src/batdetect2/evaluate/config.py

@@ -4,7 +4,7 @@ from pydantic import Field
 from soundevent import data
 
 from batdetect2.configs import BaseConfig, load_config
-from batdetect2.evaluate.match import MatchConfig
+from batdetect2.evaluate.match import DEFAULT_MATCH_CONFIG, MatchConfig
 
 __all__ = [
     "EvaluationConfig",
@@ -13,7 +13,9 @@ __all__ = [
 
 
 class EvaluationConfig(BaseConfig):
-    match: MatchConfig = Field(default_factory=MatchConfig)
+    match: MatchConfig = Field(
+        default_factory=lambda: DEFAULT_MATCH_CONFIG.model_copy(),
+    )
 
 
 def load_evaluation_config(

src/batdetect2/evaluate/match.py

@@ -1,12 +1,8 @@
-from collections.abc import Callable, Iterable, Mapping
+from typing import Annotated, List, Literal, Optional, Union
-from typing import List, Literal, Optional, Tuple
 
-import numpy as np
+from pydantic import Field
 from soundevent import data
-from soundevent.evaluation import compute_affinity
+from soundevent.evaluation import match_geometries
-from soundevent.evaluation import (
-    match_geometries as optimal_match,
-)
 from soundevent.geometry import compute_bounds
 
 from batdetect2.configs import BaseConfig
@@ -14,186 +10,70 @@ from batdetect2.evaluate.types import MatchEvaluation
 from batdetect2.postprocess.types import BatDetect2Prediction
 from batdetect2.targets.types import TargetProtocol
 
-MatchingStrategy = Literal["greedy", "optimal"]
-"""The type of matching algorithm to use: 'greedy' or 'optimal'."""
 
-
+class BBoxMatchConfig(BaseConfig):
-MatchingGeometry = Literal["bbox", "interval", "timestamp"]
+    match_method: Literal["BBoxIOU"] = "BBoxIOU"
-"""The geometry representation to use for matching."""
+    affinity_threshold: float = 0.5
-
+    time_buffer: float = 0.01
-
-class MatchConfig(BaseConfig):
-    """Configuration for matching geometries.
-
-    Attributes
-    ----------
-    strategy : MatchingStrategy, default="greedy"
-        The matching algorithm to use. 'greedy' prioritizes high-confidence
-        predictions, while 'optimal' finds the globally best set of matches.
-    geometry : MatchingGeometry, default="timestamp"
-        The geometric representation to use when computing affinity.
-    affinity_threshold : float, default=0.0
-        The minimum affinity score (e.g., IoU) required for a valid match.
-    time_buffer : float, default=0.005
-        Time tolerance in seconds used in affinity calculations.
-    frequency_buffer : float, default=1000
-        Frequency tolerance in Hertz used in affinity calculations.
-    """
-
-    strategy: MatchingStrategy = "greedy"
-    geometry: MatchingGeometry = "timestamp"
-    affinity_threshold: float = 0.0
-    time_buffer: float = 0.005
     frequency_buffer: float = 1_000
 
 
-def _to_bbox(geometry: data.Geometry) -> data.BoundingBox:
+class IntervalMatchConfig(BaseConfig):
+    match_method: Literal["IntervalIOU"] = "IntervalIOU"
+    affinity_threshold: float = 0.5
+    time_buffer: float = 0.01
+
+
+class StartTimeMatchConfig(BaseConfig):
+    match_method: Literal["StartTime"] = "StartTime"
+    time_buffer: float = 0.01
+
+
+MatchConfig = Annotated[
+    Union[BBoxMatchConfig, IntervalMatchConfig, StartTimeMatchConfig],
+    Field(discriminator="match_method"),
+]
+
+
+DEFAULT_MATCH_CONFIG = BBoxMatchConfig()
+
+
+def prepare_geometry(
+    geometry: data.Geometry, config: MatchConfig
+) -> data.Geometry:
     start_time, low_freq, end_time, high_freq = compute_bounds(geometry)
+
+    if config.match_method == "BBoxIOU":
         return data.BoundingBox(
             coordinates=[start_time, low_freq, end_time, high_freq]
         )
 
-
+    if config.match_method == "IntervalIOU":
-def _to_interval(geometry: data.Geometry) -> data.TimeInterval:
-    start_time, _, end_time, _ = compute_bounds(geometry)
         return data.TimeInterval(coordinates=[start_time, end_time])
 
-
+    if config.match_method == "StartTime":
-def _to_timestamp(geometry: data.Geometry) -> data.TimeStamp:
-    start_time = compute_bounds(geometry)[0]
         return data.TimeStamp(coordinates=start_time)
 
