Add spancat pipeline in spacy debug data (#10070)

* Setup debug data for spancat

* Add check for missing labels

* Add low-level data warning error

* Improve logic when compiling the gold train data

* Implement check for negative examples

* Remove breakpoint

* Remove ws_ents and missing entity checks

* Fix mypy errors

* Make variable name spans_key consistent

* Rename pipeline -> component for consistency

* Account for missing labels per spans_key

* Cleanup variable names for consistency

* Improve brevity of conditional statements

* Remove unused variables

* Include spans_key as an argument for _get_examples

* Add a conditional check for spans_key

* Update spancat debug data based on new API

- Instead of using _get_labels_from_model(), I'm now using
_get_labels_from_spancat() (cf. https://github.com/explosion/spaCy/pull10079)
- The way information is displayed was also changed (text -> table)

* Rename model_labels to ensure mypy works

* Update wording on warning messages

Use "span type" instead of "entity type" in wording the warning messages.
This is because Spans aren't necessarily entities.

* Update component type into a Literal

This is to make it clear that the component parameter should only accept
either 'spancat' or 'ner'.

* Update checks to include actual model span_keys

Instead of looking at everything in the data, we only check those
span_keys from the actual spancat component. Instead of doing the filter
inside the for-loop, I just made another dictionary,
data_labels_in_component to hold this value.

* Update spacy/cli/debug_data.py

* Show label counts only when verbose is True

Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>

Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>

Author: ljvmiranda921

spacy/cli/debug_data.py

@@ -193,6 +193,70 @@ def debug_data(
     else:
         msg.info("No word vectors present in the package")
 
+    if "spancat" in factory_names:
+        model_labels_spancat = _get_labels_from_spancat(nlp)
+        has_low_data_warning = False
+        has_no_neg_warning = False
+
+        msg.divider("Span Categorization")
+        msg.table(model_labels_spancat, header=["Spans Key", "Labels"], divider=True)
+
+        msg.text("Label counts in train data: ", show=verbose)
+        for spans_key, data_labels in gold_train_data["spancat"].items():
+            msg.text(
+                f"Key: {spans_key}, {_format_labels(data_labels.items(), counts=True)}",
+                show=verbose,
+            )
+        # Data checks: only take the spans keys in the actual spancat components
+        data_labels_in_component = {
+            spans_key: gold_train_data["spancat"][spans_key]
+            for spans_key in model_labels_spancat.keys()
+        }
+        for spans_key, data_labels in data_labels_in_component.items():
+            for label, count in data_labels.items():
+                # Check for missing labels
+                spans_key_in_model = spans_key in model_labels_spancat.keys()
+                if (spans_key_in_model) and (
+                    label not in model_labels_spancat[spans_key]
+                ):
+                    msg.warn(
+                        f"Label '{label}' is not present in the model labels of key '{spans_key}'. "
+                        "Performance may degrade after training."
+                    )
+                # Check for low number of examples per label
+                if count <= NEW_LABEL_THRESHOLD:
+                    msg.warn(
+                        f"Low number of examples for label '{label}' in key '{spans_key}' ({count})"
+                    )
+                    has_low_data_warning = True
+                # Check for negative examples
+                with msg.loading("Analyzing label distribution..."):
+                    neg_docs = _get_examples_without_label(
+                        train_dataset, label, "spancat", spans_key
+                    )
+                if neg_docs == 0:
+                    msg.warn(f"No examples for texts WITHOUT new label '{label}'")
+                    has_no_neg_warning = True
+
+        if has_low_data_warning:
+            msg.text(
+                f"To train a new span type, your data should include at "
+                f"least {NEW_LABEL_THRESHOLD} instances of the new label",
+                show=verbose,
+            )
+        else:
+            msg.good("Good amount of examples for all labels")
+
+        if has_no_neg_warning:
+            msg.text(
+                "Training data should always include examples of spans "
+                "in context, as well as examples without a given span "
+                "type.",
+                show=verbose,
+            )
+        else:
+            msg.good("Examples without ocurrences available for all labels")
+
     if "ner" in factory_names:
         # Get all unique NER labels present in the data
         labels = set(
@@ -238,7 +302,7 @@ def debug_data(
                 has_low_data_warning = True
 
                 with msg.loading("Analyzing label distribution..."):
-                    neg_docs = _get_examples_without_label(train_dataset, label)
+                    neg_docs = _get_examples_without_label(train_dataset, label, "ner")
                 if neg_docs == 0:
                     msg.warn(f"No examples for texts WITHOUT new label '{label}'")
                     has_no_neg_warning = True
@@ -573,6 +637,7 @@ def _compile_gold(
         "deps": Counter(),
         "words": Counter(),
         "roots": Counter(),
+        "spancat": dict(),
         "ws_ents": 0,
         "boundary_cross_ents": 0,
         "n_words": 0,
@@ -617,6 +682,15 @@ def _compile_gold(
                     data["boundary_cross_ents"] += 1
                 elif label == "-":
                     data["ner"]["-"] += 1
+        if "spancat" in factory_names:
+            for span_key in list(eg.reference.spans.keys()):
+                if span_key not in data["spancat"]:
+                    data["spancat"][span_key] = Counter()
+                for i, span in enumerate(eg.reference.spans[span_key]):
+                    if span.label_ is None:
+                        continue
+                    else:
+                        data["spancat"][span_key][span.label_] += 1
         if "textcat" in factory_names or "textcat_multilabel" in factory_names:
             data["cats"].update(gold.cats)
             if any(val not in (0, 1) for val in gold.cats.values()):
@@ -687,14 +761,28 @@ def _format_labels(
     return ", ".join([f"'{l}'" for l in cast(Iterable[str], labels)])
 
 
-def _get_examples_without_label(data: Sequence[Example], label: str) -> int:
+def _get_examples_without_label(
+    data: Sequence[Example],
+    label: str,
+    component: Literal["ner", "spancat"] = "ner",
+    spans_key: Optional[str] = "sc",
+) -> int:
     count = 0
     for eg in data:
-        labels = [
-            label.split("-")[1]
-            for label in eg.get_aligned_ner()
-            if label not in ("O", "-", None)
-        ]
+        if component == "ner":
+            labels = [
+                label.split("-")[1]
+                for label in eg.get_aligned_ner()
+                if label not in ("O", "-", None)
+            ]
+
+        if component == "spancat":
+            labels = (
+                [span.label_ for span in eg.reference.spans[spans_key]]
+                if spans_key in eg.reference.spans
+                else []
+            )
+
         if label not in labels:
             count += 1
     return count