Machine Learning with Golang: Linear Regression and Classification

This blog teaches you how to use Golang and Gonum to implement linear regression and classification models for machine learning tasks.

Table of Contents

1. Introduction

Machine learning is a branch of artificial intelligence that enables computers to learn from data and make predictions or decisions. Machine learning can be applied to various domains, such as natural language processing, computer vision, recommender systems, and more.

One of the most common tasks in machine learning is regression, which involves predicting a continuous value based on some input features. For example, you might want to predict the price of a house based on its size, location, and amenities. Another common task is classification, which involves predicting a discrete label based on some input features. For example, you might want to classify an email as spam or not based on its content, sender, and subject.

In this blog, you will learn how to use Golang and Gonum to implement linear regression and classification models for machine learning tasks. Golang is a fast, simple, and reliable programming language that is widely used for web development, system programming, and concurrency. Gonum is a set of packages that provide numerical and scientific computing functionalities for Golang, such as matrix manipulation, statistics, optimization, and more.

By the end of this blog, you will be able to:

Understand the basic concepts and assumptions of linear regression and classification models
Use Golang and Gonum to load, manipulate, and visualize data
Use Golang and Gonum to fit and evaluate linear regression and classification models
Compare and contrast different classification models, such as logistic regression and k-nearest neighbors

Are you ready to dive into machine learning with Golang and Gonum? Let’s get started!

2. Golang and Gonum: A Brief Overview

In this section, you will learn about the basics of Golang and Gonum, two powerful tools that you will use to implement machine learning models. You will also learn how to install and set up Golang and Gonum on your system, and how to write and run a simple Golang program.

Golang, also known as Go, is a programming language that was developed by Google in 2009. It is a compiled, statically typed, and concurrent language that aims to be simple, fast, and reliable. Golang has many features that make it suitable for web development, system programming, and concurrency, such as:

A syntax that is easy to read and write
A built-in support for concurrency using goroutines and channels
A rich set of standard libraries and tools
A cross-platform compatibility and deployment
A garbage collection and memory management
A powerful interface system and type inference

To install Golang on your system, you can follow the official instructions here. You will need to download and install the appropriate binary package for your operating system, and set up the environment variables GOROOT and GOPATH. You can also use an IDE or a code editor of your choice to write and edit Golang code, such as Visual Studio Code, Atom, or Sublime Text.

To write a simple Golang program, you can create a file with the extension .go, and use the package and import keywords to declare the package name and the libraries that you want to use. The main function is the entry point of the program, and the fmt library provides functions for input and output. For example, the following code prints “Hello, world!” to the standard output:

package main

import "fmt"

func main() {
    fmt.Println("Hello, world!")
}

To run the program, you can use the go run command, followed by the name of the file. For example, if the file is named hello.go, you can run it as follows:

$ go run hello.go
Hello, world!

Gonum is a set of packages that provide numerical and scientific computing functionalities for Golang. It is an open-source project that is inspired by other scientific computing libraries, such as NumPy, SciPy, and MATLAB. Gonum offers many features that make it useful for machine learning, such as:

A matrix package that supports various types of matrices, such as dense, sparse, symmetric, and triangular
A stat package that provides functions for descriptive and inferential statistics, such as mean, variance, correlation, hypothesis testing, and more
An optimize package that implements various optimization algorithms, such as gradient descent, Newton’s method, and quasi-Newton methods
A plot package that enables data visualization using charts, graphs, and histograms
A blas and lapack package that provide bindings to the Basic Linear Algebra Subprograms (BLAS) and Linear Algebra Package (LAPACK) libraries, which are widely used for high-performance linear algebra computations

To install Gonum on your system, you can use the go get command, followed by the name of the package that you want to use. For example, to install the matrix package, you can run the following command:

$ go get gonum.org/v1/gonum/mat

To use Gonum in your Golang program, you can import the package that you need using the import keyword, and use the package name as a prefix to access its functions and types. For example, the following code creates a 2×3 matrix using the mat package, and prints its dimensions and elements to the standard output:

package main

import (
    "fmt"
    "gonum.org/v1/gonum/mat"
)

func main() {
    // Create a 2x3 matrix with the given data
    data := []float64{1, 2, 3, 4, 5, 6}
    A := mat.NewDense(2, 3, data)

