Table Of ContentMachine Learning With Go
Implement Regression, Classification, Clustering,
Time-series Models, Neural Networks and more
using the Go Programming Language
Daniel Whitenack
BIRMINGHAM - MUMBAI
Machine Learning With Go
Copyright © 2017 Packt Publishing
First published: September 2017
Production reference: 1210917
Published by Packt Publishing Ltd.
Livery Place
35 Livery Street
Birmingham
B3 2PB, UK.
ISBN 978-1-78588-210-4
www.packtpub.com
Contents
Preface
1
Chapter 1: Gathering and Organizing Data
8
Handling data - Gopher style 9
Best practices for gathering and organizing data with Go 11
CSV files 12
Reading in CSV data from a file 13
Handling unexpected fields 14
Handling unexpected types 15
Manipulating CSV data with data frames 18
JSON 20
Parsing JSON 20
JSON output 23
SQL-like databases 24
Connecting to an SQL database 24
Querying the database 25
Modifying the database 26
Caching 27
Caching data in memory 28
Caching data locally on disk 28
Data versioning 30
Pachyderm jargon 31
Deploying/installing Pachyderm 31
Creating data repositories for data versioning 32
Putting data into data repositories 33
Getting data out of versioned data repositories 33
References 33
Summary 34
Chapter 2: Matrices, Probability, and Statistics
35
Matrices and vectors 35
Vectors 36
Vector operations 36
Matrices 38
Matrix operations 40
Statistics 41
Distributions 42
Statistical measures 43
Measures of central tendency 43
Measures of spread or dispersion 45
Visualizing distributions 48
Histograms 48
Box plots 52
Probability 55
Random variables 55
Probability measures 56
Independent and conditional probability 56
Hypothesis testing 57
Test statistics 58
Calculating p-values 58
References 60
Summary 61
Chapter 3: Evaluation and Validation
62
Evaluation 62
Continuous metrics 63
Categorical metrics 67
Individual evaluation metrics for categorical variables 67
Confusion matrices, AUC, and ROC 72
Validation 75
Training and test sets 76
Holdout set 80
Cross validation 81
References 83
Summary 84
Chapter 4: Regression
85
Understanding regression model jargon 85
Linear regression 86
Overview of linear regression 87
Linear regression assumptions and pitfalls 89
Linear regression example 90
Profiling the data 90
Choosing our independent variable 94
Creating our training and test sets 97
Training our model 99
Evaluating the trained model 100
Multiple linear regression 104
Nonlinear and other types of regression 109
References 114
Summary 114
Chapter 5: Classification
115
Understanding classification model jargon 116
Logistic regression 116
Overview of logistic regression 117
Logistic regression assumptions and pitfalls 121
Logistic regression example 121
Cleaning and profiling the data 122
Creating our training and test sets 127
Training and testing the logistic regression model 129
k-nearest neighbors 135
Overview of kNN 135
kNN assumptions and pitfalls 137
kNN example 138
Decision trees and random forests 140
Overview of decision trees and random forests 140
Decision tree and random forest assumptions and pitfalls 141
Decision tree example 142
Random forest example 143
Naive bayes 144
Overview of naive bayes and its big assumption 144
Naive bayes example 144
References 146
Summary 147
Chapter 6: Clustering
148
Understanding clustering model jargon 149
Measuring Distance or Similarity 149
Evaluating clustering techniques 151
Internal clustering evaluation 151
External clustering evaluation 156
k-means clustering 157
Overview of k-means clustering 157
k-means assumptions and pitfalls 160
k-means clustering example 161
Profiling the data 161
Generating clusters with k-means 164
Evaluating the generated clusters 166
Other clustering techniques 169
References 170
Summary 170
Chapter 7: Time Series and Anomaly Detection
171
Representing time series data in Go 172
Understanding time series jargon 175
Statistics related to time series 176
Autocorrelation 176
Partial autocorrelation 181
Auto-regressive models for forecasting 184
Auto-regressive model overview 184
Auto-regressive model assumptions and pitfalls 185
Auto-regressive model example 186
Transforming to a stationary series 186
Analyzing the ACF and choosing an AR order 189
Fitting and evaluating an AR(2) model 190
Auto-regressive moving averages and other time series models 196
Anomaly detection 197
References 199
Summary 199
Chapter 8: Neural Networks and Deep Learning
201
Understanding neural net jargon 202
Building a simple neural network 203
Nodes in the network 204
Network architecture 206
Why do we expect this architecture to work? 207
Training our neural network 208
Utilizing the simple neural network 215
Training the neural network on real data 216
Evaluating the neural network 218
Introducing deep learning 220
What is a deep learning model? 221
Deep learning with Go 222
Setting up TensorFlow for use with Go 224
Retrieving and calling a pretrained TensorFlow model 224
Object detection using TensorFlow from Go 226
References 230
Summary 230
Chapter 9: Deploying and Distributing Analyses and Models
231
Running models reliably on remote machines 232
A brief introduction to Docker and Docker jargon 232
Docker-izing a machine learning application 234
Docker-izing the model training and export 234
Docker-izing model predictions 239
Testing the Docker images locally 244
Running the Docker images on remote machines 246
Building a scalable and reproducible machine learning pipeline 247
Setting up a Pachyderm and Kubernetes cluster 248
Building a Pachyderm machine learning pipeline 250
Creating and filling the input repositories 251
Creating and running the processing stages 255
Updating pipelines and examining provenance 260
Scaling pipeline stages 262
References 264
Summary 265
Chapter 10: Algorithms/Techniques Related to Machine Learning
266
Gradient descent 266
Entropy, information gain, and related methods 269
Backpropagation 271
Index
275
Preface
It seems like machine learning and artificial intelligence is all the rage, both in hip tech
companies and increasingly in larger enterprise companies. Data scientists are using
machine learning to do everything from drive cars to draw cats. However, if you follow the
data science community, you have very likely seen something like language wars unfold
between Python and R users. These languages dominate the machine learning conversation
and often seem to be the only choices to integrate machine learning in your organization.
