scripts for calculating likelihood ratios
LIR Python Likelihood Ratio Library
This library provides a collection of scripts to aid calibration, and calculation and evaluation of Likelihood Ratios.
A simple score-based LR system
A score-based LR system needs a scorer and a calibrator. The most basic setup uses a training set and a test set. Both the scorer and the calibrator are fitted on the training set.
import lir import numpy as np from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split # generate some data randomly from a normal distribution X = np.concatenate([np.random.normal(loc=0, size=(100, 1)), np.random.normal(loc=1, size=(100, 1))]) y = np.concatenate([np.zeros(100), np.ones(100)]) # split the data into train and test X_train, X_test, y_train, y_test = train_test_split(X, y) # initialize a scorer and a calibrator scorer = LogisticRegression(solver='lbfgs') # choose any sklearn style classifier calibrator = lir.KDECalibrator() # use plain KDE for calibration calibrated_scorer = lir.CalibratedScorer(scorer, calibrator) # fit and predict calibrated_scorer.fit(X_train, y_train) lrs_test = calibrated_scorer.predict_lr(X_test) # print the quality of the system as log likelihood ratio cost (lower is better) print('The log likelihood ratio cost is', lir.cllr(lrs_test, y_test), '(lower is better)') print('The discriminative power is', lir.cllr_min(lrs_test, y_test), '(lower is better)') # plot calibration import lir.plotting lir.plotting.plot_pav(lrs_test, y_test)
The log likelihood ratio cost (CLLR) may be used as a metric of performance. In this case it should yield a value of around .8, but highly variable due to the small number of samples. Increase the sample size to get more stable results.
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