layout: global title: ML Pipelines displayTitle: ML Pipelines license: | Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements. See the NOTICE file distributed with this work for additional information regarding copyright ownership. The ASF licenses this file to You under the Apache License, Version 2.0 (the “License”); you may not use this file except in compliance with the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
\[ \newcommand{\R}{\mathbb{R}} \newcommand{\E}{\mathbb{E}} \newcommand{\x}{\mathbf{x}} \newcommand{\y}{\mathbf{y}} \newcommand{\wv}{\mathbf{w}} \newcommand{\av}{\mathbf{\alpha}} \newcommand{\bv}{\mathbf{b}} \newcommand{\N}{\mathbb{N}} \newcommand{\id}{\mathbf{I}} \newcommand{\ind}{\mathbf{1}} \newcommand{\0}{\mathbf{0}} \newcommand{\unit}{\mathbf{e}} \newcommand{\one}{\mathbf{1}} \newcommand{\zero}{\mathbf{0}} \]
In this section, we introduce the concept of ML Pipelines. ML Pipelines provide a uniform set of high-level APIs built on top of DataFrames that help users create and tune practical machine learning pipelines.
Table of Contents
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 scikit-learn project.
DataFrame
: This ML API uses DataFrame
from Spark SQL as an ML dataset, which can hold a variety of data types. E.g., a DataFrame
could have different columns storing text, feature vectors, true labels, and predictions.
Transformer
: A Transformer
is an algorithm which can transform one DataFrame
into another DataFrame
. E.g., an ML model is a Transformer
which transforms a DataFrame
with features into a DataFrame
with predictions.
Estimator
: An Estimator
is an algorithm which can be fit on a DataFrame
to produce a Transformer
. E.g., a learning algorithm is an Estimator
which trains on a DataFrame
and produces a model.
Pipeline
: A Pipeline
chains multiple Transformer
s and Estimator
s together to specify an ML workflow.
Parameter
: All Transformer
s and Estimator
s now share a common API for specifying parameters.
Machine learning can be applied to a wide variety of data types, such as vectors, text, images, and structured data. This API adopts the DataFrame
from Spark SQL in order to support a variety of data types.
DataFrame
supports many basic and structured types; see the Spark SQL datatype reference for a list of supported types. In addition to the types listed in the Spark SQL guide, DataFrame
can use ML Vector
types.
A DataFrame
can be created either implicitly or explicitly from a regular RDD
. See the code examples below and the Spark SQL programming guide for examples.
Columns in a DataFrame
are named. The code examples below use names such as “text”, “features”, and “label”.
A Transformer
is an abstraction that includes feature transformers and learned models. Technically, a Transformer
implements a method transform()
, which converts one DataFrame
into another, generally by appending one or more columns. For example:
DataFrame
, read a column (e.g., text), map it into a new column (e.g., feature vectors), and output a new DataFrame
with the mapped column appended.DataFrame
, read the column containing feature vectors, predict the label for each feature vector, and output a new DataFrame
with predicted labels appended as a column.An Estimator
abstracts the concept of a learning algorithm or any algorithm that fits or trains on data. Technically, an Estimator
implements a method fit()
, which accepts a DataFrame
and produces a Model
, which is a Transformer
. For example, a learning algorithm such as LogisticRegression
is an Estimator
, and calling fit()
trains a LogisticRegressionModel
, which is a Model
and hence a Transformer
.
Transformer.transform()
s and Estimator.fit()
s are both stateless. In the future, stateful algorithms may be supported via alternative concepts.
Each instance of a Transformer
or Estimator
has a unique ID, which is useful in specifying parameters (discussed below).
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:
MLlib represents such a workflow as a Pipeline
, which consists of a sequence of PipelineStage
s (Transformer
s and Estimator
s) to be run in a specific order. We will use this simple workflow as a running example in this section.
A Pipeline
is specified as a sequence of stages, and each stage is either a Transformer
or an Estimator
. These stages are run in order, and the input DataFrame
is transformed as it passes through each stage. For Transformer
stages, the transform()
method is called on the DataFrame
. 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
.
We illustrate this for the simple text document workflow. The figure below is for the training time usage of a Pipeline
.
