Reload pipeline on threshold change. Adjust tests. Remove confection reference.

spacy/cli/find_threshold.py

@@ -6,7 +6,6 @@ from typing import Optional, Tuple, Any, Dict, List
 
 import numpy
 import wasabi.tables
-from confection import Config
 
 from ..pipeline import TrainablePipe, Pipe
 from ..errors import Errors
@@ -87,7 +86,7 @@ def find_threshold(
     use_gpu: int = _DEFAULTS["use_gpu"],  # type: ignore
     gold_preproc: bool = _DEFAULTS["gold_preproc"],  # type: ignore
     silent: bool = True,
-) -> Tuple[float, float]:
+) -> Tuple[float, float, Dict[float, float]]:
     """
     Runs prediction trials for `textcat` models with varying tresholds to maximize the specified metric.
     model (Union[str, Path]): Path to file with trained model.
@@ -102,7 +101,8 @@ def find_threshold(
         tokenization, and may result in sequences of more consistent length. However, it may reduce runtime accuracy due
         to train/test skew.
     silent (bool): Whether to print non-error-related output to stdout.
-    RETURNS (Tuple[float, float]): Best found threshold with corresponding F-score.
+    RETURNS (Tuple[float, float, Dict[float, float]]): Best found threshold, the corresponding score, scores for all
+        evaluated thresholds.
     """
 
     setup_gpu(use_gpu, silent=silent)
@@ -138,18 +138,39 @@ def find_threshold(
     ) -> Dict[str, Any]:
         """Set item in nested dictionary. Adapated from https://stackoverflow.com/a/54138200.
         config (Dict[str, Any]): Configuration dictionary.
-        keys (List[Any]):
+        keys (List[Any]): Path to value to set.
         value (float): Value to set.
         RETURNS (Dict[str, Any]): Updated dictionary.
         """
         functools.reduce(operator.getitem, keys[:-1], config)[keys[-1]] = value
         return config
 
+    def filter_config(config: Dict[str, Any], keys: List[str]) -> Dict[str, Any]:
+        """Filters provided config dictionary so that only the specified keys path remains.
+        config (Dict[str, Any]): Configuration dictionary.
+        keys (List[Any]): Path to value to set.
+        RETURNS (Dict[str, Any]): Filtered dictionary.
+        """
+        return {
+            keys[0]: filter_config(config[keys[0]], keys[1:])
+            if len(keys) > 1
+            else config[keys[0]]
+        }
+
     # Evaluate with varying threshold values.
     scores: Dict[float, float] = {}
+    config_keys_full = ["components", pipe_name, *config_keys]
     for threshold in numpy.linspace(0, 1, n_trials):
-        pipe.cfg = set_nested_item(pipe.cfg, config_keys, threshold)
+        # Reload pipeline with overrides specifying the new threshold.
-        nlp._pipe_configs[pipe_name] = Config(pipe.cfg)
+        nlp = util.load_model(
+            model,
+            config=set_nested_item(
+                filter_config(nlp.config, config_keys_full).copy(),
+                config_keys_full,
+                threshold,
+            ),
+        )
+        nlp.get_pipe(pipe_name).cfg = set_nested_item(pipe.cfg, config_keys, threshold)
         scores[threshold] = nlp.evaluate(dev_dataset)[scores_key]
         if not (
             isinstance(scores[threshold], float) or isinstance(scores[threshold], int)
@@ -170,4 +191,4 @@ def find_threshold(
             ),
         )
 
-    return best_threshold, scores[best_threshold]
+    return best_threshold, scores[best_threshold], scores

spacy/tests/test_cli.py

@@ -36,7 +36,7 @@ from spacy.tokens.span import Span
 from spacy.training import Example, docs_to_json, offsets_to_biluo_tags
 from spacy.training.converters import conll_ner_to_docs, conllu_to_docs
 from spacy.training.converters import iob_to_docs
-from spacy.pipeline import TextCategorizer, Pipe, SpanCategorizer
+from spacy.pipeline import TextCategorizer
 from spacy.util import ENV_VARS, get_minor_version, load_model_from_config, load_config
 
 from ..cli.init_pipeline import _init_labels
@@ -860,6 +860,8 @@ def test_span_length_freq_dist_output_must_be_correct():
 
 
 def test_cli_find_threshold(capsys):
+    thresholds = numpy.linspace(0, 1, 10)
+
     def make_examples(_nlp: Language) -> List[Example]:
         docs: List[Example] = []
 
@@ -921,33 +923,35 @@ def test_cli_find_threshold(capsys):
         )
         with make_tempdir() as nlp_dir:
             nlp.to_disk(nlp_dir)
-            assert (
+            res = find_threshold(
-                find_threshold(
+                model=nlp_dir,
-                    model=nlp_dir,
+                data_path=docs_dir / "docs.spacy",
-                    data_path=docs_dir / "docs.spacy",
+                pipe_name="tc_multi",
-                    pipe_name="tc_multi",
+                threshold_key="threshold",
-                    threshold_key="threshold",
+                scores_key="cats_macro_f",
-                    scores_key="cats_macro_f",
+                silent=False,
-                    silent=False,
-                )[0]
-                == numpy.linspace(0, 1, 10)[1]
             )
+            assert res[0] != thresholds[0]
+            assert thresholds[0] < res[0] < thresholds[9]
+            assert res[1] == 1.0
+            assert res[2][1.0] == 0.0
 
         # Test with spancat.
         nlp, _ = init_nlp((("spancat", {}),))
         with make_tempdir() as nlp_dir:
             nlp.to_disk(nlp_dir)
-            assert (
+            res = find_threshold(
-                find_threshold(
+                model=nlp_dir,
-                    model=nlp_dir,
+                data_path=docs_dir / "docs.spacy",
-                    data_path=docs_dir / "docs.spacy",
+                pipe_name="spancat",
-                    pipe_name="spancat",
+                threshold_key="threshold",
-                    threshold_key="threshold",
+                scores_key="spans_sc_f",
-                    scores_key="spans_sc_f",
+                silent=True,
-                    silent=True,
-                )[0]
-                == numpy.linspace(0, 1, 10)[1]
             )
+            assert res[0] != thresholds[0]
+            assert thresholds[0] < res[0] < thresholds[8]
+            assert res[1] == 1.0
+            assert res[2][1.0] == 0.0
 
         # Having multiple textcat_multilabel components should work, since the name has to be specified.
         nlp, _ = init_nlp((("textcat_multilabel", {}),))