7de73e9ef7
* Bayesian Binary Sensor Why: * It would be beneficial to leverage various sensor outputs in a Bayesian manner in order to sense more complex events. This change addresses the need by: * `BayesianBinarySensor` class in `./homeassistant/components/binary_sensor/bayesian.py` * Tests in `./tests/components/binary_sensor/test_bayesian.py` Caveats: This is my first time in this code-base. I did try to follow conventions that I was able to find, but I'm sure there will be some issues to straighten out. * minor cleanup * Address reviewer's comments This change addresses the need by: * Removing `CONF_SENSOR_CLASS` and its usage in `get_deprecated`. * Make probability update function a static method, and use single `_` to match project conventions. * Address linter failures * fix `device_class` declaration * Address Comments Why: * Not validating config schema enough. * Not following common practices for async initialization. * Naive implementation of Bayes' rule. This change addresses the need by: * Improving config validation for observations. * Moving initialization logic into `async_added_to_hass`. * Re-configuring Bayesian updates to allow true P|Q usage. * address linting issues * Improve DRYness by adding `_update_current_obs` method * update doc strings and ensure functions are set up properly for async * Make only 1 state change handle * fix style * fix style part 2 * fix lint
176 lines
5.7 KiB
Python
176 lines
5.7 KiB
Python
"""The test for the bayesian sensor platform."""
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import unittest
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from homeassistant.setup import setup_component
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from homeassistant.components.binary_sensor import bayesian
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from tests.common import get_test_home_assistant
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class TestBayesianBinarySensor(unittest.TestCase):
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"""Test the threshold sensor."""
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def setup_method(self, method):
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"""Set up things to be run when tests are started."""
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self.hass = get_test_home_assistant()
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def teardown_method(self, method):
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"""Stop everything that was started."""
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self.hass.stop()
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def test_sensor_numeric_state(self):
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"""Test sensor on numeric state platform observations."""
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config = {
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'binary_sensor': {
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'platform':
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'bayesian',
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'name':
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'Test_Binary',
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'observations': [{
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'platform': 'numeric_state',
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'entity_id': 'sensor.test_monitored',
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'below': 10,
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'above': 5,
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'prob_given_true': 0.6
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}, {
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'platform': 'numeric_state',
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'entity_id': 'sensor.test_monitored1',
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'below': 7,
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'above': 5,
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'prob_given_true': 0.9,
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'prob_given_false': 0.1
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}],
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'prior':
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0.2,
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}
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}
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assert setup_component(self.hass, 'binary_sensor', config)
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self.hass.states.set('sensor.test_monitored', 4)
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self.hass.block_till_done()
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state = self.hass.states.get('binary_sensor.test_binary')
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self.assertEqual([], state.attributes.get('observations'))
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self.assertEqual(0.2, state.attributes.get('probability'))
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assert state.state == 'off'
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self.hass.states.set('sensor.test_monitored', 6)
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self.hass.block_till_done()
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self.hass.states.set('sensor.test_monitored', 4)
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self.hass.block_till_done()
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self.hass.states.set('sensor.test_monitored', 6)
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self.hass.states.set('sensor.test_monitored1', 6)
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self.hass.block_till_done()
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state = self.hass.states.get('binary_sensor.test_binary')
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self.assertEqual([{
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'prob_false': 0.4,
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'prob_true': 0.6
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}, {
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'prob_false': 0.1,
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'prob_true': 0.9
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}], state.attributes.get('observations'))
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self.assertAlmostEqual(0.7714285714285715,
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state.attributes.get('probability'))
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assert state.state == 'on'
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self.hass.states.set('sensor.test_monitored', 6)
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self.hass.states.set('sensor.test_monitored1', 0)
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self.hass.block_till_done()
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self.hass.states.set('sensor.test_monitored', 4)
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self.hass.block_till_done()
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state = self.hass.states.get('binary_sensor.test_binary')
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self.assertEqual(0.2, state.attributes.get('probability'))
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assert state.state == 'off'
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self.hass.states.set('sensor.test_monitored', 15)
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self.hass.block_till_done()
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state = self.hass.states.get('binary_sensor.test_binary')
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assert state.state == 'off'
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def test_sensor_state(self):
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"""Test sensor on state platform observations."""
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config = {
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'binary_sensor': {
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'name':
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'Test_Binary',
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'platform':
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'bayesian',
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'observations': [{
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'platform': 'state',
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'entity_id': 'sensor.test_monitored',
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'to_state': 'off',
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'prob_given_true': 0.8,
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'prob_given_false': 0.4
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}],
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'prior':
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0.2,
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'probability_threshold':
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0.32,
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}
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}
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assert setup_component(self.hass, 'binary_sensor', config)
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self.hass.states.set('sensor.test_monitored', 'on')
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state = self.hass.states.get('binary_sensor.test_binary')
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self.assertEqual([], state.attributes.get('observations'))
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self.assertEqual(0.2, state.attributes.get('probability'))
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assert state.state == 'off'
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self.hass.states.set('sensor.test_monitored', 'off')
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self.hass.block_till_done()
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self.hass.states.set('sensor.test_monitored', 'on')
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self.hass.block_till_done()
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self.hass.states.set('sensor.test_monitored', 'off')
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self.hass.block_till_done()
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state = self.hass.states.get('binary_sensor.test_binary')
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self.assertEqual([{
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'prob_true': 0.8,
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'prob_false': 0.4
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}], state.attributes.get('observations'))
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self.assertAlmostEqual(0.33333333, state.attributes.get('probability'))
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assert state.state == 'on'
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self.hass.states.set('sensor.test_monitored', 'off')
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self.hass.block_till_done()
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self.hass.states.set('sensor.test_monitored', 'on')
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self.hass.block_till_done()
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state = self.hass.states.get('binary_sensor.test_binary')
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self.assertAlmostEqual(0.2, state.attributes.get('probability'))
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assert state.state == 'off'
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def test_probability_updates(self):
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"""Test probability update function."""
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prob_true = [0.3, 0.6, 0.8]
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prob_false = [0.7, 0.4, 0.2]
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prior = 0.5
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for pt, pf in zip(prob_true, prob_false):
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prior = bayesian.update_probability(prior, pt, pf)
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self.assertAlmostEqual(0.720000, prior)
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prob_true = [0.8, 0.3, 0.9]
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prob_false = [0.6, 0.4, 0.2]
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prior = 0.7
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for pt, pf in zip(prob_true, prob_false):
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prior = bayesian.update_probability(prior, pt, pf)
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self.assertAlmostEqual(0.9130434782608695, prior)
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