Highlighted arcs
PlotAPI Chord supports highlighting arcs and chords by default as a way to highlight or group elements.
Technical writing on data science, visualization, and artificial intelligence.
A collection of technical notebooks from my books and publications, presented in chronological order.
Pareto-Front Visualisation with PlotAPI
Dominance relations can be clearly visualised when working in a two-objective space. Let's do this with some arbitrary solutions. We'll use the `ParetoFront` visualisation from PlotAPI.
Node sorting
The nodes in a PlotAPI Sankey diagram can be sorted (e.g. alphabetically) within their columns.
Goal Rush
Everything you need to create beautiful, engaging, and interactive Goal Rush visualizations.
Details and thumbnails
The popup supports displaying additional details and thumbnails. This makes exploration even better with PlotAPI Chord.
Troubleshooting
Frequently asked questions and their answers.
Node start index
BarFight allows you to scroll up and down the number of bars, and set a starting point.
Split Chord
Everything you need to create beautiful, engaging, and interactive Split Chord visualizations.
Chord
Everything you need to create beautiful, engaging, and interactive Chord visualizations.
Pulled out arcs
PlotAPI Chord supports pulling out arcs as a way to highlight or group elements.
Showcase
Explore the different demonstrations of PlotAPI in this showcase.
WebGL or canvas
We may want to switch between the WebGL or D3.js canvas animations depending on our requirements. These have been configured to look the same.
Vertical pipes
Let's take a look at the vertical property of the Terminus diagram. Using it can give the impression that the particles are "falling" through the pipes.
Terminus
Everything you need to create beautiful, engaging, and interactive Terminus visualizations.
Stats styling
The stats panel that appears in a default Terminus diagram can be adjusted or hidden entirely. This gives us even more control over the presentation of our Terminus diagram.
Pixels per unit
Let's look at a unit-based approach to handling data with very high values or many sources and targets.
Pixels per percentage
Let's look at a percentage-based approach to handling data with very high values or many sources and targets.
Pixel size
The Terminus pixel size can be modified to improve the presentation of our diagram, this works as intended in both WebGL and D3.js canvas mode.
Pixel colors
The pixel color scheme can be set to one of many beautiful presets, or even a customized by supplying a list of colors (HEX, RGB, etc.).
Pipe styling
The Terminus pipes can be modified to improve the presentation of the diagram, giving control over the spacing and slope positions.
Pipe colors
The pipe color and opacity can be changed to improve the presentation of our Terminus diagram.
Pipe bundling
Let's take a look at the bundling parameter. This will group together the input, giving the impression that they are entering through the same pipe.
Pipe alignment
Pipes are aligned to the top-left by default, however, changing their alignment can often significantly improve the presentation of a Terminus diagram.
Layout properties
The title, width, height, margin, and position can be changed with these layout properties.
Dynamic title count
The Terminus diagram supports setting the title and also including the dynamic count of pixels currently on their journey.
Bar styling
The bars that appear at each terminus can be modified or hidden entirely. This gives us even more control over the presentation of our Terminus diagram.
Animation
The Terminus animation can be modified to improve the presentation of our diagram, giving us control over a pixel's journey duration, how many pixels are dispatched at once, and the delay before the animation begins.
Text formatting
The many text formatting options include multi-line label wrapping, size, and color, and much more.
Sankey
Everything you need to create beautiful, engaging, and interactive Sankey visualizations.
Reverse gradients
Depending on our data, it may make more sense to reverse the gradient directions used for colouring the links.
Popup layout and format
The popup supports many customizations, including the text format, width, and disabling it entirely.
Node styling
The nodes in a PlotAPI Sankey diagram can be styled with many different parameters.
Node and link colors
The color scheme can be set to one of many beautiful presets, or even a customized by supplying a list of colors (HEX, RGB, etc.). Overrides also allow colouring specific nodes.
Node aligment
PlotAPI Sankey supports different layouts using the specification of node alignment.
