Torch metrics package
This package provides utility functions to evaluate your machine learning models.
Disclaimer:
Use at your own risk. The code is not extensively tested, therefore it might contain bugs. If you find any, please let me know and I will try to fix it.
Installation:
git clone https://github.com/hpenedones/metrics.git
cd metrics
luarocks make
Receiver Operator Curves (ROC)
Used to evalute performance of binary classifiers, and their trade-offs in terms of false-positive and false-negative rates.
require 'torch'
metrics = require 'metrics'
gfx = require 'gfx.js'
resp = torch.DoubleTensor { -0.9, -0.8, -0.8, -0.5, -0.1, 0.0, 0.2, 0.2, 0.51, 0.74, 0.89}
labels = torch.IntTensor { -1, -1, 1, -1, -1, 1, 1, -1, -1, 1, 1}
roc_points, thresholds = metrics.roc.points(resp, labels)
area = metrics.roc.area(roc_points)
print(roc_points)
print(thresholds)
print(area)
gfx.chart(roc_points)
Confusion matrix (TODO)
Used to evaluate performance of multi-class classifiers.