.
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`Pipeline`s and `PipelineModel`s help to ensure that training and test data go through identical feature processing steps.
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### Details
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*DAG `Pipeline`s*: A `Pipeline`'s stages are specified as an ordered array. The examples given here are all for linear `Pipeline`s, i.e., `Pipeline`s in which each stage uses data produced by the previous stage. It is possible to create non-linear `Pipeline`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 `Pipeline` forms a DAG, then the stages must be specified in topological order.
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*Runtime checking*: Since `Pipeline`s can operate on `DataFrame`s with varied types, they cannot use
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compile-time type checking.
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`Pipeline`s and `PipelineModel`s instead do runtime checking before actually running the `Pipeline`.
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This type checking is done using the `DataFrame` *schema*, a description of the data types of columns in the `DataFrame`.
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*Unique Pipeline stages*: A `Pipeline`'s stages should be unique instances. E.g., the same instance
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`myHashingTF` should not be inserted into the `Pipeline` twice since `Pipeline` stages must have
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unique IDs. However, different instances `myHashingTF1` and `myHashingTF2` (both of type `HashingTF`)
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can be put into the same `Pipeline` since different instances will be created with different IDs.
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## Parameters
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Spark ML `Estimator`s and `Transformer`s use a uniform API for specifying parameters.
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A `Param` is a named parameter with self-contained documentation.
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A `ParamMap` is a set of (parameter, value) pairs.
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There are two main ways to pass parameters to an algorithm:
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1. Set parameters for an instance. E.g., if `lr` is an instance of `LogisticRegression`, one could
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call `lr.setMaxIter(10)` to make `lr.fit()` use at most 10 iterations.
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This API resembles the API used in `spark.mllib` package.
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2. Pass a `ParamMap` to `fit()` or `transform()`. Any parameters in the `ParamMap` will override parameters previously specified via setter methods.
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Parameters belong to specific instances of `Estimator`s and `Transformer`s.
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For example, if we have two `LogisticRegression` instances `lr1` and `lr2`, then we can build a `ParamMap` with both `maxIter` parameters specified: `ParamMap(lr1.maxIter -> 10, lr2.maxIter -> 20)`.
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This is useful if there are two algorithms with the `maxIter` parameter in a `Pipeline`.
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# Code examples
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This section gives code examples illustrating the functionality discussed above.
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For more info, please refer to the API documentation
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([Scala](api/scala/index.html#org.apache.spark.ml.package),
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[Java](api/java/org/apache/spark/ml/package-summary.html),
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and [Python](api/python/pyspark.ml.html)).
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Some Spark ML algorithms are wrappers for `spark.mllib` algorithms, and the
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[MLlib programming guide](mllib-guide.html) has details on specific algorithms.
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## Example: Estimator, Transformer, and Param
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This example covers the concepts of `Estimator`, `Transformer`, and `Param`.
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<div class="codetabs">
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<div data-lang="scala">
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{% highlight scala %}
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import org.apache.spark.ml.classification.LogisticRegression
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import org.apache.spark.ml.param.ParamMap
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import org.apache.spark.mllib.linalg.{Vector, Vectors}
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import org.apache.spark.sql.Row
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// Prepare training data from a list of (label, features) tuples.
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val training = sqlContext.createDataFrame(Seq(
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(1.0, Vectors.dense(0.0, 1.1, 0.1)),
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(0.0, Vectors.dense(2.0, 1.0, -1.0)),
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(0.0, Vectors.dense(2.0, 1.3, 1.0)),
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(1.0, Vectors.dense(0.0, 1.2, -0.5))
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)).toDF("label", "features")
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// Create a LogisticRegression instance. This instance is an Estimator.
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val lr = new LogisticRegression()
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// Print out the parameters, documentation, and any default values.
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println("LogisticRegression parameters:\n" + lr.explainParams() + "\n")
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// We may set parameters using setter methods.
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lr.setMaxIter(10)
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.setRegParam(0.01)
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// Learn a LogisticRegression model. This uses the parameters stored in lr.
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val model1 = lr.fit(training)
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// Since model1 is a Model (i.e., a Transformer produced by an Estimator),
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// we can view the parameters it used during fit().
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// This prints the parameter (name: value) pairs, where names are unique IDs for this
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// LogisticRegression instance.
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println("Model 1 was fit using parameters: " + model1.parent.extractParamMap)
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// We may alternatively specify parameters using a ParamMap,
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// which supports several methods for specifying parameters.
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val paramMap = ParamMap(lr.maxIter -> 20)
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.put(lr.maxIter, 30) // Specify 1 Param. This overwrites the original maxIter.
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.put(lr.regParam -> 0.1, lr.threshold -> 0.55) // Specify multiple Params.
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// One can also combine ParamMaps.
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val paramMap2 = ParamMap(lr.probabilityCol -> "myProbability") // Change output column name
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val paramMapCombined = paramMap ++ paramMap2
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// Now learn a new model using the paramMapCombined parameters.
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// paramMapCombined overrides all parameters set earlier via lr.set* methods.
