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Introduction to Functional Programming in F# – Part 5

Introduction

Welcome to the fifth post in this introductory series on Functional Programming in F#. In this post we will be building upon some of the concepts we have learned in previous posts whilst investigating functional collections.

Before We Start

We are going to use the solution we created in the last post of this series: https://trustbit.tech/blog/2019/10/01/introduction-to-functional-programming-in-f-sharp-part-4.

Add Orders.fs, OrderTests.fsx and lists.fsx to the code project and OrderTests.fs to the test project.

The Basics

F# has a hugely deserved reputation for being great for data-centric use cases like finance and data science due in a large part to the power of it's support for data structures and collection handling. In this post we will look at how we can harness some of this power in normal line of business apps.

There are a number of collections in F# that we can make use of but the three primary ones are:

  • Sequence (Seq) - Lazily evaluated - Equivalent to IEnumerable.

  • Array - Great for numerics/data science. There are built-in modules for 2d, 3d and 4d arrays.

  • List - Eagerly evaluated and immutable structure and data. F# specific, not same as List.

Each of these types has a module that contains a wide range of functions including some to convert to/from each other.

In this post we are going to concentrate on the List type and module.

Core Functionality

We will be using lists.fsx for this section. Remember to highlight the code and run it in F# Interactive (FSI).

Create an empty list:

let items = []

Create a list with five integers:

let items = [1;2;3;4;5]

In this case, we could also do this:

let items = [1..5]

Or we could use a List comprehension:

let items = [ for x in 1..5 do yield x ]

List comprehensions are really powerful but we are not going to use them in this post.

To add an item to a list, we use the cons operator:

let items' = 6 :: items

The original list remains unaffected by the new item as it is immutable.

A list is made up of a head (single item) and a tail (list of items). We can pattern match on a list to show this:

let readList items =
    match items with
    | [] -> sprintf "Empty list"
    | head :: tail -> sprintf "Head: %A and Tail: %A" head tail

let emptyList = readList [] // "Empty list"
let multipleList = readList [1;2;3;4;5] // "Head: 1 and Tail: 
[2;3;4;5]"
let singleItemList = readList [1] // "Head: 1 and Tail: []"

We can join (concatenate) two lists together:

let list1 = [1..5]
let list2 = [3..7]
let emptyList = []

let joined = list1 @ list2 // [1;2;3;4;5;3;4;5;6;7]
let joinedEmpty = list1 @ emptyList // [1;2;3;4;5]
let emptyJoined = emptyList @ list1 // [1;2;3;4;5]

As lists are immutable, we can re-use them knowing that there values/structure will never change.

We could also use the concat function on the List module to do the same job as the @ operator:

let joined = List.concat [list1;list2]

We can filter a list using an ('a -> bool) function and the filter function from the List module:

let myList = [1..9]

let getEvens items =
    items
    |> List.filter (fun x -> x % 2 = 0)

let evens = getEvens myList // [2;4;6;8]

We can add up the items in a list using the sum function:

let sum items =
    items |> List.sum

let mySum = sum myList // 45

Other aggregation functions are as easy to use but we are not going to look at them here.

Sometimes we want to perform an operation on each item in a list. If we want to return the new list, we use the map function:

let triple items =
    items
    |> List.map (fun x -> x * 3)

let myTriples = triple myList

If we don't want to return a new list, we use the iter function:

let print items =
    items
    |> List.iter (fun x -> (sprintf "My value is %i" x))

print myList |> ignore

Let's take a look at a more complicated example using map that changes the structure of the output list. We will use a list of tuples (int * decimal) which might represent quantity and unit price.

let items = [(1,0.25M);(5,0.25M);(1,2.25M);(1,125M);(7,10.9M)]

To calculate the total price of the items, we can use map to convert (int * decimal) list to decimal list and then sum the items:

let sum items =
    items
    |> List.map (fun (q, p) -> decimal q * p)
    |> List.sum

Note the explicit conversion of the integer to a decimal. F# is strict about types in calculations and does support implicit conversion. In this particular case, there is an easier way to do the calculation in one step:

let sum items =
    items
    |> List.sumBy (fun (q, p) -> decimal q * p)

Folding

A very powerful functional concept that we can use to do similar aggregation tasks (and lots more that we won't cover) is the fold function:

let total items =
    items
    |> List.fold (fun acc (q, p) -> acc + decimal q * p) 0M

let total = getTotal items

The lambda function uses an accumulator and the deconstructed tuple and simply adds the intermediate calculation to the accumulator. The 0M parameter is the initial value of the accumulator. If we were folding using multiplication, the initial value would probably have been 1M.

