AI News, Spark Pipelines: Elegant Yet Powerful

Spark Pipelines: Elegant Yet Powerful

We’ve all suffered through the experience of reopening a machine learning project and trying to trace back our thought process.

Often times it feels like a jungle where dozens of feature engineering steps are criss-crossed with a grab-bag of hand-tuned models.

In other words, it lets us focus more on solving a machine learning task, instead of wasting time spent on organizing code.

Typically during the exploratory stages of a machine learning problem, we find ourselves iterating through dozens, if not hundreds, of features and model combinations.

Our thinking process could resemble something like this: Before long, our Jupyter notebook is filled with spaghetti code that takes up hundreds of cells.

This gives us a declarative interface where it's easy to see the entire data extraction, transformation, and model training workflow.

The raw data consists of restaurant reviews (String) and ratings (Integers), like so: During the feature engineering process, text features are extracted from the raw reviews using both the HashingTF and Word2Vec algorithms.

accept a DataFrame as input and return a DataFrame as output The following code snippet demonstrates a naive implementation of a word count Transformer.

Main concepts in Pipelines

Table of Contents MLlib standardizes APIs for machine learning algorithms to make it easier to combine multiple algorithms

section covers the key concepts introduced by the Pipelines API, where the pipeline concept is mostly

Machine learning can be applied to a wide variety of data types, such as vectors, text, images, and structured data. This

example: An Estimator abstracts the concept of a learning algorithm or any algorithm that fits or trains on data. Technically,

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 PipelineStages

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.

bottom row represents data flowing through the pipeline, where cylinders indicate DataFrames. 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,

Pipelines and PipelineModels help to ensure that training and test data go through identical feature processing steps.

The examples given here are all for linear Pipelines, i.e., Pipelines in which each stage uses data produced by the previous stage.

It is possible to create non-linear Pipelines 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).

type checking is done using the DataFrame schema, a description of the data types of columns in the DataFrame.

There are two main ways to pass parameters to an algorithm: Parameters belong to specific instances of Estimators and Transformers. 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 ->

This example follows the simple text document Pipeline illustrated in the figures above.

Introduction to Spark’s Machine Learning Pipeline

(Or if you want to take a shortcut and skip reading that you could just use the maintenance_data.csv as both the test and training data.) The Spark pipeline object is{Pipeline, PipelineModel}.

In general a machine learning pipeline describes the process of writing code, releasing it to production, doing data extractions, creating training models, and tuning the algorithm.

or other benefits to doing this, but the Spark documentation does not spell that out.) But at least it mimics the pipeline from, at least regarding the data transformation operations.

In other words, in the graphic above the dataframe is created through reading data from Hadoop or whatever and then transform() and fit() operations are performed on it to add feature and label columns, which is the format required for the logistic regression ML algorithm.

To illustrate using our own code, we rewrite the code from the blog posts mentioned above, which was two separate programs (create model and make predictions) into one program shown here.

Next, we first read in the dataframe from a text file as usual but instead of performing transform() operations by themselves on the dataframe, we feed the VectorAssembler(), StringIndexer(), and LogisticRegression() into new Pipeline().setStages(Array(assembler, labelIndexer, lr)). Stepan Pushkarev, Multi Runtime Serving Pipelines

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