Linked data table
PlotAPI Chord supports a linked data table. This means as you hover over arcs and chords in the Chord diagram, a data table will be filtering in real-time to show more information.
Link opacity
The opacity of the links in the Sankey diagram can be changed. The opacity will change on mouse-over to highlight the selected connections.
Link background color
Until we mouseover a Sankey link, what we actually see is the background color. By default, this background colour is set to be the same as the link foreground color, as defined by the `colors` parameter, which by default is `"gradient"`.
Layout properties
The title, width, height, margin, position, background color, and border can be changed with these layout properties.
Flat link colors
PlotAPI Sankey uses gradients to colour the links by default. This can be changed to use flat colors instead.
Animation
PlotAPI Sankey supports animations - both for interactions and for nice introductions to your visualisation.
Temporal format
Temporal formats can be used for order values and presentation.
Pie Fight
Everything you need to create beautiful, engaging, and interactive Pie Chart Race visualizations.
Node icons and colors
Let's take a look at how we can change the colours and icons for nodes in our Pie Fight diagram. These configurations override the color scheme.
Node text and values
Let's take a look at how we can change the presentation of the text and values in our Pie Fight diagram.
Theme colors
The color scheme can be set to one of many beautiful presets, or even a customized by supplying a list of colors (HEX, RGB, etc.).
Event mode
Event information can be displayed using different modes, these include paused, running, and interactive.
Event information
Let's take a look at how we can display event content during our visualisation at different times. This can be useful for displaying additional information or images that are relevant to specific events.
Pareto Front
Everything you need to create beautiful, engaging, and interactive Pareto Front visualizations.
Temporal format
Temporal formats can be used for order values and presentation.
Node icons and colors
Let's take a look at how we can change the colours and icons for nodes in our Line Fight diagram. These configurations override the color scheme.
Node colors
The color scheme can be set to one of many beautiful presets, or even a customized by supplying a list of colors (HEX, RGB, etc.).
Line Fight
Everything you need to create beautiful, engaging, and interactive Line Chart Race visualizations.
Event mode
Event information can be displayed using different modes, these include paused, running, and interactive.
Event information
Let's take a look at how we can display event content during our visualisation at different times. This can be useful for displaying additional information or images that are relevant to specific events.
Popup layout and format
The popup supports many customizations, including the text format, width, and disabling it entirely.
Heat Map
Everything you need to create beautiful, engaging, and interactive Heat Map visualizations.
Ticks
PlotAPI Chord supports displaying and formatting ticks around the diagram.
Text formatting
The many text formatting options include multi-line label wrapping, size, and color.
Reverse gradients
Depending on our data, it may make more sense to reverse the gradient directions used for colouring the ribbons.
Radius scales
The inner and outer radius scales of the Chord diagram can be adjusted. This will change the thickness of the arcs.
Popup layout and format
The popup supports many customizations, including the text format, width, and disabling it entirely.
Linked data table
PlotAPI Chord supports a linked data table. This means as you hover over arcs and chords in the Chord diagram, a data table will be filtering in real-time to show more information.
Layout properties
The title, width, margin, position, and rotation can be changed with these layout properties.
Equal-sized arcs
The arcs of a Chord diagram can be customised to be of equal size, regardless of the value of their relationships.
Directed chords
The default behaviour of PlotAPI chord is to represent both sides of a relationship with a single chord. However, it may be more suitable to use two different chords with arrows to indicate the dimension of a relationship.
Label styles
Different label styles are more suitable for different chord diagrams, and choosing the right one will improve the look of a PlotAPI Chord diagram.
Hidden diagonals
By default, Chord displays occurrences where a category is not related to another category. These values appear in the diagonal of the matrix. It may be desirable to hide (but not remove) these values.
Chord opacity
The opacity of the inner section of the Chord diagram, or the _chords_, can be changed. The opacity will change on `mouseover` to highlight the selected connections.