We will explore a third option in this book: Go, the open source programming language
created at Google.
The unique features of Go, along with the mindset of Go programmers, can help data
scientists overcome some of the common struggles that they encounter. In particular, data
scientists are (unfortunately) known to produce bad, inefficient, and unmaintainable code.
This book will address this issue, and will clearly show you how to be productive in
machine learning while also producing applications that maintain a high level of integrity.
It will also allow you to overcome the common challenges of integrating analysis and
machine learning code within an existing engineering organization.
This book will develop readers into productive, innovative data analysts who leverage Go
to build robust and valuable applications. To this end, the book will clearly introduce the
technical, programming aspects of machine learning in Go, but it will also guide the reader
to understand sound workflows and philosophies for real-world analysis.
What this book covers
Preparing and analyzing data in machine learning workflows:
Chapter 1, Gathering and Organizing Data, covers the gathering, organization, and
parsing of data to/from a local and remote sources. Once the reader is done with
this chapter, they will understand how to interact with data stored in various
places and in various formats, how to parse and clean that data, and how to
output that cleaned and parsed data.
Chapter 2, Matrices, Probability, and Statistics, covers the organization of data into
matrices and matrix operations. Once the reader is done with this material, they
will understand how to form matrices within Go programs and how to utilize
these matrices to perform various types of matrix operations. This chapter also
covers statistical measures and operations key to day-to-day data analysis work.
Once the reader is done with this chapter, they will understand how to perform
solid summary data analysis, describe and visualize distributions, quantify
hypotheses, and transform datasets with, for example, dimensionality reductions.
Chapter 3, Evaluation and Validation, covers evaluation and validation, which are
key to measuring the performance of machine applications and ensuring that
they generalize. Once the reader is done with this chapter, they will understand
various metrics to gauge the performance of models (in other words, evaluate the
model) as well as various techniques to validate the model more generally.
Machine learning techniques:
Chapter 4, Regression, explains regression, a widely used technique to model
continuous variables and a basis for other models. Regression produces models
that are immediately interpretable. Thus, it can provide an excellent starting
point when introducing predictive capabilities in a organization.
Chapter 5, Classification, covers classification, a machine learning technique
distinct from regression in that the target variable is typically categorical or
labeled. For example, a classification model may classify emails into spam and
not spam categories or classify network traffic as fraudulent or not fraudulent.
Chapter 6, Clustering, covers clustering, an unsupervised machine learning
technique used to form groupings of samples. At the end of this chapter, readers
will be able to automatically form groupings of data points to better understand
their structure.
Chapter 7, Time Series and Anomaly Detection, introduces techniques utilized to
model time series data, such as stock prices, user events, and so on. After reading
this chapter, the reader will understand how to evaluate various terms in a time
series, build up a model of the time series, and detect anomalies in a time series.
Taking machine learning to the next level:
Chapter 8, Neural Networks and Deep Learning, introduces techniques utilized to
perform regression, classification, and image processing with neural networks.
After reading this chapter, the reader will understand how and when to apply
these more complicated modeling techniques.
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Chapter 9, Deploying and Distributing Analyses and Models, empowers readers to
deploy the models that we have developed throughout the class to production
environments and distribute processing over production scale data. This chapter
illustrates how both of these things can be done easily, without significant
modifications to the code utilized throughout the book.
The Appendix, Algorithms/Techniques Related to Machine Learning, can be referenced
throughout the text of the book and will provide information about algorithms,
optimizations, and techniques that are relevant to machine learning workflows.
What you need for this book
To run the examples in this book and experiment with the techniques covered in the book,
you will generally need the following:
Access to a bash-like shell.
A complete Go environment including Go, an editor, and related default or
custom environment variables defined. You can, for example, follow this guide at
https://www.goinggo.net/2016/05/installing-go-and-your-workspace.html.
Various Go dependencies. These can be obtained as they are needed via go get
....
Then, to run the examples related to some of the advanced topics, such as data pipelining
and deep learning, you will need a few additional things:
An installation or deployment of Pachyderm. You can follow these docs to get
Pachyderm up and running locally or in the cloud,
http://pachyderm.readthedocs.io/en/latest/.
A working Docker installation
(https://www.docker.com/community-edition#/download).
An installation of TensorFlow. To install TensorFlow locally, you can follow this
guide at https://www.tensorflow.org/install/.
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