Ruby ML for Python Coders

Python and Ruby

Curious to try machine learning in Ruby? Here’s a short cheatsheet for Python coders.

Data structure basics

Libraries

Category	Python	Ruby
Multi-dimensional arrays	NumPy	Numo
Data frames	Pandas	Daru, Rover
Visualization	Altair	Vega
Predictive modeling	Scikit-learn	Rumale
Gradient boosting	XGBoost, LightGBM	XGBoost, LightGBM
Deep learning	PyTorch, TensorFlow	Torch.rb, TensorFlow (TensorFlow )
Recommendations	Surprise, Implicit	Disco
Approximate nearest neighbors	NGT, Faiss, Annoy	NGT, Faiss, Annoy.rb
Factorization machines	xLearn	xLearn
Natural language processing	Transformers, spaCy, NTLK	Informers, many others
Text tokenization	Bling Fire, YouTokenToMe	Bling Fire, YouTokenToMe
Text classification	fastText	fastText
Topic modeling	Gemsim, tomotopy	tomoto
Forecasting	Prophet	Prophet.rb
Optimization	OR-Tools, CVXPY, PuLP, SCS, OSQP	OR-Tools, CBC, SCS, OSQP
Reinforcement learning	Vowpal Wabbit	Vowpal Wabbit
Bayesian inference	PyStan, CmdStanPy	CmdStan.rb
t-SNE	Multicore t-SNE	t-SNE
CUDA arrays	CuPy	Cumo
Scoring engine	ONNX Runtime	ONNX Runtime, Menoh

This list is by no means comprehensive. Many Ruby libraries are ones I created, as mentioned here and here.

If you’re planning to add Ruby support to your ML library:

Give Ruby a shot for your next maching learning project!

Updates

All code examples are public domain.
Use them however you’d like (licensed under CC0).

Jupyter + Rails