    // Print the dimensions and the elements of the matrix
    r, c := A.Dims()
    fmt.Printf("A is a %d x %d matrix\n", r, c)
    fmt.Println(A)
}

To run the program, you can use the go run command, followed by the name of the file. For example, if the file is named matrix.go, you can run it as follows:

$ go run matrix.go
A is a 2 x 3 matrix
⎡1  2  3⎤
⎣4  5  6⎦

Now that you have a basic understanding of Golang and Gonum, you are ready to use them to implement machine learning models. In the next section, you will learn how to perform linear regression with Golang and Gonum.

2.1. What is Golang?

Golang, also known as Go, is a programming language that was developed by Google in 2009. It is a compiled, statically typed, and concurrent language that aims to be simple, fast, and reliable. Golang has many features that make it suitable for web development, system programming, and concurrency, such as:

A syntax that is easy to read and write
A built-in support for concurrency using goroutines and channels
A rich set of standard libraries and tools
A cross-platform compatibility and deployment
A garbage collection and memory management
A powerful interface system and type inference

package main

import "fmt"

func main() {
    fmt.Println("Hello, world!")
}

To run the program, you can use the go run command, followed by the name of the file. For example, if the file is named hello.go, you can run it as follows:

$ go run hello.go
Hello, world!

In this section, you learned about the basics of Golang, such as its syntax, features, installation, and usage. In the next section, you will learn about Gonum, a set of packages that provide numerical and scientific computing functionalities for Golang.

2.2. What is Gonum?

Gonum is a set of packages that provide numerical and scientific computing functionalities for Golang. It is an open-source project that is inspired by other scientific computing libraries, such as NumPy, SciPy, and MATLAB. Gonum offers many features that make it useful for machine learning, such as:

A matrix package that supports various types of matrices, such as dense, sparse, symmetric, and triangular
A stat package that provides functions for descriptive and inferential statistics, such as mean, variance, correlation, hypothesis testing, and more
An optimize package that implements various optimization algorithms, such as gradient descent, Newton’s method, and quasi-Newton methods
A plot package that enables data visualization using charts, graphs, and histograms
A blas and lapack package that provide bindings to the Basic Linear Algebra Subprograms (BLAS) and Linear Algebra Package (LAPACK) libraries, which are widely used for high-performance linear algebra computations

$ go get gonum.org/v1/gonum/mat

package main

import (
    "fmt"
    "gonum.org/v1/gonum/mat"
)

func main() {
    // Create a 2x3 matrix with the given data
    data := []float64{1, 2, 3, 4, 5, 6}
    A := mat.NewDense(2, 3, data)

    // Print the dimensions and the elements of the matrix
    r, c := A.Dims()
    fmt.Printf("A is a %d x %d matrix\n", r, c)
    fmt.Println(A)
}

To run the program, you can use the go run command, followed by the name of the file. For example, if the file is named matrix.go, you can run it as follows:

$ go run matrix.go
A is a 2 x 3 matrix
⎡1  2  3⎤
⎣4  5  6⎦

In this section, you learned about the basics of Gonum, such as its features, installation, and usage. In the next section, you will learn how to perform linear regression with Golang and Gonum.

3. Linear Regression with Golang and Gonum

Linear regression is one of the most basic and widely used machine learning models. It is a supervised learning technique that aims to find a linear relationship between a set of input features and a continuous output variable. Linear regression can be used for various purposes, such as predicting future values, estimating trends, or finding the effect of a variable on another.

In this section, you will learn how to perform linear regression with Golang and Gonum. You will follow these steps:

Understand the basic concepts and assumptions of the linear regression model
Load, manipulate, and visualize a sample dataset using Golang and Gonum
Fit and evaluate a linear regression model using Golang and Gonum

By the end of this section, you will be able to use Golang and Gonum to perform linear regression on any dataset of your choice. You will also learn how to interpret the results and assess the quality of the model.

