def test_config(self):
s_obj = metrics.SpecificityAtSensitivity(
0.4, num_thresholds=100, name='specificity_at_sensitivity_1')
self.assertEqual(s_obj.name, 'specificity_at_sensitivity_1')
self.assertLen(s_obj.variables, 4)
self.assertEqual(s_obj.value, 0.4)
self.assertLen(s_obj.thresholds, 100)
def test_unweighted_all_correct(self): s_obj = metrics.SpecificityAtSensitivity(0.7) inputs = np.random.randint(0, 2, size=(100, 1)) y_pred = constant_op.constant(inputs, dtype=dtypes.float32) y_true = constant_op.constant(inputs) self.evaluate(variables.variables_initializer(s_obj.variables)) result = s_obj(y_true, y_pred) self.assertAlmostEqual(1, self.evaluate(result))
def test_unweighted_high_sensitivity(self):
s_obj = metrics.SpecificityAtSensitivity(0.8)
pred_values = [0.0, 0.1, 0.2, 0.3, 0.4, 0.1, 0.45, 0.5, 0.8, 0.9]
label_values = [0, 0, 0, 0, 0, 1, 1, 1, 1, 1]
y_pred = constant_op.constant(pred_values, dtype=dtypes.float32)
y_true = constant_op.constant(label_values)
self.evaluate(variables.variables_initializer(s_obj.variables))
result = s_obj(y_true, y_pred)
self.assertAlmostEqual(0.4, self.evaluate(result))
def test_weighted(self, label_dtype):
s_obj = metrics.SpecificityAtSensitivity(0.4)
pred_values = [0.0, 0.1, 0.2, 0.3, 0.4, 0.01, 0.02, 0.25, 0.26, 0.26]
label_values = [0, 0, 0, 0, 0, 1, 1, 1, 1, 1]
weight_values = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
y_pred = constant_op.constant(pred_values, dtype=dtypes.float32)
y_true = math_ops.cast(label_values, dtype=label_dtype)
weights = constant_op.constant(weight_values)
self.evaluate(variables.variables_initializer(s_obj.variables))
result = s_obj(y_true, y_pred, sample_weight=weights)
self.assertAlmostEqual(0.4, self.evaluate(result))
def test_reset_states_specificity_at_sensitivity(self):
s_obj = metrics.SpecificityAtSensitivity(0.5, num_thresholds=1)
model = _get_model([s_obj])
x = np.concatenate((np.ones((25, 4)), np.zeros(
(25, 4)), np.zeros((25, 4)), np.ones((25, 4))))
y = np.concatenate((np.ones((25, 1)), np.zeros(
(25, 1)), np.ones((25, 1)), np.zeros((25, 1))))
for _ in range(2):
model.evaluate(x, y)
self.assertEqual(self.evaluate(s_obj.true_positives), 25.)
self.assertEqual(self.evaluate(s_obj.false_positives), 25.)
self.assertEqual(self.evaluate(s_obj.false_negatives), 25.)
self.assertEqual(self.evaluate(s_obj.true_negatives), 25.)
def test_config(self):
s_obj = metrics.SpecificityAtSensitivity(
0.4, num_thresholds=100, name='specificity_at_sensitivity_1')
self.assertEqual(s_obj.name, 'specificity_at_sensitivity_1')
self.assertLen(s_obj.variables, 4)
self.assertEqual(s_obj.sensitivity, 0.4)
self.assertEqual(s_obj.num_thresholds, 100)
# Check save and restore config
s_obj2 = metrics.SpecificityAtSensitivity.from_config(s_obj.get_config())
self.assertEqual(s_obj2.name, 'specificity_at_sensitivity_1')
self.assertLen(s_obj2.variables, 4)
self.assertEqual(s_obj2.sensitivity, 0.4)
self.assertEqual(s_obj2.num_thresholds, 100)
def test_reset_states(self):
s_obj = metrics.SpecificityAtSensitivity(0.5, num_thresholds=1)
model = _get_simple_sequential_model([s_obj])
x = np.concatenate((np.ones((25, 4)), np.zeros((25, 4)), np.zeros((25, 4)),
np.ones((25, 4))))
y = np.concatenate((np.ones((25, 1)), np.zeros((25, 1)), np.ones((25, 1)),
np.zeros((25, 1))))
model.evaluate(x, y)
self.assertEqual(self.evaluate(s_obj.tp), 25.)
self.assertEqual(self.evaluate(s_obj.fp), 25.)
self.assertEqual(self.evaluate(s_obj.fn), 25.)
self.assertEqual(self.evaluate(s_obj.tn), 25.)
model.evaluate(x, y)
self.assertEqual(self.evaluate(s_obj.tp), 25.)
self.assertEqual(self.evaluate(s_obj.fp), 25.)
self.assertEqual(self.evaluate(s_obj.fn), 25.)
self.assertEqual(self.evaluate(s_obj.tn), 25.)
def test_value_is_idempotent(self):
s_obj = metrics.SpecificityAtSensitivity(0.7)
y_pred = random_ops.random_uniform((10, 3),
maxval=1,
dtype=dtypes.float32,
seed=1)
y_true = random_ops.random_uniform((10, 3),
maxval=2,
dtype=dtypes.int64,
seed=1)
update_op = s_obj.update_state(y_true, y_pred)
self.evaluate(variables.variables_initializer(s_obj.variables))
# Run several updates.
for _ in range(10):
self.evaluate(update_op)
# Then verify idempotency.
initial_specificity = self.evaluate(s_obj.result())
for _ in range(10):
self.assertAlmostEqual(initial_specificity, self.evaluate(s_obj.result()),
1e-3)
def test_invalid_num_thresholds(self):
with self.assertRaisesRegexp(ValueError, '`num_thresholds` must be > 0.'):
metrics.SpecificityAtSensitivity(0.4, num_thresholds=-1)
def test_invalid_sensitivity(self):
with self.assertRaisesRegexp(
ValueError, r'`sensitivity` must be in the range \[0, 1\].'):
metrics.SpecificityAtSensitivity(-1)