delegate to taggingbackends

src/maggotuba/models/train_model.py

@@ -6,8 +6,7 @@ import json
 import glob
 
 def train_model(backend, layers=1, pretrained_model_instance="default",
-                subsets=(1, 0, 0), rng_seed=None, iterations=1000,
-                balancing_strategy='maggotuba', **kwargs):
+                subsets=(1, 0, 0), rng_seed=None, iterations=1000, **kwargs):
     # make_dataset generated or moved the larva_dataset file into data/interim/{instance}/
     #larva_dataset_file = backend.list_interim_files("larva_dataset_*.hdf5") # recursive
     larva_dataset_file = glob.glob(str(backend.interim_data_dir() / "larva_dataset_*.hdf5")) # not recursive (faster)
@@ -15,7 +14,6 @@ def train_model(backend, layers=1, pretrained_model_instance="default",
     # subsets=(1, 0, 0) => all data are training data; no validation or test subsets
     dataset = LarvaDataset(larva_dataset_file[0], new_generator(rng_seed),
                            subsets=subsets, **kwargs)
-    dataset.weight_classes = isinstance(balancing_strategy, str) and (balancing_strategy.lower() == 'auto')
     labels = dataset.labels
     assert 0 < len(labels)
     labels = labels if isinstance(labels[0], str) else [s.decode() for s in labels]