To make it clearer what we are trying to do, we could use the ||> operator:

let getTotal items =
    (0M, items) ||> List.fold (fun acc (q, p) -> acc + 
decimal q * p)

Grouping Data and Uniqueness

Rather than try to explain what the groupBy function does, it will be easier to show you:

let myList = [1;2;3;4;5;7;6;5;4;3]    

let groupBy items = // 'a list -> ('a * 'a list) list
    items
    |> List.groupBy (fun x -> x)

let gbResult = groupBy myList // [1,[1];2,[2];3,[3;3];
4,[4;4];5,[5;5];6,[6];7,[7]]

To get the list of unique items from the result list, we can use the map function:

let unique items =
    items
    |> groupBy // or List.groupBy (fun x -> x)
    |> List.map (fun (i, _) -> i)

let unResult = unique myList // [1;2;3;4;5;6;7]

There is a built-in collection type called Set that will do this as well:

let uniqueSet items =
    items
    |> Set.ofList

let setResult = uniqueSet myList // [1;2;3;4;5;6;7]

Note the use of the ofList function to convert a list to a set.

We now have enough information to move onto a practical example of using an F# list.

Practical Example

In this example code we are going to manage an order with an immutable list of items. The functionality we need to add is:

  • Add an item

  • Remove an item

  • Reduce quantity of an item

  • Clear all of the items

First, create a module called Orders in Orders.fs and add record types for Order and Order Item:

type Item = {
    ProductId : int
    Quantity : int
}

type Order = {
    Id : int
    Items : Item list
}

Now we need to add a function to add an item to the order. This function needs to cater for products that exist in the order as well as those that don't. Let's create a couple of helpers bindings to help us get started:

let order = { Id = 1; Items = [ {ProductId = 1; Quantity = 1}]}
let newItem = { ProductId = 1; Quantity = 1 }

Firstly we need to group the items by the productId:

let addItem item order = // Item -> Order -> 
(int * Item list) list
    let items = 
        item :: order.Items
        |> List.groupBy (fun i -> i.ProductId)

let result = addItem newItem order // [(1,[ 
{ ProductId = 1; Quantity = 1 };
{ ProductId = 1; Quantity = 1 } ])]

Then we use the map and sumBy functions to aggregate per product:

let addItem item order = // Item -> Order -> Item list
    let items = 
        item :: order.Items
        |> List.groupBy (fun i -> i.ProductId) 
// (int * Item list) list
        |> List.map (fun (id, items) -> 
{ ProductId = id; Quantity = items |> 
List.sumBy (fun i -> i.Quantity) })

let result = addItem newItem order // [ 
{ ProductId = 1; Quantity = 2 } ]

Finally, we need to copy and update the order with our newly calculated items:

let addItem item order = // Item -> Order -> Order
    let items = 
        item :: order.Items
        |> List.groupBy (fun i -> i.ProductId) 
// (int * Item list) list
        |> List.map (fun (id, items) -> 
{ ProductId = id; Quantity = items |> List.sumBy 
(fun i -> i.Quantity) })
    { order with Items = items }

let result = addItem newItem order 
// { Id = 1; Items = [ { ProductId = 1; Quantity = 2 } ] }

Remove the helpers for order, newItem and result as we are going to create the following asserts in the OrderTests.fsx file:

let addNewItemAssert = 
    let myEmptyOrder = { Id = 1; Items = [] }
    let expected = { Id = 1; Items = [ 
{ ProductId = 1; Quantity = 1 } ] }
    let actual = myEmptyOrder |> addItem { 
ProductId = 1; Quantity = 1 } 
    actual = expected

let addExistingItemAssert = 
    let myOrder = { Id = 1; Items = [ { 
ProductId = 1; Quantity = 1 } ] }
    let expected = { Id = 1; Items = [ { 
ProductId = 1; Quantity = 2 } ] }
    let actual = myOrder |> addItem { 
ProductId = 1; Quantity = 1 } 
    actual = expected

We can easily add multiple items to an order:

let addItems items order = // Item list -> Order -> Order
    let items = 
        items @ order.Items 
        |> List.groupBy (fun i -> i.ProductId)
        |> List.map (fun (id, items) -> { ProductId = id; 
Quantity = items |> List.sumBy (fun i -> i.Quantity) })
        |> List.sortBy (fun i -> i.ProductId)
    { order with Items = items }

