20230129 pretrained encoder made new default pretrained model; ABC->AAC...

20230129 pretrained encoder made new default pretrained model; ABC->AAC post-prediction rule made default

pretrained_models/default/autoencoder_config.json

 {
   "project_dir": "",
   "seed": 100,
-  "exp_name": "20230111",
-  "data_dir": "/pasteur/appa/scratch/flaurent/MaggotUBA-adapter/data/20230111/pretrain/20/1/larva_dataset_2023_01_12_20_20_100000.hdf5",
-  "raw_data_dir": "/pasteur/zeus/projets/p02/hecatonchire/screens",
+  "exp_name": "20230129 -- see https://gitlab.pasteur.fr/nyx/MaggotUBA-adapter/-/blob/design/scripts/maestro/make_20230129_datasets.py and related scripts",
+  "data_dir": "/pasteur/appa/scratch/flaurent/MaggotUBA-adapter/data/20230129/pretrain/20/1/larva_dataset_2023_01_29_20_20_108440.hdf5",
+  "raw_data_dir": "/pasteur/zeus/projets/p02/hecatonchire/screens/t15",
   "log_dir": "",
   "exp_folder": "",
-  "config": "models/20230111/autoencoder_config.json",
+  "config": "models/20230129/autoencoder_config.json",
   "num_workers": 4,
   "n_features": 10,
   "len_traj": 20,
@@ -87,7 +87,7 @@
   "init": "kaiming",
   "n_clusters": 2,
   "dim_reduc": "UMAP",
-  "optim_iter": 1000,
+  "optim_iter": 10000,
   "pseudo_epoch": 100,
   "batch_size": 128,
   "lr": 0.005,

src/maggotuba/models/train_model.py

@@ -26,8 +26,11 @@ def train_model(backend, layers=1, pretrained_model_instance="default",
         pretrained_model_instances = pretrained_model_instance
         config_files = import_pretrained_models(backend, pretrained_model_instances)
         model = make_trainer(config_files, labels, layers)
-    # fine-tune and save the model
+    # fine-tune the model
     model.train(dataset)
+    # add post-prediction rule ABC -> AAC
+    model.clf_config['post_filters'] = ['ABC->AAC']
+    # save the model
     print(f"saving model \"{backend.model_instance}\"")
     model.save()