Above, the top row represents a Pipeline
with three stages. The first two (Tokenizer
and HashingTF
) are Transformer
s (blue), and the third (LogisticRegression
) is an Estimator
(red). The bottom row represents data flowing through the pipeline, where cylinders indicate DataFrame
s. The Pipeline.fit()
method is called on the original DataFrame
, which has raw text documents and labels. The Tokenizer.transform()
method splits the raw text documents into words, adding a new column with words to the DataFrame
. The HashingTF.transform()
method converts the words column into feature vectors, adding a new column with those vectors to the DataFrame
. Now, since LogisticRegression
is an Estimator
, the Pipeline
first calls LogisticRegression.fit()
to produce a LogisticRegressionModel
. If the Pipeline
had more Estimator
s, it would call the LogisticRegressionModel
's transform()
method on the DataFrame
before passing the DataFrame
to the next stage.
A Pipeline
is an Estimator
. Thus, after a Pipeline
's fit()
method runs, it produces a PipelineModel
, which is a Transformer
. This PipelineModel
is used at test time; the figure below illustrates this usage.
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. When the PipelineModel
‘s transform()
method is called on a test dataset, the data are passed through the fitted pipeline in order. Each stage’s transform()
method updates the dataset and passes it to the next stage.
Pipeline
s and PipelineModel
s help to ensure that training and test data go through identical feature processing steps.
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.
Runtime checking: Since Pipeline
s can operate on DataFrame
s with varied types, they cannot use compile-time type checking. Pipeline
s and PipelineModel
s instead do runtime checking before actually running the Pipeline
. This type checking is done using the DataFrame
schema, a description of the data types of columns in the DataFrame
.
Unique Pipeline stages: A Pipeline
's stages should be unique instances. E.g., the same instance myHashingTF
should not be inserted into the Pipeline
twice since Pipeline
stages must have unique IDs. However, different instances myHashingTF1
and myHashingTF2
(both of type HashingTF
) can be put into the same Pipeline
since different instances will be created with different IDs.
MLlib Estimator
s and Transformer
s use a uniform API for specifying parameters.
A Param
is a named parameter with self-contained documentation. A ParamMap
is a set of (parameter, value) pairs.
There are two main ways to pass parameters to an algorithm:
lr
is an instance of LogisticRegression
, one could call lr.setMaxIter(10)
to make lr.fit()
use at most 10 iterations. This API resembles the API used in spark.mllib
package.ParamMap
to fit()
or transform()
. Any parameters in the ParamMap
will override parameters previously specified via setter methods.Parameters belong to specific instances of Estimator
s and Transformer
s. For example, if we have two LogisticRegression
instances lr1
and lr2
, then we can build a ParamMap
with both maxIter
parameters specified: ParamMap(lr1.maxIter -> 10, lr2.maxIter -> 20)
. This is useful if there are two algorithms with the maxIter
parameter in a Pipeline
.
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 spark.ml
and pyspark.ml
has complete coverage.
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 SPARK-15572.
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.
Model persistence: Is a model or Pipeline saved using Apache Spark ML persistence in Spark version X loadable by Spark version Y?
Model behavior: Does a model or Pipeline in Spark version X behave identically in Spark version Y?
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.
This section gives code examples illustrating the functionality discussed above. For more info, please refer to the API documentation (Scala, Java, and Python).
This example covers the concepts of Estimator
, Transformer
, and Param
.
Refer to the Estimator
Scala docs, the Transformer
Scala docs and the Params
Scala docs for details on the API.
{% include_example scala/org/apache/spark/examples/ml/EstimatorTransformerParamExample.scala %}
Refer to the Estimator
Java docs, the Transformer
Java docs and the Params
Java docs for details on the API.
{% include_example java/org/apache/spark/examples/ml/JavaEstimatorTransformerParamExample.java %}
Refer to the Estimator
Python docs, the Transformer
Python docs and the Params
Python docs for more details on the API.
{% include_example python/ml/estimator_transformer_param_example.py %}
This example follows the simple text document Pipeline
illustrated in the figures above.
Refer to the Pipeline
Scala docs for details on the API.
{% include_example scala/org/apache/spark/examples/ml/PipelineExample.scala %}
Refer to the Pipeline
Java docs for details on the API.
{% include_example java/org/apache/spark/examples/ml/JavaPipelineExample.java %}
Refer to the Pipeline
Python docs for more details on the API.
{% include_example python/ml/pipeline_example.py %}
A big benefit of using ML Pipelines is hyperparameter optimization. See the ML Tuning Guide for more information on automatic model selection.