Add Bayesian type for binary_sensor_map component (#4640)

* initial support for Bayesian type

* Cast bool state of binary_sensor to uint64_t

* Rename channels to observations with Bayesian

* Improve/standardize comments for all types

* Use black to correct sensor.py formatting

* Add SUM and BAYESIAN binary sensor map tests

* Remove unused variable

* Update esphome/components/binary_sensor_map/binary_sensor_map.cpp

Co-authored-by: Jesse Hills <3060199+jesserockz@users.noreply.github.com>

---------

Co-authored-by: Jesse Hills <3060199+jesserockz@users.noreply.github.com>

esphome/components/binary_sensor_map/binary_sensor_map.cpp

@@ -16,6 +16,9 @@ void BinarySensorMap::loop() {
     case BINARY_SENSOR_MAP_TYPE_SUM:
       this->process_sum_();
       break;
+    case BINARY_SENSOR_MAP_TYPE_BAYESIAN:
+      this->process_bayesian_();
+      break;
   }
 }
 
@@ -23,46 +26,51 @@ void BinarySensorMap::process_group_() {
   float total_current_value = 0.0;
   uint8_t num_active_sensors = 0;
   uint64_t mask = 0x00;
-  // check all binary_sensors for its state. when active add its value to total_current_value.
-  // create a bitmask for the binary_sensor status on all channels
+
+  // - check all binary_sensors for its state
+  //  - if active, add its value to total_current_value.
+  // - creates a bitmask for the binary_sensor states on all channels
   for (size_t i = 0; i < this->channels_.size(); i++) {
     auto bs = this->channels_[i];
     if (bs.binary_sensor->state) {
       num_active_sensors++;
-      total_current_value += bs.sensor_value;
+      total_current_value += bs.parameters.sensor_value;
       mask |= 1ULL << i;
     }
   }
-  // check if the sensor map was touched
+
+  // potentially update state only if a binary_sensor is active
   if (mask != 0ULL) {
-    // did the bit_mask change or is it a new sensor touch
+    // publish the average if the bitmask has changed
     if (this->last_mask_ != mask) {
       float publish_value = total_current_value / num_active_sensors;
       this->publish_state(publish_value);
     }
   } else if (this->last_mask_ != 0ULL) {
-    // is this a new sensor release
+    // no buttons are pressed and the states have changed since last run, so publish NAN
     ESP_LOGV(TAG, "'%s' - No binary sensor active, publishing NAN", this->name_.c_str());
     this->publish_state(NAN);
   }
+
   this->last_mask_ = mask;
 }
 
 void BinarySensorMap::process_sum_() {
   float total_current_value = 0.0;
   uint64_t mask = 0x00;
+
   // - check all binary_sensor states
   // - if active, add its value to total_current_value
-  // - creates a bitmask for the binary_sensor status on all channels
+  // - creates a bitmask for the binary_sensor states on all channels
   for (size_t i = 0; i < this->channels_.size(); i++) {
     auto bs = this->channels_[i];
     if (bs.binary_sensor->state) {
-      total_current_value += bs.sensor_value;
+      total_current_value += bs.parameters.sensor_value;
       mask |= 1ULL << i;
     }
   }
 
-  // update state only if the binary sensor states have changed or if no state has ever been sent on boot
+  // update state only if any binary_sensor states have changed or if no state has ever been sent on boot
   if ((this->last_mask_ != mask) || (!this->has_state())) {
     this->publish_state(total_current_value);
   }
@@ -70,15 +78,65 @@ void BinarySensorMap::process_sum_() {
   this->last_mask_ = mask;
 }
 
+void BinarySensorMap::process_bayesian_() {
+  float posterior_probability = this->bayesian_prior_;
+  uint64_t mask = 0x00;
+
+  // - compute the posterior probability by taking the product of the predicate probablities for each observation
+  // - create a bitmask for the binary_sensor states on all channels/observations
+  for (size_t i = 0; i < this->channels_.size(); i++) {
+    auto bs = this->channels_[i];
+
+    posterior_probability *=
+        this->bayesian_predicate_(bs.binary_sensor->state, posterior_probability,
+                                  bs.parameters.probabilities.given_true, bs.parameters.probabilities.given_false);
+
+    mask |= ((uint64_t) (bs.binary_sensor->state)) << i;
+  }
+
+  // update state only if any binary_sensor states have changed or if no state has ever been sent on boot
+  if ((this->last_mask_ != mask) || (!this->has_state())) {
+    this->publish_state(posterior_probability);
+  }
+
+  this->last_mask_ = mask;
+}
+
+float BinarySensorMap::bayesian_predicate_(bool sensor_state, float prior, float prob_given_true,
+                                           float prob_given_false) {
+  float prob_state_source_true = prob_given_true;
+  float prob_state_source_false = prob_given_false;
+
+  // if sensor is off, then we use the probabilities for the observation's complement
+  if (!sensor_state) {
+    prob_state_source_true = 1 - prob_given_true;
+    prob_state_source_false = 1 - prob_given_false;
+  }
+
+  return prob_state_source_true / (prior * prob_state_source_true + (1.0 - prior) * prob_state_source_false);
+}
+
 void BinarySensorMap::add_channel(binary_sensor::BinarySensor *sensor, float value) {
   BinarySensorMapChannel sensor_channel{
       .binary_sensor = sensor,
+      .parameters{
           .sensor_value = value,
+      },
   };
   this->channels_.push_back(sensor_channel);
 }
 
