Bayesian Binary Sensor

homeassistant/components/binary_sensor/bayesian.py

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+"""
+Use Bayesian Inference to trigger a binary sensor.
+
+For more details about this platform, please refer to the documentation at
+https://home-assistant.io/components/binary_sensor.bayesian/
+"""
+import asyncio
+import logging
+from collections import OrderedDict
+
+import voluptuous as vol
+
+import homeassistant.helpers.config_validation as cv
+from homeassistant.components.binary_sensor import (
+    BinarySensorDevice, PLATFORM_SCHEMA)
+from homeassistant.const import (
+    CONF_ABOVE, CONF_BELOW, CONF_DEVICE_CLASS, CONF_ENTITY_ID, CONF_NAME,
+    CONF_PLATFORM, CONF_STATE, STATE_UNKNOWN)
+from homeassistant.core import callback
+from homeassistant.helpers import condition
+from homeassistant.helpers.event import async_track_state_change
+
+_LOGGER = logging.getLogger(__name__)
+
+CONF_OBSERVATIONS = 'observations'
+CONF_PRIOR = 'prior'
+CONF_PROBABILITY_THRESHOLD = 'probability_threshold'
+CONF_P_GIVEN_F = 'prob_given_false'
+CONF_P_GIVEN_T = 'prob_given_true'
+CONF_TO_STATE = 'to_state'
+
+DEFAULT_NAME = 'BayesianBinary'
+
+NUMERIC_STATE_SCHEMA = vol.Schema({
+    CONF_PLATFORM: 'numeric_state',
+    vol.Required(CONF_ENTITY_ID): cv.entity_id,
+    vol.Optional(CONF_ABOVE): vol.Coerce(float),
+    vol.Optional(CONF_BELOW): vol.Coerce(float),
+    vol.Required(CONF_P_GIVEN_T): vol.Coerce(float),
+    vol.Optional(CONF_P_GIVEN_F): vol.Coerce(float)
+}, required=True)
+
+STATE_SCHEMA = vol.Schema({
+    CONF_PLATFORM: CONF_STATE,
+    vol.Required(CONF_ENTITY_ID): cv.entity_id,
+    vol.Required(CONF_TO_STATE): cv.string,
+    vol.Required(CONF_P_GIVEN_T): vol.Coerce(float),
+    vol.Optional(CONF_P_GIVEN_F): vol.Coerce(float)
+}, required=True)
+
+PLATFORM_SCHEMA = PLATFORM_SCHEMA.extend({
+    vol.Optional(CONF_NAME, default=DEFAULT_NAME):
+        cv.string,
+    vol.Optional(CONF_DEVICE_CLASS): cv.string,
+    vol.Required(CONF_OBSERVATIONS): vol.Schema(
+        vol.All(cv.ensure_list, [vol.Any(NUMERIC_STATE_SCHEMA,
+                                         STATE_SCHEMA)])
+    ),
+    vol.Required(CONF_PRIOR): vol.Coerce(float),
+    vol.Optional(CONF_PROBABILITY_THRESHOLD):
+        vol.Coerce(float),
+})
+
+
+def update_probability(prior, prob_true, prob_false):
+    """Update probability using Bayes' rule."""
+    numerator = prob_true * prior
+    denominator = numerator + prob_false * (1 - prior)
+
+    probability = numerator / denominator
+    return probability
+
+
+@asyncio.coroutine
+def async_setup_platform(hass, config, async_add_devices, discovery_info=None):
+    """Set up the Threshold sensor."""
+    name = config.get(CONF_NAME)
+    observations = config.get(CONF_OBSERVATIONS)
+    prior = config.get(CONF_PRIOR)
+    probability_threshold = config.get(CONF_PROBABILITY_THRESHOLD, 0.5)
+    device_class = config.get(CONF_DEVICE_CLASS)
+
+    async_add_devices([
+        BayesianBinarySensor(name, prior, observations, probability_threshold,
+                             device_class)
+    ], True)
+
+
+class BayesianBinarySensor(BinarySensorDevice):
+    """Representation of a Bayesian sensor."""
+
+    def __init__(self, name, prior, observations, probability_threshold,
+                 device_class):
+        """Initialize the Bayesian sensor."""
+        self._name = name
+        self._observations = observations
+        self._probability_threshold = probability_threshold
+        self._device_class = device_class
+        self._deviation = False
+        self.prior = prior
+        self.probability = prior
+
+        self.current_obs = OrderedDict({})
+
+        self.entity_obs = {obs['entity_id']: obs for obs in self._observations}
+
+        self.watchers = {
+            'numeric_state': self._process_numeric_state,
+            'state': self._process_state
+        }
+
+    @asyncio.coroutine
+    def async_added_to_hass(self):
+        """Call when entity about to be added to hass."""
