Name: orange3-conformal
Owner: Bioinformatics Laboratory
Description: Conformal Prediction library for Orange3
Created: 2017-02-03 13:52:33.0
Updated: 2017-11-20 06:41:58.0
Pushed: 2017-07-25 08:28:07.0
Size: 4011
Language: Python
GitHub Committers
| User | Most Recent Commit | # Commits |
|---|
Other Committers
| User | Most Recent Commit | # Commits |
|---|
Conformal Prediction is an add-on for Orange3 data mining software package. It provides an extensive toolset for conformal prediction.
To install the add-on, run
python setup.py install
To register this add-on with Orange, but keep the code in the development directory (do not copy it to Python's site-packages directory), run
python setup.py develop
The library in the add-on can be used in Python scripts. The add-on does not provide any GUI widgets.
The example below evaluates an inductive conformal predictor at 0.1 significance level on the Iris dataset (spliting it into a training and testing set in ratio 2:1). The nonconformity scores used by the conformal predictor are based on the probabilities returned by a Naive Bayes classifier.
import Orange
import orangecontrib.conformal as cp
tab = Orange.data.Table('iris')
nc = cp.nonconformity.InverseProbability(Orange.classification.NaiveBayesLearner())
ic = cp.classification.InductiveClassifier(nc)
r = cp.evaluation.run(ic, 0.1, cp.evaluation.RandomSampler(tab, 2, 1))
print(r.accuracy())
Please see doc/Orange-ConformalPrediction.pdf.
Documentation in other formats can also be built using Sphinx from the doc directory.