Incorporate feedback.

spacy/cli/find_threshold.py

@@ -30,7 +30,7 @@ def find_threshold_cli(
     data_path: Path = Arg(..., help="Location of binary evaluation data in .spacy format", exists=True),
     pipe_name: str = Arg(..., help="Name of pipe to examine thresholds for"),
     threshold_key: str = Arg(..., help="Key of threshold attribute in component's configuration"),
-    scores_key: str = Arg(..., help="Name of score to metric to optimize"),
+    scores_key: str = Arg(..., help="Metric to optimize"),
     n_trials: int = Opt(_DEFAULTS["n_trials"], "--n_trials", "-n", help="Number of trials to determine optimal thresholds"),
     code_path: Optional[Path] = Opt(None, "--code", "-c", help="Path to Python file with additional code (registered functions) to be imported"),
     use_gpu: int = Opt(_DEFAULTS["use_gpu"], "--gpu-id", "-g", help="GPU ID or -1 for CPU"),
@@ -39,12 +39,12 @@ def find_threshold_cli(
     # fmt: on
 ):
     """
-    Runs prediction trials for `textcat` models with varying tresholds to maximize the specified metric from CLI.
+    Runs prediction trials for models with varying tresholds to maximize the specified metric from CLI.
     model (Path): Path to file with trained model.
     data_path (Path): Path to file with DocBin with docs to use for threshold search.
     pipe_name (str): Name of pipe to examine thresholds for.
     threshold_key (str): Key of threshold attribute in component's configuration.
-    scores_key (str): Name of score to metric to optimize.
+    scores_key (str): Metric to optimize.
     n_trials (int): Number of trials to determine optimal thresholds
     code_path (Optional[Path]): Path to Python file with additional code (registered functions) to be imported.
     use_gpu (int): GPU ID or -1 for CPU.
@@ -82,7 +82,7 @@ def find_threshold(
     silent: bool = True,
 ) -> Tuple[float, float, Dict[float, float]]:
     """
-    Runs prediction trials for `textcat` models with varying tresholds to maximize the specified metric.
+    Runs prediction trials for models with varying tresholds to maximize the specified metric.
     model (Union[str, Path]): Path to file with trained model.
     data_path (Union[str, Path]): Path to file with DocBin with docs to use for threshold search.
     pipe_name (str): Name of pipe to examine thresholds for.
@@ -104,11 +104,9 @@ def find_threshold(
         wasabi.msg.fail("Evaluation data not found", data_path, exits=1)
     nlp = util.load_model(model)
 
-    pipe: Optional[Any] = None
+    if pipe_name not in nlp.component_names:
-    try:
+        raise AttributeError(Errors.E001.format(name=pipe_name))
-        pipe = nlp.get_pipe(pipe_name)
+    pipe = nlp.get_pipe(pipe_name)
-    except KeyError as err:
-        wasabi.msg.fail(title=str(err), exits=1)
     if not hasattr(pipe, "scorer"):
         raise AttributeError(Errors.E1045)
 
@@ -141,14 +139,24 @@ def find_threshold(
         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]:
+    def filter_config(
+        config: Dict[str, Any], keys: List[str], full_key: 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.
+        full_key (str): Full user-specified key.
         RETURNS (Dict[str, Any]): Filtered dictionary.
         """
+        if keys[0] not in config:
+            wasabi.msg.fail(
+                title=f"Failed to look up `{full_key}` in config: sub-key {[keys[0]]} not found.",
+                text=f"Make sure you specified {[keys[0]]} correctly. The following sub-keys are available instead: "
+                f"{config.keys()}",
+                exits=1,
+            )
         return {
-            keys[0]: filter_config(config[keys[0]], keys[1:])
+            keys[0]: filter_config(config[keys[0]], keys[1:], full_key)
             if len(keys) > 1
             else config[keys[0]]
         }
@@ -164,7 +172,9 @@ def find_threshold(
         nlp = util.load_model(
             model,
             config=set_nested_item(
-                filter_config(nlp.config, config_keys_full).copy(),
+                filter_config(
+                    nlp.config, config_keys_full, ".".join(config_keys_full)
+                ).copy(),
                 config_keys_full,
                 threshold,
             ),
@@ -176,7 +186,16 @@ def find_threshold(
                 set_nested_item(getattr(pipe, "cfg"), config_keys, threshold),
             )
 
-        scores[threshold] = nlp.evaluate(dev_dataset)[scores_key]
+        eval_scores = nlp.evaluate(dev_dataset)
+        if scores_key not in eval_scores:
+            wasabi.msg.fail(
+                title=f"Failed to look up score `{scores_key}` in evaluation results.",
+                text=f"Make sure you specified the correct value for `scores_key` correctly. The following scores are "
+                f"available: {eval_scores.keys()}",
+                exits=1,
+            )
+        scores[threshold] = eval_scores[scores_key]
+
         if not isinstance(scores[threshold], (float, int)):
             wasabi.msg.fail(
                 f"Returned score for key '{scores_key}' is not numeric. Threshold optimization only works for numeric "