def test_threshold_limit(self):
with self.assertRaisesRegexp(
ValueError,
r'Threshold values must be in \[0, 1\]. Invalid values: \[-1, 2\]'):
metrics.FalsePositives(thresholds=[-1, 0.5, 2])
with self.assertRaisesRegexp(
ValueError,
r'Threshold values must be in \[0, 1\]. Invalid values: \[None\]'):
metrics.FalsePositives(thresholds=[None])
def test_reset_states(self): fp_obj = metrics.FalsePositives() model = _get_simple_sequential_model([fp_obj]) x = np.ones((100, 4)) y = np.zeros((100, 1)) model.evaluate(x, y) self.assertEqual(self.evaluate(fp_obj.accumulator), 100.) model.evaluate(x, y) self.assertEqual(self.evaluate(fp_obj.accumulator), 100.)
def test_weighted(self):
fp_obj = metrics.FalsePositives()
self.evaluate(variables.variables_initializer(fp_obj.variables))
y_true = constant_op.constant(((0, 1, 0, 1, 0), (0, 0, 1, 1, 1),
(1, 1, 1, 1, 0), (0, 0, 0, 0, 1)))
y_pred = constant_op.constant(((0, 0, 1, 1, 0), (1, 1, 1, 1, 1),
(0, 1, 0, 1, 0), (1, 1, 1, 1, 1)))
sample_weight = constant_op.constant((1., 1.5, 2., 2.5))
result = fp_obj(y_true, y_pred, sample_weight=sample_weight)
self.assertAllClose([14.], self.evaluate(result))
def test_config(self):
fp_obj = metrics.FalsePositives(name='my_fp', thresholds=[0.4, 0.9])
self.assertEqual(fp_obj.name, 'my_fp')
self.assertEqual(len(fp_obj.variables), 1)
self.assertEqual(fp_obj.thresholds, [0.4, 0.9])
# Check save and restore config
fp_obj2 = metrics.FalsePositives.from_config(fp_obj.get_config())
self.assertEqual(fp_obj2.name, 'my_fp')
self.assertEqual(len(fp_obj2.variables), 1)
self.assertEqual(fp_obj2.thresholds, [0.4, 0.9])
def test_weighted_with_thresholds(self):
fp_obj = metrics.FalsePositives(thresholds=[0.15, 0.5, 0.85])
self.evaluate(variables.variables_initializer(fp_obj.variables))
y_pred = constant_op.constant(((0.9, 0.2, 0.8, 0.1), (0.2, 0.9, 0.7, 0.6),
(0.1, 0.2, 0.4, 0.3), (0, 1, 0.7, 0.3)))
y_true = constant_op.constant(((0, 1, 1, 0), (1, 0, 0, 0), (0, 0, 0, 0),
(1, 1, 1, 1)))
sample_weight = ((1.0, 2.0, 3.0, 5.0), (7.0, 11.0, 13.0, 17.0),
(19.0, 23.0, 29.0, 31.0), (5.0, 15.0, 10.0, 0))
result = fp_obj(y_true, y_pred, sample_weight=sample_weight)
self.assertAllClose([125., 42., 12.], self.evaluate(result))
def test_unweighted_with_thresholds(self):
fp_obj = metrics.FalsePositives(thresholds=[0.15, 0.5, 0.85])
self.evaluate(variables.variables_initializer(fp_obj.variables))
y_pred = constant_op.constant(((0.9, 0.2, 0.8, 0.1), (0.2, 0.9, 0.7, 0.6),
(0.1, 0.2, 0.4, 0.3), (0, 1, 0.7, 0.3)))
y_true = constant_op.constant(((0, 1, 1, 0), (1, 0, 0, 0), (0, 0, 0, 0),
(1, 1, 1, 1)))
update_op = fp_obj.update_state(y_true, y_pred)
self.evaluate(update_op)
result = fp_obj.result()
self.assertAllClose([7., 4., 2.], result)
def test_unweighted(self):
fp_obj = metrics.FalsePositives()
self.evaluate(variables.variables_initializer(fp_obj.variables))
y_true = constant_op.constant(((0, 1, 0, 1, 0), (0, 0, 1, 1, 1),
(1, 1, 1, 1, 0), (0, 0, 0, 0, 1)))
y_pred = constant_op.constant(((0, 0, 1, 1, 0), (1, 1, 1, 1, 1),
(0, 1, 0, 1, 0), (1, 1, 1, 1, 1)))
update_op = fp_obj.update_state(y_true, y_pred)
self.evaluate(update_op)
result = fp_obj.result()
self.assertAllClose([7.], result)
def test_config(self): fp_obj = metrics.FalsePositives(name='my_fp', thresholds=[0.4, 0.9]) self.assertEqual(fp_obj.name, 'my_fp') self.assertEqual(len(fp_obj.variables), 1) self.assertEqual(fp_obj.thresholds, [0.4, 0.9])