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Home > Data > Artificial Intelligence > Machine Learning with Swift
Machine Learning with Swift
Machine Learning with Swift

Machine Learning with Swift: Artificial Intelligence for iOS

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Machine Learning with Swift

Classification – Decision Tree Learning

In the previous chapter, we discussed different types of machine learning, including supervised classification tasks; in this chapter, we will build our first Swift application for this. We will discuss main components of machine learning development stack, and will also exercise in data generation, exploratory analysis, preprocessing, and models training and evaluation in Python. After this, we will transfer our model to Swift. We will also discuss a specific class of supervised learning algorithms—decision tree learning and its extension: random forest.

The following topics are waiting for us in this chapter:

  • Machine learning software development stack
  • Python toolbox for machine learning: IPython, SciPy, scikit-learn
  • Dataset generation and exploratory analysis
  • Data preprocessing
  • Decision tree learning and random forest
  • Assessing...

Machine learning toolbox

For many years, the programming language of choice for machine learning was one of the following: Python, R, MATLAB, C++. This is not due to some specific language features, but because of the infrastructure around it: libraries and tools. Swift is a relatively young programming language, and anyone who chooses it as a primary tool for machine learning development should start from the very basic building blocks, and build his own tools and libraries. Recently, Apple became more open to third-party Python machine learning tools: Core ML can work with some of them.

Here is a list of components that are needed for the successful machine learning research and development, and examples of popular libraries and tools of the type:

  • Linear algebra: Machine learning developer needs data structures like vectors, matrices, and tensors with compact syntax and hardware...

Prototyping the first machine learning app

Usually, before implementing a machine learning application for mobile devices, you want to do a quick and dirty prototype just to check your ideas. This allows to save a lot of time when you realize that the model you initially thought works perfectly for your problem, in reality doesn't. The quickest way to do a prototype is to use Python or R tools listed in the previous section.

Python is a general-purpose programming language with rich infrastructure and vibrant community. Its syntax is similar in many ways to Swift's one. Throughout this book, we'll use it for prototyping, and Swift for actual development.

When you have tested your ideas and a model prototype works as you expect, you can start thinking about how to port it to an iOS. You have several options here:

Inference-only options:

  • Check the Core ML, and a...

IPython notebook crash course

Feel free to skip this section if you're familiar with the Python and Jupyter notebooks.

IPython notebook and its web-based GUI Jupyter are standard tools for data-driven machine learning development. Jupyter is also a handy tool for learning Python and its libraries. You can combine pieces of code with comments in markdown format. You can also execute pieces of code in place, chaining them one after another, and immediately seeing the results of computations. It also allows to embed interactive charts, tables, videos, and other multimedia objects inside the notebook. We will use Jupyter notebooks for writing quick prototypes of our models.

To create a new notebook, run in the Terminal:

> jupyter notebook

You will see output similar to this:

[I 10:51:23.269 NotebookApp] Serving notebooks from local directory: ...
[I 10:51:23.269 NotebookApp...

Time to practice

In the following sections, we'll dive into machine learning practice, to get a feeling of what it looks like. Just like in a theater play, in machine learning you have a list of characters and a list of acts.

Two main characters are:

  • Dataset
  • Model

Three main acts are:

  • Dataset preparation
  • Model training
  • Model evaluation

We'll go through all these acts, and by the end of the chapter we'll have our first trained model. First, we need to define a problem, and then we can start coding a prototype in Python. Our destination point is a working model in Swift. Don't take the problem itself too seriously, though, because as the first exercise, we're going to solve a fictional problem.

Machine learning for extra-terrestrial life explorers

Swift is undoubtedly the programming language of the future. In the nearest years, we're expecting to see Swift being employed to program-intelligent scout robots that will explore alien planets and life forms on them. These robots should be able to recognize and classify aliens they will encounter. Let's build a model to distinguish between two alien species using their characteristic features.

The biosphere of the distant planet consists mainly of two species: night predators rabbosauruses, and peaceful, herbivorous platyhogs (see the following diagram). Roboscouts are equipped with sensors to measure only three features of each individual: length (in meters), color, and fluffiness.

Figure 2.1: Objects of interest in our first machine learning task. Picture by Mykola Sosnovshchenko.
The full code of the Python...

Loading the dataset

Create and open a new IPython notebook. In the chapter's supplementary materials, you can see the file extraterrestrials.csv. Copy it to the same folder where you created your notebook. In the first cell of your notebook, execute the magical command:

In []: 
%matplotlib inline

This is needed to see inline plots right in the notebook in the future.

The library we are using for datasets loading and manipulation is pandas. Let's import it, and load the .csv file:

In []: 
import pandas as pd 
df = pd.read_csv('extraterrestrials.csv', sep='t', encoding='utf-8', index_col=0)

Object df is a data frame. This is a table-like data structured for efficient manipulations over the different data types. To see what's inside, execute:

In []: 
df.head() 
Out[]:

Length

Color

Fluffy

Label

0

27.545139

Pink gold...

