Project: Apache Spark (App Definition and Development / Streaming & Messaging)
File: ml-pipeline.md

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 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. `\[ \newcommand{\R}{\mathbb{R}} \newcommand{\E}{\mathbb{E}} \newcommand{\x}{\mathbf{x}} \newcommand{\y}{\mathbf{y}} \newcommand{\wv}{\mathbf{w}} \newcommand{\av}{\mathbf{\alpha}} \newcommand{\bv}{\mathbf{b}} \newcommand{\N}{\mathbb{N}} \newcommand{\id}{\mathbf{I}} \newcommand{\ind}{\mathbf{1}} \newcommand{\0}{\mathbf{0}} \newcommand{\unit}{\mathbf{e}} \newcommand{\one}{\mathbf{1}} \newcommand{\zero}{\mathbf{0}} \]` In this section, we introduce the concept of *ML Pipelines*. ML Pipelines provide a uniform set of high-level APIs built on top of that help users create and tune practical machine learning pipelines. Table of Contents This will become a table of contents (this text will be scraped). {:toc} 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 project. *: 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. *: 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. *: 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. *: A `Pipeline` chains multiple `Transformer`s and `Estimator`s together to specify an ML workflow. *: 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 for a list of supported types. In addition to the types listed in the Spark SQL guide, `DataFrame` can use ML types. A `DataFrame` can be created either implicitly or explicitly from a regular `RDD`. See the code examples below and the 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

Technically, a `Transformer` implements a method `transform()`, which converts one `DataFrame` into another, generally by appending one or more columns. For example: A feature transformer might take a `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. A learning model might take a `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: Split each document's text into words. Convert each document's words into a numerical feature vector. Learn a prediction model using the feature vectors and labels. 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`. <p style="text-align: center;"> <img src="img/ml-Pipeline.png" title="ML Pipeline Example" alt="ML Pipeline Example" width="80%" /> </p> 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

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. <p style="text-align: center;"> <img src="img/ml-PipelineModel.png" title="ML PipelineModel Example" alt="ML PipelineModel Example" width="80%" /> </p> 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: Set parameters for an instance. E.g., if `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. Pass a `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

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 . 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? Major versions: No guarantees, but best-effort. Minor and patch versions: Yes; these are backwards compatible. Note about the format: There are no guarantees for a stable persistence format, but model loading itself is designed to be backwards compatible. Model behavior: Does a model or Pipeline in Spark version X behave identically in Spark version Y? Major versions: No guarantees, but best-effort. Minor and patch versions: Identical behavior, except for bug fixes. 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 (, , and ). This example covers the concepts of `Estimator`, `Transformer`, and `Param`. <div class="codetabs"> <div data-lang="python" markdown="1"> Refer to the , the and the for more details on the API. {% includeexample python/ml/estimatortransformerparamexample.py %} </div> <div data-lang="scala" markdown="1"> Refer to the , the and the for details on the API. {% include_example scala/org/apache/spark/examples/ml/EstimatorTransformerParamExample.scala %} </div> <div data-lang="java" markdown="1"> Refer to the , the and the for details on the API. {% include_example java/org/apache/spark/examples/ml/JavaEstimatorTransformerParamExample.java %} </div> </div> This example follows the simple text document `Pipeline` illustrated in the figures above. <div class="codetabs"> <div data-lang="python" markdown="1"> Refer to the for more details on the API. {% includeexample python/ml/pipelineexample.py %} </div> <div data-lang="scala" markdown="1"> Refer to the for details on the API. {% include_example scala/org/apache/spark/examples/ml/PipelineExample.scala %} </div> <div data-lang="java" markdown="1"> Refer to the for details on the API. {% include_example java/org/apache/spark/examples/ml/JavaPipelineExample.java %} </div> </div> A big benefit of using ML Pipelines is hyperparameter optimization. See the for more information on automatic model selection.