+        @callback
+        # pylint: disable=invalid-name
+        def async_threshold_sensor_state_listener(entity, old_state,
+                                                  new_state):
+            """Handle sensor state changes."""
+            if new_state.state == STATE_UNKNOWN:
+                return
+
+            entity_obs = self.entity_obs[entity]
+            platform = entity_obs['platform']
+
+            self.watchers[platform](entity_obs)
+
+            prior = self.prior
+            print(self.current_obs.values())
+            for obs in self.current_obs.values():
+                prior = update_probability(prior, obs['prob_true'],
+                                           obs['prob_false'])
+
+            self.probability = prior
+
+            self.hass.async_add_job(self.async_update_ha_state, True)
+
+        entities = [obs['entity_id'] for obs in self._observations]
+        async_track_state_change(
+            self.hass, entities, async_threshold_sensor_state_listener)
+
+    def _update_current_obs(self, entity_observation, should_trigger):
+        """Update current observation."""
+        entity = entity_observation['entity_id']
+
+        if should_trigger:
+            prob_true = entity_observation['prob_given_true']
+            prob_false = entity_observation.get(
+                'prob_given_false', 1 - prob_true)
+
+            self.current_obs[entity] = {
+                'prob_true': prob_true,
+                'prob_false': prob_false
+            }
+
+        else:
+            self.current_obs.pop(entity, None)
+
+    def _process_numeric_state(self, entity_observation):
+        """Add entity to current_obs if numeric state conditions are met."""
+        entity = entity_observation['entity_id']
+
+        should_trigger = condition.async_numeric_state(
+            self.hass, entity,
+            entity_observation.get('below'),
+            entity_observation.get('above'), None, entity_observation)
+
+        self._update_current_obs(entity_observation, should_trigger)
+
+    def _process_state(self, entity_observation):
+        """Add entity to current observations if state conditions are met."""
+        entity = entity_observation['entity_id']
+
+        should_trigger = condition.state(
+            self.hass, entity, entity_observation.get('to_state'))
+
+        self._update_current_obs(entity_observation, should_trigger)
+
+    @property
+    def name(self):
+        """Return the name of the sensor."""
+        return self._name
+
+    @property
+    def is_on(self):
+        """Return true if sensor is on."""
+        return self._deviation
+
+    @property
+    def should_poll(self):
+        """No polling needed."""
+        return False
+
+    @property
+    def device_class(self):
+        """Return the sensor class of the sensor."""
+        return self._device_class
+
+    @property
+    def device_state_attributes(self):
+        """Return the state attributes of the sensor."""
+        return {
+            'observations': [val for val in self.current_obs.values()],
+            'probability': self.probability,
+            'probability_threshold': self._probability_threshold
+        }
+
+    @asyncio.coroutine
+    def async_update(self):
+        """Get the latest data and update the states."""
+        self._deviation = bool(self.probability > self._probability_threshold)

tests/components/binary_sensor/test_bayesian.py

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+"""The test for the bayesian sensor platform."""
+import unittest
+
+from homeassistant.setup import setup_component
+from homeassistant.components.binary_sensor import bayesian
+
+from tests.common import get_test_home_assistant
+
+
+class TestBayesianBinarySensor(unittest.TestCase):
+    """Test the threshold sensor."""
+
+    def setup_method(self, method):
+        """Set up things to be run when tests are started."""
+        self.hass = get_test_home_assistant()
+
+    def teardown_method(self, method):
+        """Stop everything that was started."""
+        self.hass.stop()
+
+    def test_sensor_numeric_state(self):
+        """Test sensor on numeric state platform observations."""
