def update_state(self, y_true, y_pred, sample_weight=None):
if type(y_true)==np.ndarray:
y_true = tf.constant(y_true)
if type(y_pred)==np.ndarray:
y_pred = tf.constant(y_pred)
sh = tf.shape(y_true)
if len(sh)>1:
y_true = tf.reshape(y_true,sh[:1])
y_true = tf.cast(y_true,dtype=tf.int32)
brier_score = 1 + um.brier_score(labels=y_true, probabilities=y_pred)
super(BrierScore, self).update_state(brier_score)
def test_brier_decomposition(self, temperature, nlabels, nsamples):
"""Test the accuracy of the estimated Brier decomposition."""
tf.random.set_seed(1)
logits = tf.random.normal((nsamples, nlabels)) / temperature
labels = tf.random.uniform((nsamples, ),
maxval=nlabels,
dtype=tf.int32)
uncertainty, resolution, reliability = um.brier_decomposition(
labels=labels, logits=logits)
uncertainty = float(uncertainty)
resolution = float(resolution)
reliability = float(reliability)
# Recover an estimate of the Brier score from the decomposition
brier = uncertainty - resolution + reliability
# Estimate Brier score directly
brier_direct = um.brier_score(labels=labels, logits=logits)
brier_direct = float(brier_direct)
logging.info(
"Brier, n=%d k=%d T=%.2f, Unc %.4f - Res %.4f + Rel %.4f = "
"Brier %.4f, Brier-direct %.4f", nsamples, nlabels, temperature,
uncertainty, resolution, reliability, brier, brier_direct)
self.assertGreaterEqual(resolution,
0.0,
msg="Brier resolution negative")
self.assertGreaterEqual(reliability,
0.0,
msg="Brier reliability negative")
self.assertAlmostEqual(
brier,
brier_direct,
delta=1.0e-2,
msg=
"Brier from decomposition (%.4f) and Brier direct (%.4f) disagree "
"beyond estimation error." % (brier, brier_direct))
def update_state(self, y_true, y_pred, sample_weight=None):
brier_score = um.brier_score(labels=y_true, probabilities=y_pred)
super(BrierScore, self).update_state(brier_score)
