def test_brier_score_loss_from_scikit_learn_example():
"""
from sklearn docs...
Examples
--------
>>> import numpy as np
>>> from sklearn.metrics import brier_score_loss
>>> y_true = np.array([0, 1, 1, 0])
>>> y_prob = np.array([0.1, 0.9, 0.8, 0.3])
>>> brier_score_loss(y_true, y_prob)
0.037...
"""
y_true = T.fvector('y_true')
y_predicted = T.fvector('y_predicted')
brier_score = tmetrics.brier_score_loss(y_true, y_predicted)
f = theano.function([y_true, y_predicted], brier_score)
yt = np.array([0, 1, 1, 0], 'float32')
yp = np.array([.1, .9, .8, .3], theano.config.floatX)
refscore = sklearn.metrics.brier_score_loss(yt, yp)
tol = .01
score = f(yt, yp)
assert (refscore - tol) < score < (refscore + tol)
#also test the function is numpy/pandas compatible
assert (refscore - tol) < tmetrics.brier_score_loss(yt, yp) < (refscore + tol)
def test_brier_score_loss_2D():
yt = np.array([0, 1, 1, 0], 'float32')
yp = np.array([.1, .9, .8, .3], theano.config.floatX)
refscore = sklearn.metrics.brier_score_loss(yt, yp)
yt = np.concatenate([yt.reshape(1, 4), yt.reshape(1, 4)], axis=0)
yp = np.concatenate([yp.reshape(1, 4), yp.reshape(1, 4)], axis=0)
y_true = T.fmatrix('y_true')
y_predicted = T.fmatrix('y_predicted')
brier_score = tmetrics.brier_score_loss(y_true, y_predicted)
f = theano.function([y_true, y_predicted], brier_score)
tol = .01
score = f(yt, yp)
assert (refscore - tol) < score < (refscore + tol)