Refactor bayesian observations using dataclass (#79590)
* refactor * remove some changes * remove typehint * improve codestyle * move docstring to comment * < 88 chars * avoid short var names * more readable * fix rename * Update homeassistant/components/bayesian/helpers.py Co-authored-by: epenet <6771947+epenet@users.noreply.github.com> * Update homeassistant/components/bayesian/binary_sensor.py Co-authored-by: epenet <6771947+epenet@users.noreply.github.com> * Update homeassistant/components/bayesian/binary_sensor.py Co-authored-by: epenet <6771947+epenet@users.noreply.github.com> * no intermediate * comment why set before list Co-authored-by: epenet <6771947+epenet@users.noreply.github.com>pull/79602/head
HarvsG
GitHub
parent
56dd0a6867
commit
dd1463da28
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@ -35,24 +35,24 @@ from homeassistant.helpers.template import result_as_boolean
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from homeassistant.helpers.typing import ConfigType, DiscoveryInfoType
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from . import DOMAIN, PLATFORMS
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from .const import (
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ATTR_OBSERVATIONS,
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ATTR_OCCURRED_OBSERVATION_ENTITIES,
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ATTR_PROBABILITY,
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ATTR_PROBABILITY_THRESHOLD,
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CONF_OBSERVATIONS,
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CONF_P_GIVEN_F,
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CONF_P_GIVEN_T,
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CONF_PRIOR,
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CONF_PROBABILITY_THRESHOLD,
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CONF_TEMPLATE,
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CONF_TO_STATE,
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DEFAULT_NAME,
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DEFAULT_PROBABILITY_THRESHOLD,
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)
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from .helpers import Observation
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from .repairs import raise_mirrored_entries, raise_no_prob_given_false
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ATTR_OBSERVATIONS = "observations"
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ATTR_OCCURRED_OBSERVATION_ENTITIES = "occurred_observation_entities"
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ATTR_PROBABILITY = "probability"
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ATTR_PROBABILITY_THRESHOLD = "probability_threshold"
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CONF_OBSERVATIONS = "observations"
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CONF_PRIOR = "prior"
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CONF_TEMPLATE = "template"
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CONF_PROBABILITY_THRESHOLD = "probability_threshold"
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CONF_P_GIVEN_F = "prob_given_false"
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CONF_P_GIVEN_T = "prob_given_true"
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CONF_TO_STATE = "to_state"
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DEFAULT_NAME = "Bayesian Binary Sensor"
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DEFAULT_PROBABILITY_THRESHOLD = 0.5
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_LOGGER = logging.getLogger(__name__)
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@ -156,7 +156,20 @@ class BayesianBinarySensor(BinarySensorEntity):
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def __init__(self, name, prior, observations, probability_threshold, device_class):
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"""Initialize the Bayesian sensor."""
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self._attr_name = name
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self._observations = observations
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self._observations = [
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Observation(
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entity_id=observation.get(CONF_ENTITY_ID),
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platform=observation[CONF_PLATFORM],
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prob_given_false=observation[CONF_P_GIVEN_F],
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prob_given_true=observation[CONF_P_GIVEN_T],
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observed=None,
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to_state=observation.get(CONF_TO_STATE),
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above=observation.get(CONF_ABOVE),
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below=observation.get(CONF_BELOW),
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value_template=observation.get(CONF_VALUE_TEMPLATE),
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)
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for observation in observations
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]
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self._probability_threshold = probability_threshold
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self._attr_device_class = device_class
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self._attr_is_on = False
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@ -230,13 +243,18 @@ class BayesianBinarySensor(BinarySensorEntity):
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self.entity_id,
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)
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observation = None
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observed = None
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else:
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observation = result_as_boolean(result)
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observed = result_as_boolean(result)
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for obs in self.observations_by_template[template]:
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obs_entry = {"entity_id": entity, "observation": observation, **obs}
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self.current_observations[obs["id"]] = obs_entry
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for observation in self.observations_by_template[template]:
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observation.observed = observed
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# in some cases a template may update because of the absence of an entity
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if entity is not None:
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observation.entity_id = str(entity)
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self.current_observations[observation.id] = observation
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if event:
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self.async_set_context(event.context)
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@ -270,7 +288,7 @@ class BayesianBinarySensor(BinarySensorEntity):
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raise_mirrored_entries(
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self.hass,
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all_template_observations,
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text=f"{self._attr_name}/{all_template_observations[0]['value_template']}",
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text=f"{self._attr_name}/{all_template_observations[0].value_template}",
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)
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@callback
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@ -289,42 +307,38 @@ class BayesianBinarySensor(BinarySensorEntity):
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def _record_entity_observations(self, entity):
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local_observations = OrderedDict({})
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for entity_obs in self.observations_by_entity[entity]:
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platform = entity_obs["platform"]
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for observation in self.observations_by_entity[entity]:
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platform = observation.platform
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observation = self.observation_handlers[platform](entity_obs)
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observed = self.observation_handlers[platform](observation)
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observation.observed = observed
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obs_entry = {
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"entity_id": entity,
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"observation": observation,
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**entity_obs,
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}
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local_observations[entity_obs["id"]] = obs_entry
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local_observations[observation.id] = observation
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return local_observations
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def _calculate_new_probability(self):
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prior = self.prior
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for obs in self.current_observations.values():
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if obs is not None:
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if obs["observation"] is True:
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for observation in self.current_observations.values():
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if observation is not None:
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if observation.observed is True:
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prior = update_probability(
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prior,
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obs["prob_given_true"],
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obs["prob_given_false"],
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observation.prob_given_true,
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observation.prob_given_false,
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)
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elif obs["observation"] is False:
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elif observation.observed is False:
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prior = update_probability(
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prior,
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1 - obs["prob_given_true"],
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1 - obs["prob_given_false"],
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1 - observation.prob_given_true,
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1 - observation.prob_given_false,
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)
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elif obs["observation"] is None:
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if obs["entity_id"] is not None:
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elif observation.observed is None:
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if observation.entity_id is not None:
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_LOGGER.debug(
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"Observation for entity '%s' returned None, it will not be used for Bayesian updating",
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obs["entity_id"],
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observation.entity_id,
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)
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else:
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_LOGGER.debug(
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@ -338,8 +352,8 @@ class BayesianBinarySensor(BinarySensorEntity):
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Build and return data structure of the form below.