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val model2 = lr.fit(training, paramMapCombined)
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println("Model 2 was fit using parameters: " + model2.parent.extractParamMap)
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// Prepare test data.
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val test = sqlContext.createDataFrame(Seq(
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(1.0, Vectors.dense(-1.0, 1.5, 1.3)),
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(0.0, Vectors.dense(3.0, 2.0, -0.1)),
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(1.0, Vectors.dense(0.0, 2.2, -1.5))
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)).toDF("label", "features")
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// Make predictions on test data using the Transformer.transform() method.
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// LogisticRegression.transform will only use the 'features' column.
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// Note that model2.transform() outputs a 'myProbability' column instead of the usual
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// 'probability' column since we renamed the lr.probabilityCol parameter previously.
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model2.transform(test)
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.select("features", "label", "myProbability", "prediction")
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.collect()
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.foreach { case Row(features: Vector, label: Double, prob: Vector, prediction: Double) =>
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println(s"($features, $label) -> prob=$prob, prediction=$prediction")
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}
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{% endhighlight %}
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</div>
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<div data-lang="java">
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{% highlight java %}
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import java.util.Arrays;
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import java.util.List;
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import org.apache.spark.ml.classification.LogisticRegressionModel;
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import org.apache.spark.ml.param.ParamMap;
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import org.apache.spark.ml.classification.LogisticRegression;
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import org.apache.spark.mllib.linalg.Vectors;
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import org.apache.spark.mllib.regression.LabeledPoint;
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import org.apache.spark.sql.DataFrame;
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import org.apache.spark.sql.Row;
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// Prepare training data.
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// We use LabeledPoint, which is a JavaBean. Spark SQL can convert RDDs of JavaBeans
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// into DataFrames, where it uses the bean metadata to infer the schema.
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DataFrame training = sqlContext.createDataFrame(Arrays.asList(
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new LabeledPoint(1.0, Vectors.dense(0.0, 1.1, 0.1)),
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new LabeledPoint(0.0, Vectors.dense(2.0, 1.0, -1.0)),
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new LabeledPoint(0.0, Vectors.dense(2.0, 1.3, 1.0)),
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new LabeledPoint(1.0, Vectors.dense(0.0, 1.2, -0.5))
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), LabeledPoint.class);
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// Create a LogisticRegression instance. This instance is an Estimator.
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LogisticRegression lr = new LogisticRegression();
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// Print out the parameters, documentation, and any default values.
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System.out.println("LogisticRegression parameters:\n" + lr.explainParams() + "\n");
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// We may set parameters using setter methods.
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lr.setMaxIter(10)
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.setRegParam(0.01);
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// Learn a LogisticRegression model. This uses the parameters stored in lr.
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LogisticRegressionModel model1 = lr.fit(training);
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// Since model1 is a Model (i.e., a Transformer produced by an Estimator),
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// we can view the parameters it used during fit().
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// This prints the parameter (name: value) pairs, where names are unique IDs for this
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// LogisticRegression instance.
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System.out.println("Model 1 was fit using parameters: " + model1.parent().extractParamMap());
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// We may alternatively specify parameters using a ParamMap.
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ParamMap paramMap = new ParamMap()
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.put(lr.maxIter().w(20)) // Specify 1 Param.
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.put(lr.maxIter(), 30) // This overwrites the original maxIter.
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.put(lr.regParam().w(0.1), lr.threshold().w(0.55)); // Specify multiple Params.
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// One can also combine ParamMaps.
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ParamMap paramMap2 = new ParamMap()
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.put(lr.probabilityCol().w("myProbability")); // Change output column name
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ParamMap paramMapCombined = paramMap.$plus$plus(paramMap2);
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// Now learn a new model using the paramMapCombined parameters.
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// paramMapCombined overrides all parameters set earlier via lr.set* methods.
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LogisticRegressionModel model2 = lr.fit(training, paramMapCombined);
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System.out.println("Model 2 was fit using parameters: " + model2.parent().extractParamMap());
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// Prepare test documents.
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DataFrame test = sqlContext.createDataFrame(Arrays.asList(
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new LabeledPoint(1.0, Vectors.dense(-1.0, 1.5, 1.3)),
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new LabeledPoint(0.0, Vectors.dense(3.0, 2.0, -0.1)),
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new LabeledPoint(1.0, Vectors.dense(0.0, 2.2, -1.5))
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), LabeledPoint.class);
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// Make predictions on test documents using the Transformer.transform() method.
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// LogisticRegression.transform will only use the 'features' column.
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// Note that model2.transform() outputs a 'myProbability' column instead of the usual
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// 'probability' column since we renamed the lr.probabilityCol parameter previously.
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DataFrame results = model2.transform(test);
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for (Row r: results.select("features", "label", "myProbability", "prediction").collect()) {
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System.out.println("(" + r.get(0) + ", " + r.get(1) + ") -> prob=" + r.get(2)
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+ ", prediction=" + r.get(3));
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}
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{% endhighlight %}
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</div>
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<div data-lang="python">
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{% highlight python %}
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from pyspark.mllib.linalg import Vectors
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from pyspark.ml.classification import LogisticRegression
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from pyspark.ml.param import Param, Params
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# Prepare training data from a list of (label, features) tuples.
