Log the similarity between anchor and negatives

tests/conftest.py

@@ -114,3 +114,58 @@ def tracks_hcs_dataset(tmp_path_factory: TempPathFactory) -> Path:
         )
         fake_tracks.to_csv(dataset_path / fov_name / "tracks.csv", index=False)
     return dataset_path
+
+
+@fixture(scope="function")
+def tracks_with_gaps_dataset(tmp_path_factory: TempPathFactory) -> Path:
+    """Provides a HCS OME-Zarr dataset with tracking results with gaps in time."""
+    dataset_path = tmp_path_factory.mktemp("tracks_gaps.zarr")
+    _build_hcs(dataset_path, ["nuclei_labels"], (1, 256, 256), np.uint16, 3)
+
+    # Define different track patterns for different FOVs
+    track_patterns = {
+        "A/1/0": [
+            # Track 0: complete sequence t=[0,1,2,3]
+            {"track_id": 0, "t": 0, "y": 128, "x": 128, "id": 0},
+            {"track_id": 0, "t": 1, "y": 128, "x": 128, "id": 1},
+            {"track_id": 0, "t": 2, "y": 128, "x": 128, "id": 2},
+            {"track_id": 0, "t": 3, "y": 128, "x": 128, "id": 3},
+            # Track 1: ends early t=[0,1]
+            {"track_id": 1, "t": 0, "y": 100, "x": 100, "id": 4},
+            {"track_id": 1, "t": 1, "y": 100, "x": 100, "id": 5},
+        ],
+        "A/1/1": [
+            # Track 0: gap at t=2, has t=[0,1,3]
+            {"track_id": 0, "t": 0, "y": 128, "x": 128, "id": 0},
+            {"track_id": 0, "t": 1, "y": 128, "x": 128, "id": 1},
+            {"track_id": 0, "t": 3, "y": 128, "x": 128, "id": 2},
+            # Track 1: even timepoints only t=[0,2,4]
+            {"track_id": 1, "t": 0, "y": 100, "x": 100, "id": 3},
+            {"track_id": 1, "t": 2, "y": 100, "x": 100, "id": 4},
+            {"track_id": 1, "t": 4, "y": 100, "x": 100, "id": 5},
+        ],
+        "A/2/0": [
+            # Track 0: single timepoint t=[0]
+            {"track_id": 0, "t": 0, "y": 128, "x": 128, "id": 0},
+            # Track 1: complete short sequence t=[0,1,2]
+            {"track_id": 1, "t": 0, "y": 100, "x": 100, "id": 1},
+            {"track_id": 1, "t": 1, "y": 100, "x": 100, "id": 2},
+            {"track_id": 1, "t": 2, "y": 100, "x": 100, "id": 3},
+        ],
+    }
+
+    for fov_name, _ in open_ome_zarr(dataset_path).positions():
+        if fov_name in track_patterns:
+            tracks_data = track_patterns[fov_name]
+        else:
+            # Default tracks for other FOVs
+            tracks_data = [
+                {"track_id": 0, "t": 0, "y": 128, "x": 128, "id": 0},
+            ]
+
+        tracks_df = pd.DataFrame(tracks_data)
+        tracks_df["parent_track_id"] = -1
+        tracks_df["parent_id"] = -1
+        tracks_df.to_csv(dataset_path / fov_name / "tracks.csv", index=False)
+
+    return dataset_path

tests/data/test_triplet.py

@@ -2,7 +2,7 @@
 from iohub import open_ome_zarr
 from pytest import mark
 
-from viscy.data.triplet import TripletDataModule
+from viscy.data.triplet import TripletDataModule, TripletDataset
 
