def test_unweighted_all_correct(self):
s_obj = metrics.SensitivityAtSpecificity(0.7, num_thresholds=1)
inputs = np.random.randint(0, 2, size=(100, 1))
y_pred = K.constant(inputs, dtype='float32')
y_true = K.constant(inputs)
result = s_obj(y_true, y_pred)
assert np.isclose(1, K.eval(result))
def test_SensitivityAtSpecificity(self, distribution):
label_prediction = ([0, 0, 0, 1, 1], [0, 0.3, 0.8, 0.3, 0.8])
with distribution.scope():
metric = metrics.SensitivityAtSpecificity(0.5)
self.evaluate([v.initializer for v in metric.variables])
updates = distribution.run(metric, args=label_prediction)
self.evaluate(updates)
self.assertAllClose(metric.result(), 0.5)
def test_unweighted_low_specificity(self):
s_obj = metrics.SensitivityAtSpecificity(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]
y_pred = K.constant(pred_values, dtype='float32')
y_true = K.constant(label_values)
result = s_obj(y_true, y_pred)
assert np.isclose(0.6, K.eval(result))
def test_weighted(self):
s_obj = metrics.SensitivityAtSpecificity(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 = K.constant(pred_values, dtype='float32')
y_true = K.constant(label_values, dtype='float32')
weights = K.constant(weight_values)
result = s_obj(y_true, y_pred, sample_weight=weights)
assert np.isclose(0.675, K.eval(result))
def test_sensitivity_metrics():
metrics_list = [
metrics.SensitivityAtSpecificity(0.5),
metrics.SpecificityAtSensitivity(0.5),
]
model = Sequential([Dense(2, input_shape=(3, ))])
model.compile('rmsprop', 'mse', metrics=metrics_list)
x = np.random.random((10, 3))
y = np.random.random((10, 2))
model.fit(x, y)
model.evaluate(x, y)
def test_config(self):
s_obj = metrics.SensitivityAtSpecificity(
0.4, num_thresholds=100, name='sensitivity_at_specificity_1')
assert s_obj.name == 'sensitivity_at_specificity_1'
assert len(s_obj.weights) == 4
assert s_obj.specificity == 0.4
assert s_obj.num_thresholds == 100
# Check save and restore config
s_obj2 = metrics.SensitivityAtSpecificity.from_config(
s_obj.get_config())
assert s_obj2.name == 'sensitivity_at_specificity_1'
assert len(s_obj2.weights) == 4
assert s_obj2.specificity == 0.4
assert s_obj2.num_thresholds == 100
def test_invalid_num_thresholds(self):
with pytest.raises(Exception):
metrics.SensitivityAtSpecificity(0.4, num_thresholds=-1)
def test_invalid_specificity(self):
with pytest.raises(Exception):
metrics.SensitivityAtSpecificity(-1)