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training = sqlContext.createDataFrame([
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(1.0, Vectors.dense([0.0, 1.1, 0.1])),
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(0.0, Vectors.dense([2.0, 1.0, -1.0])),
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(0.0, Vectors.dense([2.0, 1.3, 1.0])),
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(1.0, Vectors.dense([0.0, 1.2, -0.5]))], ["label", "features"])
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# Create a LogisticRegression instance. This instance is an Estimator.
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lr = LogisticRegression(maxIter=10, regParam=0.01)
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# Print out the parameters, documentation, and any default values.
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print "LogisticRegression parameters:\n" + lr.explainParams() + "\n"
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# Learn a LogisticRegression model. This uses the parameters stored in lr.
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model1 = lr.fit(training)
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# Since model1 is a Model (i.e., a transformer produced by an Estimator),
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# we can view the parameters it used during fit().
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# This prints the parameter (name: value) pairs, where names are unique IDs for this
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# LogisticRegression instance.
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print "Model 1 was fit using parameters: "
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print model1.extractParamMap()
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|
|
|
# We may alternatively specify parameters using a Python dictionary as a paramMap
|
|
paramMap = {lr.maxIter: 20}
|
|
paramMap[lr.maxIter] = 30 # Specify 1 Param, overwriting the original maxIter.
|
|
paramMap.update({lr.regParam: 0.1, lr.threshold: 0.55}) # Specify multiple Params.
|
|
|
|
# You can combine paramMaps, which are python dictionaries.
|
|
paramMap2 = {lr.probabilityCol: "myProbability"} # Change output column name
|
|
paramMapCombined = paramMap.copy()
|
|
paramMapCombined.update(paramMap2)
|
|
|
|
# Now learn a new model using the paramMapCombined parameters.
|
|
# paramMapCombined overrides all parameters set earlier via lr.set* methods.
|
|
model2 = lr.fit(training, paramMapCombined)
|
|
print "Model 2 was fit using parameters: "
|
|
print model2.extractParamMap()
|
|
|
|
# Prepare test data
|
|
test = sqlContext.createDataFrame([
|
|
(1.0, Vectors.dense([-1.0, 1.5, 1.3])),
|
|
(0.0, Vectors.dense([3.0, 2.0, -0.1])),
|
|
(1.0, Vectors.dense([0.0, 2.2, -1.5]))], ["label", "features"])
|
|
|
|
# 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.
|
|
prediction = model2.transform(test)
|
|
selected = prediction.select("features", "label", "myProbability", "prediction")
|
|
for row in selected.collect():
|
|
print row
|
|
|
|
{% endhighlight %}
|
|
</div>
|
|
|
|
</div>
|
|
|
|
## Example: Pipeline
|
|
|
|
This example follows the simple text document `Pipeline` illustrated in the figures above.
|
|
|
|
<div class="codetabs">
|
|
|
|
<div data-lang="scala">
|
|
{% highlight scala %}
|
|
import org.apache.spark.ml.Pipeline
|
|
import org.apache.spark.ml.classification.LogisticRegression
|
|
import org.apache.spark.ml.feature.{HashingTF, Tokenizer}
|
|
import org.apache.spark.mllib.linalg.Vector
|
|
import org.apache.spark.sql.Row
|
|
|
|
// Prepare training documents from a list of (id, text, label) tuples.
|
|
val training = sqlContext.createDataFrame(Seq(
|
|
(0L, "a b c d e spark", 1.0),
|
|
(1L, "b d", 0.0),
|
|
(2L, "spark f g h", 1.0),
|
|
(3L, "hadoop mapreduce", 0.0)
|
|
)).toDF("id", "text", "label")
|
|
|
|
// Configure an ML pipeline, which consists of three stages: tokenizer, hashingTF, and lr.
|
|
val tokenizer = new Tokenizer()
|
|
.setInputCol("text")
|
|
.setOutputCol("words")
|
|
val hashingTF = new HashingTF()
|
|
.setNumFeatures(1000)
|
|
.setInputCol(tokenizer.getOutputCol)
|
|
.setOutputCol("features")
|
|
val lr = new LogisticRegression()
|
|
.setMaxIter(10)
|
|
.setRegParam(0.01)
|
|
val pipeline = new Pipeline()
|
|
.setStages(Array(tokenizer, hashingTF, lr))
|
|
|
|
// Fit the pipeline to training documents.
|
|
val model = pipeline.fit(training)
|
|
|
|
// Prepare test documents, which are unlabeled (id, text) tuples.
|
|
val test = sqlContext.createDataFrame(Seq(
|
|
(4L, "spark i j k"),
|
|
(5L, "l m n"),
|
|
(6L, "mapreduce spark"),
|
|
(7L, "apache hadoop")
|
|
)).toDF("id", "text")
|
|
|
|
// Make predictions on test documents.