Add some asserts to the OrderTests.fsx file for the addItems function:

let addNewItemsAssert = 
    let myEmptyOrder = { Id = 1; Items = [] }
    let expected = { Id = 1; Items = [ { ProductId = 1; 
Quantity = 1 }; { ProductId = 2; Quantity = 5 } ] }
    let actual = myEmptyOrder |> addItems [ { ProductId = 1; 
Quantity = 1 }; { ProductId = 2; Quantity = 5 } ]
    actual = expected

let addItemsAssert = 
    let myOrder = { Id = 1; Items = [ { ProductId = 1; 
Quantity = 1 } ] }
    let expected = { Id = 1; Items = [ { ProductId = 1; 
Quantity = 2 }; { ProductId = 2; Quantity = 5 } ] }
    let actual = myOrder |> addItems [ { ProductId = 1; 
Quantity = 1 }; { ProductId = 2; Quantity = 5 } ]
    actual = expected

Let's extract the common functionality (group by and map) into a new function:

let recalculate items = // Item list -> Item list
    items
    |> List.groupBy (fun i -> i.ProductId)
    |> List.map (fun (id, items) -> { ProductId = id; 
Quantity = items |> List.sumBy (fun i -> i.Quantity) })

let addItem item order =
    let items = 
        item :: order.Items
        |> recalculate
    { order with Items = items }

let addItems items order =
    let items = 
        items @ order.Items 
        |> recalculate
        |> List.sortBy (fun i -> i.ProductId)
    { order with Items = items }

Run the changes into FSI and the verify using the asserts.

We could simplify/modify the addItem function to the following:

let addItem item order =
    { order with Items = item :: order.Items |> recalculate }

Removing an item can be easily achieved by filtering out the unwanted item by the productId:

let removeItem productId order =
    let items = 
        order.Items
        |> filter (fun x -> x.ProductId <> productId)
    { order with Items = items }

Again we write some asserts to verify our new function works as expected:

let removeExistingItemAssert = 
    let myEmptyOrder = { Id = 1; Items = [ { ProductId = 1; 
Quantity = 1 } ] }
    let expected = { Id = 1; Items = [] }
    let actual = myEmptyOrder |> removeItem 1 
    actual = expected

let removeNonexistantItemAssert = 
    let myOrder = { Id = 2; Items = [ { ProductId = 1; 
Quantity = 1 } ] }
    let expected = { Id = 2; Items = [ { ProductId = 1; 
Quantity = 1 } ] }
    let actual = myOrder |> removeItem 2 
    actual = expected

Reducing an item quantity is slightly more complex. Firstly we add an item with negative quantity, recalculate the items and then filter out any items with a quantity less than or equal to 0:

let reduceItem productId quantity order =
    let items = 
        { ProductId = productId; Quantity = -quantity } 
:: order.Items
        |> recalculate
        |> filter (fun x -> x.Quantity > 0)
    { order with Items = items }

Again we write some asserts to verify our new function works as expected:

let reduceSomeExistingItemAssert = 
    let myOrder = { Id = 1; Items = [ { ProductId = 1; 
Quantity = 5 } ] }
    let expected = { Id = 1; Items = [ { ProductId = 1; 
Quantity = 2 } ] }
    let actual = myEmptyOrder |> reduceItem 1 3
    actual = expected

let reduceAllExistingItemAssert = 
    let myOrder = { Id = 2; Items = [ { ProductId = 1; 
Quantity = 5 } ] }
    let expected = { Id = 2; Items = [] }
    let actual = myEmptyOrder |> reduceItem 1 5
    actual = expected

let reduceNonexistantItemAssert = 
    let myOrder = { Id = 3; Items = [ { ProductId = 1; 
Quantity = 1 } ] }
    let expected = { Id = 3; Items = [ { ProductId = 1; 
Quantity = 1 } ] }
    let actual = myOrder |> reduceItem 2 5
    actual = expected

let reduceNonexistantItemAssert = 
    let myEmptyOrder = { Id = 4; Items = [] }
    let expected = { Id = 4; Items = [] }
    let actual = myEmptyOrder |> reduceItem 2 5
    actual = expected

Clearing all of the items is really simple:

let clearItems order = 
    { order with Items = [] }

Write some asserts to verify our new function works as expected:

let clearItemsAssert = 
    let myOrder = { Id = 1; Items = [ { ProductId = 1; 
Quantity = 1 } ] }
    let expected = { Id = 1; Items = [] }
    let actual = myOrder |> clearItems
    actual = expected

let clearEmptyItemsAssert = 
    let myEmptyOrder = { Id = 2; Items = [] }
    let expected = { Id = 2; Items = [] }
    let actual = myEmptyOrder |> clearItems
    actual = expected

You should now convert all of the asserts we have written to real tests in OrderTests.fs in the test project.