Chord colors
The color scheme can be set to one of many beautiful presets, or even a customized by supplying a list of colors (HEX, RGB, etc.).
Asymmetric relationships
By turning symmetric mode off, each end of a chord can be of different size, and it also changes the information presented in the popup.
Arc padding
Adjusting padding may not sound exciting, but in this case, it can have some interesting effects.
Arc numbers
PlotAPI Chord supports displaying the quantity associated with each arc as a label.
Animation
PlotAPI Chord supports animations - both for looping and for nice introductions to your visualisation.
Temporal format
Temporal formats can be used for order values and presentation.
Node visibility
Let's take a look at how we can change the number of bars that are visible by default.
Node icons and colors
Let's take a look at how we can change the colours and icons for nodes in our Bar Fight diagram. These configurations override the color scheme.
Node colors
The color scheme can be set to one of many beautiful presets, or even a customized by supplying a list of colors (HEX, RGB, etc.).
Layout properties
The title, width, height, margin, position, background color, and border can be changed with these layout properties.
Event mode
Event information can be displayed using different modes, these include paused, running, and interactive.
Event information
Let's take a look at how we can display event content during our visualisation at different times. This can be useful for displaying additional information or images that are relevant to specific events.
Bar Fight
Everything you need to create beautiful, engaging, and interactive Bar Chart Race visualizations.
Animation
The BarFight animation can be modified to improve the presentation of our diagram, giving us control over animations durations, delays, and behavior.
Visualizations
Easily turn your data into engaging visualizations with PlotAPI's friendly interface — with or without code.
Getting started
Welcome to the PlotAPI documentation. It contains all the information you need to get started using PlotAPI.
Uploading to cloud
PlotAPI supports uploading visualizations to the cloud, where they can be embedded and shared privately, publicly, or within your Team.
Saving locally
PlotAPI supports saving visualizations locally. This includes saving to PNG, PDF, SVG, animated MP4, and interactive HTML.
REST API
PlotAPI supports more than just Python and Rust. With the REST API, you can create beautiful visualizations from any language.
Julia
How to use PlotAPI with Julia.
JavaScript
How to use PlotAPI with JavaScript.
HTTP
How to use PlotAPI with HTTP.
License activation
Activate your license and get access to Pro and Business features with the instructions below.
Installation
Get access to the API with the instructions below. Libraries available for Python and Rust. For everything else, there's the REST API.
Displaying in notebook
PlotAPI supports displaying interactive visualizations directly within Jupyter Notebooks. This includes Jupyter Lab, Jupyter Notebook Classic, Google Colab, and more.
Desktop Browsers Market Share with Pie Fight
In this notebook we're going to use PlotAPI Pie Fight to visualise desktop browser market share over time. We"ll use Python, but PlotAPI can be used from any programming language.
Pokemon Trends with Bar Fight
In this notebook we're going to use PlotAPI Bar Fight to visualise Pokémon search trends over time. We"ll use Python, but PlotAPI can be used from any programming language.
Pokemon Types with Chord
In this notebook we're going to use PlotAPI Chord to visualise the co-occurrences between Pokémon types. We"ll use Python, but PlotAPI can be used from any programming language.
Animal Crossing Villager Species and Personality
In this notebook we're going to use PlotAPI Chord to visualise the co-occurrences between the species and personality of Animal Crossing villagers. We"ll use Python, but PlotAPI can be used from any programming language.
Apple 2021 Q3 Results with Sankey
In this notebook we're going to use PlotAPI Sankey to visualise some of the Apple's filings for the third quarter of 2021.
Apple 2021 Q4 Results with Sankey
In this notebook we're going to use PlotAPI Sankey to visualise some of the Apple's filings for the fourth quarter of 2021.
League of Legends Classes
In this notebook we're going to use PlotAPI Chord to visualise the co-occurrences between League of Legends classes. We"ll use Python, but PlotAPI can be used from any programming language.