-
-_geometry_cast_functions: Mapping[
-    MatchingGeometry, Callable[[data.Geometry], data.Geometry]
-] = {
-    "bbox": _to_bbox,
-    "interval": _to_interval,
-    "timestamp": _to_timestamp,
-}
-
-
-def match_geometries(
-    source: List[data.Geometry],
-    target: List[data.Geometry],
-    config: MatchConfig,
-    scores: Optional[List[float]] = None,
-) -> Iterable[Tuple[Optional[int], Optional[int], float]]:
-    geometry_cast = _geometry_cast_functions[config.geometry]
-
-    if config.strategy == "optimal":
-        return optimal_match(
-            source=[geometry_cast(geom) for geom in source],
-            target=[geometry_cast(geom) for geom in target],
-            time_buffer=config.time_buffer,
-            freq_buffer=config.frequency_buffer,
-            affinity_threshold=config.affinity_threshold,
-        )
-
-    if config.strategy == "greedy":
-        return greedy_match(
-            source=[geometry_cast(geom) for geom in source],
-            target=[geometry_cast(geom) for geom in target],
-            time_buffer=config.time_buffer,
-            freq_buffer=config.frequency_buffer,
-            affinity_threshold=config.affinity_threshold,
-            scores=scores,
-        )
-
     raise NotImplementedError(
-        f"Matching strategy not implemented {config.strategy}"
+        f"Invalid matching configuration. Unknown match method: {config.match_method}"
     )
 
 
-def greedy_match(
+def _get_frequency_buffer(config: MatchConfig) -> float:
-    source: List[data.Geometry],
+    if config.match_method == "BBoxIOU":
-    target: List[data.Geometry],
+        return config.frequency_buffer
-    scores: Optional[List[float]] = None,
-    affinity_threshold: float = 0.5,
-    time_buffer: float = 0.001,
-    freq_buffer: float = 1000,
-) -> Iterable[Tuple[Optional[int], Optional[int], float]]:
-    """Performs a greedy, one-to-one matching of source to target geometries.
 
-    Iterates through source geometries, prioritizing by score if provided. Each
+    return 0
-    source is matched to the best available target, provided the affinity
-    exceeds the threshold and the target has not already been assigned.
 
-    Parameters
-    ----------
-    source
-        A list of source geometries (e.g., predictions).
-    target
-        A list of target geometries (e.g., ground truths).
-    scores
-        Confidence scores for each source geometry for prioritization.
-    affinity_threshold
-        The minimum affinity score required for a valid match.
-    time_buffer
-        Time tolerance in seconds for affinity calculation.
-    freq_buffer
-        Frequency tolerance in Hertz for affinity calculation.
 
-    Yields
+def _get_affinity_threshold(config: MatchConfig) -> float:
-    ------
+    if (
-    Tuple[Optional[int], Optional[int], float]
+        config.match_method == "BBoxIOU"
-        A 3-element tuple describing a match or a miss. There are three
+        or config.match_method == "IntervalIOU"
-        possible formats:
+    ):
-        - Successful Match: `(source_idx, target_idx, affinity)`
+        return config.affinity_threshold
-        - Unmatched Source (False Positive): `(source_idx, None, 0)`
-        - Unmatched Target (False Negative): `(None, target_idx, 0)`
-    """
-    assigned = set()
 
-    if not source:
+    return 0
-        for target_idx in range(len(target)):
-            yield None, target_idx, 0
-
-        return
-
-    if not target:
-        for source_idx in range(len(source)):
-            yield source_idx, None, 0
-
-        return
-
-    if scores is None:
-        indices = np.arange(len(source))
-    else:
-        indices = np.argsort(scores)[::-1]
-
-    for source_idx in indices:
-        source_geometry = source[source_idx]
-
-        affinities = np.array(
-            [
-                compute_affinity(
-                    source_geometry,
-                    target_geometry,
-                    time_buffer=time_buffer,
-                    freq_buffer=freq_buffer,
-                )
-                for target_geometry in target
-            ]
-        )
-
-        closest_target = int(np.argmax(affinities))
-        affinity = affinities[closest_target]
-
-        if affinities[closest_target] <= affinity_threshold:
-            yield source_idx, None, 0
-            continue
-
-        if closest_target in assigned:
-            yield source_idx, None, 0
-            continue
-
-        assigned.add(closest_target)
-        yield source_idx, closest_target, affinity
-
-    missed_ground_truth = set(range(len(target))) - assigned
-    for target_idx in missed_ground_truth:
-        yield None, target_idx, 0
 