Are you ready to learn linear regression with Golang and Gonum? Let’s begin!

3.1. The Linear Regression Model

The linear regression model is a mathematical equation that describes the relationship between a dependent variable and one or more independent variables. The dependent variable, also called the response or the outcome, is the variable that you want to predict or explain. The independent variables, also called the predictors or the features, are the variables that affect or influence the dependent variable.

The linear regression model assumes that the dependent variable is a linear function of the independent variables, plus some random error. The error term represents the variation in the dependent variable that is not explained by the independent variables. The linear regression model can be written as follows:

$$y = \beta_0 + \beta_1 x_1 + \beta_2 x_2 + … + \beta_n x_n + \epsilon$$

where:

$y$ is the dependent variable
$x_1, x_2, …, x_n$ are the independent variables
$\beta_0, \beta_1, …, \beta_n$ are the coefficients or the parameters of the model
$\epsilon$ is the error term

The coefficients of the model represent the slope or the effect of each independent variable on the dependent variable. The intercept or the constant term $\beta_0$ represents the value of the dependent variable when all the independent variables are zero. The error term $\epsilon$ represents the variation in the dependent variable that is not explained by the model.

The goal of linear regression is to find the best values for the coefficients that minimize the sum of squared errors (SSE) between the observed and the predicted values of the dependent variable. The SSE is a measure of how well the model fits the data. The lower the SSE, the better the fit. The SSE can be calculated as follows:

$$SSE = \sum_{i=1}^n (y_i – \hat{y}_i)^2$$

where:

$n$ is the number of observations
$y_i$ is the observed value of the dependent variable for the $i$-th observation
$\hat{y}_i$ is the predicted value of the dependent variable for the $i$-th observation

There are different methods to find the best values for the coefficients, such as the ordinary least squares (OLS) method, the gradient descent method, or the normal equation method. In this tutorial, you will use the optimize package from Gonum to implement the gradient descent method, which is an iterative algorithm that updates the coefficients by moving in the direction of the steepest descent of the SSE function.

In this section, you learned about the basic concepts and assumptions of the linear regression model. In the next section, you will learn how to load, manipulate, and visualize a sample dataset using Golang and Gonum.

3.2. Data Preparation and Visualization

Before you can fit and evaluate a linear regression model, you need to prepare and visualize the data that you will use. Data preparation involves loading, cleaning, transforming, and splitting the data into training and testing sets. Data visualization involves exploring and understanding the data using charts, graphs, and histograms.

In this section, you will learn how to perform data preparation and visualization with Golang and Gonum. You will use a sample dataset that contains information about the advertising budget and sales of a product. The dataset has four columns: TV, Radio, Newspaper, and Sales. The first three columns represent the advertising budget (in thousands of dollars) spent on different media channels, and the last column represents the sales (in thousands of units) of the product. The dataset has 200 rows, each corresponding to a different market.

You can download the dataset from here. You will need to save the file as advertising.csv in your working directory. Alternatively, you can use the download_file function from the net/http package to download the file programmatically.

To load the dataset into your Golang program, you will use the csv package, which provides functions for reading and writing comma-separated values (CSV) files. You will also use the strconv package, which provides functions for converting strings to other data types, such as floats. You will store the data as a slice of slices of floats, where each slice represents a row of the dataset.

To clean the dataset, you will check for any missing or invalid values, and remove or replace them as needed. You will also remove the first column of the dataset, which contains the row numbers, as it is not relevant for the analysis.

To transform the dataset, you will convert the slice of slices of floats into a matrix, using the mat package from Gonum. A matrix is a two-dimensional array of numbers that can be manipulated using various operations, such as addition, multiplication, inversion, and more. You will also separate the independent variables (TV, Radio, and Newspaper) from the dependent variable (Sales), and store them as two matrices: X and y.

To split the dataset, you will divide the data into two subsets: a training set and a testing set. The training set is used to fit the model, and the testing set is used to evaluate the model. You will use a 80/20 split, meaning that 80% of the data will be used for training, and 20% of the data will be used for testing. You will also use a random seed to ensure that the split is reproducible.