Pokemon Types with Heat Map
In this notebook we're going to use PlotAPI Heat Map to visualise the co-occurrences between Pokémon types. We"ll use Python, but PlotAPI can be used from any programming language.
Animal Crossing Villager Style
In this notebook we're going to use PlotAPI Chord to visualise the style co-occurrences of Animal Crossing villagers. We"ll use Python, but PlotAPI can be used from any programming language.
IMDb Top 1000 with Chord
In this notebook we're going to use PlotAPI Chord to visualise the co-occurrences of genres in the IMDb "Top 1000" (Sorted by IMDb Rating Descending). We"ll use Python, but PlotAPI can be used from any programming language.
IMDb Top 1000 with Heat Map
In this notebook we're going to use PlotAPI Heat Map to visualise the co-occurrences of genres in the IMDb "Top 1000" (Sorted by IMDb Rating Descending). We"ll use Python, but PlotAPI can be used from any programming language.
Degree Classification by Graduate Gender with Terminus
In this notebook we're going to use PlotAPI Terminus to visualise how degree classes vary by graduates' gender. We"ll use Python, but PlotAPI can be used from any programming language.
Degree Classification by Graduate Ethnicity with Terminus
In this notebook we're going to use PlotAPI Terminus to visualise how degree classes vary by graduates' ethnicity. We"ll use Python, but PlotAPI can be used from any programming language.
Global Email Spam with Terminus
In this notebook we're going to use PlotAPI Terminus to visualise the average daily email & spam volume for August 2021.
DataFrame to Samples Dict
Data often needs wrangling prior to visualisation. Let's take a look at how we can transform our data from a DataFrame to a Dictionary for PlotAPI.
Generating Background Colours
Choosing the right colours can often make all the difference when adding finishing touches to a visualization. Finding colours that work together can be difficult, even more so when we need to find colours for hundreds of elements. So let's see how we can generate some colours with code!
Creating a Dataset by Web Scraping
Data analysis and visualization tutorials often begin with loading an existing dataset. Sometimes, especially when the dataset isn't shared, it can feel like being taught how to draw an owl. So let's take a step back and think about how we may create a dataset for ourselves.
Path Construction with 2D Arrays
Whilst you could say that it's possible to draw a zigzag using multiple rect elements at different positions and rotations, it is certainly an infeasible and inefficient exercise. This is where the SVG path element comes in. A path describes an outline of some shape that can be filled and/or stroked.
CVSS Exploratory Data Analysis
A comparison between CVSS-2 and CVSS-3 using Python and the data science stack.
Advanced Features For Chord Diagrams
Chord Pro Features For Chord Diagrams
Colour Transitions
Whilst we could use the string literal magenta as our argument to change the fill colour, we'll use its hexadecimal equivalent, #ff00ff instead. This will give us more opportunity should we want to tinker with the colours. We could also use an RGB value, e.g. rgb(255, 0, 255).
Animated Transitions with Easing
D3.js transitions support easing functions which can change the speed at which an animation progresses throughout its duration. There are many different easing functions available in D3.js. Examples, descriptions, and visualisations of each one can be found in the API reference.
Looping Animated Transitions
We've introduced this new invocation of the transition.on(typenames, listener) function. This can add a listener function to the selection, which is invoked based on the event type. We've used an event type of end because we want our transition to end before calling the next one.
Arabica Coffee Beans - Origin and Variety
Arabica Coffee Beans - Origin and Variety
Animated Transitions
Much like selections, transitions can be used to modify attributes and styles. The difference is that whilst selections apply the changes instantly, transitions apply the changes gradually (and smoothly) over a specified duration.
Grouping Elements
The g SVG element is a container used to group other SVG elements. Transformations applied to the g element are performed on its child elements, and its attributes are inherited by its children. We can create a group element with D3.js by appending a g element using any selection.