Exploratory data analysis

First, we want to see how many individuals of each class we have. This is important, because if the class distribution is very imbalanced (like 1 to 100, for example), we will have problems training our classification models. You can get data frame columns via the dot notation. For example, df.label will return you the label column as a new data frame. The data frame class has all kinds of useful methods for calculating the summary statistics. The value_counts() method returns the counts of each element type in the data frame:

In []: 
df.label.value_counts() 
Out[]: 
platyhog       520 
rabbosaurus    480 
Name: label, dtype: int64

The class distribution looks okay for our purposes. Now let's explore the features.

We need to group our data by classes, and calculate feature statistics separately to see the difference between the creature...

Machine learning toolbox

For many years, the programming language of choice for machine learning was one of the following: Python, R, MATLAB, C++. This is not due to some specific language features, but because of the infrastructure around it: libraries and tools. Swift is a relatively young programming language, and anyone who chooses it as a primary tool for machine learning development should start from the very basic building blocks, and build his own tools and libraries. Recently, Apple became more open to third-party Python machine learning tools: Core ML can work with some of them.

Here is a list of components that are needed for the successful machine learning research and development, and examples of popular libraries and tools of the type:

  • Linear algebra: Machine learning developer needs data structures like vectors, matrices, and tensors with compact syntax and hardware-accelerated operations on them. Examples in other languages: NumPy, MATLAB, and R standard libraries, Torch.
  • Probability...

Prototyping the first machine learning app

Usually, before implementing a machine learning application for mobile devices, you want to do a quick and dirty prototype just to check your ideas. This allows to save a lot of time when you realize that the model you initially thought works perfectly for your problem, in reality doesn't. The quickest way to do a prototype is to use Python or R tools listed in the previous section.

Python is a general-purpose programming language with rich infrastructure and vibrant community. Its syntax is similar in many ways to Swift's one. Throughout this book, we'll use it for prototyping, and Swift for actual development.

When you have tested your ideas and a model prototype works as you expect, you can start thinking about how to port it to an iOS. You have several options here:

Inference-only options:

  • Check the Core ML, and a list of the Python libraries it supports. Maybe, you will be able to export your model in Core ML format, and run it on a device.
  • Write...

IPython notebook crash course

Feel free to skip this section if you're familiar with the Python and Jupyter notebooks.

IPython notebook and its web-based GUI Jupyter are standard tools for data-driven machine learning development. Jupyter is also a handy tool for learning Python and its libraries. You can combine pieces of code with comments in markdown format. You can also execute pieces of code in place, chaining them one after another, and immediately seeing the results of computations. It also allows to embed interactive charts, tables, videos, and other multimedia objects inside the notebook. We will use Jupyter notebooks for writing quick prototypes of our models.

To create a new notebook, run in the Terminal:

> jupyter notebook

You will see output similar to this:

[I 10:51:23.269 NotebookApp] Serving notebooks from local directory: ...[I 10:51:23.269 NotebookApp] 0 active kernels [I 10:51:23.270 NotebookApp] The Jupyter Notebook is running at: http://localhost:8888/?token=3c073db5636e366fd750e661cc597652025fdbf41162c125...

Time to practice

In the following sections, we'll dive into machine learning practice, to get a feeling of what it looks like. Just like in a theater play, in machine learning you have a list of characters and a list of acts.

Two main characters are:

  • Dataset
  • Model

Three main acts are:

  • Dataset preparation
  • Model training
  • Model evaluation

We'll go through all these acts, and by the end of the chapter we'll have our first trained model. First, we need to define a problem, and then we can start coding a prototype in Python. Our destination point is a working model in Swift. Don't take the problem itself too seriously, though, because as the first exercise, we're going to solve a fictional problem.

Machine learning for extra-terrestrial life explorers

Swift is undoubtedly the programming language of the future. In the nearest years, we're expecting to see Swift being employed to program-intelligent scout robots that will explore alien planets and life forms on them. These robots should be able to recognize and classify aliens they will encounter. Let's build a model to distinguish between two alien species using their characteristic features.

The biosphere of the distant planet consists mainly of two species: night predators rabbosauruses, and peaceful, herbivorous platyhogs (see the following diagram). Roboscouts are equipped with sensors to measure only three features of each individual: length (in meters), color, and fluffiness.

Figure 2.1: Objects of interest in our first machine learning task. Picture by Mykola Sosnovshchenko.

Note

The full code of the Python part of this chapter can be found here: ML_Intro.ipynb.

Loading the dataset

Create and open a new IPython notebook. In the chapter's supplementary materials, you can see the file extraterrestrials.csv. Copy it to the same folder where you created your notebook. In the first cell of your notebook, execute the magical command:

In []: 
%matplotlib inline

This is needed to see inline plots right in the notebook in the future.

