ENH: Convert post-processing to transforms, fix #537

- Factor all functions out of labeled_timebins except has_unlabeled
- Only test has_unlabeled in test_labeled_timebins,
  parametrize the unit test
- Add 'multi_char_labels_to_single_char' to vak.labels
  - and add type annotations and clean up docstrings
- Clean up / add tests to tests/test_labels.py
- Add src/vak/transforms/labeled_timebins/functional.py
- Add src/vak/transforms/labeled_timebins/transforms.py
- Fix imports in vak/transforms/ __init__.py files
  - Import labeled_timebins transforms in transforms/__init__.py
  - Fix other imports
  - Import classes from transforms in
    src/vak/transforms/labeled_timebins/__init__.py
- TST: Move paths outside fixtures in fixtures/annot.py
  to be able to import directly in test modules
  to parametrize there.
- TST: Move path to constant outside fixture in fixtures/config.py
- Add tests/test_transforms/__init__.py
  so we can do relative import from tests/fixtures
- Add tests/test_transforms/test_labeled_timebins/test_functional.py
- Add tests/test_transforms/test_labeled_timebins/test_transforms.py
- Use `vak.transforms.labeled_timebins.from_segments`
  in vocal_dataset.py
- Use vak.transforms.labeled_timebins in engine/model.py
- Use transforms.labeled_timebins.from_segments
  in datasets/window_dataset.py
- Use transforms.labeled_timebins.to_segments in core/predict.py
- Use labeled_timebins transforms 'postprocess' and 'to_segments'
  in core/predict.py

src/vak/core/predict.py

@@ -14,7 +14,6 @@
     constants,
     files,
     io,
-    labeled_timebins,
     validators
 )
 from .. import models
@@ -223,12 +222,19 @@ def predict(
 
             spect_dict = files.spect.load(spect_path)
             t = spect_dict[timebins_key]
-            labels, onsets_s, offsets_s = labeled_timebins.lbl_tb2segments(
+
+            if majority_vote or min_segment_dur:
+                y_pred = transforms.labeled_timebins.postprocess(
+                    y_pred,
+                    timebin_dur=timebin_dur,
+                    min_segment_dur=min_segment_dur,
+                    majority_vote=majority_vote,
+                )
+
+            labels, onsets_s, offsets_s = transforms.labeled_timebins.to_segments(
                 y_pred,
                 labelmap=labelmap,
                 t=t,
-                min_segment_dur=min_segment_dur,
-                majority_vote=majority_vote,
             )
             if labels is None and onsets_s is None and offsets_s is None:
                 # handle the case when all time bins are predicted to be unlabeled

src/vak/datasets/vocal_dataset.py

@@ -2,7 +2,7 @@
 
 from .. import annotation
 from .. import files
-from .. import labeled_timebins
+from .. import transforms
 
 
 class VocalDataset:
@@ -80,7 +80,7 @@ def __getitem__(self, idx):
             annot = self.annots[idx]
             lbls_int = [self.labelmap[lbl] for lbl in annot.seq.labels]
             # "lbl_tb": labeled timebins. Target for output of network
-            lbl_tb = labeled_timebins.label_timebins(
+            lbl_tb = transforms.labeled_timebins.from_segments(
                 lbls_int,
                 annot.seq.onsets_s,
                 annot.seq.offsets_s,

src/vak/datasets/window_dataset.py

@@ -4,11 +4,13 @@
 import torch
 from torchvision.datasets.vision import VisionDataset
 
-from .. import annotation
-from .. import files
-from .. import io
-from .. import labeled_timebins
-from .. import validators
+from .. import (
+    annotation,
+    files,
+    io,
+    transforms,
+    validators
+)
 
 
 class WindowDataset(VisionDataset):
@@ -206,7 +208,7 @@ def __get_window_labelvec(self, idx):
             spect_id
         ]  # "annot id" == spect_id if both were taken from rows of DataFrame
         lbls_int = [self.labelmap[lbl] for lbl in annot.seq.labels]
-        lbl_tb = labeled_timebins.label_timebins(
+        lbl_tb = transforms.labeled_timebins.from_segments(
             lbls_int,
             annot.seq.onsets_s,
             annot.seq.offsets_s,
@@ -694,7 +696,7 @@ def spect_vectors_from_df(
                 lbls_int = [labelmap[lbl] for lbl in annot.seq.labels]
                 timebins = spect_dict[timebins_key]
                 lbl_tb.append(
-                    labeled_timebins.label_timebins(
+                    transforms.labeled_timebins.from_segments(
                         lbls_int,
                         annot.seq.onsets_s,
                         annot.seq.offsets_s,

src/vak/engine/model.py

@@ -7,7 +7,7 @@
 from tqdm import tqdm
 
 from ..device import get_default as get_default_device
-from ..labeled_timebins import lbl_tb2labels
+from .. import transforms
 
 
 logger = logging.getLogger(__name__)
@@ -267,10 +267,10 @@ def _eval(self, eval_data):
                         for metric_name in self.metrics.keys()
                     ]
                 ):
-                    y_labels = lbl_tb2labels(
+                    y_labels = transforms.labeled_timebins.lbl_tb2labels(
                         y.cpu().numpy(), eval_data.dataset.labelmap
                     )
-                    y_pred_labels = lbl_tb2labels(
+                    y_pred_labels = transforms.labeled_timebins.lbl_tb2labels(
                         y_pred.cpu().numpy(), eval_data.dataset.labelmap
                     )
                 else: