Update app.py

app.py

@@ -15,7 +15,7 @@
 __author__ = 'Dmitry Ustalov'
 __license__ = 'Apache 2.0'
 
-from typing import IO, Tuple, List, cast, Dict, Set
+from typing import IO, Tuple, List, cast, Dict, Set, Callable
 
 import gradio as gr
 import networkx as nx
@@ -68,19 +68,30 @@ def bradley_terry(wins: npt.NDArray[np.int64], ties: npt.NDArray[np.int64],
     return p
 
 
-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 centrality(algorithm: Callable[..., Dict[int, float]],
+               wins: npt.NDArray[np.int64], ties: npt.NDArray[np.int64],
+               tolerance: float = 10e-6, limit: int = 100) -> npt.NDArray[np.float64]:
     A = wins + .5 * ties
 
     G = nx.from_numpy_array(A, create_using=nx.DiGraph)
 
-    scores: Dict[int, float] = nx.algorithms.pagerank(G, max_iter=limit, tol=tolerance)
+    scores: Dict[int, float] = algorithm(G, max_iter=limit, tol=tolerance)
 
     p = np.array([scores[i] for i in range(len(G))])
 
     return p
 
 
+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]:
+    return centrality(nx.algorithms.eigenvector_centrality_numpy, wins, ties, tolerance, limit)
+
+
+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]:
+    return centrality(nx.algorithms.pagerank, wins, ties, tolerance, limit)
+
+
 # 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]:
@@ -131,6 +142,7 @@ def newman(wins: npt.NDArray[np.int64], ties: npt.NDArray[np.int64],
 
 ALGORITHMS = {
     'Bradley-Terry (1952)': bradley_terry,
+    'Eigenvector (1986)': eigen,
     'PageRank (1998)': pagerank,
     'Newman (2023)': newman,
 }
@@ -232,7 +244,8 @@ def main() -> None:
             gr.Checkbox(
                 value=False,
                 label='Largest SCC',
-                info='Bradley-Terry and Newman algorithms require the comparison graph to be strongly-connected. '
+                info='Bradley-Terry, Eigenvector, and Newman algorithms require the comparison graph '
+                     'to be strongly-connected. '
                      'This option keeps only the largest strongly-connected component (SCC) of the input graph. '
                      'Some items might be missing as a result of this filtering.'
             ),
@@ -252,6 +265,7 @@ def main() -> None:
         ],
         examples=[
             ['food.csv', 'Bradley-Terry (1952)', False],
+            ['food.csv', 'Eigenvector (1986)', False],
             ['food.csv', 'PageRank (1998)', False],
             ['food.csv', 'Newman (2023)', False]
         ],