+        config = {
+            'binary_sensor': {
+                'platform':
+                'bayesian',
+                'name':
+                'Test_Binary',
+                'observations': [{
+                    'platform': 'numeric_state',
+                    'entity_id': 'sensor.test_monitored',
+                    'below': 10,
+                    'above': 5,
+                    'prob_given_true': 0.6
+                }, {
+                    'platform': 'numeric_state',
+                    'entity_id': 'sensor.test_monitored1',
+                    'below': 7,
+                    'above': 5,
+                    'prob_given_true': 0.9,
+                    'prob_given_false': 0.1
+                }],
+                'prior':
+                0.2,
+            }
+        }
+
+        assert setup_component(self.hass, 'binary_sensor', config)
+
+        self.hass.states.set('sensor.test_monitored', 4)
+        self.hass.block_till_done()
+
+        state = self.hass.states.get('binary_sensor.test_binary')
+
+        self.assertEqual([], state.attributes.get('observations'))
+        self.assertEqual(0.2, state.attributes.get('probability'))
+
+        assert state.state == 'off'
+
+        self.hass.states.set('sensor.test_monitored', 6)
+        self.hass.block_till_done()
+        self.hass.states.set('sensor.test_monitored', 4)
+        self.hass.block_till_done()
+        self.hass.states.set('sensor.test_monitored', 6)
+        self.hass.states.set('sensor.test_monitored1', 6)
+        self.hass.block_till_done()
+
+        state = self.hass.states.get('binary_sensor.test_binary')
+        self.assertEqual([{
+            'prob_false': 0.4,
+            'prob_true': 0.6
+        }, {
+            'prob_false': 0.1,
+            'prob_true': 0.9
+        }], state.attributes.get('observations'))
+        self.assertAlmostEqual(0.7714285714285715,
+                               state.attributes.get('probability'))
+
+        assert state.state == 'on'
+
+        self.hass.states.set('sensor.test_monitored', 6)
+        self.hass.states.set('sensor.test_monitored1', 0)
+        self.hass.block_till_done()
+        self.hass.states.set('sensor.test_monitored', 4)
+        self.hass.block_till_done()
+
+        state = self.hass.states.get('binary_sensor.test_binary')
+        self.assertEqual(0.2, state.attributes.get('probability'))
+
+        assert state.state == 'off'
+
+        self.hass.states.set('sensor.test_monitored', 15)
+        self.hass.block_till_done()
+
+        state = self.hass.states.get('binary_sensor.test_binary')
+
+        assert state.state == 'off'
+
+    def test_sensor_state(self):
+        """Test sensor on state platform observations."""
+        config = {
+            'binary_sensor': {
+                'name':
+                'Test_Binary',
+                'platform':
+                'bayesian',
+                'observations': [{
+                    'platform': 'state',
+                    'entity_id': 'sensor.test_monitored',
+                    'to_state': 'off',
+                    'prob_given_true': 0.8,
+                    'prob_given_false': 0.4
+                }],
+                'prior':
+                0.2,
+                'probability_threshold':
+                0.32,
+            }
+        }
+
+        assert setup_component(self.hass, 'binary_sensor', config)
+
+        self.hass.states.set('sensor.test_monitored', 'on')
+
+        state = self.hass.states.get('binary_sensor.test_binary')
+
+        self.assertEqual([], state.attributes.get('observations'))
+        self.assertEqual(0.2, state.attributes.get('probability'))
+
+        assert state.state == 'off'
+
+        self.hass.states.set('sensor.test_monitored', 'off')
+        self.hass.block_till_done()
+        self.hass.states.set('sensor.test_monitored', 'on')
+        self.hass.block_till_done()
+        self.hass.states.set('sensor.test_monitored', 'off')
+        self.hass.block_till_done()
+
+        state = self.hass.states.get('binary_sensor.test_binary')
+        self.assertEqual([{
+            'prob_true': 0.8,
+            'prob_false': 0.4
+        }], state.attributes.get('observations'))
+        self.assertAlmostEqual(0.33333333, state.attributes.get('probability'))
+
+        assert state.state == 'on'
+
+        self.hass.states.set('sensor.test_monitored', 'off')
+        self.hass.block_till_done()
+        self.hass.states.set('sensor.test_monitored', 'on')
+        self.hass.block_till_done()
+
+        state = self.hass.states.get('binary_sensor.test_binary')
+        self.assertAlmostEqual(0.2, state.attributes.get('probability'))
+
+        assert state.state == 'off'
+
+    def test_probability_updates(self):
+        """Test probability update function."""
+        prob_true = [0.3, 0.6, 0.8]
+        prob_false = [0.7, 0.4, 0.2]
+        prior = 0.5
+
+        for pt, pf in zip(prob_true, prob_false):
+            prior = bayesian.update_probability(prior, pt, pf)
+
+        self.assertAlmostEqual(0.720000, prior)
+
+        prob_true = [0.8, 0.3, 0.9]
+        prob_false = [0.6, 0.4, 0.2]
+        prior = 0.7
+
+        for pt, pf in zip(prob_true, prob_false):
+            prior = bayesian.update_probability(prior, pt, pf)
+
+        self.assertAlmostEqual(0.9130434782608695, prior)