def _set_weight(self, feature, weight):
logger.debug("Setting feature: %s frow weight: %s to weight: %s",
util.pp(feature), util.pp(self._get_weight(feature)),
util.pp(weight))
assert not np.isnan(weight.mean)
assert weight.variance >= 0.0
self._weights[util.serialize_feature(feature)] = weight
def _set_weight(self, feature, weight):
logger.debug("Setting feature: %s frow weight: %s to weight: %s",
util.pp(feature),
util.pp(self._get_weight(feature)),
util.pp(weight))
assert not np.isnan(weight.mean)
assert weight.variance >= 0.0
self._weights[util.serialize_feature(feature)] = weight
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 _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 get_bias_for_feature(self, feature):
return self._biased_weights.get(util.serialize_feature(feature))
def get_bias_for_feature(self, feature):
return self._biased_weights.get(util.serialize_feature(feature))
def _get_weight(self, feature):
return self._weights.get(util.serialize_feature(feature),
util.prior_weight())
def _get_weight(self, feature):
return self._weights.get(
util.serialize_feature(feature),
util.prior_weight())