Attributes and Styles
We can set CSS style properties by invoking .style(name, value) on the selection, and set SVG attributes by invoking .attr(name, value) where the argument to the first parameter should be the name of the attribute we want to set, and the argument to the second parameter should be the value we want to set it to.
Selections and Selecting Elements
Besides the d3.create() and d3.append() functions which return selections, we can use the d3.select() and d3.selectAll() functions to return selections by matching a CSS selector.
Creating Shape Elements
To create a circle element with D3.js we can invoke the append(name) function on our svg selection and pass in the name of the element. In this case, we're passing in circle as our argument for the name parameter.
Creating an Empty SVG
To create an element with D3.js we invoke the create(name) function and pass in the name of the element. In this case, we're passing in svg as our argument for the name parameter.
Software and Page Setup
It helps to have the right tools and templates available so that we can focus on the examples and exercises. Getting the hang of D3.js may involve plenty of tinkering, saving, and refreshing of HTML documents - don't be discouraged if your visualisations don't look right the first, second, or third time!
Preface
There is a wealth of cookbook-style resources available for D3.js visualisations, meaning you can create some interesting visualisations by copying some code and passing in your data. However, what this book aims to be is a practical journey through the many components of D3.js. By the end of this book, we want to be able to create new visualisations from the ground up and modify the behaviour of existing ones.
Box Plots at the Olympics
We work towards illustrating the age and height of athletes grouped by games in the 120 years of Olympic history.
Getting Started with PlotAPI and Rust
Generate visualisations through the API from Python, Rust, and more. Super-charge your notebooks with inline visualisations!
Olympic Weightlifting Medals with Stacked Bar Charts
We're going to use 120 years of Olympic history to create a visualisation. Let's set our sights on something that illustrates the distribution of Olympic medals awarded for the weightlifting sport.
Video Game Publishers and Genres with SplitChord
In this notebook we're going to use PlotAPI SplitChord to visualise the co-occurrences between genres and publishers in video game titles. We"ll use Python, but PlotAPI can be used from any programming language.
Top Olympic Medal Earning Countries
In this notebook we're going to use PlotAPI Chord to visualise the co-occurrences between countries and medals earned in the olympic games. We"ll use Python, but PlotAPI can be used from any programming language.
League of Legends World Championship
In this notebook we're going to use PlotAPI Chord to visualise the matches between teams throughout the Legends World Championship 2019. We"ll use Python, but PlotAPI can be used from any programming language.
StamiStudios Panels and Colours
In this notebook we're going to use PlotAPI Chord to visualise the co-occurrences between panels and colours purchased for the StamiStudios Everyday Ita Bag. We"ll use Python, but PlotAPI can be used from any programming language.
Visualisation of Co-occurring Types
We're going to use the Complete Pokemon Dataset dataset to visualise the co-occurrence of Pokémon types from generations one to eight. We'll make this happen using a chord diagram.
Co-occurrence of Anime Genres with Chord Diagrams
Co-occurrence of Anime Genres with Chord Diagrams
Co-occurrence of Movie Genres with Chord Diagrams
Co-occurrence of Movie Genres with Chord Diagrams
Interactive Chord Diagrams
Chord diagrams are useful when trying to convey relationships between different entities, and they can be beautiful and eye-catching.
Co-occurrence of Pokemon Types (Gen 1-8) with Chord Diagrams
Co-occurrence of Pokemon Types (Gen 1-8) with Chord Diagrams
Occurrence and Co-occurrence of Pokemon Types with Chord Diagrams
Occurrence and Co-occurrence of Pokemon Types with Chord Diagrams
Co-occurrence of Pokemon Types (Gen 1-6) with Chord Diagrams
Co-occurrence of Pokemon Types (Gen 1-6) with Chord Diagrams
Interactive Chord Diagrams
Interactive Chord Diagrams
NDArray Index Arrays and Mask Index Arrays
NumPy has many features that Rust's NDArray doesn't have yet, e.g. index arrays and mask index arrays. However, there is more than one way to index an array!
