def test_strict_log_probability(basic_model):
basic_model.set_priors([Normal(1, 0.5), Exponential(0.5)])
x = np.array([1.0, 0.5])
assert basic_model.strict_log_probability(
('item 0', 'item 1'), x) == pytest.approx(-0.1413058, 0.001)
assert basic_model.strict_log_probability(
('item 1', 'item 0'), x) == pytest.approx(-2.026650, 0.001)
def test_inference_with_different_priors(basic_model):
basic_model.set_priors([Normal(1, 1), Exponential(-0.5)])
assert basic_model.weights is None
with pytest.warns(UserWarning):
basic_model.infer_weights()
assert all(a - b < 1e-4
for a, b in zip(basic_model.weights.tolist(), [1, 0]))
def test_probability(basic_model):
basic_model.set_priors([Normal(1, 0.5), Exponential(0.5)])
x = np.array([1.0, 0.5])
assert basic_model.probability(x) == np.exp(basic_model.log_probability(x))
def test_indifferent_log_probability(basic_model):
basic_model.set_priors([Normal(1, 0.5), Exponential(0.5)])
x = np.array([1.0, 0.5])
assert basic_model.indifferent_log_probability(
('item 0', 'item 1'), x) == pytest.approx(-0.7188213, 0.001)
import pandas as pd
from pypbl.priors import Normal, Exponential
from pypbl.elicitation import BayesPreference
data = pd.read_csv('data/mtcars.csv')
print(data)
# set index of the data frame to be the item names
data.set_index('model', inplace=True)
p = BayesPreference(data=data)
p.set_priors([
Exponential(1), # MPG - high miles per gallon is preferred
Normal(), # number of cylinders
Normal(), # displacement
Exponential(2), # horsepower - high horsepower is preferred
Normal(), # real axle ratio
Normal(), # weight
Exponential(-3), # quarter mile time - high acceleration is preferred
Normal(), # engine type
Normal(), # transmission type
Normal(), # number of gears
Normal() # number of carburetors
])
# add some preferences and infer the weights for each parameter
p.add_strict_preference('Pontiac Firebird', 'Fiat 128')
p.add_strict_preference('Mazda RX4', 'Mazda RX4 Wag')
p.add_indifferent_preference('Merc 280', 'Merc 280C')
p.infer_weights(method='mean')
def test_exponential_prior_with_negative_weights():
prior = Exponential(mu=-0.5)
assert prior.mu == -0.5
assert prior(-0.5) > prior(-1)
assert prior(-0.5) > prior(1)
def test_exponential_prior():
prior = Exponential(mu=0.5)
assert prior.mu == 0.5
assert prior(0.5) > prior(-1)
assert prior(0.5) > prior(1)
def test_default_exponential_prior():
prior = Exponential()
assert prior.mu == 1
assert prior(0) > prior(-1)
assert prior(0) > prior(1)
assert prior(np.array([0])) > prior(np.array([-1]))