Generate synthetic datasets for machine learning

Open-source data generator working in terminal and as a JavaScript module

Using synthetic datasets is the easiest and the most robust way to test machine learning models and statistical methods. Instead of relying on real-world data with not fully known dependencies between variables, artificial data has clear rules under the hood. Another benefit is there's almost no size limit. We can generate as many observations as we need for our purposes.

With mkdata, you can generate new data based on nine well-known problems (e.g. Friedman datasets). You can use it as a JavaScript module producing 2D arrays ready for model training. Or create CSV files calling mkdata as a CLI app without writing code at all.

Install mkdata

• npm i mkdata -S or
• npm i mkdata -g or run using npx without installation
• npx mkdata -d moons -s 1000 -o friedman1.csv

CLI

mkdata -d friedman1 -s 1000 -o friedman1.csv

or using stdout

mkdata -d friedman1 -s 1000 > friedman1.csv

Params:

• -d, --dataset - dataset name
• -f, --nFeatures - number of features
• -s, --nSamples - number of samples
• -n, --noise - noise size
• -o, --output - output file name
• -r, --randomState - seed

API

const make = require('mkdata')
const [X, y] = make.spirals({ nSamples: 1000 })

friedman1, friedman2, friedman3 methods also return data generating functions:

const [X, y, f] = make.friedman3({ nSamples: 1000 })
const yt = X.map(f)

Synthetic datasets

• Friedman 1 (y = 10 * sin(Pi * x1 * x2) + 20 * (x3 - 0.5) ** 2 + 10 * x4 + 5 * x5 + e)
• Friedman 2 (y = sqrt(x1 ** 2 + (x2 * x3 - 1 / (x2 * x4)) ** 2) + e)
• Friedman 3 (y = atan(x2 * x3 - 1 / (x2 * x4) / x1) + e)
• Hastie (binary classification problem used in Hastie et al 2009)
• Moons (two interleaving half circles)
• Peak (peak benchmark problem)
• Ringnorm (from Breiman 1996)
• Spirals (two entangled spirals)
• Swissroll (from S. Marsland 2009)

Web demo

Generate same synthetic datasets without installing mkdata here: https://statsim.com/gen/ (CSV format)