refactor: move random sampling to outside caller of visualiser

kazu/training/config.py

@@ -45,6 +45,8 @@ class TrainingConfig:
     architecture: str = "bert"
     #: fraction of epoch to complete before evaluations begin
     epoch_completion_fraction_before_evals: float = 0.75
+    #: The random seed to use
+    seed: int = 42
 
 
 @dataclass

kazu/training/modelling_utils.py

@@ -1,7 +1,6 @@
 import copy
 import json
 import logging
-import random
 from collections.abc import Iterable
 from pathlib import Path
 from typing import Any, Optional, Union
@@ -102,26 +101,24 @@ def get_gold_ents_for_side_by_side_view(self, docs: list[Document]) -> list[list
         return result
 
     def update(
-        self, test_docs: list[Document], global_step: Union[int, str], has_gs: bool = True
+        self, docs: list[Document], global_step: Union[int, str], has_gs: bool = True
     ) -> None:
         ls_manager = LabelStudioManager(
             headers=self.ls_manager.headers,
             project_name=f"{self.ls_manager.project_name}_test_{global_step}",
         )
-
         ls_manager.delete_project_if_exists()
         ls_manager.create_linking_project()
-        docs_subset = random.sample(test_docs, min([len(test_docs), 100]))
-        if not docs_subset:
+        if not docs:
             logger.info("no results to represent yet")
             return
         if has_gs:
-            side_by_side = self.get_gold_ents_for_side_by_side_view(docs_subset)
+            side_by_side = self.get_gold_ents_for_side_by_side_view(docs)
             ls_manager.update_view(self.view, side_by_side)
             ls_manager.update_tasks(side_by_side)
         else:
-            ls_manager.update_view(self.view, docs_subset)
-            ls_manager.update_tasks(docs_subset)
+            ls_manager.update_view(self.view, docs)
+            ls_manager.update_tasks(docs)
 
 
 def create_wrapper(cfg: DictConfig, label_list: list[str]) -> Optional[LSManagerViewWrapper]:

kazu/training/train_multilabel_ner.py

@@ -4,6 +4,7 @@
 import logging
 import math
 import pickle
+import random
 import shutil
 import tempfile
 from collections import defaultdict
@@ -337,6 +338,7 @@ def __init__(
         self.label_list = label_list
         self.pretrained_model_name_or_path = pretrained_model_name_or_path
         self.keys_to_use = _select_keys_to_use(self.training_config.architecture)
+        random.seed(training_config.seed)
 
     def _write_to_tensorboard(
         self, global_step: int, main_tag: str, tag_scalar_dict: dict[str, NumericMetric]
@@ -360,7 +362,8 @@ def evaluate_model(
 
         model_test_docs = self._process_docs(model)
         if self.ls_wrapper:
-            self.ls_wrapper.update(model_test_docs, global_step)
+            sample_test_docs = random.sample(model_test_docs, min([len(model_test_docs), 100]))
+            self.ls_wrapper.update(sample_test_docs, global_step)
 
         all_results, tensorboad_loggables = calculate_metrics(
             epoch_loss, model_test_docs, self.label_list