|
|
model.transform(test)
|
|
.select("id", "text", "probability", "prediction")
|
|
.collect()
|
|
.foreach { case Row(id: Long, text: String, prob: Vector, prediction: Double) =>
|
|
println(s"($id, $text) --> prob=$prob, prediction=$prediction")
|
|
}
|
|
|
|
{% endhighlight %}
|
|
</div>
|
|
|
|
<div data-lang="java">
|
|
{% highlight java %}
|
|
import java.util.Arrays;
|
|
import java.util.List;
|
|
|
|
import org.apache.spark.ml.Pipeline;
|
|
import org.apache.spark.ml.PipelineModel;
|
|
import org.apache.spark.ml.PipelineStage;
|
|
import org.apache.spark.ml.classification.LogisticRegression;
|
|
import org.apache.spark.ml.feature.HashingTF;
|
|
import org.apache.spark.ml.feature.Tokenizer;
|
|
import org.apache.spark.sql.DataFrame;
|
|
import org.apache.spark.sql.Row;
|
|
|
|
// Labeled and unlabeled instance types.
|
|
// Spark SQL can infer schema from Java Beans.
|
|
public class Document implements Serializable {
|
|
private long id;
|
|
private String text;
|
|
|
|
public Document(long id, String text) {
|
|
this.id = id;
|
|
this.text = text;
|
|
}
|
|
|
|
public long getId() { return this.id; }
|
|
public void setId(long id) { this.id = id; }
|
|
|
|
public String getText() { return this.text; }
|
|
public void setText(String text) { this.text = text; }
|
|
}
|
|
|
|
public class LabeledDocument extends Document implements Serializable {
|
|
private double label;
|
|
|
|
public LabeledDocument(long id, String text, double label) {
|
|
super(id, text);
|
|
this.label = label;
|
|
}
|
|
|
|
public double getLabel() { return this.label; }
|
|
public void setLabel(double label) { this.label = label; }
|
|
}
|
|
|
|
// Prepare training documents, which are labeled.
|
|
DataFrame training = sqlContext.createDataFrame(Arrays.asList(
|
|
new LabeledDocument(0L, "a b c d e spark", 1.0),
|
|
new LabeledDocument(1L, "b d", 0.0),
|
|
new LabeledDocument(2L, "spark f g h", 1.0),
|
|
new LabeledDocument(3L, "hadoop mapreduce", 0.0)
|
|
), LabeledDocument.class);
|
|
|
|
// Configure an ML pipeline, which consists of three stages: tokenizer, hashingTF, and lr.
|
|
Tokenizer tokenizer = new Tokenizer()
|
|
.setInputCol("text")
|
|
.setOutputCol("words");
|
|
HashingTF hashingTF = new HashingTF()
|
|
.setNumFeatures(1000)
|
|
.setInputCol(tokenizer.getOutputCol())
|
|
.setOutputCol("features");
|
|
LogisticRegression lr = new LogisticRegression()
|
|
.setMaxIter(10)
|
|
.setRegParam(0.01);
|
|
Pipeline pipeline = new Pipeline()
|
|
.setStages(new PipelineStage[] {tokenizer, hashingTF, lr});
|
|
|
|
// Fit the pipeline to training documents.
|
|
PipelineModel model = pipeline.fit(training);
|
|
|
|
// Prepare test documents, which are unlabeled.
|
|
DataFrame test = sqlContext.createDataFrame(Arrays.asList(
|
|
new Document(4L, "spark i j k"),
|
|
new Document(5L, "l m n"),
|
|
new Document(6L, "mapreduce spark"),
|
|
new Document(7L, "apache hadoop")
|
|
), Document.class);
|
|
|
|
// Make predictions on test documents.
|
|
DataFrame predictions = model.transform(test);
|
|
for (Row r: predictions.select("id", "text", "probability", "prediction").collect()) {
|
|
System.out.println("(" + r.get(0) + ", " + r.get(1) + ") --> prob=" + r.get(2)
|
|
+ ", prediction=" + r.get(3));
|
|
}
|
|
|
|
{% endhighlight %}
|
|
</div>
|
|
|
|
<div data-lang="python">
|
|
{% highlight python %}
|
|
from pyspark.ml import Pipeline
|
|
from pyspark.ml.classification import LogisticRegression
|
|
from pyspark.ml.feature import HashingTF, Tokenizer
|
|
from pyspark.sql import Row
|
|
|
|
# Prepare training documents from a list of (id, text, label) tuples.
|
|
LabeledDocument = Row("id", "text", "label")
|
|
training = sqlContext.createDataFrame([
|
|
(0L, "a b c d e spark", 1.0),
|
|
(1L, "b d", 0.0),
|
|
(2L, "spark f g h", 1.0),
|
|
(3L, "hadoop mapreduce", 0.0)], ["id", "text", "label"])
