239 lines
8.3 KiB
Python
239 lines
8.3 KiB
Python
"""
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Use Bayesian Inference to trigger a binary sensor.
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For more details about this platform, please refer to the documentation at
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https://home-assistant.io/components/binary_sensor.bayesian/
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"""
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from collections import OrderedDict
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import voluptuous as vol
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from homeassistant.components.binary_sensor import (
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PLATFORM_SCHEMA, BinarySensorDevice)
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from homeassistant.const import (
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CONF_ABOVE, CONF_BELOW, CONF_DEVICE_CLASS, CONF_ENTITY_ID, CONF_NAME,
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CONF_PLATFORM, CONF_STATE, CONF_VALUE_TEMPLATE, STATE_UNKNOWN)
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from homeassistant.core import callback
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from homeassistant.helpers import condition
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import homeassistant.helpers.config_validation as cv
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from homeassistant.helpers.event import async_track_state_change
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ATTR_OBSERVATIONS = 'observations'
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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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NUMERIC_STATE_SCHEMA = vol.Schema({
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CONF_PLATFORM: 'numeric_state',
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vol.Required(CONF_ENTITY_ID): cv.entity_id,
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vol.Optional(CONF_ABOVE): vol.Coerce(float),
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vol.Optional(CONF_BELOW): vol.Coerce(float),
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vol.Required(CONF_P_GIVEN_T): vol.Coerce(float),
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vol.Optional(CONF_P_GIVEN_F): vol.Coerce(float)
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}, required=True)
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STATE_SCHEMA = vol.Schema({
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CONF_PLATFORM: CONF_STATE,
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vol.Required(CONF_ENTITY_ID): cv.entity_id,
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vol.Required(CONF_TO_STATE): cv.string,
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vol.Required(CONF_P_GIVEN_T): vol.Coerce(float),
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vol.Optional(CONF_P_GIVEN_F): vol.Coerce(float)
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}, required=True)
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TEMPLATE_SCHEMA = vol.Schema({
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CONF_PLATFORM: CONF_TEMPLATE,
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vol.Required(CONF_VALUE_TEMPLATE): cv.template,
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vol.Required(CONF_P_GIVEN_T): vol.Coerce(float),
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vol.Optional(CONF_P_GIVEN_F): vol.Coerce(float)
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}, required=True)
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PLATFORM_SCHEMA = PLATFORM_SCHEMA.extend({
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vol.Optional(CONF_NAME, default=DEFAULT_NAME): cv.string,
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vol.Optional(CONF_DEVICE_CLASS): cv.string,
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vol.Required(CONF_OBSERVATIONS):
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vol.Schema(vol.All(cv.ensure_list,
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[vol.Any(NUMERIC_STATE_SCHEMA, STATE_SCHEMA,
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TEMPLATE_SCHEMA)])),
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vol.Required(CONF_PRIOR): vol.Coerce(float),
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vol.Optional(CONF_PROBABILITY_THRESHOLD,
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default=DEFAULT_PROBABILITY_THRESHOLD): vol.Coerce(float),
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})
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def update_probability(prior, prob_true, prob_false):
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"""Update probability using Bayes' rule."""
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numerator = prob_true * prior
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denominator = numerator + prob_false * (1 - prior)
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probability = numerator / denominator
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return probability
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async def async_setup_platform(hass, config, async_add_entities,
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discovery_info=None):
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"""Set up the Bayesian Binary sensor."""
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name = config.get(CONF_NAME)
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observations = config.get(CONF_OBSERVATIONS)
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prior = config.get(CONF_PRIOR)
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probability_threshold = config.get(CONF_PROBABILITY_THRESHOLD)
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device_class = config.get(CONF_DEVICE_CLASS)
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async_add_entities([
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BayesianBinarySensor(
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name, prior, observations, probability_threshold, device_class)
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], True)
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class BayesianBinarySensor(BinarySensorDevice):
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"""Representation of a Bayesian sensor."""
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def __init__(self, name, prior, observations, probability_threshold,
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device_class):
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"""Initialize the Bayesian sensor."""