Confirmed Cases of Coronavirus in England with Scattergeo
Let's use a bubble plot on a map of the UK to plot the confirmed cases of Coronavirus in the UK.
Coronavirus Time Series Line and Bar Chart
Coronavirus Time Series Line and Bar Chart
Unique Array Elements and their Frequency
Let's demonstrate a few approaches to identifying the unique elements in an array, counting the number of unique elements, and the frequency of these unique elements.
Coronavirus Time Series Map Animation
Coronavirus Time Series Map Animation
API Requests and JSON Data
We'll often need to make HTTP requests to retrieve data - let's see one common approach in Python.
Software Setup
Software Setup
Preface
Preface
Descriptive Statistics with NDArray
We're going to take a look at some of the tools we have for descriptive statistics.
Typed Arrays from String Arrays for Dataset Operation
Let's demonstrate how to get parts of our raw string array into multiple arrays of various type.
Loading Datasets from CSV into NDArray
We shift our focus to dealing with real-world datasets and how to load them into an NDArray.
Better Output for 2D Arrays
Let's improve the presentation of the cell output for our arrays. This will generally improve the presentation of our notebooks.
Multidimensional Arrays and Operations with NDArray
The ndarray crate provides us with a multidimensional container that can contain general or numerical elements.
Finishing Touches for Visualisation
Let's take our Plotly workaround a step further to its final destination - a reusable function that we can use throughout our analyses.
Better Plotting with Plotly
Let's improve our workaround for data visualisation with Plotly for Rust in Jupyter notebooks.
Plotting with Plotly
How to embed Plotly visualisations in a Jupyter Notebook with a small workaround.
Plotting with Plotters
I had originally planned to use Plotters for all the graphing in this book. However, shortly after finding Plotters, I found out that a Rust library had enabled Plotly support.
Setup Anaconda, Jupyter, and Rust
We are taking a practical approach in the following sections. As such, we need the right tools and environments available in order to keep up with the examples and exercises.
Preface
The Rust programming language has become a popular choice amongst software engineers since its release in 2010. Besides being something new and interesting, Rust promised to offer exceptional performance and reliability.
YAML for Configuration Files
What this section is most interested in is using YAML for configuration files, enabling us to extract parameters that we use within our programs so that they can be separated.
Using a Framework with a Custom Objective Function
Let's use the Platypus framework to apply the Non-dominated Sorting Genetic Algorithm II (NSGA-II)1 to a custom objective function.
MNE DataFrame Scaling
MNE DataFrame scaling with Pandas.
Time Domain Analysis with Plotly
Plotting multiple channels in the time domain with Plotly.
Time Domain Analysis
Plotting multiple channels in the time domain.
Event Related Potentials
Plotting event related potentials with MNE.
Topographical Plots
Plotting topographical maps with MNE.
Fast Fourier Transform
How to plot multiple different sine waves onto different subplots.
Algorithm Performance and Statistical Significance
Let's test the significance of our pairwise comparison. The significance test you select depends on the nature of your data-set and other criteria. We will use the Wilcoxon signed-rank.
Summing Sine Waves
We move on from creating and plotting individual sine waves, to summing them and plotting our new and more complicated wave.
Multiple Sine Waves in the Time Domain
How to plot multiple different sine waves onto different subplots.
Sine Waves in the Time Domain
Let's look at a simple sine wave, how to create one in Python and how to visualise it in the time domain using a line chart.
Sample Size Sufficiency
Before conducting a comparison between algorithms we need to determine whether our sample size will be sufficient, i.e. is our sample size large enough to support our hypothesis?
Using a Framework to Compare Algorithm Performance
Let's use the Platypus framework to compare the performance of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and the Pareto Archived Evolution Strategy (PAES).
Using a Framework to Generate Results
Let's demonstrate how we can use a popular multi-objective optimisation algorithm, NSGA-II, to approximate multiple trade-off solutions to the DTLZ2 test problem.