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{
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"sensor.sensor1": [{"id": 0, ...}, {"id": 1, ...}],
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"sensor.sensor2": [{"id": 2, ...}],
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"sensor.sensor1": [Observation, Observation],
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"sensor.sensor2": [Observation],
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...
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}
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@ -347,21 +361,20 @@ class BayesianBinarySensor(BinarySensorEntity):
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for all relevant observations to be looked up via their `entity_id`.
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"""
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observations_by_entity: dict[str, list[OrderedDict]] = {}
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for i, obs in enumerate(self._observations):
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obs["id"] = i
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observations_by_entity: dict[str, list[Observation]] = {}
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for observation in self._observations:
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if "entity_id" not in obs:
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if (key := observation.entity_id) is None:
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continue
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observations_by_entity.setdefault(obs["entity_id"], []).append(obs)
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observations_by_entity.setdefault(key, []).append(observation)
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for li_of_dicts in observations_by_entity.values():
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if len(li_of_dicts) == 1:
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for entity_observations in observations_by_entity.values():
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if len(entity_observations) == 1:
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continue
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for ord_dict in li_of_dicts:
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if ord_dict["platform"] != "state":
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for observation in entity_observations:
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if observation.platform != "state":
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continue
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ord_dict["platform"] = "multi_state"
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observation.platform = "multi_state"
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return observations_by_entity
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@ -370,8 +383,8 @@ class BayesianBinarySensor(BinarySensorEntity):
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Build and return data structure of the form below.
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{
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"template": [{"id": 0, ...}, {"id": 1, ...}],
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"template2": [{"id": 2, ...}],
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"template": [Observation, Observation],
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"template2": [Observation],
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...
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}
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@ -380,20 +393,18 @@ class BayesianBinarySensor(BinarySensorEntity):
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"""
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observations_by_template = {}
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for ind, obs in enumerate(self._observations):
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obs["id"] = ind
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if "value_template" not in obs:
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for observation in self._observations:
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if observation.value_template is None:
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continue
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template = obs.get(CONF_VALUE_TEMPLATE)
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observations_by_template.setdefault(template, []).append(obs)
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template = observation.value_template
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observations_by_template.setdefault(template, []).append(observation)
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return observations_by_template
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def _process_numeric_state(self, entity_observation):
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"""Return True if numeric condition is met, return False if not, return None otherwise."""
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entity = entity_observation["entity_id"]
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entity = entity_observation.entity_id
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try:
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if condition.state(self.hass, entity, [STATE_UNKNOWN, STATE_UNAVAILABLE]):
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@ -401,61 +412,67 @@ class BayesianBinarySensor(BinarySensorEntity):
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return condition.async_numeric_state(
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self.hass,
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entity,
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entity_observation.get("below"),
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entity_observation.get("above"),
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entity_observation.below,
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entity_observation.above,
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None,
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entity_observation,
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entity_observation.to_dict(),
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)
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except ConditionError:
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return None
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def _process_state(self, entity_observation):
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"""Return True if state conditions are met."""
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entity = entity_observation["entity_id"]
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"""Return True if state conditions are met, return False if they are not.
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Returns None if the state is unavailable.
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"""
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entity = entity_observation.entity_id
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try:
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if condition.state(self.hass, entity, [STATE_UNKNOWN, STATE_UNAVAILABLE]):
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return None
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return condition.state(
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self.hass, entity, entity_observation.get("to_state")
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)
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return condition.state(self.hass, entity, entity_observation.to_state)
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except ConditionError:
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return None
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def _process_multi_state(self, entity_observation):
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"""Return True if state conditions are met."""
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entity = entity_observation["entity_id"]
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"""Return True if state conditions are met, otherwise return None.
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Never return False as all other states should have their own probabilities configured.
