go through possible teams with brute force

brute_force.py

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+import numpy as np
+import json
+import itertools
+from pathlib import Path
+from rich.console import Console
+from rich.table import Table
+from rich.text import Text
+from rich import box
+
+rgen = np.random.default_rng(seed=42)
+
+
+def load_stats():
+    db = Path("local_team.db")
+    players_list = "prefs_page/src/players.json"
+    with open(players_list, "r") as f:
+        players = json.load(f)
+
+    preferences = {}
+
+    for line in open(db, "r"):
+        date, person, prefs = line.split("\t")
+        if not person.strip() or not prefs.strip():
+            continue
+        preferences[person] = [p.strip() for p in prefs.split(",")]
+
+    for player in players:
+        if player not in preferences:
+            preferences[player] = []
+
+    return players, preferences
+
+
+# synthetical data
+# rgen = np.random.default_rng(seed=42)
+
+# n_prefs = 8
+# people = {
+#     player: rgen.choice(players, size=n_prefs, replace=False) for player in players
+# }
+
+
+def team_table(mean, team0, team1):
+    console = Console(record=True)
+    console.print(Text(f"mean wishes fulfilled: {score:.02f}"))
+    for i, team in enumerate([team0, team1]):
+        table = Table(title=f"Team {i}", box=box.ROUNDED, show_lines=True)
+        table.add_column("player", justify="right", style="cyan")
+        table.add_column("wishes fulfilled", justify="center", style="magenta")
+        table.add_column("in %", justify="center", style="green")
+        for p in sorted(list(team)):
+            prefs = people[p]
+            matches = sum([pref in team for pref in people[p]])
+            table.add_row(
+                p, f"{matches:d}", f"{matches/len(prefs):.2%}" if prefs else ""
+            )
+        console.print(table)
+    console.save_html("tables.html")
+
+
+def team_table_json():
+    players, preferences = load_stats()
+    mean, team0, team1 = apply_brute_force(players, preferences)
+    data = {}
+    for i, team in enumerate([team0, team1]):
+        tablename = f"Team {i+1}"
+        data[tablename] = []
+        for p in sorted(list(team)):
+            prefs = preferences[p]
+            matches = sum([pref in team for pref in preferences[p]])
+            data[tablename].append([p, matches, len(prefs)])
+
+    with open("prefs_page/src/table.json", "w") as f:
+        json.dump(data, f)
+
+
+def apply_brute_force(players, preferences):
+    def evaluate_teams(team0, team1):
+        scores = []
+        percentages = []
+        for team in [team0, team1]:
+            for p in team:
+                scores.append(sum([pref in team for pref in preferences[p]]))
+                if len(preferences[p]) > 0:
+                    percentages.append(scores[-1] / len(preferences[p]))
+        return np.mean(scores), np.mean(percentages) * 100
+
+    best_score = [(0, [], [])]
+    best_percentage = [(0, [], [])]
+    for team0 in itertools.combinations(players, 9):
+        team1 = {player for player in players if player not in team0}
+        score, percentage = evaluate_teams(team0, team1)
+        if score > best_score[0][0]:
+            best_score = [(score, team0, team1)]
+        if score == best_score[0][0] and set(team0) != set(best_score[0][1]):
+            best_score.append((score, team0, team1))
+        if percentage > best_percentage[0][0]:
+            best_percentage = [(percentage, team0, team1)]
+        if percentage == best_percentage[0][0] and set(team0) != set(best_score[0][1]):
+            best_percentage.append((percentage, team0, team1))
+
+    for result in best_score:
+        print(result[0])
+        print(result[1])
+        print(result[2])
+
+    for result in best_percentage:
+        print(result[0])
+        print(result[1])
+        print(result[2])
+
+    # team_table(score, team0, team1)
+    return best_score[0]
+
+
+if __name__ == "__main__":
+    team_table_json()