def test_reset_states(self): tp_obj = metrics.TruePositives() model = _get_simple_sequential_model([tp_obj]) x = np.ones((100, 4)) y = np.ones((100, 1)) model.evaluate(x, y) self.assertEqual(self.evaluate(tp_obj.accumulator), 100.) model.evaluate(x, y) self.assertEqual(self.evaluate(tp_obj.accumulator), 100.)
def test_weighted(self):
tp_obj = metrics.TruePositives()
self.evaluate(variables.variables_initializer(tp_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 = tp_obj(y_true, y_pred, sample_weight=sample_weight)
self.assertAllClose([12.], self.evaluate(result))
def test_weighted_with_thresholds(self):
tp_obj = metrics.TruePositives(thresholds=[0.15, 0.5, 0.85])
self.evaluate(variables.variables_initializer(tp_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)))
result = tp_obj(y_true, y_pred, sample_weight=37.)
self.assertAllClose([222., 111., 37.], self.evaluate(result))
def test_config(self):
tp_obj = metrics.TruePositives(name='my_tp', thresholds=[0.4, 0.9])
self.assertEqual(tp_obj.name, 'my_tp')
self.assertEqual(len(tp_obj.variables), 1)
self.assertEqual(tp_obj.thresholds, [0.4, 0.9])
# Check save and restore config
tp_obj2 = metrics.TruePositives.from_config(tp_obj.get_config())
self.assertEqual(tp_obj2.name, 'my_tp')
self.assertEqual(len(tp_obj2.variables), 1)
self.assertEqual(tp_obj2.thresholds, [0.4, 0.9])
def test_unweighted_with_thresholds(self):
tp_obj = metrics.TruePositives(thresholds=[0.15, 0.5, 0.85])
self.evaluate(variables.variables_initializer(tp_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 = tp_obj.update_state(y_true, y_pred)
self.evaluate(update_op)
result = tp_obj.result()
self.assertAllClose([6., 3., 1.], result)
def test_unweighted(self):
tp_obj = metrics.TruePositives()
self.evaluate(variables.variables_initializer(tp_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 = tp_obj.update_state(y_true, y_pred)
self.evaluate(update_op)
result = tp_obj.result()
self.assertAllClose([7.], result)
def test_config(self): tp_obj = metrics.TruePositives(name='my_tp', thresholds=[0.4, 0.9]) self.assertEqual(tp_obj.name, 'my_tp') self.assertEqual(len(tp_obj.variables), 1) self.assertEqual(tp_obj.thresholds, [0.4, 0.9])