|
|
|
|
# Configure an ML pipeline, which consists of tree stages: tokenizer, hashingTF, and lr.
|
|
tokenizer = Tokenizer(inputCol="text", outputCol="words")
|
|
hashingTF = HashingTF(inputCol=tokenizer.getOutputCol(), outputCol="features")
|
|
lr = LogisticRegression(maxIter=10, regParam=0.01)
|
|
pipeline = Pipeline(stages=[tokenizer, hashingTF, lr])
|
|
|
|
# Fit the pipeline to training documents.
|
|
model = pipeline.fit(training)
|
|
|
|
# Prepare test documents, which are unlabeled (id, text) tuples.
|
|
test = sqlContext.createDataFrame([
|
|
(4L, "spark i j k"),
|
|
(5L, "l m n"),
|
|
(6L, "mapreduce spark"),
|
|
(7L, "apache hadoop")], ["id", "text"])
|
|
|
|
# Make predictions on test documents and print columns of interest.
|
|
prediction = model.transform(test)
|
|
selected = prediction.select("id", "text", "prediction")
|
|
for row in selected.collect():
|
|
print(row)
|
|
|
|
{% endhighlight %}
|
|
</div>
|
|
|
|
</div>
|
|
|
|
## Example: model selection via cross-validation
|
|
|
|
An important task in ML is *model selection*, or using data to find the best model or parameters for a given task. This is also called *tuning*.
|
|
`Pipeline`s facilitate model selection by making it easy to tune an entire `Pipeline` at once, rather than tuning each element in the `Pipeline` separately.
|
|
|
|
Currently, `spark.ml` supports model selection using the [`CrossValidator`](api/scala/index.html#org.apache.spark.ml.tuning.CrossValidator) class, which takes an `Estimator`, a set of `ParamMap`s, and an [`Evaluator`](api/scala/index.html#org.apache.spark.ml.evaluation.Evaluator).
|
|
`CrossValidator` begins by splitting the dataset into a set of *folds* which are used as separate training and test datasets; e.g., with `$k=3$` folds, `CrossValidator` will generate 3 (training, test) dataset pairs, each of which uses 2/3 of the data for training and 1/3 for testing.
|
|
`CrossValidator` iterates through the set of `ParamMap`s. For each `ParamMap`, it trains the given `Estimator` and evaluates it using the given `Evaluator`.
|
|
|
|
The `Evaluator` can be a [`RegressionEvaluator`](api/scala/index.html#org.apache.spark.ml.evaluation.RegressionEvaluator)
|
|
for regression problems, a [`BinaryClassificationEvaluator`](api/scala/index.html#org.apache.spark.ml.evaluation.BinaryClassificationEvaluator)
|
|
for binary data, or a [`MultiClassClassificationEvaluator`](api/scala/index.html#org.apache.spark.ml.evaluation.MultiClassClassificationEvaluator)
|
|
for multiclass problems. The default metric used to choose the best `ParamMap` can be overriden by the `setMetric`
|
|
method in each of these evaluators.
|
|
|
|
The `ParamMap` which produces the best evaluation metric (averaged over the `$k$` folds) is selected as the best model.
|
|
`CrossValidator` finally fits the `Estimator` using the best `ParamMap` and the entire dataset.
|
|
|
|
The following example demonstrates using `CrossValidator` to select from a grid of parameters.
|
|
To help construct the parameter grid, we use the [`ParamGridBuilder`](api/scala/index.html#org.apache.spark.ml.tuning.ParamGridBuilder) utility.
|
|
|
|
Note that cross-validation over a grid of parameters is expensive.
|
|
E.g., in the example below, the parameter grid has 3 values for `hashingTF.numFeatures` and 2 values for `lr.regParam`, and `CrossValidator` uses 2 folds. This multiplies out to `$(3 \times 2) \times 2 = 12$` different models being trained.
|
|
In realistic settings, it can be common to try many more parameters and use more folds (`$k=3$` and `$k=10$` are common).
|
|
In other words, using `CrossValidator` can be very expensive.
|
|
However, it is also a well-established method for choosing parameters which is more statistically sound than heuristic hand-tuning.
|
|
|
|
<div class="codetabs">
|
|
|
|
<div data-lang="scala">
|
|
{% highlight scala %}
|
|
import org.apache.spark.ml.Pipeline
|
|
import org.apache.spark.ml.classification.LogisticRegression
|
|
import org.apache.spark.ml.evaluation.BinaryClassificationEvaluator
|
|
import org.apache.spark.ml.feature.{HashingTF, Tokenizer}
|
|
import org.apache.spark.ml.tuning.{ParamGridBuilder, CrossValidator}
|
|
import org.apache.spark.mllib.linalg.Vector
|
|
import org.apache.spark.sql.Row
|
|
|
|
// Prepare training data from a list of (id, text, label) tuples.
