def _importance(self, feature):
prior_prediction = self.predict([util.bias_feature()] +
[pb.Feature()] *
(self._config.num_features - 1))
with_weight_prediction = self.predict([util.bias_feature()] +
[pb.Feature()] *
(self._config.num_features - 2) +
[feature])
return util.kl_divergence(with_weight_prediction, prior_prediction)
def test_importance_is_monotonic_in_mean(self):
f = pb.Feature(feature=10, value=5)
importances = []
for mean in np.linspace(0.0, 3.0, 10):
self._predictor._set_weight(f, pb.Gaussian(mean=0.5, variance=0.5))
importances.append(self._predictor._importance(f))
self.assertEqual(sorted(importances), importances)
def next(self):
"""Implementing the Python iterator protocol
"""
feature_vector = [
pb.Feature(feature=f,
value=np.random.randint(0, self._cardinality(f)))
for f in range(self._simulation.predictor_config.num_features)
]
label = self._label(feature_vector)
self._num_samples += 1
return (feature_vector, label)
def _construct_biased_weights(simulation):
biased_weights = {}
for feature, value in itertools.product(
range(1, simulation.predictor_config.num_features),
range(simulation.feature_cardinality)):
key = util.serialize_feature(
pb.Feature(feature=feature, value=value))
if np.random.rand() < simulation.biased_feature_proportion:
direction = np.random.rand() < \
simulation.predictor_config.prior_probability
biased_weights[key] = direction
logger.info("Biased truth feature (%s, %s) to %s", feature,
value, direction)
return biased_weights
def test_importance_of_set_feature(self):
f = pb.Feature(feature=10, value=5)
self._predictor._set_weight(f, pb.Gaussian(mean=0.5, variance=0.5))
self.assertGreater(self._predictor._importance(f), 0.0)
def test_importance_of_empty_feature(self):
self.assertEqual(self._predictor._importance(pb.Feature()), 0.0)
def _create_feature_vector(num_features):
return [util.bias_feature()] + \
[pb.Feature()] * (num_features - 1)
def deserialize_feature(string):
f = pb.Feature()
f.ParseFromString(string)
return f
def bias_feature():
return pb.Feature(feature=0, value=0)