Contributing Hypervolume Indicator
The Contributing Hypervolume (CHV) indicator is a population sorting mechanism based on an adaptation of the hypervolume indicator.
Hypervolume Indicator
The hypervolume indicator is a performance metric for indicating the quality of a non-dominated approximation set.
Non-Dominated Sorting
Non-dominated sorting is important during the selection stage of an Evolutionary Algorithm because it allows us to prioritise the selection of solutions based on their dominance relations with respect to the rest of the population.
Pareto Optimality and Dominance Relations
We often look for a single solution which has the best objective value, whereas this is not possible in multi-objective problems because they often involve conflicts between multiple objectives.
Single Objective Problems: Rastrigin
In single-objective problems, the objective is to find a single solution which represents the global optimum in the entire search space. Let's take the Rastrigin function as an example.
Single Objective Problems
In single-objective problems, the objective is to find a single solution which represents the global optimum in the entire search space. Let's take the Sphere function as an example.
Using a Framework and the ZDT Test Suite
Let's use the Platypus implementation of ZDT1, which will save us from having to implement it in Python ourselves.
Populations in Objective and Decision Space
Let's demonstate how to generate and visualise a population in the objective space and decision space.
Population Initialisation
Before the main optimisation process can begin, we need to complete the initialisation stage of the algorithm. There are many schemes for generating the initial population - let's start simple.
Synthetic Objective Functions and ZDT2
We will be using a synthetic test problem throughout this notebook called ZDT2. It is part of the ZDT test suite, consisting of six different two-objective synthetic test problems.
Synthetic Objective Functions and ZDT1
We will be using a synthetic test problem throughout this notebook called ZDT1. It is part of the ZDT test suite, consisting of six different two-objective synthetic test problems.
Objective Functions
Objective functions are perhaps the most important part of any Evolutionary Algorithm, whilst simultaneously being the least important part too.
Block Diagrams in Notebooks
Throughout this book, we will programmatically generate block diagrams to illustrate concepts and processes. Let's see how they're generated.
Python Crash Course
This crash-course makes the assumption that you already have some programming experience, but perhaps none with Python.
Software Setup
We are taking a practical approach in the following sections. As such, we need the right tools and environments available in order to keep up with the examples and exercises.
Preface
Evolutionary Algorithms (EAs) are a fascinating class of algorithms for meta-heuristic optimisation. Perhaps the most difficult question to answer is where do we start? There is so much to cover, and many potential starting points.
Oversampling the Cardiotocography Data Set
We use adaptive synthetic sampling (ADASYN) to resample and balance our Cardiotocography dataset.
Class Imbalance and Oversampling
Let's take a quick look at the problem of imbalanced datasets and one way to address it with oversampling.
Results Analysis
Supported by figures and statistics, we will have a look at how our solution performed and discuss anything interesting about the results.
Data Wrangling and Experiment Implementation
We will use the Keras API on top of TensorFlow to implement our experiment. All code will be in Python, and at the time of publishing everything is guaranteed to work within a Kaggle Notebook.
Experimental Design
It's important to know what we're looking for, how we're going to use our dataset, what algorithms we will be employing, and how we will determine whether the performance of our approach is successful.
Exploratory Data Analysis
We present and discuss a dataset selected for our machine learning experiment. This includes some analysis and visualisations to give us a better understanding of what we're dealing with.
Getting Started with Kaggle
Let's go through the process of signing up to Kaggle, firing up a Kernel, and executing a Hello World program in Python.
Pairwise Comparison
Pairwise comparison of data-sets is very important. It allows us to compare two sets of data and make decisions based on the outcome.
Standard Deviation
A brief re-cap on calculating the standard deviation with and without numpy.
Software Setup
We are taking a practical approach in the following sections. As such, we need the right tools and environments available in order to keep up with the examples and exercises.