To visualize the dataset, you will use the plot package from Gonum, which provides functions for creating and customizing various types of plots, such as scatter plots, line plots, bar plots, and more. You will create scatter plots to show the relationship between each independent variable and the dependent variable, and histograms to show the distribution of each variable.

The following code shows how to perform data preparation and visualization with Golang and Gonum:

package main

import (
"encoding/csv"
"fmt"
"gonum.org/v1/gonum/mat"
"gonum.org/v1/gonum/stat"
"gonum.org/v1/plot"
"gonum.org/v1/plot/plotter"
"gonum.org/v1/plot/vg"
"io"
"log"
"math/rand"
"net/http"
"os"
"strconv"
)

// downloadFile downloads a file from a given URL and saves it to a given filename
func downloadFile(url string, filename string) error {
// Get the data from the URL
resp, err := http.Get(url)
if err != nil {
return err
}
defer resp.Body.Close()

// Create the file
out, err := os.Create(filename)
if err != nil {
return err
}
defer out.Close()

// Write the data to the file
_, err = io.Copy(out, resp.Body)
if ...

3.3. Model Fitting and Evaluation

After you have prepared and visualized the data, you can fit and evaluate a linear regression model using Golang and Gonum. Fitting a model involves finding the best values for the coefficients that minimize the sum of squared errors (SSE) between the observed and the predicted values of the dependent variable. Evaluating a model involves measuring how well the model fits the data and how well it generalizes to new data.

In this section, you will learn how to fit and evaluate a linear regression model with Golang and Gonum. You will follow these steps:

Define a function that calculates the SSE given the coefficients and the data
Use the optimize package from Gonum to find the optimal coefficients using the gradient descent method
Use the stat package from Gonum to calculate the coefficient of determination (R-squared) and the root mean squared error (RMSE) as metrics of model fit and performance
Use the plot package from Gonum to create a scatter plot of the observed vs. the predicted values of the dependent variable

By the end of this section, you will be able to fit and evaluate a linear regression model using Golang and Gonum. You will also be able to interpret the results and assess the quality of the model.

Are you ready to fit and evaluate a linear regression model with Golang and Gonum? Let’s begin!

4. Classification with Golang and Gonum

Classification is another common and important machine learning task. It is a supervised learning technique that aims to find a rule or a function that assigns a discrete label to a set of input features. Classification can be used for various purposes, such as identifying objects, detecting spam, diagnosing diseases, and more.

In this section, you will learn how to perform classification with Golang and Gonum. You will use two different classification models: logistic regression and k-nearest neighbors. Logistic regression is a linear model that predicts the probability of a binary outcome based on some input features. K-nearest neighbors is a non-parametric model that predicts the label of a new instance based on the labels of its closest neighbors in the feature space.

You will follow these steps:

Understand the basic concepts and assumptions of the logistic regression and k-nearest neighbors models
Load, manipulate, and visualize a sample dataset using Golang and Gonum
Fit and evaluate the logistic regression and k-nearest neighbors models using Golang and Gonum
Compare and contrast the two models and select the best one for the given dataset

By the end of this section, you will be able to use Golang and Gonum to perform classification on any dataset of your choice. You will also learn how to interpret the results and assess the quality of the models.

Are you ready to learn classification with Golang and Gonum? Let’s begin!

4.1. The Classification Problem

Classification is a machine learning task that aims to find a rule or a function that assigns a discrete label to a set of input features. For example, you might want to classify an email as spam or not based on its content, sender, and subject. Classification can be used for various purposes, such as identifying objects, detecting spam, diagnosing diseases, and more.

There are different types of classification problems, depending on the number and nature of the labels. The most common types are:

Binary classification: The label has only two possible values, such as yes or no, true or false, positive or negative, etc.
Multi-class classification: The label has more than two possible values, such as red, green, or blue, cat, dog, or bird, etc.
Multi-label classification: The label can have more than one value at the same time, such as comedy and romance, action and thriller, etc.

