def test_ratio_is_zero_if_probability_is_out_of_bounds(self):
loss_curve = shapes.Curve([(0.21, 0.131), (0.24, 0.108),
(0.27, 0.089), (0.30, 0.066)])
self.assertEqual(0.0,
common.compute_conditional_loss(loss_curve, 0.050))
self.assertEqual(0.0,
common.compute_conditional_loss(loss_curve, 0.200))
def test_conditional_loss_duplicates(self):
# we feed compute_conditional_loss with some duplicated data to see if
# it's handled correctly
closs1 = common.compute_conditional_loss(shapes.Curve([(0.21, 0.131),
(0.24, 0.108), (0.27, 0.089), (0.30, 0.066)]), 0.100)
# duplicated y values, different x values, (0.19, 0.131), (0.20, 0.131)
#should be skipped
closs2 = common.compute_conditional_loss(shapes.Curve([(0.19, 0.131),
(0.20, 0.131), (0.21, 0.131), (0.24, 0.108), (0.27, 0.089),
(0.30, 0.066)]), 0.100)
self.assertEquals(closs1, closs2)
def compute_conditional_loss(self, col, row, loss_curve, asset, loss_poe):
""" Compute the conditional loss for a loss curve and probability of
exceedance """
loss_conditional = common.compute_conditional_loss(loss_curve, loss_poe)
key = kvs.tokens.loss_key(self.id, row, col, asset["AssetID"], loss_poe)
LOGGER.debug("RESULT: conditional loss is %s, write to key %s" % (
loss_conditional, key))
kvs.set(key, loss_conditional)
def compute_conditional_loss(self, col, row, loss_curve, asset, loss_poe):
""" Compute the conditional loss for a loss curve and probability of
exceedance """
loss_conditional = common.compute_conditional_loss(
loss_curve, loss_poe)
key = kvs.tokens.loss_key(self.id, row, col, asset["assetID"],
loss_poe)
LOGGER.debug("RESULT: conditional loss is %s, write to key %s" %
(loss_conditional, key))
kvs.set(key, loss_conditional)
def compute_conditional_loss(job_id, col, row, loss_curve, asset, loss_poe):
"""Compute the conditional loss for a loss curve and Probability of
Exceedance (PoE)."""
loss_conditional = common.compute_conditional_loss(
loss_curve, loss_poe)
key = kvs.tokens.loss_key(
job_id, row, col, asset["assetID"], loss_poe)
LOG.debug("Conditional loss is %s, write to key %s" %
(loss_conditional, key))
kvs.set(key, loss_conditional)
def test_ratio_is_max_if_probability_is_too_low(self):
loss_curve = shapes.Curve([(0.21, 0.131), (0.24, 0.108),
(0.27, 0.089), (0.30, 0.066)])
self.assertEqual(0.30,
common.compute_conditional_loss(loss_curve, 0.050))
def test_conditional_loss_computation(self):
loss_curve = shapes.Curve([(0.21, 0.131), (0.24, 0.108),
(0.27, 0.089), (0.30, 0.066)])
self.assertAlmostEqual(0.2526, common.compute_conditional_loss(
loss_curve, 0.100), 4)
def test_conditional_loss_computation(self):
loss_curve = shapes.Curve([(0.21, 0.131), (0.24, 0.108),
(0.27, 0.089), (0.30, 0.066)])
self.assertAlmostEqual(0.2526, common.compute_conditional_loss(
loss_curve, 0.100), 4)
def test_ratio_is_max_if_probability_is_too_low(self):
loss_curve = shapes.Curve([(0.21, 0.131), (0.24, 0.108),
(0.27, 0.089), (0.30, 0.066)])
self.assertEqual(0.30,
common.compute_conditional_loss(loss_curve, 0.050))