The library we are using for datasets loading and manipulation is pandas. Let's import it, and load the .csv file:

In []: 
import pandas as pd 
df = pd.read_csv('extraterrestrials.csv', sep='t', encoding='utf-8', index_col=0)

Object df is a data frame. This is a table-like data structured for efficient manipulations over the different data types. To see what's inside, execute:

In []: 
df.head() 
Out[]:

Length

Color

Fluffy

Label

0

27.545139

Pink gold

True

Rabbosaurus

1

12.147357

Pink gold

False

Platyhog

2

23.454173

Light black

True

Rabbosaurus

3

29.956698

Pink gold

True

Rabbosaurus

4

34.884065

Light black

True

Rabbosaurus

This prints the first five rows of the...

Exploratory data analysis

First, we want to see how many individuals of each class we have. This is important, because if the class distribution is very imbalanced (like 1 to 100, for example), we will have problems training our classification models. You can get data frame columns via the dot notation. For example, df.label will return you the label column as a new data frame. The data frame class has all kinds of useful methods for calculating the summary statistics. The value_counts() method returns the counts of each element type in the data frame:

In []: 
df.label.value_counts() 
Out[]: 
platyhog       520 
rabbosaurus    480 
Name: label, dtype: int64

The class distribution looks okay for our purposes. Now let's explore the features.

We need to group our data by classes, and calculate feature statistics separately to see the difference between the creature classes. This can be done using the groupby() method. It takes the label of the column by which you want to group your data:

In [...
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Key benefits

  • Implement effective machine learning solutions for your iOS applications
  • Use Swift and Core ML to build and deploy popular machine learning models
  • Develop neural networks for natural language processing and computer vision

Description

Machine learning as a field promises to bring increased intelligence to the software by helping us learn and analyse information efficiently and discover certain patterns that humans cannot. This book will be your guide as you embark on an exciting journey in machine learning using the popular Swift language. We’ll start with machine learning basics in the first part of the book to develop a lasting intuition about fundamental machine learning concepts. We explore various supervised and unsupervised statistical learning techniques and how to implement them in Swift, while the third section walks you through deep learning techniques with the help of typical real-world cases. In the last section, we will dive into some hard core topics such as model compression, GPU acceleration and provide some recommendations to avoid common mistakes during machine learning application development. By the end of the book, you'll be able to develop intelligent applications written in Swift that can learn for themselves.

Who is this book for?

iOS developers who wish to create smarter iOS applications using the power of machine learning will find this book to be useful. This book will also benefit data science professionals who are interested in performing machine learning on mobile devices. Familiarity with Swift programming is all you need to get started with this book.

What you will learn

  • Learn rapid model prototyping with Python and Swift
  • Deploy pre-trained models to iOS using Core ML
  • Find hidden patterns in the data using unsupervised learning
  • Get a deeper understanding of the clustering techniques
  • Learn modern compact architectures of neural networks for iOS devices
  • Train neural networks for image processing and natural language processing
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Publication date : Feb 28, 2018
Length: 378 pages
Edition : 1st
Language : English
ISBN-13 : 9781787121515
Category :
Data
Languages :
Swift
Concepts :
Artificial Intelligence
Tools :
iOS

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Publication date : Feb 28, 2018
Length: 378 pages
Edition : 1st
Language : English
ISBN-13 : 9781787121515
Category :
Data
Languages :
Swift
Concepts :
Artificial Intelligence
Tools :
iOS