 
 def match_sound_events_and_raw_predictions(
@@ -202,7 +82,7 @@ def match_sound_events_and_raw_predictions(
     targets: TargetProtocol,
     config: Optional[MatchConfig] = None,
 ) -> List[MatchEvaluation]:
-    config = config or MatchConfig()
+    config = config or DEFAULT_MATCH_CONFIG
 
     target_sound_events = [
         targets.transform(sound_event_annotation)
@@ -212,34 +92,30 @@ def match_sound_events_and_raw_predictions(
     ]
 
     target_geometries: List[data.Geometry] = [  # type: ignore
-        sound_event_annotation.sound_event.geometry
+        prepare_geometry(
+            sound_event_annotation.sound_event.geometry,
+            config=config,
+        )
         for sound_event_annotation in target_sound_events
         if sound_event_annotation.sound_event.geometry is not None
     ]
 
     predicted_geometries = [
-        raw_prediction.raw.geometry for raw_prediction in raw_predictions
+        prepare_geometry(raw_prediction.raw.geometry, config=config)
-    ]
-
-    scores = [
-        raw_prediction.raw.detection_score
         for raw_prediction in raw_predictions
     ]
 
     matches = []
 
-    for source_idx, target_idx, affinity in match_geometries(
+    for id1, id2, affinity in match_geometries(
-        source=predicted_geometries,
+        target_geometries,
-        target=target_geometries,
+        predicted_geometries,
-        config=config,
+        time_buffer=config.time_buffer,
-        scores=scores,
+        freq_buffer=_get_frequency_buffer(config),
+        affinity_threshold=_get_affinity_threshold(config),
     ):
-        target = (
+        target = target_sound_events[id1] if id1 is not None else None
-            target_sound_events[target_idx] if target_idx is not None else None
+        prediction = raw_predictions[id2] if id2 is not None else None
-        )
-        prediction = (
-            raw_predictions[source_idx] if source_idx is not None else None
-        )
 
         gt_det = target is not None
         gt_class = targets.encode_class(target) if target is not None else None
@@ -282,7 +158,7 @@ def match_predictions_and_annotations(
     clip_prediction: data.ClipPrediction,
     config: Optional[MatchConfig] = None,
 ) -> List[data.Match]:
-    config = config or MatchConfig()
+    config = config or DEFAULT_MATCH_CONFIG
 
     annotated_sound_events = [
         sound_event_annotation
@@ -297,46 +173,29 @@ def match_predictions_and_annotations(
     ]
 
     annotated_geometries: List[data.Geometry] = [
-        sound_event.sound_event.geometry
+        prepare_geometry(sound_event.sound_event.geometry, config=config)
         for sound_event in annotated_sound_events
         if sound_event.sound_event.geometry is not None
     ]
 
     predicted_geometries: List[data.Geometry] = [
-        sound_event.sound_event.geometry
+        prepare_geometry(sound_event.sound_event.geometry, config=config)
-        for sound_event in predicted_sound_events
-        if sound_event.sound_event.geometry is not None
-    ]
-
-    scores = [
-        sound_event.score
         for sound_event in predicted_sound_events
         if sound_event.sound_event.geometry is not None
     ]
 
     matches = []
-    for source_idx, target_idx, affinity in match_geometries(
+    for id1, id2, affinity in match_geometries(
-        source=predicted_geometries,
+        annotated_geometries,
-        target=annotated_geometries,
+        predicted_geometries,
-        config=config,
+        time_buffer=config.time_buffer,
-        scores=scores,
+        freq_buffer=_get_frequency_buffer(config),
+        affinity_threshold=_get_affinity_threshold(config),
     ):
-        target = (
+        target = annotated_sound_events[id1] if id1 is not None else None
-            annotated_sound_events[target_idx]
+        source = predicted_sound_events[id2] if id2 is not None else None
-            if target_idx is not None
-            else None
-        )
-        source = (
-            predicted_sound_events[source_idx]
-            if source_idx is not None
-            else None
-        )
         matches.append(
-            data.Match(
+            data.Match(source=source, target=target, affinity=affinity)
-                source=source,
-                target=target,
-                affinity=affinity,
-            )
         )
 
     return matches

src/batdetect2/train/callbacks.py

@@ -177,10 +177,7 @@ def _match_all_collected_examples(
         match
         for clip_annotation, raw_predictions in pre_matches
         for match in match_sound_events_and_raw_predictions(
-            clip_annotation,
+            clip_annotation, raw_predictions, targets=targets, config=config
-            raw_predictions,
-            targets=targets,
-            config=config,
         )
     ]