In this tutorial, you will focus on binary classification, which is the simplest and most common type of classification problem. You will use a sample dataset that contains information about the admission status of students based on their exam scores and previous education. The dataset has four columns: Exam 1, Exam 2, Education, and Admission. The first three columns represent the scores of the students on two exams and their level of education (1 for high school, 2 for college, 3 for master’s degree, and 4 for PhD degree). The last column represents the admission status of the students (0 for rejected, 1 for accepted). The dataset has 100 rows, each corresponding to a different student.

You can download the dataset from here. You will need to save the file as admission.csv in your working directory. Alternatively, you can use the download_file function from the net/http package to download the file programmatically.

In this section, you learned about the basic concepts and types of the classification problem. In the next section, you will learn how to load, manipulate, and visualize the dataset using Golang and Gonum.

4.2. Logistic Regression with Golang and Gonum

Logistic regression is a linear model that predicts the probability of a binary outcome based on some input features. For example, you might want to predict the admission status of a student based on their exam scores and previous education. Logistic regression can be used for various purposes, such as detecting spam, diagnosing diseases, and more.

In this section, you will learn how to fit and evaluate a logistic regression model with Golang and Gonum. You will use the same dataset that you used for the classification problem, which contains information about the admission status of students based on their exam scores and previous education. You will follow these steps:

Define a function that calculates the logistic function, which is the inverse of the logit function, and returns the probability of a positive outcome given the input features and the coefficients
Define a function that calculates the log-likelihood function, which is the logarithm of the likelihood function, and returns the negative log-likelihood given the data and the coefficients
Use the optimize package from Gonum to find the optimal coefficients that maximize the log-likelihood function using the gradient descent method
Use the stat package from Gonum to calculate the accuracy, precision, recall, and F1-score as metrics of model performance
Use the plot package from Gonum to create a scatter plot of the exam scores and the admission status, and a contour plot of the decision boundary

By the end of this section, you will be able to fit and evaluate a logistic regression model using Golang and Gonum. You will also be able to interpret the results and assess the quality of the model.

Are you ready to learn logistic regression with Golang and Gonum? Let’s begin!

4.3. K-Nearest Neighbors with Golang and Gonum

K-nearest neighbors (KNN) is a non-parametric model that predicts the label of a new instance based on the labels of its closest neighbors in the feature space. For example, you might want to predict the admission status of a student based on their exam scores and previous education, by looking at the admission status of the students who have similar exam scores and previous education. KNN can be used for various purposes, such as identifying objects, detecting spam, diagnosing diseases, and more.

In this section, you will learn how to fit and evaluate a KNN model with Golang and Gonum. You will use the same dataset that you used for the classification problem, which contains information about the admission status of students based on their exam scores and previous education. You will follow these steps:

Define a function that calculates the Euclidean distance between two points in the feature space
Define a function that finds the k nearest neighbors of a given point in the dataset, and returns their labels and distances
Define a function that predicts the label of a given point based on the majority vote of its k nearest neighbors
Use the stat package from Gonum to calculate the accuracy, precision, recall, and F1-score as metrics of model performance
Use the plot package from Gonum to create a scatter plot of the exam scores and the admission status, and a contour plot of the decision boundary

By the end of this section, you will be able to fit and evaluate a KNN model using Golang and Gonum. You will also be able to interpret the results and assess the quality of the model.

Are you ready to learn KNN with Golang and Gonum? Let’s begin!

5. Conclusion

In this blog, you learned how to use Golang and Gonum to implement linear regression and classification models for machine learning tasks. You learned how to:

Understand the basic concepts and assumptions of linear regression and classification models
Use Golang and Gonum to load, manipulate, and visualize data
Use Golang and Gonum to fit and evaluate linear regression and classification models
Compare and contrast different classification models, such as logistic regression and k-nearest neighbors

You also learned how to use Golang and Gonum to perform various numerical and scientific computing operations, such as matrix manipulation, statistics, optimization, and plotting. You saw how Golang and Gonum can be powerful and useful tools for machine learning, as they offer simplicity, speed, reliability, and functionality.

We hope you enjoyed this blog and learned something new and interesting. If you want to learn more about Golang and Gonum, you can check out their official websites and documentation:

You can also find the complete code and data for this blog on GitHub: