Update app.py

app.py

@@ -15,6 +15,7 @@
 __author__ = 'Dmitry Ustalov'
 __license__ = 'Apache 2.0'
 
+from functools import partial
 from typing import IO, Tuple, List, cast, Dict, Set, Callable
 
 import gradio as gr
@@ -68,28 +69,38 @@ def bradley_terry(wins: npt.NDArray[np.int64], ties: npt.NDArray[np.int64],
     return p
 
 
-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]:
+def centrality(algorithm: Callable[[nx.DiGraph], Dict[int, float]],
+               wins: npt.NDArray[np.int64], ties: npt.NDArray[np.int64]) -> npt.NDArray[np.float64]:
     A = wins + .5 * ties
 
     G = nx.from_numpy_array(A, create_using=nx.DiGraph)
 
-    scores: Dict[int, float] = algorithm(G, max_iter=limit, tol=tolerance)
+    scores: Dict[int, float] = algorithm(G)
 
     p = np.array([scores[i] for i in range(len(G))])
 
     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]:
+    M = wins + .5 * ties
+
+    return M.sum(axis=0)
+
+
 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)
+    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]:
-    return centrality(nx.algorithms.pagerank, wins, ties, tolerance, limit)
+    algorithm = partial(nx.algorithms.pagerank, max_iter=limit, tol=tolerance)
+
+    return centrality(algorithm, wins, ties)
 
 
 # https://gist.github.com/dustalov/41678b70c40ba5a55430fa5e77b121d9#file-newman-py
@@ -141,6 +152,7 @@ def newman(wins: npt.NDArray[np.int64], ties: npt.NDArray[np.int64],
 
 
 ALGORITHMS = {
+    'Counting': counting,
     'Bradley-Terry (1952)': bradley_terry,
     'Eigenvector (1986)': eigen,
     'PageRank (1998)': pagerank,
@@ -156,7 +168,7 @@ def largest_strongly_connected_component(df: pd.DataFrame) -> Set[str]:
     return cast(Set[str], largest)
 
 
-def handler(file: IO[bytes], algorithm: str, filtered: bool, seed: int) -> Tuple[pd.DataFrame, Figure]:
+def handler(file: IO[bytes], algorithm: str, filtered: bool, truncated: bool, seed: int) -> Tuple[pd.DataFrame, Figure]:
     if file is None:
         raise gr.Error('File must be uploaded')
 
@@ -219,6 +231,10 @@ def handler(file: IO[bytes], algorithm: str, filtered: bool, seed: int) -> Tuple
     df_result.sort_values(by=['rank', 'score'], ascending=[True, False], inplace=True)
     df_result.reset_index(inplace=True)
 
+    if truncated:
+        df_result = pd.concat((df_result.head(5), df_result.tail(5)), copy=False)
+        df_result = df_result[~df_result.index.duplicated(keep='last')]
+
     df_pairwise = pd.DataFrame(data=scores[:, np.newaxis] / (scores + scores[:, np.newaxis]),
                                index=index, columns=index)
     df_pairwise = df_pairwise.reindex(labels=df_result['item'], columns=df_result['item'], copy=False)
@@ -249,6 +265,12 @@ def main() -> None:
                      '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.'
             ),
+            gr.Checkbox(
+                value=False,
+                label='Truncate Output',
+                info='Perform the entire computation but output only five head and five tail items, '
+                     'avoiding overlap.'
+            ),
             gr.Number(
                 label='Seed',
                 precision=0
@@ -264,6 +286,7 @@ def main() -> None:
             )
         ],
         examples=[
+            ['food.csv', 'Counting', False],
             ['food.csv', 'Bradley-Terry (1952)', False],
             ['food.csv', 'Eigenvector (1986)', False],
             ['food.csv', 'PageRank (1998)', False],