Practical Java Machine Learning: Projects with Google Cloud Platform and Amazon Web Services

410 Pages·2018·14.57 MB·English

by  Mark Wickham

Practical Java
Machine
Learning
Projects with Google Cloud Platform and
Amazon Web Services
—
Mark Wickham
Practical Java
Machine Learning
Projects with Google Cloud
Platform and Amazon Web Services
Mark Wickham
Practical Java Machine Learning: Projects with Google Cloud Platform and
Amazon Web Services
Mark Wickham
Irving, TX, USA
ISBN-13 (pbk): 978-1-4842-3950-6 ISBN-13 (electronic): 978-1-4842-3951-3
https://doi.org/10.1007/978-1-4842-3951-3
Library of Congress Control Number: 2018960994
Copyright © 2018 by Mark Wickham
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Printed on acid-free paper
Table of Contents
About the Author xi
About the Technical Reviewer xiii
Preface xv
Chapter 1: Introduction1
1.1 Terminology .............................................................................................................................1
1.2 Historical .................................................................................................................................5
1.3 Machine Learning Business Case ...........................................................................................7
Machine Learning Hype ...........................................................................................................7
Challenges and Concerns ........................................................................................................8
Data Science Platforms ...........................................................................................................9
ML Monetization ....................................................................................................................13
The Case for Classic Machine Learning on Mobile ................................................................14
1.4 Deep Learning .......................................................................................................................18
Identifying DL Applications ....................................................................................................19
1.5 ML-Gates Methodology .........................................................................................................22
ML-Gate 6: Identify the Well-Defined Problem ......................................................................23
ML-Gate 5: Acquire Sufficient Data .......................................................................................24
ML-Gate 4: Process/Clean/Visualize the Data .......................................................................25
ML-Gate 3: Generate a Model ................................................................................................25
ML-Gate 2: Test/Refine the Model .........................................................................................25
ML-Gate 1: Integrate the Model .............................................................................................26
ML-Gate 0: Deployment .........................................................................................................26
Methodology Summary .........................................................................................................27
1.6 The Case for Java ..................................................................................................................27
Java Market ...........................................................................................................................27
Java Versions .........................................................................................................................29
iii
Table of ConTenTs
Installing Java .......................................................................................................................31
Java Performance .................................................................................................................33
1.7 Development Environments ..................................................................................................35
Android Studio .......................................................................................................................36
Eclipse ...................................................................................................................................39
Net Beans IDE ........................................................................................................................43
1.8 Competitive Advantage .........................................................................................................44
Standing on the Shoulders of Giants .....................................................................................44
Bridging Domains ..................................................................................................................45
1.9 Chapter Summary .................................................................................................................46
Key Findings ..........................................................................................................................46
Chapter 2: Data: The Fuel for Machine Learning 47
2.1 Megatrends ...........................................................................................................................48
Explosion of Data ...................................................................................................................48
Highly Scalable Computing Resources ..................................................................................51
Advancement in Algorithms ...................................................................................................52
2.2 Think Like a Data Scientist ....................................................................................................52
Data Nomenclature ................................................................................................................53
Defining Data .........................................................................................................................54
2.3 Data Formats ........................................................................................................................55
CSV Files and Apache OpenOffice .........................................................................................57
ARFF Files ..............................................................................................................................62
JSON ......................................................................................................................................63
2.4 JSON Integration ...................................................................................................................69
JSON with Android SDK .........................................................................................................69
JSON with Java JDK ..............................................................................................................70
2.5 D ata Preprocessing ...............................................................................................................72
Instances, Attributes, Labels, and Features ...........................................................................73
Data Type Identification .........................................................................................................74
Missing Values and Duplicates ..............................................................................................74
Erroneous Values and Outliers ...............................................................................................76
iv
Table of ConTenTs
Macro Processing with OpenOffice Calc ...............................................................................77
JSON Validation .....................................................................................................................79
2.6 Creating Your Own Data ........................................................................................................80
Wifi Gathering ........................................................................................................................80
2.7 Visualization ..........................................................................................................................84
JavaScript Visualization Libraries ..........................................................................................84
D3 Plus ..................................................................................................................................86
2.8 Project: D3 Visualization ........................................................................................................86
2.9 Project: Android Data Visualization .......................................................................................97
