Collate best model after training

spacy/cli/train.py

@@ -185,6 +185,52 @@ def train(lang, output_dir, train_data, dev_data, n_iter=30, n_sents=0,
         with nlp.use_params(optimizer.averages):
             final_model_path = output_path / 'model-final'
             nlp.to_disk(final_model_path)
+        components = []
+        if not no_parser:
+            components.append('parser')
+        if not no_tagger:
+            components.append('tagger')
+        if not no_entity:
+            components.append('ner')
+        _collate_best_model(meta, output_path, components)
+
+def _collate_best_model(meta, output_path, components):
+    bests = {}
+    for component in components:
+        bests[component] = _find_best(output_path, component)
+    best_dest = output_path / 'model-best'
+    shutil.copytree(output_path / 'model-final', best_dest)
+    for component, best_component_src in bests.items():
+        shutil.rmtree(best_dir / component)
+        shutil.copytree(best_component_src, best_dest / component)
+        with (best_component_src / 'accuracy.json').open() as file_:
+            accs = json.load(file_)
+        for metric in _get_metrics(component):
+            meta['accuracy'][metric] = accs[metric]
+    with (best_dest / 'meta.json').open('w') as file_:
+        file_.write(json_dumps(meta))
+
+
+def _find_best(experiment_dir, component):
+    accuracies = []
+    for epoch_model in experiment_dir.iterdir():
+        if epoch_model.is_dir() and epoch_model.parts[-1] != "model-final":
+            accs = json.load((epoch_model / "accuracy.json").open())
+            scores = [accs.get(metric, 0.0) for metric in _get_metrics(component)]
+            accuracies.append((scores, epoch_model))
+    if accuracies:
+        return max(accuracies)[1]
+    else:
+        return None
+
+def _get_metrics(component):
+    if component == "parser":
+        return ("las", "uas", "token_acc")
+    elif component == "tagger":
+        return ("tags_acc",)
+    elif component == "ner":
+        return ("ents_f", "ents_p", "ents_r")
+    return ("token_acc",)
 
 
 def _render_parses(i, to_render):