 
 @mark.parametrize("include_wells", [None, ["A/1", "A/2", "B/1"]])
@@ -109,3 +109,244 @@ def test_datamodule_z_window_size(
             expected_z_shape,
             *yx_patch_size,
         )
+
+
+def test_filter_anchors_time_interval_any(
+    preprocessed_hcs_dataset, tracks_with_gaps_dataset
+):
+    """Test that time_interval='any' returns all tracks unchanged."""
+    with open_ome_zarr(preprocessed_hcs_dataset) as dataset:
+        channel_names = dataset.channel_names
+        positions = list(dataset.positions())
+
+    # Create dataset with time_interval="any"
+    tracks_tables = []
+    for fov_name, _ in positions:
+        tracks_df = pd.read_csv(
+            next((tracks_with_gaps_dataset / fov_name).glob("*.csv"))
+        ).astype(int)
+        tracks_tables.append(tracks_df)
+
+    total_tracks = sum(len(df) for df in tracks_tables)
+
+    ds = TripletDataset(
+        positions=[pos for _, pos in positions],
+        tracks_tables=tracks_tables,
+        channel_names=channel_names,
+        initial_yx_patch_size=(64, 64),
+        z_range=slice(4, 9),
+        fit=True,
+        time_interval="any",
+    )
+
+    # Should return all tracks
+    assert len(ds.valid_anchors) == total_tracks
+
+
+def test_filter_anchors_time_interval_1(
+    preprocessed_hcs_dataset, tracks_with_gaps_dataset
+):
+    """Test filtering with time_interval=1."""
+    with open_ome_zarr(preprocessed_hcs_dataset) as dataset:
+        channel_names = dataset.channel_names
+        positions = list(dataset.positions())
+
+    tracks_tables = []
+    for fov_name, _ in positions:
+        tracks_df = pd.read_csv(
+            next((tracks_with_gaps_dataset / fov_name).glob("*.csv"))
+        ).astype(int)
+        tracks_tables.append(tracks_df)
+
+    ds = TripletDataset(
+        positions=[pos for _, pos in positions],
+        tracks_tables=tracks_tables,
+        channel_names=channel_names,
+        initial_yx_patch_size=(64, 64),
+        z_range=slice(4, 9),
+        fit=True,
+        time_interval=1,
+    )
+
+    # Check expected anchors per FOV/track
+    valid_anchors = ds.valid_anchors
+
+    # FOV A/1/0, Track 0: t=[0,1,2,3] -> valid anchors at t=[0,1,2]
+    fov_a10_track0 = valid_anchors[
+        (valid_anchors["fov_name"] == "A/1/0") & (valid_anchors["track_id"] == 0)
+    ]
+    assert set(fov_a10_track0["t"]) == {0, 1, 2}
+
+    # FOV A/1/0, Track 1: t=[0,1] -> valid anchor at t=[0]
+    fov_a10_track1 = valid_anchors[
+        (valid_anchors["fov_name"] == "A/1/0") & (valid_anchors["track_id"] == 1)
+    ]
+    assert set(fov_a10_track1["t"]) == {0}
+
+    # FOV A/1/1, Track 0: t=[0,1,3] -> valid anchor at t=[0] only (t=1 has no t+1=2)
+    fov_a11_track0 = valid_anchors[
+        (valid_anchors["fov_name"] == "A/1/1") & (valid_anchors["track_id"] == 0)
+    ]
+    assert set(fov_a11_track0["t"]) == {0}
+
+    # FOV A/1/1, Track 1: t=[0,2,4] -> no valid anchors (gaps of 2, no consecutive t+1)
+    fov_a11_track1 = valid_anchors[
+        (valid_anchors["fov_name"] == "A/1/1") & (valid_anchors["track_id"] == 1)
+    ]
+    assert len(fov_a11_track1) == 0
+
+    # FOV A/2/0, Track 0: t=[0] -> no valid anchors (no t+1)
+    fov_a20_track0 = valid_anchors[
+        (valid_anchors["fov_name"] == "A/2/0") & (valid_anchors["track_id"] == 0)
+    ]
+    assert len(fov_a20_track0) == 0
+
+    # FOV A/2/0, Track 1: t=[0,1,2] -> valid anchors at t=[0,1]
+    fov_a20_track1 = valid_anchors[
+        (valid_anchors["fov_name"] == "A/2/0") & (valid_anchors["track_id"] == 1)
+    ]
+    assert set(fov_a20_track1["t"]) == {0, 1}
+
+
+def test_filter_anchors_time_interval_2(
+    preprocessed_hcs_dataset, tracks_with_gaps_dataset
+):
+    """Test filtering with time_interval=2."""
+    with open_ome_zarr(preprocessed_hcs_dataset) as dataset:
+        channel_names = dataset.channel_names
+        positions = list(dataset.positions())
+
+    tracks_tables = []
+    for fov_name, _ in positions:
+        tracks_df = pd.read_csv(
+            next((tracks_with_gaps_dataset / fov_name).glob("*.csv"))
+        ).astype(int)
+        tracks_tables.append(tracks_df)
+
+    ds = TripletDataset(
+        positions=[pos for _, pos in positions],
+        tracks_tables=tracks_tables,
+        channel_names=channel_names,
+        initial_yx_patch_size=(64, 64),
+        z_range=slice(4, 9),
+        fit=True,
+        time_interval=2,
+    )
+
+    valid_anchors = ds.valid_anchors
