Usage

import * as sklearn from 'sklearn'
 
const data = [
  [0, 0, 0],
  [0, 1, 1],
  [1, 0, 1],
  [1, 1, 1]
]
 
const py = await sklearn.createPythonBridge()
 
const model = new sklearn.TSNE({
  n_components: 2,
  perplexity: 2
})
await model.init(py)
 
const x = await model.fit_transform({ X: data })
console.log(x)
 
await model.dispose()
await py.disconnect()

Since the TS classes are auto-generated from the Python docs, the code will look almost identical to the Python version, so use their excellent API docs (opens in a new tab) as a reference.

All class names, method names, attribute (accessor) names and types are the same as the official Python version.

The main differences are:

You need to call createPythonBridge() before using any sklearn classes
- This spawns a Python child process and validates all of the Python dependencies
- You can pass a custom python path via createPythonBridge({ python: '/path/to/your/python3' })
You need to pass this bridge to a class's async init method before using it
- This creates an underlying Python variable representing your class instance
Instead of using numpy or pandas, we're just using plain JavaScript arrays
- Anywhere the Python version would input or output a nympy array, we instead just use number[], number[][], etc
- We take care of converting to and from numpy arrays automatically where necessary
Whenever you're done using an instance, call dispose() to free the underlying Python resources
Whenever you're done using your Python bridge, call disconnect() on the bridge to cleanly exit the Python child process

Install Restrictions