2.10 Summary...........................................................................................................................102
Key Data Findings ................................................................................................................103
Chapter 3: Leveraging Cloud Platforms 105
3.1 Introduction .........................................................................................................................105
Commercial Cloud Providers ...............................................................................................106
Competitive Positioning .......................................................................................................109
Pricing .................................................................................................................................110
3.2 Google Cloud Platform (GCP) ...............................................................................................112
Google Compute Engine (GCE) Virtual Machines (VM) .........................................................114
Google Cloud SDK ................................................................................................................116
Google Cloud Client Libraries ..............................................................................................120
Cloud Tools for Eclipse (CT4E) .............................................................................................120
GCP Cloud Machine Learning Engine (ML Engine) ...............................................................121
GCP Free Tier Pricing Details ...............................................................................................122
3.3 Amazon AWS .......................................................................................................................123
AWS Machine Learning........................................................................................................124
AWS ML Building and Deploying Models .............................................................................126
AWS EC2 AMI .......................................................................................................................131
Running Weka ML in the AWS Cloud ....................................................................................135
AWS SageMaker ..................................................................................................................141
AWS SDK for Java ................................................................................................................143
AWS Free Tier Pricing Details ..............................................................................................147
v
Table of ConTenTs
3.4 Machine Learning APIs........................................................................................................148
Using ML REST APIs.............................................................................................................150
Alternative ML API Providers ...............................................................................................151
3.5 Project: GCP Cloud Speech API for Android .........................................................................152
Cloud Speech API App Overview ..........................................................................................153
GCP Machine Learning APIs .................................................................................................155
Cloud Speech API Authentication .........................................................................................156
Android Audio ......................................................................................................................161
Cloud Speech API App Summary .........................................................................................165
3.6 Cloud Data for Machine Learning ........................................................................................166
Unstructured Data ...............................................................................................................167
NoSQL Databases ................................................................................................................168
NoSQL Data Store Methods .................................................................................................170
Apache Cassandra Java Interface .......................................................................................172
3.7 Cloud Platform Summary ....................................................................................................175
Chapter 4: Algorithms: The Brains of Machine Learning 177
4.1 Introduction .........................................................................................................................177
ML-Gate 3 ............................................................................................................................178
4.2 Algorithm Styles ..................................................................................................................179
Labeled vs. Unlabeled Data .................................................................................................179
4.3 Supervised Learning ...........................................................................................................180
4.4 Unsupervised Learning .......................................................................................................182
4.5 Semi-Supervised Learning .................................................................................................184
4.6 A lternative Learning Styles .................................................................................................185
Linear Regression Algorithm ...............................................................................................185
Deep Learning Algorithms ...................................................................................................186
Reinforcement Learning ......................................................................................................188
4.7 C ML Algorithm Overview .....................................................................................................189
4.8 Choose the Right Algorithm ................................................................................................192
Functional Algorithm Decision Process ...............................................................................193
vi
Table of ConTenTs
4.9 The Seven Most Useful CML Algorithms .............................................................................195
Naive Bayes Algorithm (NB) .................................................................................................195
Random Forest Algorithm (RF) .............................................................................................197
K-Nearest Neighbors Algorithm (KNN) .................................................................................199
Support Vector Machine Algorithm (SVM) ............................................................................202
K-Means Algorithm ..............................................................................................................204
DBSCAN Algorithm ...............................................................................................................206
Expectation-Maximization (EM) Algorithm...........................................................................208
4.10 Algorithm Performance .....................................................................................................209
MNIST Algorithm Evaluation ................................................................................................209
4.11 Algorithm Analysis ............................................................................................................214
Confusion Matrix .................................................................................................................215
ROC Curves ..........................................................................................................................216
K-Fold Cross-Validation .......................................................................................................218
4.12 Java Source Code .............................................................................................................220
Classification Algorithms .....................................................................................................222
Clustering Algorithms ..........................................................................................................223
Java Algorithm Modification ................................................................................................224
Chapter 5: Machine Learning Environments227