+
+    # FOV A/1/0, Track 0: t=[0,1,2,3] -> valid anchors at t=[0,1] (t+2 available)
+    fov_a10_track0 = valid_anchors[
+        (valid_anchors["fov_name"] == "A/1/0") & (valid_anchors["track_id"] == 0)
+    ]
+    assert set(fov_a10_track0["t"]) == {0, 1}
+
+    # FOV A/1/0, Track 1: t=[0,1] -> no valid anchors (no t+2)
+    fov_a10_track1 = valid_anchors[
+        (valid_anchors["fov_name"] == "A/1/0") & (valid_anchors["track_id"] == 1)
+    ]
+    assert len(fov_a10_track1) == 0
+
+    # FOV A/1/1, Track 0: t=[0,1,3] -> valid anchor at t=[1] (t=1+2=3 exists)
+    fov_a11_track0 = valid_anchors[
+        (valid_anchors["fov_name"] == "A/1/1") & (valid_anchors["track_id"] == 0)
+    ]
+    assert set(fov_a11_track0["t"]) == {1}
+
+    # FOV A/1/1, Track 1: t=[0,2,4] -> valid anchors at t=[0,2]
+    fov_a11_track1 = valid_anchors[
+        (valid_anchors["fov_name"] == "A/1/1") & (valid_anchors["track_id"] == 1)
+    ]
+    assert set(fov_a11_track1["t"]) == {0, 2}
+
+    # FOV A/2/0, Track 1: t=[0,1,2] -> valid anchor at t=[0]
+    fov_a20_track1 = valid_anchors[
+        (valid_anchors["fov_name"] == "A/2/0") & (valid_anchors["track_id"] == 1)
+    ]
+    assert set(fov_a20_track1["t"]) == {0}
+
+
+def test_filter_anchors_cross_fov_independence(
+    preprocessed_hcs_dataset, tracks_with_gaps_dataset
+):
+    """Test that same track_id in different FOVs are treated independently."""
+    with open_ome_zarr(preprocessed_hcs_dataset) as dataset:
+        channel_names = dataset.channel_names
+        positions = list(dataset.positions())
+
+    tracks_tables = []
+    for fov_name, _ in positions:
+        tracks_df = pd.read_csv(
+            next((tracks_with_gaps_dataset / fov_name).glob("*.csv"))
+        ).astype(int)
+        tracks_tables.append(tracks_df)
+
+    ds = TripletDataset(
+        positions=[pos for _, pos in positions],
+        tracks_tables=tracks_tables,
+        channel_names=channel_names,
+        initial_yx_patch_size=(64, 64),
+        z_range=slice(4, 9),
+        fit=True,
+        time_interval=1,
+    )
+
+    # Check global_track_id format and uniqueness
+    assert "global_track_id" in ds.tracks.columns
+    global_track_ids = ds.tracks["global_track_id"].unique()
+
+    # Verify format: should be "fov_name_track_id"
+    for gid in global_track_ids:
+        assert "_" in gid
+        fov_part, track_id_part = gid.rsplit("_", 1)
+        assert "/" in fov_part  # FOV names contain slashes like "A/1/0"
+
+    # Track 0 exists in multiple FOVs (A/1/0, A/1/1, A/2/0) but should have different global_track_ids
+    track0_global_ids = ds.tracks[ds.tracks["track_id"] == 0][
+        "global_track_id"
+    ].unique()
+    assert len(track0_global_ids) >= 3  # At least 3 different FOVs with track_id=0
+
+    # Verify that filtering is independent per FOV
+    # A/1/0 Track 0 (continuous) should have more valid anchors than A/1/1 Track 0 (with gap)
+    valid_a10_track0 = ds.valid_anchors[
+        (ds.valid_anchors["fov_name"] == "A/1/0") & (ds.valid_anchors["track_id"] == 0)
+    ]
+    valid_a11_track0 = ds.valid_anchors[
+        (ds.valid_anchors["fov_name"] == "A/1/1") & (ds.valid_anchors["track_id"] == 0)
+    ]
+    # A/1/0 Track 0 has t=[0,1,2] valid (3 anchors)
+    # A/1/1 Track 0 has t=[0] valid (1 anchor, gap at t=2)
+    assert len(valid_a10_track0) == 3
+    assert len(valid_a11_track0) == 1
+
+
+def test_filter_anchors_predict_mode(
+    preprocessed_hcs_dataset, tracks_with_gaps_dataset
+):
+    """Test that predict mode (fit=False) returns all tracks regardless of time_interval."""
+    with open_ome_zarr(preprocessed_hcs_dataset) as dataset:
+        channel_names = dataset.channel_names
+        positions = list(dataset.positions())
+
+    tracks_tables = []
+    for fov_name, _ in positions:
+        tracks_df = pd.read_csv(
+            next((tracks_with_gaps_dataset / fov_name).glob("*.csv"))
+        ).astype(int)
+        tracks_tables.append(tracks_df)
+
+    total_tracks = sum(len(df) for df in tracks_tables)
+
+    ds = TripletDataset(
+        positions=[pos for _, pos in positions],
+        tracks_tables=tracks_tables,
+        channel_names=channel_names,
+        initial_yx_patch_size=(64, 64),
+        z_range=slice(4, 9),
+        fit=False,  # Predict mode
+        time_interval=1,
+    )
+
+    # Should return all tracks even with time_interval=1
+    assert len(ds.valid_anchors) == total_tracks

