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Add micro PRF for morph scoring (#9546)
* Add micro PRF for morph scoring For pipelines where morph features are added by more than one component and a reference training corpus may not contain all features, a micro PRF score is more flexible than a simple accuracy score. An example is the reading and inflection features added by the Japanese tokenizer. * Use `morph_micro_f` as the default morph score for Japanese morphologizers. * Update docstring * Fix typo in docstring * Update Scorer API docs * Fix results type * Organize score list by attribute prefix
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Adriane Boyd
GitHub
parent
554fa414ec
commit
12974bf4d9
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@ -203,7 +203,7 @@ class Japanese(Language):
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"extend": True,
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"scorer": {"@scorers": "spacy.morphologizer_scorer.v1"},
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},
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default_score_weights={"pos_acc": 0.5, "morph_acc": 0.5, "morph_per_feat": None},
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default_score_weights={"pos_acc": 0.5, "morph_micro_f": 0.5, "morph_per_feat": None},
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)
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def make_morphologizer(
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nlp: Language,
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@ -247,18 +247,21 @@ class Scorer:
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missing_values: Set[Any] = MISSING_VALUES, # type: ignore[assignment]
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**cfg,
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) -> Dict[str, Any]:
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"""Return PRF scores per feat for a token attribute in UFEATS format.
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"""Return micro PRF and PRF scores per feat for a token attribute in
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UFEATS format.
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examples (Iterable[Example]): Examples to score
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attr (str): The attribute to score.
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getter (Callable[[Token, str], Any]): Defaults to getattr. If provided,
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getter(token, attr) should return the value of the attribute for an
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individual token.
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missing_values (Set[Any]): Attribute values to treat as missing annotation
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in the reference annotation.
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RETURNS (dict): A dictionary containing the per-feat PRF scores under
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the key attr_per_feat.
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missing_values (Set[Any]): Attribute values to treat as missing
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annotation in the reference annotation.
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RETURNS (dict): A dictionary containing the micro PRF scores under the
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key attr_micro_p/r/f and the per-feat PRF scores under
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attr_per_feat.
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"""
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micro_score = PRFScore()
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per_feat = {}
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for example in examples:
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pred_doc = example.predicted
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@ -300,15 +303,22 @@ class Scorer:
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pred_per_feat[field] = set()
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pred_per_feat[field].add((gold_i, feat))
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for field in per_feat:
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micro_score.score_set(pred_per_feat.get(field, set()), gold_per_feat.get(field, set()))
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per_feat[field].score_set(
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pred_per_feat.get(field, set()), gold_per_feat.get(field, set())
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)
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score_key = f"{attr}_per_feat"
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if any([len(v) for v in per_feat.values()]):
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result = {k: v.to_dict() for k, v in per_feat.items()}
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return {score_key: result}
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result: Dict[str, Any] = {}
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if len(micro_score) > 0:
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result[f"{attr}_micro_p"] = micro_score.precision
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result[f"{attr}_micro_r"] = micro_score.recall
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result[f"{attr}_micro_f"] = micro_score.fscore
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result[f"{attr}_per_feat"] = {k: v.to_dict() for k, v in per_feat.items()}
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else:
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return {score_key: None}
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result[f"{attr}_micro_p"] = None
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result[f"{attr}_micro_r"] = None
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result[f"{attr}_micro_f"] = None
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result[f"{attr}_per_feat"] = None
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return result
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@staticmethod
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def score_spans(
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@ -249,6 +249,7 @@ def test_tag_score(tagged_doc):
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assert results["tag_acc"] == 1.0
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assert results["pos_acc"] == 1.0
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assert results["morph_acc"] == 1.0
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assert results["morph_micro_f"] == 1.0
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assert results["morph_per_feat"]["NounType"]["f"] == 1.0
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# Gold annotation is modified
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@ -272,6 +273,7 @@ def test_tag_score(tagged_doc):
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assert results["tag_acc"] == 0.9
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assert results["pos_acc"] == 0.9
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assert results["morph_acc"] == approx(0.8)
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assert results["morph_micro_f"] == approx(0.8461538)
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assert results["morph_per_feat"]["NounType"]["f"] == 1.0
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assert results["morph_per_feat"]["Poss"]["f"] == 0.0
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assert results["morph_per_feat"]["Number"]["f"] == approx(0.72727272)
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@ -41,15 +41,20 @@ Calculate the scores for a list of [`Example`](/api/example) objects using the
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scoring methods provided by the components in the pipeline.