For more details on the List module, have a look at the Language Reference in the F# Guide (https://docs.microsoft.com/en-us/dotnet/fsharp/language-reference/index).

Summary

In this post we have looked at some of the most useful functions on the List module and we have seen that it is possible to use immutable data structures to provide important business functionality. We are five posts into the series and we haven't mutated anything yet!

In the next post we will look at how to handle streams of data from a json source.

If you have any comments on this series of posts or suggestions for new ones, send me a tweet (@ijrussell) and let me know.

Part 4Table of ContentsPart 6

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Nina DemuthBlog
Blog

7 Positive effects of visualizing the interests of your team

Interests maps unleash hidden potentials and interests, but they also make it clear which topics are not of interest to your colleagues.

Felix KrauseBlog
Blog

Part 2: Detecting Truck Parking Lots on Satellite Images

In the previous blog post, we created an already pretty powerful image segmentation model in order to detect the shape of truck parking lots on satellite images. However, we will now try to run the code on new hardware and get even better as well as more robust results.

Rinat AbdullinRinat AbdullinBlog
Blog

State of Fast Feedback in Data Science Projects

DSML projects can be quite different from the software projects: a lot of R&D in a rapidly evolving landscape, working with data, distributions and probabilities instead of code. However, there is one thing in common: iterative development process matters a lot.

Felix KrauseBlog
Blog

License Plate Detection for Precise Car Distance Estimation

When it comes to advanced driver-assistance systems or self-driving cars, one needs to find a way of estimating the distance to other vehicles on the road.

Felix KrauseBlog
Blog

Creating a Cross-Domain Capable ML Pipeline

As classifying images into categories is a ubiquitous task occurring in various domains, a need for a machine learning pipeline which can accommodate for new categories is easy to justify. In particular, common general requirements are to filter out low-quality (blurred, low contrast etc.) images, and to speed up the learning of new categories if image quality is sufficient. In this blog post we compare several image classification models from the transfer learning perspective.

Felix KrauseBlog
Blog

Boosting speed of scikit-learn regression algorithms

The purpose of this blog post is to investigate the performance and prediction speed behavior of popular regression algorithms, i.e. models that predict numerical values based on a set of input variables.

Aqeel AlazreeBlog
Blog

Database Analysis Report

This report comprehensively analyzes the auto parts sales database. The primary focus is understanding sales trends, identifying high-performing products, Analyzing the most profitable products for the upcoming quarter, and evaluating inventory management efficiency.

Rinat AbdullinRinat AbdullinBlog
Blog

Strategic Impact of Large Language Models

This blog discusses the rapid advancements in large language models, particularly highlighting the impact of OpenAI's GPT models.

Aqeel AlazreeBlog
Blog

Part 1: Data Analysis with ChatGPT

In this new blog series we will give you an overview of how to analyze and visualize data, create code manually and how to make ChatGPT work effectively. Part 1 deals with the following: In the data-driven era, businesses and organizations are constantly seeking ways to extract meaningful insights from their data. One powerful tool that can facilitate this process is ChatGPT, a state-of-the-art natural language processing model developed by OpenAI. In Part 1 pf this blog, we'll explore the proper usage of data analysis with ChatGPT and how it can help you make the most of your data.

Matus ZilinskyBlog
Blog

Creating a Social Media Posts Generator Website with ChatGPT

Using the GPT-3-turbo and DALL-E models in Node.js to create a social post generator for a fictional product can be really helpful. The author uses ChatGPT to create an API that utilizes the openai library for Node.js., a Vue component with an input for the title and message of the post. This article provides step-by-step instructions for setting up the project and includes links to the code repository.

Jörg EgretzbergerJörg EgretzbergerBlog
Blog

8 tips for developing AI assistants

AI assistants for businesses are hype, and many teams were already eagerly and enthusiastically working on their implementation. Unfortunately, however, we have seen that many teams we have observed in Europe and the US have failed at the task. Read about our 8 most valuable tips, so that you will succeed.

Rinat AbdullinRinat AbdullinBlog
Blog

5 Inconvenient Questions when hiring an AI company

This article discusses five questions you should ask when buying an AI. These questions are inconvenient for providers of AI products, but they are necessary to ensure that you are getting the best product for your needs. The article also discusses the importance of testing the AI system on your own data to see how it performs.

