🧠 UnitRefine – Active Learning Classifier (Mice)

This repository supports an active learning pipeline for refining spike sorting classification using human-in-the-loop curation. It includes both training data and a model trained on mouse electrophysiological recordings, annotated with labels such as sua and not-sua.

📁 Files and Descriptions

File	Description
`X_labeled.csv`	Full feature matrix used for training the classifier
`y_labeled.csv`	Corresponding labels for `X_labeled.csv` (`sua as 1`, `not-sua as 0`)
`X_new.csv`	Features of newly queried units labeled by a human using SortingView
`y_new.csv`	Human-verified labels for the units in `X_new.csv`
`learner.skops`	Trained `RandomForestClassifier` serialized using `skops`

🔁 Active Learning Workflow

Query uncertain units using modAL.
Present units to human curators via SortingView.
Incorporate corrected labels into the training set.
Update and re-train the model.
Push updates to the Hugging Face Hub.

🐭 Dataset Info

Species: Mouse
Features: Quality metrics + Template metrics from Spikeinterface
Label classes: not-sua, sua, psua

🚀 Model Use

To use the trained classifier:

from skops.io import load

clf = load("learner.skops")
preds = clf.predict(X_test[feature_columns])