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self._name = name
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self._observations = observations
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self._probability_threshold = probability_threshold
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self._device_class = device_class
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self._deviation = False
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self.prior = prior
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self.probability = prior
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self.current_obs = OrderedDict({})
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to_observe = set()
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for obs in self._observations:
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if 'entity_id' in obs:
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to_observe.update(set([obs.get('entity_id')]))
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if 'value_template' in obs:
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to_observe.update(
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set(obs.get(CONF_VALUE_TEMPLATE).extract_entities()))
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self.entity_obs = dict.fromkeys(to_observe, [])
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for ind, obs in enumerate(self._observations):
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obs['id'] = ind
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if 'entity_id' in obs:
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self.entity_obs[obs['entity_id']].append(obs)
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if 'value_template' in obs:
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for ent in obs.get(CONF_VALUE_TEMPLATE).extract_entities():
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self.entity_obs[ent].append(obs)
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self.watchers = {
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'numeric_state': self._process_numeric_state,
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'state': self._process_state,
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'template': self._process_template
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}
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async def async_added_to_hass(self):
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"""Call when entity about to be added."""
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@callback
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def async_threshold_sensor_state_listener(entity, old_state,
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new_state):
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"""Handle sensor state changes."""
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if new_state.state == STATE_UNKNOWN:
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return
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entity_obs_list = self.entity_obs[entity]
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for entity_obs in entity_obs_list:
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platform = entity_obs['platform']
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self.watchers[platform](entity_obs)
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prior = self.prior
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for obs in self.current_obs.values():
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prior = update_probability(
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prior, obs['prob_true'], obs['prob_false'])
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self.probability = prior
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self.hass.async_add_job(self.async_update_ha_state, True)
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async_track_state_change(
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self.hass, self.entity_obs, async_threshold_sensor_state_listener)
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def _update_current_obs(self, entity_observation, should_trigger):
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"""Update current observation."""
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obs_id = entity_observation['id']
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if should_trigger:
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prob_true = entity_observation['prob_given_true']
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prob_false = entity_observation.get(
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'prob_given_false', 1 - prob_true)
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self.current_obs[obs_id] = {
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'prob_true': prob_true,
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'prob_false': prob_false
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}
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else:
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self.current_obs.pop(obs_id, None)
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def _process_numeric_state(self, entity_observation):
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"""Add entity to current_obs if numeric state conditions are met."""
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entity = entity_observation['entity_id']
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should_trigger = condition.async_numeric_state(
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self.hass, entity,
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entity_observation.get('below'),
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entity_observation.get('above'), None, entity_observation)
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self._update_current_obs(entity_observation, should_trigger)
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def _process_state(self, entity_observation):
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"""Add entity to current observations if state conditions are met."""
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entity = entity_observation['entity_id']
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should_trigger = condition.state(
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self.hass, entity, entity_observation.get('to_state'))
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self._update_current_obs(entity_observation, should_trigger)
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def _process_template(self, entity_observation):
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"""Add entity to current_obs if template is true."""
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template = entity_observation.get(CONF_VALUE_TEMPLATE)
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template.hass = self.hass
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should_trigger = condition.async_template(
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self.hass, template, entity_observation)
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self._update_current_obs(entity_observation, should_trigger)
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@property
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def name(self):
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"""Return the name of the sensor."""
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return self._name
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@property
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def is_on(self):
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"""Return true if sensor is on."""
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return self._deviation
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@property
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def should_poll(self):
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"""No polling needed."""
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return False
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@property
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def device_class(self):
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"""Return the sensor class of the sensor."""
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return self._device_class
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@property
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def device_state_attributes(self):
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"""Return the state attributes of the sensor."""
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return {
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ATTR_OBSERVATIONS: [val for val in self.current_obs.values()],
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ATTR_PROBABILITY: round(self.probability, 2),
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ATTR_PROBABILITY_THRESHOLD: self._probability_threshold,
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}
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async def async_update(self):
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"""Get the latest data and update the states."""
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self._deviation = bool(self.probability >= self._probability_threshold)
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