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Table of Contents

13 Chapters
Getting Started with Machine Learning Chevron down icon Chevron up icon
Getting Started with Machine Learning
What is AI?
The motivation behind ML
What is ML ?
Applications of ML
Using ML to build smarter iOS applications
Getting to know your data
Choosing a model
Summary
Bibliography
Classification – Decision Tree Learning Chevron down icon Chevron up icon
Classification – Decision Tree Learning
Machine learning toolbox
Prototyping the first machine learning app
IPython notebook crash course
Time to practice
Machine learning for extra-terrestrial life explorers
Loading the dataset
Exploratory data analysis
Data preprocessing
Decision trees everywhere
Training the decision tree classifier
How decision tree learning works
Implementing first machine learning app in Swift
Introducing Core ML
Summary
K-Nearest Neighbors Classifier Chevron down icon Chevron up icon
K-Nearest Neighbors Classifier
Calculating the distance
Using instance-based models for classification and clustering
People motion recognition using inertial sensors
Understanding the KNN algorithm
Recognizing human motion using KNN
Reasoning in high-dimensional spaces
KNN pros
KNN cons
Improving our solution
Summary
Bibliography
K-Means Clustering Chevron down icon Chevron up icon
K-Means Clustering
Unsupervised learning
K-means clustering
Implementing k-means in Swift
Clustering objects on a map
Choosing the number of clusters
K-means clustering – problems
K-means++
Image segmentation using k-means
Summary
Association Rule Learning Chevron down icon Chevron up icon
Association Rule Learning
Seeing association rules
Defining data structures
Using association measures to assess rules
Decomposing the problem
Generating all possible rules
Finding frequent item sets
The Apriori algorithm
Implementing Apriori in Swift
Running Apriori
Running Apriori on real-world data
The pros and cons of Apriori
Building an adaptable user experience
Summary
Bibliography
Linear Regression and Gradient Descent Chevron down icon Chevron up icon
Linear Regression and Gradient Descent
Understanding the regression task
Introducing simple linear regression
Feature scaling
Feature standardization
Implementing multiple linear regression in Swift
Fixing linear regression problems with regularization
Summary
Bibliography
Linear Classifier and Logistic Regression Chevron down icon Chevron up icon
Linear Classifier and Logistic Regression
Revisiting the classification task
Implementing logistic regression in Swift
Predicting user intents
Choosing the regression model for your problem
Bias-variance trade-off
Summary
Neural Networks Chevron down icon Chevron up icon
Neural Networks
What are artificial NNs anyway?
Building the neuron
Building the network
Building a neural layer in Swift
Using neurons to build logical functions
Implementing layers in Swift
Training the network
Basic neural network subroutines (BNNS)
Summary
Convolutional Neural Networks Chevron down icon Chevron up icon
Convolutional Neural Networks
Understanding users emotions
Introducing computer vision problems
Introducing convolutional neural networks
Pooling operation
Convolution operation
Building the network
Loss functions
Training the network
Training the CNN for facial expression recognition
Environment setup
Deep learning frameworks
Loading the data
Splitting the data
Data augmentation
Creating the network
Plotting the network structure
Training the network
Plotting loss
Making predictions
Saving the model in HDF5 format
Converting to Core ML format
Visualizing convolution filters
Deploying CNN to iOS
Summary
Bibliography
Natural Language Processing Chevron down icon Chevron up icon
Natural Language Processing
NLP in the mobile development world
Word Association game
Python NLP libraries
Textual corpuses
Common NLP approaches and subtasks
Distributional semantics hypothesis
Word vector representations
Autoencoder neural networks
Word2Vec
Word2Vec in Gensim
Vector space properties
iOS application
Word2Vec friends and relatives
Where to go from here?
Summary
Machine Learning Libraries Chevron down icon Chevron up icon
Machine Learning Libraries
Machine learning and AI APIs
Libraries
General-purpose machine learning libraries
Inference-only libraries
NLP libraries
Speech recognition
Computer vision
Low-level subroutine libraries
Choosing a deep learning framework
Summary
Optimizing Neural Networks for Mobile Devices Chevron down icon Chevron up icon
Optimizing Neural Networks for Mobile Devices
Delivering perfect user experience
Calculating the size of a convolutional neural network
Lossless compression
Compact CNN architectures
Preventing a neural network from growing big
Lossy compression
An example of the network compression
Summary
Bibliography
Best Practices Chevron down icon Chevron up icon
Best Practices
Mobile machine learning project life cycle
Best practices
Machine learning gremlins
Recommended learning resources
Summary

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Me Apr 28, 2018
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Just a few minutes into the examples and walk-throughs and I'm running into errors and oversights. I hope the entire book isn't like this. I buy technical books to save time, not spend more time debugging misdirections. So far the issues are minor and have only cost about an hour to resolve, and perhaps less for someone who regularly works with the prescribed tools, but again, the point is to guide the user off a cliff... I mean through the material.
Amazon Verified review Amazon

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About the 3 authors

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Profile icon Alexander Sosnovshchenko
Alexander Sosnovshchenko
Alexander Sosnovshchenko has been working as an iOS software engineer since 2012. Later he made his foray into data science, from the first experiments with mobile machine learning in 2014, to complex deep learning solutions for detecting anomalies in video surveillance data. He lives in Lviv, Ukraine, and has a wife and a daughter.
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Profile icon Jojo Moolayil
Jojo Moolayil
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Jojo Moolayil is an artificial intelligence, deep learning, machine learning, and decision science professional with over six years of industrial experience. He is the author of Learn Keras for Deep Neural Networks, published by Apress, and Smarter Decisions – The Intersection of IoT and Decision Science, published by Packt Publishing. He has worked with several industry leaders on high-impact, critical data science and machine learning projects across multiple verticals. He is currently associated with Amazon Web Services as a research scientist in Canada. Apart from writing books on AI, decision science, and the internet of things, Jojo has been a technical reviewer for various books in the same fields published by Apress and Packt Publishing.
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Profile icon Oleksandr Baiev
Oleksandr Baiev
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