Aqeel AlazreeBlog
Blog

Part 4: Save Time and Analyze the Database File

ChatGPT-4 enables you to analyze database contents with just two simple steps (copy and paste), facilitating well-informed decision-making.

Rinat AbdullinRinat AbdullinBlog
Blog

Announcing Domain-Driven Design Exercises

Interested in Domain Driven Design? Then this DDD exercise is perfect for you!

TIMETOACT
Service
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Service

Application Modernization

Application Modernization focuses on modernizing existing applications. The key to success in Application Modernization is the strategy and selection of projects.

TIMETOACT
Technologie
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Technologie

Confluence from Atlassian

Create, organize, and collaborate on tasks - all in a single place. Confluence is a workspace for teams and organizations where you can store your documentation and collaboratively develop and share knowledge. Dynamic pages give your team a place to create, capture, and collaborate around projects or idea development.

TIMETOACT
News
News

TIMETOACT is Mendix training partner

We are convinced of Mendix's no-code/low-code platforms and are therefore not only a Mendix partner, but also a Mendix training partner.

TIMETOACT
Technologie
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Technologie

Jira Service Management from Atlassian

Enable developers, operators, and other teams from different departments to collaborate and improve their service management responsiveness. Respond quickly to incidents, requests, and changes, and provide your customers with an optimized service experience.

TIMETOACT
Service
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Service

Application Development

Application Development refers to the process of modifying, designing and/or developing one or more applications. Gaps in the software landscape can be closed by tailoring applications individually to the customer.

TIMETOACT
Service
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Service

Requirement Engineering

Requirement Engineering, also known as Requirements Analysis, is a central component of the Software Development process. In this process, the requirements for the system to be developed are defined using a systematic procedure.

TIMETOACT
Technologie
Technologie

Advice around Mendix

Develop your solutions quickly and independently in low-code with the leading technology vendor. Use the Mendix toolkit and model your applications with visual elements.

TIMETOACT
Technologie
Technologie

Our service offer for Mendix

The Dutch software manufacturer gives us the possibilities to create platform-independent low/no-code solutions for you with its products. In addition, we offer a wide range of services related to Mendix and are available to you from conceptual design to hosting and operation of your new solution.

TIMETOACT
Referenz
Referenz

Interactive online portal identifies suitable employees

TIMETOACT digitizes several test procedures for KI.TEST to determine professional intelligence and personality.

TIMETOACT
Service
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Service

Fullstack Development

The trend in Software Development is towards Full-Stack Development. Full-stack developers are programmers who work in both frontend and backend development and thus have competencies in the areas of databases, servers, systems and clients.

TIMETOACT
Technologie
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Technologie

Bamboo, Bitbucket, Sourcetree

Continuous Integration and a Continuous Delivery Pipeline with Bamboo, Bitbucket and Sourcetree. We can help you with our years of experience as a user as well as a solution partner of Atlassian products in many areas.

TIMETOACT
Referenz
Referenz

The digital customer file with IBM Content Manager

The prefabricated house specialist SchwörerHaus KG has relied on IBM technology for many years to set up a digital customer file.

TIMETOACT
Technologie
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Technologie

Containerisation with Open Source

Containerization is the next stage of virtualization and provides secure and easy compartmentalization of individual applications. The process of deploying an app has been simplified many times in recent years.

TIMETOACT
News
News

HCL license and price adjustments as of 8.8.2024

HCL Software has informed us that the license and maintenance prices for the various product categories will be increased as of 8.8.2024.

Service
Service

Cloud Transformation & Container Technologies

Public, private or hybrid? We can help you develop your cloud strategy so you can take full advantage of the technology.

TIMETOACT
Technologie
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Technologie

Incident communication management

Statuspage allows you to keep track of the status of individual system-relevant components as well as a history of past incidents. Our self-created solution also allows you to connect various monitoring tools and query them in specific cycles. The component failure automatically generates an e-mail to your ticket system.

TIMETOACT
Referenz
Referenz

TIMETOACT implements integrated insurance software

Less than one year from project start to system implementation: TIMETOACT developed the integrated, browser-based insurance software "HERMES" for the VOV D&O insurance association. The cross-departmental individual software completely covers all core processes of the insurance company. Users particularly appreciate the intuitive user interface and the high performance of HERMES.

Service
Service

Application Integration & Process Automation

Digitizing and improving business processes and responding agilely to change – more and more companies are facing these kind of challenges. This makes it all the more important to take new business opportunities through integrated and optimized processes based on intelligent, digitally networked systems.