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