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.
```python
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.metrics.cllr(lrs_test, y_test), '(lower is better)')
print('The discriminative power is', lir.metrics.cllr_min(lrs_test, y_test), '(lower is better)')
# plot calibration
import lir.plotting
with lir.plotting.show() as ax:
ax.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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"description": "LIR Python Likelihood Ratio Library\n===================================\n\nThis library provides a collection of scripts to aid calibration, and\ncalculation and evaluation of Likelihood Ratios.\n\n## A simple score-based LR system\n\nA score-based LR system needs a scorer and a calibrator. The most basic setup\nuses a training set and a test set. Both the scorer and the calibrator are\nfitted on the training set.\n\n```python\nimport lir\nimport numpy as np\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.model_selection import train_test_split\n\n# generate some data randomly from a normal distribution\nX = np.concatenate([np.random.normal(loc=0, size=(100, 1)),\n np.random.normal(loc=1, size=(100, 1))])\ny = np.concatenate([np.zeros(100), np.ones(100)])\n\n# split the data into train and test\nX_train, X_test, y_train, y_test = train_test_split(X, y)\n\n# initialize a scorer and a calibrator\nscorer = LogisticRegression(solver='lbfgs') # choose any sklearn style classifier\ncalibrator = lir.KDECalibrator() # use plain KDE for calibration\ncalibrated_scorer = lir.CalibratedScorer(scorer, calibrator)\n\n# fit and predict\ncalibrated_scorer.fit(X_train, y_train)\nlrs_test = calibrated_scorer.predict_lr(X_test)\n\n# print the quality of the system as log likelihood ratio cost (lower is better)\nprint('The log likelihood ratio cost is', lir.metrics.cllr(lrs_test, y_test), '(lower is better)')\nprint('The discriminative power is', lir.metrics.cllr_min(lrs_test, y_test), '(lower is better)')\n\n# plot calibration\nimport lir.plotting\nwith lir.plotting.show() as ax:\n ax.pav(lrs_test, y_test)\n```\n\nThe log likelihood ratio cost (Cllr) may be used as a metric of performance.\nIn this case it should yield a value of around .8, but highly variable due to\nthe small number of samples. Increase the sample size to get more stable\nresults.\n",
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