Add warnings for pointless applications of find_threshold().

spacy/cli/find_threshold.py

@@ -7,6 +7,7 @@ from typing import Optional, Tuple, Any, Dict, List
 import numpy
 import wasabi.tables
 
+from pipeline import TextCategorizer, MultiLabel_TextCategorizer
 from ..errors import Errors
 from ..training import Corpus
 from ._util import app, Arg, Opt, import_code, setup_gpu
@@ -111,6 +112,12 @@ def find_threshold(
     if not hasattr(pipe, "scorer"):
         raise AttributeError(Errors.E1045)
 
+    if isinstance(pipe, TextCategorizer):
+        wasabi.msg.warn(
+            "The `textcat` component doesn't use a threshold as it's not applicable to the concept of "
+            "exclusive classes. All thresholds will yield the same results."
+        )
+
     if not silent:
         wasabi.msg.info(
             title=f"Optimizing for {scores_key} for component '{pipe_name}' with {n_trials} "
@@ -150,8 +157,9 @@ def find_threshold(
     scores: Dict[float, float] = {}
     config_keys_full = ["components", pipe_name, *config_keys]
     table_col_widths = (10, 10)
+    thresholds = numpy.linspace(0, 1, n_trials)
     print(wasabi.tables.row(["Threshold", f"{scores_key}"], widths=table_col_widths))
-    for threshold in numpy.linspace(0, 1, n_trials):
+    for threshold in thresholds:
         # Reload pipeline with overrides specifying the new threshold.
         nlp = util.load_model(
             model,
@@ -183,6 +191,20 @@ def find_threshold(
         )
 
     best_threshold = max(scores.keys(), key=(lambda key: scores[key]))
+
+    # If all scores are identical, emit warning.
+    if all([score == scores[thresholds[0]] for score in scores.values()]):
+        wasabi.msg.warn(
+            title="All scores are identical. Verify that all settings are correct.",
+            text=""
+            if (
+                not isinstance(pipe, MultiLabel_TextCategorizer)
+                or scores_key in ("cats_macro_f", "cats_micro_f")
+            )
+            else "Use `cats_macro_f` or `cats_micro_f` when optimizing the threshold for `textcat_multilabel`.",
+        )
+
+    else:
         if not silent:
             print(
                 f"\nBest threshold: {round(best_threshold, ndigits=4)} with value of {scores[best_threshold]}."