viscy/representation/engine.py

@@ -36,6 +36,7 @@ def __init__(
         log_batches_per_epoch: int = 8,
         log_samples_per_batch: int = 1,
         log_embeddings: bool = False,
+        log_negative_metrics_every_n_epochs: int = 2,
         example_input_array_shape: Sequence[int] = (1, 2, 15, 256, 256),
     ) -> None:
         super().__init__()
@@ -49,6 +50,7 @@ def __init__(
         self.training_step_outputs = []
         self.validation_step_outputs = []
         self.log_embeddings = log_embeddings
+        self.log_negative_metrics_every_n_epochs = log_negative_metrics_every_n_epochs
 
     def forward(self, x: Tensor) -> tuple[Tensor, Tensor]:
         """Return both features and projections.
@@ -94,8 +96,8 @@ def _log_metrics(
         cosine_sim_pos = F.cosine_similarity(anchor, positive, dim=1).mean()
         euclidean_dist_pos = F.pairwise_distance(anchor, positive).mean()
         log_metric_dict = {
-            f"metrics/cosine_similarity_positive/{stage}": cosine_sim_pos,
-            f"metrics/euclidean_distance_positive/{stage}": euclidean_dist_pos,
+            f"metrics/cosine_similarity/positive/{stage}": cosine_sim_pos,
+            f"metrics/euclidean_distance/positive/{stage}": euclidean_dist_pos,
         }
 
         if negative is not None:
@@ -107,6 +109,46 @@ def _log_metrics(
             log_metric_dict[f"metrics/euclidean_distance_negative/{stage}"] = (
                 euclidean_dist_neg
             )
+        elif isinstance(self.loss_function, NTXentLoss):
+            if self.current_epoch % self.log_negative_metrics_every_n_epochs == 0:
+                batch_size = anchor.size(0)
+
+                # Cosine similarity metrics
+                anchor_norm = F.normalize(anchor, dim=1)
+                positive_norm = F.normalize(positive, dim=1)
+                all_embeddings_norm = torch.cat([anchor_norm, positive_norm], dim=0)
+                sim_matrix = torch.mm(anchor_norm, all_embeddings_norm.t())
+
+                mask = torch.ones_like(sim_matrix, dtype=torch.bool)
+                mask[range(batch_size), range(batch_size)] = False  # Exclude self
+                mask[range(batch_size), range(batch_size, 2 * batch_size)] = (
+                    False  # Exclude positive
+                )
+
+                negative_sims = sim_matrix[mask].view(batch_size, -1)
+
+                mean_neg_sim = negative_sims.mean()
+                sum_neg_sim = negative_sims.sum(dim=1).mean()
+                margin_cosine = cosine_sim_pos - mean_neg_sim
+
+                all_embeddings = torch.cat([anchor, positive], dim=0)
+                dist_matrix = torch.cdist(anchor, all_embeddings, p=2)
+                negative_dists = dist_matrix[mask].view(batch_size, -1)
+
+                mean_neg_dist = negative_dists.mean()
+                sum_neg_dist = negative_dists.sum(dim=1).mean()
+                margin_euclidean = mean_neg_dist - euclidean_dist_pos
+
+                log_metric_dict.update(
+                    {
+                        f"metrics/cosine_similarity/negative_mean/{stage}": mean_neg_sim,
+                        f"metrics/cosine_similarity/negative_sum/{stage}": sum_neg_sim,
+                        f"metrics/margin_positive/negative/{stage}": margin_cosine,
+                        f"metrics/euclidean_distance/negative_mean/{stage}": mean_neg_dist,
+                        f"metrics/euclidean_distance/negative_sum/{stage}": sum_neg_dist,
+                        f"metrics/margin_euclidean_positive/negative/{stage}": margin_euclidean,
+                    }
+                )
 
         self.log_dict(
             log_metric_dict,