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Add observed entities to bayesian sensor (#27721)

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* Add observed entities to bayesian sensor

* Update binary_sensor.py to comply with style guidelines and test_binary_sensor.py to verify the entity_id

* Update binary_sensor.py and test_binary_sensor.py to include an additional attribute for observed entities

* Use of ATTR_ENTITY_ID and numeric key of observed entity

* Update binary_sensor.py

* Update test_binary_sensor.py to verify behavior

* Update to return a list without duplicates in the state attribute

* Update binary_sensor.py: rename of ATTR_ENTITY_ID into ATTR_OBSERVED_ENTITIES

* Rename new attribute into ATTR_OCCURRED_OBSERVATION_ENTITIES and fix test
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paolog89 2020-02-25 06:27:59 +01:00 • committed by GitHub
parent 5776b9f17d
commit 0e6a48c688
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18
homeassistant/components/bayesian/binary_sensor.py
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@ -1,5 +1,6 @@
"""Use Bayesian Inference to trigger a binary sensor."""
from collections import OrderedDict
from itertools import chain
import voluptuous as vol
@ -21,6 +22,7 @@ import homeassistant.helpers.config_validation as cv
from homeassistant.helpers.event import async_track_state_change
ATTR_OBSERVATIONS = "observations"
ATTR_OCCURRED_OBSERVATION_ENTITIES = "occurred_observation_entities"
ATTR_PROBABILITY = "probability"
ATTR_PROBABILITY_THRESHOLD = "probability_threshold"
@ -126,6 +128,15 @@ class BayesianBinarySensor(BinarySensorDevice):
self.probability = prior
self.current_obs = OrderedDict({})
self.entity_obs_dict = []
for obs in self._observations:
if "entity_id" in obs:
self.entity_obs_dict.append([obs.get("entity_id")])
if "value_template" in obs:
self.entity_obs_dict.append(
list(obs.get(CONF_VALUE_TEMPLATE).extract_entities())
)
to_observe = set()
for obs in self._observations:
@ -251,6 +262,13 @@ class BayesianBinarySensor(BinarySensorDevice):
"""Return the state attributes of the sensor."""
return {
ATTR_OBSERVATIONS: list(self.current_obs.values()),
ATTR_OCCURRED_OBSERVATION_ENTITIES: list(
set(
chain.from_iterable(
self.entity_obs_dict[obs] for obs in self.current_obs.keys()
)
)
),
ATTR_PROBABILITY: round(self.probability, 2),
ATTR_PROBABILITY_THRESHOLD: self._probability_threshold,
}