|
|
val training = sqlContext.createDataFrame(Seq(
|
|
(0L, "a b c d e spark", 1.0),
|
|
(1L, "b d", 0.0),
|
|
(2L, "spark f g h", 1.0),
|
|
(3L, "hadoop mapreduce", 0.0),
|
|
(4L, "b spark who", 1.0),
|
|
(5L, "g d a y", 0.0),
|
|
(6L, "spark fly", 1.0),
|
|
(7L, "was mapreduce", 0.0),
|
|
(8L, "e spark program", 1.0),
|
|
(9L, "a e c l", 0.0),
|
|
(10L, "spark compile", 1.0),
|
|
(11L, "hadoop software", 0.0)
|
|
)).toDF("id", "text", "label")
|
|
|
|
// Configure an ML pipeline, which consists of three stages: tokenizer, hashingTF, and lr.
|
|
val tokenizer = new Tokenizer()
|
|
.setInputCol("text")
|
|
.setOutputCol("words")
|
|
val hashingTF = new HashingTF()
|
|
.setInputCol(tokenizer.getOutputCol)
|
|
.setOutputCol("features")
|
|
val lr = new LogisticRegression()
|
|
.setMaxIter(10)
|
|
val pipeline = new Pipeline()
|
|
.setStages(Array(tokenizer, hashingTF, lr))
|
|
|
|
// We use a ParamGridBuilder to construct a grid of parameters to search over.
|
|
// With 3 values for hashingTF.numFeatures and 2 values for lr.regParam,
|
|
// this grid will have 3 x 2 = 6 parameter settings for CrossValidator to choose from.
|
|
val paramGrid = new ParamGridBuilder()
|
|
.addGrid(hashingTF.numFeatures, Array(10, 100, 1000))
|
|
.addGrid(lr.regParam, Array(0.1, 0.01))
|
|
.build()
|
|
|
|
// We now treat the Pipeline as an Estimator, wrapping it in a CrossValidator instance.
|
|
// This will allow us to jointly choose parameters for all Pipeline stages.
|
|
// A CrossValidator requires an Estimator, a set of Estimator ParamMaps, and an Evaluator.
|
|
// Note that the evaluator here is a BinaryClassificationEvaluator and its default metric
|
|
// is areaUnderROC.
|
|
val cv = new CrossValidator()
|
|
.setEstimator(pipeline)
|
|
.setEvaluator(new BinaryClassificationEvaluator)
|
|
.setEstimatorParamMaps(paramGrid)
|
|
.setNumFolds(2) // Use 3+ in practice
|
|
|
|
// Run cross-validation, and choose the best set of parameters.
|
|
val cvModel = cv.fit(training)
|
|
|
|
// Prepare test documents, which are unlabeled (id, text) tuples.
|
|
val test = sqlContext.createDataFrame(Seq(
|
|
(4L, "spark i j k"),
|
|
(5L, "l m n"),
|
|
(6L, "mapreduce spark"),
|
|
(7L, "apache hadoop")
|
|
)).toDF("id", "text")
|
|
|
|
// Make predictions on test documents. cvModel uses the best model found (lrModel).
|
|
cvModel.transform(test)
|
|
.select("id", "text", "probability", "prediction")
|
|
.collect()
|
|
.foreach { case Row(id: Long, text: String, prob: Vector, prediction: Double) =>
|
|
println(s"($id, $text) --> prob=$prob, prediction=$prediction")
|
|
}
|
|
|
|
{% endhighlight %}
|
|
</div>
|
|
|
|
<div data-lang="java">
|
|
{% highlight java %}
|
|
import java.util.Arrays;
|
|
import java.util.List;
|
|
|
|
import org.apache.spark.ml.Pipeline;
|
|
import org.apache.spark.ml.PipelineStage;
|
|
import org.apache.spark.ml.classification.LogisticRegression;
|
|
import org.apache.spark.ml.evaluation.BinaryClassificationEvaluator;
|
|
import org.apache.spark.ml.feature.HashingTF;
|
|
import org.apache.spark.ml.feature.Tokenizer;
|
|
import org.apache.spark.ml.param.ParamMap;
|
|
import org.apache.spark.ml.tuning.CrossValidator;
|
|
import org.apache.spark.ml.tuning.CrossValidatorModel;
|
|
import org.apache.spark.ml.tuning.ParamGridBuilder;
|
|
import org.apache.spark.sql.DataFrame;
|
|
import org.apache.spark.sql.Row;
|
|
|
|
// Labeled and unlabeled instance types.
|
|
// Spark SQL can infer schema from Java Beans.
|
|
public class Document implements Serializable {
|
|
private long id;
|
|
private String text;
|
|
|
|
public Document(long id, String text) {
|
|
this.id = id;
|
|
this.text = text;
|
|
}
|
|
|
|
public long getId() { return this.id; }
|
|
public void setId(long id) { this.id = id; }
|
|
|
|
public String getText() { return this.text; }
|
|
public void setText(String text) { this.text = text; }
|
|
}
|
|
|
|
public class LabeledDocument extends Document implements Serializable {
|
|
private double label;
|
|
|
|
public LabeledDocument(long id, String text, double label) {
|
|
super(id, text);
|
|
this.label = label;
|
|
}
|
|
|
|
public double getLabel() { return this.label; }
|
|
public void setLabel(double label) { this.label = label; }
|
|
}
|
|
|
|
|
|
// Prepare training documents, which are labeled.
