def test_unweighted_all_correct(self):
s_obj = metrics.PrecisionAtRecall(0.7)
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_PrecisionAtRecall(self, distribution):
label_prediction = ([0, 0, 0, 1, 1], [0, 0.3, 0.8, 0.3, 0.8])
with distribution.scope():
metric = metrics.PrecisionAtRecall(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_recall(self):
s_obj = metrics.PrecisionAtRecall(0.4)
pred_values = [0.0, 0.1, 0.2, 0.3, 0.4, 0.1, 0.15, 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(0.5, K.eval(result))
def test_unweighted_high_recall(self):
s_obj = metrics.PrecisionAtRecall(0.8)
pred_values = [0.0, 0.1, 0.2, 0.3, 0.5, 0.4, 0.5, 0.6, 0.8, 0.9]
label_values = [0, 0, 0, 0, 0, 1, 1, 1, 1, 1]
# For a score between 0.4 and 0.5, we expect 0.8 precision, 0.8 recall.
y_pred = K.constant(pred_values, dtype='float32')
y_true = K.constant(label_values)
result = s_obj(y_true, y_pred)
assert np.isclose(0.8, K.eval(result))
def test_weighted(self):
s_obj = metrics.PrecisionAtRecall(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 = [2, 2, 1, 1, 1, 1, 1, 2, 2, 2]
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(2. / 3., K.eval(result))
def test_config(self):
s_obj = metrics.PrecisionAtRecall(0.4,
num_thresholds=100,
name='precision_at_recall_1')
assert s_obj.name == 'precision_at_recall_1'
assert s_obj.recall == 0.4
assert s_obj.num_thresholds == 100
# Check save and restore config
s_obj2 = metrics.PrecisionAtRecall.from_config(s_obj.get_config())
assert s_obj2.name == 'precision_at_recall_1'
assert s_obj2.recall == 0.4
assert s_obj2.num_thresholds == 100
def test_invalid_num_thresholds(self):
with pytest.raises(Exception):
metrics.PrecisionAtRecall(0.4, num_thresholds=-1)
def test_invalid_sensitivity(self):
with pytest.raises(Exception):
metrics.PrecisionAtRecall(-1)