-void BinarySensorMap::set_sensor_type(BinarySensorMapType sensor_type) { this->sensor_type_ = sensor_type; }
-
+void BinarySensorMap::add_channel(binary_sensor::BinarySensor *sensor, float prob_given_true, float prob_given_false) {
+  BinarySensorMapChannel sensor_channel{
+      .binary_sensor = sensor,
+      .parameters{
+          .probabilities{
+              .given_true = prob_given_true,
+              .given_false = prob_given_false,
+          },
+      },
+  };
+  this->channels_.push_back(sensor_channel);
+}
 }  // namespace binary_sensor_map
 }  // namespace esphome

esphome/components/binary_sensor_map/binary_sensor_map.h

@@ -12,51 +12,88 @@ namespace binary_sensor_map {
 enum BinarySensorMapType {
   BINARY_SENSOR_MAP_TYPE_GROUP,
   BINARY_SENSOR_MAP_TYPE_SUM,
+  BINARY_SENSOR_MAP_TYPE_BAYESIAN,
 };
 
 struct BinarySensorMapChannel {
   binary_sensor::BinarySensor *binary_sensor;
+  union {
     float sensor_value;
+    struct {
+      float given_true;
+      float given_false;
+    } probabilities;
+  } parameters;
 };
 
-/** Class to group binary_sensors to one Sensor.
+/** Class to map one or more binary_sensors to one Sensor.
  *
- * Each binary sensor represents a float value in the group.
+ * Each binary sensor has configured parameters that each mapping type uses to compute the single numerical result
  */
 class BinarySensorMap : public sensor::Sensor, public Component {
  public:
   void dump_config() override;
+
   /**
-   * The loop checks all binary_sensor states
-   * When the binary_sensor reports a true value for its state, then the float value it represents is added to the
-   * total_current_value
+   * The loop calls the configured type processing method
    *
-   * Only when the total_current_value changed and at least one sensor reports an active state we publish the sensors
-   * average value. When the value changed and no sensors ar active we publish NAN.
-   * */
+   * The processing method loops through all sensors and calculates the numerical result
+   * The result is only published if a binary sensor state has changed or, for some types, on initial boot
+   */
   void loop() override;
-  float get_setup_priority() const override { return setup_priority::DATA; }
-  /** Add binary_sensors to the group.
-   * Each binary_sensor represents a float value when its state is true
+
+  /**
+   * Add binary_sensors to the group when only one parameter is needed for the configured mapping type.
    *
    * @param *sensor The binary sensor.
    * @param value  The value this binary_sensor represents
    */
   void add_channel(binary_sensor::BinarySensor *sensor, float value);
-  void set_sensor_type(BinarySensorMapType sensor_type);
+
+  /**
+   * Add binary_sensors to the group when two parameters are needed for the Bayesian mapping type.
+   *
+   * @param *sensor The binary sensor.
+   * @param prob_given_true Probability this observation is on when the Bayesian event is true
+   * @param prob_given_false Probability this observation is on when the Bayesian event is false
+   */
+  void add_channel(binary_sensor::BinarySensor *sensor, float prob_given_true, float prob_given_false);
+
+  void set_sensor_type(BinarySensorMapType sensor_type) { this->sensor_type_ = sensor_type; }
+
+  void set_bayesian_prior(float prior) { this->bayesian_prior_ = prior; };
 
  protected:
   std::vector<BinarySensorMapChannel> channels_{};
   BinarySensorMapType sensor_type_{BINARY_SENSOR_MAP_TYPE_GROUP};
-  // this gives max 64 channels per binary_sensor_map
+
+  // this allows a max of 64 channels/observations in order to keep track of binary_sensor states
   uint64_t last_mask_{0x00};
+
+  // Bayesian event prior probability before taking into account any observations
+  float bayesian_prior_{};
+
   /**
-   * methods to process the types of binary_sensor_maps
-   * GROUP: process_group_() just map to a value
+   * Methods to process the binary_sensor_maps types
+   *
+   * GROUP: process_group_() averages all the values
    * ADD: process_add_() adds all the values
+   * BAYESIAN: process_bayesian_() computes the predicate probability
    * */
   void process_group_();
   void process_sum_();
+  void process_bayesian_();
+
+  /**
+   * Computes the Bayesian predicate for a specific observation
+   * If the sensor state is false, then we use the parameters' probabilities for the observatiosn complement
+   *
+   * @param sensor_state  State of observation
+   * @param prior Prior probability before accounting for this observation
+   * @param prob_given_true Probability this observation is on when the Bayesian event is true
+   * @param prob_given_false Probability this observation is on when the Bayesian event is false
+   * */
+  float bayesian_predicate_(bool sensor_state, float prior, float prob_given_true, float prob_given_false);
 };
 
