update pyterrier dependency

pyproject.toml

@@ -27,7 +27,7 @@ classifiers = [
 ]
 
 [project.optional-dependencies]
-pyterrier = ["python-terrier>=0.10.0, <1"]
+pyterrier = ["python-terrier>=0.12.0, <0.13"]
 
 [project.urls]
 Repository = "https://github.com/mrjleo/fast-forward-indexes"

src/fast_forward/util/pyterrier.py

@@ -23,23 +23,23 @@ def __init__(self, index: "Index") -> None:
         self._index = index
         super().__init__()
 
-    def transform(self, topics_or_res: "pd.DataFrame") -> "pd.DataFrame":
+    def transform(self, inp: "pd.DataFrame") -> "pd.DataFrame":
         """Compute the scores for all query-document pairs in the data frame.
 
         The previous scores are moved to the "score_0" column.
 
-        :param topics_or_res: The PyTerrier data frame.
+        :param inp: The PyTerrier data frame.
         :return: A data frame with the computed scores.
         """
         ff_scores = self._index(
             Ranking(
-                topics_or_res.rename(columns={"qid": "q_id", "docno": "id"}),
+                inp.rename(columns={"qid": "q_id", "docno": "id"}),
                 copy=False,
                 is_sorted=True,
             )
         )._df.rename(columns={"q_id": "qid", "id": "docno"})
 
-        result = topics_or_res[["qid", "docno", "score"]].merge(
+        result = inp[["qid", "docno", "score"]].merge(
             ff_scores[["qid", "docno", "score", "query"]],
             on=["qid", "docno"],
             suffixes=("_0", None),
@@ -71,15 +71,12 @@ def __init__(self, alpha: float) -> None:
         self.alpha = alpha
         super().__init__()
 
-    def transform(self, topics_or_res: "pd.DataFrame") -> "pd.DataFrame":
+    def transform(self, inp: "pd.DataFrame") -> "pd.DataFrame":
         """Interpolate the scores as `alpha * score_0 + (1 - alpha) * score`.
 
-        :param topics_or_res: The PyTerrier data frame.
+        :param inp: The PyTerrier data frame.
         :return: A data frame with the interpolated scores.
         """
-        new_df = topics_or_res[["qid", "docno", "query"]].copy()
-        new_df["score"] = (
-            self.alpha * topics_or_res["score_0"]
-            + (1 - self.alpha) * topics_or_res["score"]
-        )
+        new_df = inp[["qid", "docno", "query"]].copy()
+        new_df["score"] = self.alpha * inp["score_0"] + (1 - self.alpha) * inp["score"]
         return pt.model.add_ranks(new_df, single_query=False)