removed label parameter from default cluster estimation

Author: Aleksa-M

main_2024.py

@@ -346,7 +346,6 @@ def main() -> int:
             MIN_NEW_POINTS_TO_RUN,
             MAX_NUM_COMPONENTS,
             RANDOM_STATE,
-            0,
         ),
         input_queues=[geolocation_to_cluster_estimation_queue],
         output_queues=[cluster_estimation_to_communications_queue],

modules/cluster_estimation/cluster_estimation.py

@@ -6,8 +6,6 @@
 
 # pylint: disable=duplicate-code
 
-# pylint: disable=duplicate-code
-
 import numpy as np
 import sklearn
 import sklearn.datasets
@@ -38,9 +36,6 @@ class ClusterEstimation:
     local_logger: Logger
         For logging error and debug messages.
 
-    label: int
-        Every cluster generated by this model will have this label
-
     METHODS
     -------
     run()
@@ -88,7 +83,6 @@ def create(
         max_num_components: int,
         random_state: int,
         local_logger: logger.Logger,
-        label: int,
     ) -> "tuple[bool, ClusterEstimation | None]":
         """
         Data requirement conditions for estimation model to run.
@@ -130,7 +124,6 @@ def create(
             max_num_components,
             random_state,
             local_logger,
-            label,
         )
 
     def __init__(
@@ -141,7 +134,6 @@ def __init__(
         max_num_components: int,
         random_state: int,
         local_logger: logger.Logger,
-        label: int,
     ) -> None:
         """
         Private constructor, use create() method.
@@ -168,7 +160,6 @@ def __init__(
         self.__min_new_points_to_run = min_new_points_to_run
         self.__has_ran_once = False
         self.__logger = local_logger
-        self.__label = label
 
     def run(
         self, detections: "list[detection_in_world.DetectionInWorld]", run_override: bool
@@ -196,6 +187,10 @@ def run(
             List containing ObjectInWorld objects, containing position and covariance value.
             None if conditions not met and model not ran or model failed to converge.
         """
+        # in use, all detections will have the same label, so the
+        # first element's label was arbitrarily selected
+        label = detections[0].label
+
         # Store new input data
         self.__current_bucket += self.__convert_detections_to_point(detections)
 
@@ -228,6 +223,7 @@ def run(
 
         # Filter out all clusters after __WEIGHT_DROP_THRESHOLD weight drop occurs
         viable_clusters = [model_output[0]]
+        print(f"len(model_output) = {len(model_output)}")
         for i in range(1, len(model_output)):
             if model_output[i][1] / model_output[i - 1][1] < self.__WEIGHT_DROP_THRESHOLD:
                 break
@@ -243,7 +239,7 @@ def run(
         objects_in_world = []
         for cluster in model_output:
             result, landing_pad = object_in_world.ObjectInWorld.create(
-                cluster[0][0], cluster[0][1], cluster[2], self.__label
+                cluster[0][0], cluster[0][1], cluster[2], label
             )
 
             if result:

modules/cluster_estimation/cluster_estimation_by_label.py

@@ -153,7 +153,6 @@ def run(
                     self.__max_num_components,
                     self.__random_state,
                     self.__local_logger,
-                    label,
                 )
                 if not result:
                     self.__local_logger.error(

modules/cluster_estimation/cluster_estimation_worker.py

@@ -19,7 +19,6 @@ def cluster_estimation_worker(
     min_new_points_to_run: int,
     max_num_components: int,
     random_state: int,
-    label: int,
     input_queue: queue_proxy_wrapper.QueueProxyWrapper,
     output_queue: queue_proxy_wrapper.QueueProxyWrapper,
     controller: worker_controller.WorkerController,
@@ -76,7 +75,6 @@ def cluster_estimation_worker(
         max_num_components,
         random_state,
         local_logger,
-        label,
     )
     if not result:
         local_logger.error("Worker failed to create class object", True)

tests/unit/test_cluster_detection.py

@@ -21,7 +21,7 @@
 
 # Test functions use test fixture signature names and access class privates
 # No enable
-# pylint: disable=protected-access,redefined-outer-name
+# pylint: disable=protected-access,redefined-outer-name,too-many-instance-attributes
 
 
 @pytest.fixture()
@@ -39,7 +39,6 @@ def cluster_model() -> cluster_estimation.ClusterEstimation:  # type: ignore
         MAX_NUM_COMPONENTS,
         RNG_SEED,
         test_logger,
-        0
     )
     assert result
     assert model is not None
@@ -62,28 +61,6 @@ def cluster_model_by_label() -> cluster_estimation_by_label.ClusterEstimationByL
         MAX_NUM_COMPONENTS,
         RNG_SEED,
         test_logger,
-        0
-    )
-    assert result
-    assert model is not None
-
-    yield model  # type: ignore
-
-
-@pytest.fixture()
-def cluster_model_by_label() -> cluster_estimation_by_label.ClusterEstimationByLabel:  # type: ignore
-    """
-    Cluster estimation by label object.
-    """
-    result, test_logger = logger.Logger.create("test_logger", False)
-    assert result
-    assert test_logger is not None
-
-    result, model = cluster_estimation_by_label.ClusterEstimationByLabel.create(
-        MIN_TOTAL_POINTS_THRESHOLD,
-        MIN_NEW_POINTS_TO_RUN,
-        RNG_SEED,
-        test_logger,
     )
     assert result
     assert model is not None