|
|
DataFrame training = sqlContext.createDataFrame(Arrays.asList(
|
|
new LabeledDocument(0L, "a b c d e spark", 1.0),
|
|
new LabeledDocument(1L, "b d", 0.0),
|
|
new LabeledDocument(2L, "spark f g h", 1.0),
|
|
new LabeledDocument(3L, "hadoop mapreduce", 0.0),
|
|
new LabeledDocument(4L, "b spark who", 1.0),
|
|
new LabeledDocument(5L, "g d a y", 0.0),
|
|
new LabeledDocument(6L, "spark fly", 1.0),
|
|
new LabeledDocument(7L, "was mapreduce", 0.0),
|
|
new LabeledDocument(8L, "e spark program", 1.0),
|
|
new LabeledDocument(9L, "a e c l", 0.0),
|
|
new LabeledDocument(10L, "spark compile", 1.0),
|
|
new LabeledDocument(11L, "hadoop software", 0.0)
|
|
), LabeledDocument.class);
|
|
|
|
// Configure an ML pipeline, which consists of three stages: tokenizer, hashingTF, and lr.
|
|
Tokenizer tokenizer = new Tokenizer()
|
|
.setInputCol("text")
|
|
.setOutputCol("words");
|
|
HashingTF hashingTF = new HashingTF()
|
|
.setNumFeatures(1000)
|
|
.setInputCol(tokenizer.getOutputCol())
|
|
.setOutputCol("features");
|
|
LogisticRegression lr = new LogisticRegression()
|
|
.setMaxIter(10)
|
|
.setRegParam(0.01);
|
|
Pipeline pipeline = new Pipeline()
|
|
.setStages(new PipelineStage[] {tokenizer, hashingTF, lr});
|
|
|
|
// We use a ParamGridBuilder to construct a grid of parameters to search over.
|
|
// With 3 values for hashingTF.numFeatures and 2 values for lr.regParam,
|
|
// this grid will have 3 x 2 = 6 parameter settings for CrossValidator to choose from.
|
|
ParamMap[] paramGrid = new ParamGridBuilder()
|
|
.addGrid(hashingTF.numFeatures(), new int[]{10, 100, 1000})
|
|
.addGrid(lr.regParam(), new double[]{0.1, 0.01})
|
|
.build();
|
|
|
|
// We now treat the Pipeline as an Estimator, wrapping it in a CrossValidator instance.
|
|
// This will allow us to jointly choose parameters for all Pipeline stages.
|
|
// A CrossValidator requires an Estimator, a set of Estimator ParamMaps, and an Evaluator.
|
|
// Note that the evaluator here is a BinaryClassificationEvaluator and its default metric
|
|
// is areaUnderROC.
|
|
CrossValidator cv = new CrossValidator()
|
|
.setEstimator(pipeline)
|
|
.setEvaluator(new BinaryClassificationEvaluator())
|
|
.setEstimatorParamMaps(paramGrid)
|
|
.setNumFolds(2); // Use 3+ in practice
|
|
|
|
// Run cross-validation, and choose the best set of parameters.
|
|
CrossValidatorModel cvModel = cv.fit(training);
|
|
|
|
// Prepare test documents, which are unlabeled.
|
|
DataFrame test = sqlContext.createDataFrame(Arrays.asList(
|
|
new Document(4L, "spark i j k"),
|
|
new Document(5L, "l m n"),
|
|
new Document(6L, "mapreduce spark"),
|
|
new Document(7L, "apache hadoop")
|
|
), Document.class);
|
|
|
|
// Make predictions on test documents. cvModel uses the best model found (lrModel).
|
|
DataFrame predictions = cvModel.transform(test);
|
|
for (Row r: predictions.select("id", "text", "probability", "prediction").collect()) {
|
|
System.out.println("(" + r.get(0) + ", " + r.get(1) + ") --> prob=" + r.get(2)
|
|
+ ", prediction=" + r.get(3));
|
|
}
|
|
|
|
{% endhighlight %}
|
|
</div>
|
|
|
|
</div>
|
|
|
|
## Example: model selection via train validation split
|
|
In addition to `CrossValidator` Spark also offers `TrainValidationSplit` for hyper-parameter tuning.
|
|
`TrainValidationSplit` only evaluates each combination of parameters once as opposed to k times in
|
|
case of `CrossValidator`. It is therefore less expensive,
|
|
but will not produce as reliable results when the training dataset is not sufficiently large.
|
|
|
|
`TrainValidationSplit` takes an `Estimator`, a set of `ParamMap`s provided in the `estimatorParamMaps` parameter,
|
|
and an `Evaluator`.
|
|
It begins by splitting the dataset into two parts using `trainRatio` parameter
|
|
which are used as separate training and test datasets. For example with `$trainRatio=0.75$` (default),
|
|
`TrainValidationSplit` will generate a training and test dataset pair where 75% of the data is used for training and 25% for validation.
|
|
Similar to `CrossValidator`, `TrainValidationSplit` also iterates through the set of `ParamMap`s.