 }  // namespace binary_sensor_map

esphome/components/binary_sensor_map/sensor.py

@@ -20,16 +20,29 @@ BinarySensorMap = binary_sensor_map_ns.class_(
 )
 SensorMapType = binary_sensor_map_ns.enum("SensorMapType")
 
+CONF_BAYESIAN = "bayesian"
+CONF_PRIOR = "prior"
+CONF_PROB_GIVEN_TRUE = "prob_given_true"
+CONF_PROB_GIVEN_FALSE = "prob_given_false"
+CONF_OBSERVATIONS = "observations"
+
 SENSOR_MAP_TYPES = {
     CONF_GROUP: SensorMapType.BINARY_SENSOR_MAP_TYPE_GROUP,
     CONF_SUM: SensorMapType.BINARY_SENSOR_MAP_TYPE_SUM,
+    CONF_BAYESIAN: SensorMapType.BINARY_SENSOR_MAP_TYPE_BAYESIAN,
 }
 
-entry = {
+entry_one_parameter = {
     cv.Required(CONF_BINARY_SENSOR): cv.use_id(binary_sensor.BinarySensor),
     cv.Required(CONF_VALUE): cv.float_,
 }
 
+entry_bayesian_parameters = {
+    cv.Required(CONF_BINARY_SENSOR): cv.use_id(binary_sensor.BinarySensor),
+    cv.Required(CONF_PROB_GIVEN_TRUE): cv.float_range(min=0, max=1),
+    cv.Required(CONF_PROB_GIVEN_FALSE): cv.float_range(min=0, max=1),
+}
+
 CONFIG_SCHEMA = cv.typed_schema(
     {
         CONF_GROUP: sensor.sensor_schema(
@@ -39,7 +52,7 @@ CONFIG_SCHEMA = cv.typed_schema(
         ).extend(
             {
                 cv.Required(CONF_CHANNELS): cv.All(
-                    cv.ensure_list(entry), cv.Length(min=1, max=64)
+                    cv.ensure_list(entry_one_parameter), cv.Length(min=1, max=64)
                 ),
             }
         ),
@@ -50,7 +63,18 @@ CONFIG_SCHEMA = cv.typed_schema(
         ).extend(
             {
                 cv.Required(CONF_CHANNELS): cv.All(
-                    cv.ensure_list(entry), cv.Length(min=1, max=64)
+                    cv.ensure_list(entry_one_parameter), cv.Length(min=1, max=64)
+                ),
+            }
+        ),
+        CONF_BAYESIAN: sensor.sensor_schema(
+            BinarySensorMap,
+            accuracy_decimals=2,
+        ).extend(
+            {
+                cv.Required(CONF_PRIOR): cv.float_range(min=0, max=1),
+                cv.Required(CONF_OBSERVATIONS): cv.All(
+                    cv.ensure_list(entry_bayesian_parameters), cv.Length(min=1, max=64)
                 ),
             }
         ),
@@ -66,6 +90,17 @@ async def to_code(config):
     constant = SENSOR_MAP_TYPES[config[CONF_TYPE]]
     cg.add(var.set_sensor_type(constant))
 
+    if config[CONF_TYPE] == CONF_BAYESIAN:
+        cg.add(var.set_bayesian_prior(config[CONF_PRIOR]))
+
+        for obs in config[CONF_OBSERVATIONS]:
+            input_var = await cg.get_variable(obs[CONF_BINARY_SENSOR])
+            cg.add(
+                var.add_channel(
+                    input_var, obs[CONF_PROB_GIVEN_TRUE], obs[CONF_PROB_GIVEN_FALSE]
+                )
+            )
+    else:
         for ch in config[CONF_CHANNELS]:
             input_var = await cg.get_variable(ch[CONF_BINARY_SENSOR])
             cg.add(var.add_channel(input_var, ch[CONF_VALUE]))

tests/test3.yaml

@@ -368,6 +368,32 @@ sensor:
       - binary_sensor: bin3
         value: 100.0
 
+  - platform: binary_sensor_map
+    name: Binary Sensor Map
+    type: sum
+    channels:
+      - binary_sensor: bin1
+        value: 10.0
+      - binary_sensor: bin2
+        value: 15.0
+      - binary_sensor: bin3
+        value: 100.0
+
+  - platform: binary_sensor_map
+    name: Binary Sensor Map
+    type: bayesian
+    prior: 0.4
+    observations:
+      - binary_sensor: bin1
+        prob_given_true: 0.9
+        prob_given_false: 0.4
+      - binary_sensor: bin2
+        prob_given_true: 0.7
+        prob_given_false: 0.05
+      - binary_sensor: bin3
+        prob_given_true: 0.8
+        prob_given_false: 0.2
+
   - platform: bl0939
     uart_id: uart_8
     voltage: