Update paper_ranking.py

src/bioregistry/analysis/paper_ranking.py

@@ -9,7 +9,7 @@
 from collections import defaultdict
 from collections.abc import Iterable
 from pathlib import Path
-from typing import Any, NamedTuple
+from typing import Any, NamedTuple, Optional, Union
 
 import click
 import numpy as np
@@ -45,8 +45,11 @@
 XTest: TypeAlias = NDArray[np.str_]
 YTest: TypeAlias = NDArray[np.str_]
 
+ClassifierHint: TypeAlias = Union[ClassifierMixin, LinearClassifierMixin]
+Classifiers: TypeAlias = list[tuple[str, ClassifierHint]]
 
-def get_publications_from_bioregistry(path: Path | None = None) -> pd.DataFrame:
+
+def get_publications_from_bioregistry(path: Optional[Path] = None) -> pd.DataFrame:
     """Load bioregistry data from a JSON file, extracting publication details and fetching abstracts if missing.
 
     :param path: Path to the bioregistry JSON file.
@@ -101,7 +104,7 @@ def load_curated_papers(file_path: Path = CURATED_PAPERS_PATH) -> pd.DataFrame:
     return curated_df
 
 
-def _get_metadata_for_ids(pubmed_ids: Iterable[int | str]) -> dict[str, dict[str, Any]]:
+def _get_metadata_for_ids(pubmed_ids: Iterable[Union[int, str]]) -> dict[str, dict[str, Any]]:
     """Get metadata for articles in PubMed, wrapping the INDRA client."""
     from indra.literature import pubmed_client
 
@@ -187,7 +190,7 @@ def load_google_curation_df() -> pd.DataFrame:
     return df
 
 
-def _map_labels(s: str) -> int | None:
+def _map_labels(s: str) -> Optional[int]:
     """Map labels to binary values.
 
     :param s: Label value.
@@ -200,9 +203,6 @@ def _map_labels(s: str) -> int | None:
     return None
 
 
-Classifiers = list[tuple[str, ClassifierMixin | LinearClassifierMixin]]
-
-
 def train_classifiers(x_train: XTrain, y_train: YTrain) -> Classifiers:
     """Train multiple classifiers on the training data.
 
@@ -241,16 +241,14 @@ def generate_meta_features(
 
 
 def _cross_val_predict(
-    clf: ClassifierMixin | LinearClassifierMixin, x_train: XTrain, y_train: YTrain, cv: int
-) -> NDArray:
+    clf: ClassifierHint, x_train: XTrain, y_train: YTrain, cv: int
+) -> NDArray[np.float64]:
     if not hasattr(clf, "predict_proba"):
         return cross_val_predict(clf, x_train, y_train, cv=cv, method="decision_function")
     return cross_val_predict(clf, x_train, y_train, cv=cv, method="predict_proba")[:, 1]
 
 
-def _predict(
-    clf: ClassifierMixin | LinearClassifierMixin, x: NDArray[np.float64]
-) -> NDArray[np.float64]:
+def _predict(clf: ClassifierHint, x: NDArray[np.float64]) -> NDArray[np.float64]:
     if hasattr(clf, "predict_proba"):
         return clf.predict_proba(x)[:, 1]
     else:
@@ -265,7 +263,7 @@ class MetaClassifierEvaluationResults(NamedTuple):
 
 
 def _evaluate_meta_classifier(
-    meta_clf: ClassifierMixin, x_test_meta: NDArray[np.float64], y_test: YTest
+    meta_clf: ClassifierMixin, x_test_meta: XTest, y_test: YTest
 ) -> MetaClassifierEvaluationResults:
     """Evaluate meta-classifier using MCC and AUC-ROC scores.
 
@@ -285,7 +283,7 @@ def predict_and_save(
     vectorizer: TfidfVectorizer,
     classifiers: Classifiers,
     meta_clf: ClassifierMixin,
-    filename: str | Path,
+    filename: str,
 ) -> None:
     """Predict and save scores for new data using trained classifiers and meta-classifier.