|
|
For each combination of parameters, it trains the given `Estimator` and evaluates it using the given `Evaluator`.
|
|
The `ParamMap` which produces the best evaluation metric is selected as the best option.
|
|
`TrainValidationSplit` finally fits the `Estimator` using the best `ParamMap` and the entire dataset.
|
|
|
|
<div class="codetabs">
|
|
|
|
<div data-lang="scala" markdown="1">
|
|
{% highlight scala %}
|
|
import org.apache.spark.ml.evaluation.RegressionEvaluator
|
|
import org.apache.spark.ml.regression.LinearRegression
|
|
import org.apache.spark.ml.tuning.{ParamGridBuilder, TrainValidationSplit}
|
|
import org.apache.spark.mllib.util.MLUtils
|
|
|
|
// Prepare training and test data.
|
|
val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
|
|
val Array(training, test) = data.randomSplit(Array(0.9, 0.1), seed = 12345)
|
|
|
|
val lr = new LinearRegression()
|
|
|
|
// We use a ParamGridBuilder to construct a grid of parameters to search over.
|
|
// TrainValidationSplit will try all combinations of values and determine best model using
|
|
// the evaluator.
|
|
val paramGrid = new ParamGridBuilder()
|
|
.addGrid(lr.regParam, Array(0.1, 0.01))
|
|
.addGrid(lr.fitIntercept)
|
|
.addGrid(lr.elasticNetParam, Array(0.0, 0.5, 1.0))
|
|
.build()
|
|
|
|
// In this case the estimator is simply the linear regression.
|
|
// A TrainValidationSplit requires an Estimator, a set of Estimator ParamMaps, and an Evaluator.
|
|
val trainValidationSplit = new TrainValidationSplit()
|
|
.setEstimator(lr)
|
|
.setEvaluator(new RegressionEvaluator)
|
|
.setEstimatorParamMaps(paramGrid)
|
|
// 80% of the data will be used for training and the remaining 20% for validation.
|
|
.setTrainRatio(0.8)
|
|
|
|
// Run train validation split, and choose the best set of parameters.
|
|
val model = trainValidationSplit.fit(training)
|
|
|
|
// Make predictions on test data. model is the model with combination of parameters
|
|
// that performed best.
|
|
model.transform(test)
|
|
.select("features", "label", "prediction")
|
|
.show()
|
|
|
|
{% endhighlight %}
|
|
</div>
|
|
|
|
<div data-lang="java" markdown="1">
|
|
{% highlight java %}
|
|
import org.apache.spark.ml.evaluation.RegressionEvaluator;
|
|
import org.apache.spark.ml.param.ParamMap;
|
|
import org.apache.spark.ml.regression.LinearRegression;
|
|
import org.apache.spark.ml.tuning.*;
|
|
import org.apache.spark.mllib.regression.LabeledPoint;
|
|
import org.apache.spark.mllib.util.MLUtils;
|
|
import org.apache.spark.rdd.RDD;
|
|
import org.apache.spark.sql.DataFrame;
|
|
|
|
DataFrame data = sqlContext.createDataFrame(
|
|
MLUtils.loadLibSVMFile(jsc.sc(), "data/mllib/sample_libsvm_data.txt"),
|
|
LabeledPoint.class);
|
|
|
|
// Prepare training and test data.
|
|
DataFrame[] splits = data.randomSplit(new double[] {0.9, 0.1}, 12345);
|
|
DataFrame training = splits[0];
|
|
DataFrame test = splits[1];
|
|
|
|
LinearRegression lr = new LinearRegression();
|
|
|
|
// We use a ParamGridBuilder to construct a grid of parameters to search over.
|
|
// TrainValidationSplit will try all combinations of values and determine best model using
|
|
// the evaluator.
|
|
ParamMap[] paramGrid = new ParamGridBuilder()
|
|
.addGrid(lr.regParam(), new double[] {0.1, 0.01})
|
|
.addGrid(lr.fitIntercept())
|
|
.addGrid(lr.elasticNetParam(), new double[] {0.0, 0.5, 1.0})
|
|
.build();
|
|
|
|
// In this case the estimator is simply the linear regression.
|
|
// A TrainValidationSplit requires an Estimator, a set of Estimator ParamMaps, and an Evaluator.
|
|
TrainValidationSplit trainValidationSplit = new TrainValidationSplit()
|
|
.setEstimator(lr)
|
|
.setEvaluator(new RegressionEvaluator())
|
|
.setEstimatorParamMaps(paramGrid)
|
|
.setTrainRatio(0.8); // 80% for training and the remaining 20% for validation
|
|
|
|
// Run train validation split, and choose the best set of parameters.
|
|
TrainValidationSplitModel model = trainValidationSplit.fit(training);
|
|
|
|
// Make predictions on test data. model is the model with combination of parameters
|
|
// that performed best.
|
|
model.transform(test)
|
|
.select("features", "label", "prediction")
|
|
.show();
|
|
|
|
{% endhighlight %}
|
|
</div>
|
|
|
|
</div>
|