def _test_tf(avg, beta, act, pred, threshold):
act = tf.constant(act, tf.float32)
pred = tf.constant(pred, tf.float32)
fbeta = FBetaScore(3, avg, beta, threshold)
fbeta.update_state(act, pred)
return fbeta.result().numpy()
def _test_tf(self, avg, beta, act, pred, threshold):
act = tf.constant(act, tf.float32)
pred = tf.constant(pred, tf.float32)
fbeta = FBetaScore(3, avg, beta, threshold)
self.evaluate(tf.compat.v1.variables_initializer(fbeta.variables))
self.evaluate(fbeta.update_state(act, pred))
return self.evaluate(fbeta.result())
def test_eq():
f1 = F1Score(3)
fbeta = FBetaScore(3, beta=1.0)
preds = [
[0.9, 0.1, 0],
[0.2, 0.6, 0.2],
[0, 0, 1],
[0.4, 0.3, 0.3],
[0, 0.9, 0.1],
[0, 0, 1],
]
actuals = [[1, 0, 0], [0, 1, 0], [0, 0, 1], [1, 0, 0], [1, 0, 0], [0, 0, 1]]
fbeta.update_state(actuals, preds)
f1.update_state(actuals, preds)
np.testing.assert_allclose(fbeta.result().numpy(), f1.result().numpy())
def test_eq(self):
f1 = F1Score(3)
fbeta = FBetaScore(3, beta=1.0)
self.evaluate(tf.compat.v1.variables_initializer(f1.variables))
self.evaluate(tf.compat.v1.variables_initializer(fbeta.variables))
preds = [[0.9, 0.1, 0], [0.2, 0.6, 0.2], [0, 0, 1], [0.4, 0.3, 0.3],
[0, 0.9, 0.1], [0, 0, 1]]
actuals = [[1, 0, 0], [0, 1, 0], [0, 0, 1], [1, 0, 0], [1, 0, 0],
[0, 0, 1]]
self.evaluate(fbeta.update_state(actuals, preds))
self.evaluate(f1.update_state(actuals, preds))
self.assertAllClose(
self.evaluate(fbeta.result()), self.evaluate(f1.result()))