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The returned `Dict` contains the scores provided by the individual pipeline
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components. For the scoring methods provided by the `Scorer` and use by the core
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pipeline components, the individual score names start with the `Token` or `Doc`
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attribute being scored:
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components. For the scoring methods provided by the `Scorer` and used by the
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core pipeline components, the individual score names start with the `Token` or
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`Doc` attribute being scored:
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- `token_acc`, `token_p`, `token_r`, `token_f`,
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- `token_acc`, `token_p`, `token_r`, `token_f`
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- `sents_p`, `sents_r`, `sents_f`
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- `tag_acc`, `pos_acc`, `morph_acc`, `morph_per_feat`, `lemma_acc`
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- `tag_acc`
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- `pos_acc`
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- `morph_acc`, `morph_micro_p`, `morph_micro_r`, `morph_micro_f`,
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`morph_per_feat`
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- `lemma_acc`
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- `dep_uas`, `dep_las`, `dep_las_per_type`
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- `ents_p`, `ents_r` `ents_f`, `ents_per_type`
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- `spans_sc_p`, `spans_sc_r`, `spans_sc_f`
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- `cats_score` (depends on config, description provided in `cats_score_desc`),
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`cats_micro_p`, `cats_micro_r`, `cats_micro_f`, `cats_macro_p`,
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`cats_macro_r`, `cats_macro_f`, `cats_macro_auc`, `cats_f_per_type`,
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@ -84,7 +89,7 @@ Docs with `has_unknown_spaces` are skipped during scoring.
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> ```
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| Name | Description |
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| ----------- | ------------------------------------------------------------------------------------------------------------------- |
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| ----------- | ------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------ |
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| `examples` | The `Example` objects holding both the predictions and the correct gold-standard annotations. ~~Iterable[Example]~~ |
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| **RETURNS** | `Dict` | A dictionary containing the scores `token_acc`, `token_p`, `token_r`, `token_f`. ~~Dict[str, float]]~~ |
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@ -124,14 +129,14 @@ scoring.
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> print(scores["morph_per_feat"])
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> ```
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| Name | Description |
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| ---------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `examples` | The `Example` objects holding both the predictions and the correct gold-standard annotations. ~~Iterable[Example]~~ |
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| `attr` | The attribute to score. ~~str~~ |
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| _keyword-only_ | |
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| `getter` | Defaults to `getattr`. If provided, `getter(token, attr)` should return the value of the attribute for an individual `Token`. ~~Callable[[Token, str], Any]~~ |
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| `missing_values` | Attribute values to treat as missing annotation in the reference annotation. Defaults to `{0, None, ""}`. ~~Set[Any]~~ |
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| **RETURNS** | A dictionary containing the per-feature PRF scores under the key `{attr}_per_feat`. ~~Dict[str, Dict[str, float]]~~ |
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| Name | Description |
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| ---------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `examples` | The `Example` objects holding both the predictions and the correct gold-standard annotations. ~~Iterable[Example]~~ |
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| `attr` | The attribute to score. ~~str~~ |
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| _keyword-only_ | |
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| `getter` | Defaults to `getattr`. If provided, `getter(token, attr)` should return the value of the attribute for an individual `Token`. ~~Callable[[Token, str], Any]~~ |
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| `missing_values` | Attribute values to treat as missing annotation in the reference annotation. Defaults to `{0, None, ""}`. ~~Set[Any]~~ |
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| **RETURNS** | A dictionary containing the micro PRF scores under the key `{attr}_micro_p/r/f` and the per-feature PRF scores under `{attr}_per_feat`. ~~Dict[str, Dict[str, float]]~~ |
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## Scorer.score_spans {#score_spans tag="staticmethod" new="3"}
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