Update app.py

app.py

@@ -37,8 +37,8 @@ def visualize(df_pairwise: pd.DataFrame) -> Figure:
 
 
 # https://gist.github.com/dustalov/41678b70c40ba5a55430fa5e77b121d9#file-bradley_terry-py
-def bradley_terry(wins: npt.NDArray[np.int64], ties: npt.NDArray[np.int64],
-                  seed: int = 0, tolerance: float = 10e-6, limit: int = 20) -> npt.NDArray[np.float64]:
+def bradley_terry(wins: npt.NDArray[np.int_], ties: npt.NDArray[np.int_],
+                  seed: int = 0, tolerance: float = 10e-6, limit: int = 20) -> npt.NDArray[np.float_]:
     M = wins + .5 * ties
 
     T = M.T + M
@@ -72,7 +72,7 @@ def bradley_terry(wins: npt.NDArray[np.int64], ties: npt.NDArray[np.int64],
 
 
 def centrality(algorithm: Callable[[nx.DiGraph], Dict[int, float]],
-               wins: npt.NDArray[np.int64], ties: npt.NDArray[np.int64]) -> npt.NDArray[np.float64]:
+               wins: npt.NDArray[np.int_], ties: npt.NDArray[np.int_]) -> npt.NDArray[np.float_]:
     A = wins + .5 * ties
 
     G = nx.from_numpy_array(A, create_using=nx.DiGraph)
@@ -84,30 +84,30 @@ def centrality(algorithm: Callable[[nx.DiGraph], Dict[int, float]],
     return p
 
 
-def counting(wins: npt.NDArray[np.int64], ties: npt.NDArray[np.int64],
-             seed: int = 0, tolerance: float = 10e-6, limit: int = 100) -> npt.NDArray[np.float64]:
+def counting(wins: npt.NDArray[np.int_], ties: npt.NDArray[np.int_],
+             seed: int = 0, tolerance: float = 10e-6, limit: int = 100) -> npt.NDArray[np.float_]:
     M = wins + .5 * ties
 
-    return cast(npt.NDArray[np.float64], M.sum(axis=1))
+    return cast(npt.NDArray[np.float_], M.sum(axis=1))
 
 
-def eigen(wins: npt.NDArray[np.int64], ties: npt.NDArray[np.int64],
-          seed: int = 0, tolerance: float = 10e-6, limit: int = 100) -> npt.NDArray[np.float64]:
+def eigen(wins: npt.NDArray[np.int_], ties: npt.NDArray[np.int_],
+          seed: int = 0, tolerance: float = 10e-6, limit: int = 100) -> npt.NDArray[np.float_]:
     algorithm = partial(nx.algorithms.eigenvector_centrality_numpy, max_iter=limit, tol=tolerance)
 
     return centrality(algorithm, wins, ties)
 
 
-def pagerank(wins: npt.NDArray[np.int64], ties: npt.NDArray[np.int64],
-             seed: int = 0, tolerance: float = 10e-6, limit: int = 100) -> npt.NDArray[np.float64]:
+def pagerank(wins: npt.NDArray[np.int_], ties: npt.NDArray[np.int_],
+             seed: int = 0, tolerance: float = 10e-6, limit: int = 100) -> npt.NDArray[np.float_]:
     algorithm = partial(nx.algorithms.pagerank, max_iter=limit, tol=tolerance)
 
     return centrality(algorithm, wins, ties)
 
 
 # https://gist.github.com/dustalov/41678b70c40ba5a55430fa5e77b121d9#file-newman-py
-def newman(wins: npt.NDArray[np.int64], ties: npt.NDArray[np.int64],
-           seed: int = 0, tolerance: float = 10e-6, limit: int = 20) -> npt.NDArray[np.float64]:
+def newman(wins: npt.NDArray[np.int_], ties: npt.NDArray[np.int_],
+           seed: int = 0, tolerance: float = 10e-6, limit: int = 20) -> npt.NDArray[np.float_]:
     rng = np.random.default_rng(seed)
 
     pi, v = rng.random(wins.shape[0]), rng.random()
@@ -214,8 +214,8 @@ def handler(file: IO[bytes], algorithm: str, filtered: bool, truncated: bool, se
                              aggfunc='count', fill_value=0)
     df_ties = df_ties.reindex(labels=index, columns=index, fill_value=0, copy=False)
 
-    wins = df_wins.to_numpy(dtype=np.int64)
-    ties = df_ties.to_numpy(dtype=np.int64)
+    wins = df_wins.to_numpy(dtype=int)
+    ties = df_ties.to_numpy(dtype=int)
     ties += ties.T
 
     assert wins.shape == ties.shape, 'wins and ties shapes are different'