5.1 Overview .............................................................................................................................228
ML Gates ..............................................................................................................................228
5.2 Java ML Environments ........................................................................................................229
Weka ....................................................................................................................................232
RapidMiner ..........................................................................................................................232
KNIME ..................................................................................................................................234
ELKI......................................................................................................................................236
Java-ML ...............................................................................................................................236
5.3 Weka Installation .................................................................................................................236
Weka Configuration .............................................................................................................238
Java Parameters Setup .......................................................................................................241
vii
Table of ConTenTs
Modifying Weka .prop Files .................................................................................................242
Weka Settings......................................................................................................................244
Weka Package Manager ......................................................................................................245
5.4 Weka Overview ...................................................................................................................247
Weka Documentation ..........................................................................................................249
Weka Explorer .....................................................................................................................249
Weka Filters .........................................................................................................................251
Weka Explorer Key Options .................................................................................................252
Weka KnowledgeFlow .........................................................................................................253
Weka Simple CLI ..................................................................................................................255
5.5 Weka Clustering Algorithms ................................................................................................257
Clustering with DBSCAN ......................................................................................................257
Clustering with KnowledgeFlow ..........................................................................................264
5.6 Weka Classification Algorithms ...........................................................................................268
Preprocessing (Data Cleaning) ............................................................................................269
Classification: Random Forest Algorithm .............................................................................274
Classification: K-Nearest Neighbor ......................................................................................278
Classification: Naive Bayes ..................................................................................................281
Classification: Support Vector Machine ...............................................................................283
5.7 Weka Model Evaluation .......................................................................................................286
Multiple ROC Curves ............................................................................................................288
5.8 Weka Importing and Exporting ............................................................................................292
Chapter 6: Integrating Models 297
6.1 Introduction .........................................................................................................................297
6.2 Managing Models ................................................................................................................298
Device Constraints ...............................................................................................................299
Optimal Model Size ..............................................................................................................300
Model Version Control ..........................................................................................................304
Updating Models ..................................................................................................................305
Managing Models: Best Practices .......................................................................................307
viii
Table of ConTenTs
6.3 Weka Java API .....................................................................................................................307
Loading Data .......................................................................................................................308
Working with Options ..........................................................................................................309
Applying Filters ....................................................................................................................309
Setting the Label Attribute ...................................................................................................310
Building a Classifier .............................................................................................................310
Training and Testing .............................................................................................................311
Building a Clusterer .............................................................................................................312
Loading Models ...................................................................................................................312
Making Predictions ..............................................................................................................313
6.4 Weka for Android .................................................................................................................314
Creating Android Weka Libraries in Eclipse .........................................................................315
Adding the Weka Library in Android Studio .........................................................................320
6.5 Android Integration .............................................................................................................321
Project: Weka Model Create .................................................................................................322
Project: Weka Model Load ...................................................................................................328
6.6 Android Weka Model Performance ......................................................................................335
6.7 Raspberry Pi Integration .....................................................................................................337
Raspberry Pi Setup for ML ...................................................................................................339
Raspberry Pi GUI Considerations .........................................................................................341
Weka API Library for Raspberry Pi .......................................................................................342
Project: Raspberry Pi Old Faithful Geyser Classifier ............................................................342
6.8 Sensor Data ........................................................................................................................363
Android Sensors ..................................................................................................................363
Raspberry Pi with Sensors ..................................................................................................365
Sensor Units of Measure .....................................................................................................369
Project: Android Activity Tracker ..........................................................................................370
6.9 Weka License Notes ............................................................................................................381
Index 383
ix