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"""
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entity = entity_observation.entity_id
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try:
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if condition.state(self.hass, entity, entity_observation.get("to_state")):
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if condition.state(self.hass, entity, entity_observation.to_state):
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return True
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except ConditionError:
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return None
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return None
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@property
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def extra_state_attributes(self):
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"""Return the state attributes of the sensor."""
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attr_observations_list = [
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obs.copy() for obs in self.current_observations.values() if obs is not None
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]
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for item in attr_observations_list:
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item.pop("value_template", None)
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return {
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ATTR_OBSERVATIONS: attr_observations_list,
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ATTR_OCCURRED_OBSERVATION_ENTITIES: list(
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{
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obs.get("entity_id")
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for obs in self.current_observations.values()
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if obs is not None
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and obs.get("entity_id") is not None
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and obs.get("observation") is not None
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}
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),
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ATTR_PROBABILITY: round(self.probability, 2),
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ATTR_PROBABILITY_THRESHOLD: self._probability_threshold,
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# An entity can be in more than one observation so set then list to deduplicate
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ATTR_OCCURRED_OBSERVATION_ENTITIES: list(
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{
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observation.entity_id
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for observation in self.current_observations.values()
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if observation is not None
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and observation.entity_id is not None
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and observation.observed is not None
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}
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),
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ATTR_OBSERVATIONS: [
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observation.to_dict()
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for observation in self.current_observations.values()
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if observation is not None
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],
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}
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async def async_update(self) -> None:
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@ -0,0 +1,17 @@
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"""Consts for using in modules."""
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ATTR_OBSERVATIONS = "observations"
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ATTR_OCCURRED_OBSERVATION_ENTITIES = "occurred_observation_entities"
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ATTR_PROBABILITY = "probability"
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ATTR_PROBABILITY_THRESHOLD = "probability_threshold"
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CONF_OBSERVATIONS = "observations"
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CONF_PRIOR = "prior"
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CONF_TEMPLATE = "template"
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CONF_PROBABILITY_THRESHOLD = "probability_threshold"
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CONF_P_GIVEN_F = "prob_given_false"
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CONF_P_GIVEN_T = "prob_given_true"
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CONF_TO_STATE = "to_state"
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DEFAULT_NAME = "Bayesian Binary Sensor"
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DEFAULT_PROBABILITY_THRESHOLD = 0.5
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@ -0,0 +1,69 @@
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"""Helpers to deal with bayesian observations."""
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from __future__ import annotations
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from dataclasses import dataclass, field
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import uuid
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from homeassistant.const import (
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CONF_ABOVE,
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CONF_BELOW,
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CONF_ENTITY_ID,
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CONF_PLATFORM,
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CONF_VALUE_TEMPLATE,
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)
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from homeassistant.helpers.template import Template
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from .const import CONF_P_GIVEN_F, CONF_P_GIVEN_T, CONF_TO_STATE
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@dataclass
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class Observation:
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"""Representation of a sensor or template observation."""
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entity_id: str | None
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platform: str
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prob_given_true: float
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prob_given_false: float
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to_state: str | None
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above: float | None
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below: float | None
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value_template: Template | None
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observed: bool | None = None
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id: str = field(default_factory=lambda: str(uuid.uuid4()))
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def to_dict(self) -> dict[str, str | float | bool | None]:
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"""Represent Class as a Dict for easier serialization."""
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# Needed because dataclasses asdict() can't serialize Templates and ignores Properties.
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dic = {
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CONF_PLATFORM: self.platform,
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CONF_ENTITY_ID: self.entity_id,
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CONF_VALUE_TEMPLATE: self.template,
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CONF_TO_STATE: self.to_state,
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CONF_ABOVE: self.above,
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CONF_BELOW: self.below,
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CONF_P_GIVEN_T: self.prob_given_true,
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CONF_P_GIVEN_F: self.prob_given_false,
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"observed": self.observed,
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}
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for key, value in dic.copy().items():
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if value is None:
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del dic[key]
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return dic
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def is_mirror(self, other: Observation) -> bool:
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"""Dectects whether given observation is a mirror of this one."""
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return (
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self.platform == other.platform
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and round(self.prob_given_true + other.prob_given_true, 1) == 1
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and round(self.prob_given_false + other.prob_given_false, 1) == 1
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)
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@property
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def template(self) -> str | None:
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"""Not all observations have templates and we want to get template strings."""
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if self.value_template is not None:
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return self.value_template.template
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return None
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@ -11,20 +11,7 @@ def raise_mirrored_entries(hass: HomeAssistant, observations, text: str = "") ->
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"""If there are mirrored entries, the user is probably using a workaround for a patched bug."""
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if len(observations) != 2:
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return
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true_sums_1: bool = (
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round(
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observations[0]["prob_given_true"] + observations[1]["prob_given_true"], 1
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)
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== 1.0
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)
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false_sums_1: bool = (
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round(
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observations[0]["prob_given_false"] + observations[1]["prob_given_false"], 1
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)
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== 1.0
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)
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same_states: bool = observations[0]["platform"] == observations[1]["platform"]
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if true_sums_1 & false_sums_1 & same_states:
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if observations[0].is_mirror(observations[1]):
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issue_registry.async_create_issue(
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hass,
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DOMAIN,
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