Initial commit: 6-Card Golf with AI opponents
Features: - Multiplayer WebSocket game server (FastAPI) - 8 AI personalities with distinct play styles - 15+ house rule variants - SQLite game logging for AI analysis - Comprehensive test suite (80+ tests) AI improvements: - Fixed Maya bug (taking bad cards, discarding good ones) - Personality traits influence style without overriding competence - Zero blunders detected in 1000+ game simulations Testing infrastructure: - Game rules verification (test_game.py) - AI decision analysis (game_analyzer.py) - Score distribution analysis (score_analysis.py) - House rules testing (test_house_rules.py) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
Aaron D. Lee
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server/score_analysis.py
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349
server/score_analysis.py
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"""
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Score distribution analysis for Golf AI.
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Generates box plots and statistics to verify AI plays reasonably.
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"""
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import random
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import sys
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from collections import defaultdict
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from game import Game, Player, GamePhase, GameOptions
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from ai import GolfAI, CPUProfile, CPU_PROFILES, get_ai_card_value
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def run_game_for_scores(num_players: int = 4) -> dict[str, int]:
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"""Run a single game and return final scores by player name."""
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# Pick random profiles
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profiles = random.sample(CPU_PROFILES, min(num_players, len(CPU_PROFILES)))
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game = Game()
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player_profiles: dict[str, CPUProfile] = {}
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for i, profile in enumerate(profiles):
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player = Player(id=f"cpu_{i}", name=profile.name)
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game.add_player(player)
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player_profiles[player.id] = profile
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options = GameOptions(initial_flips=2, flip_on_discard=False, use_jokers=False)
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game.start_game(num_decks=1, num_rounds=1, options=options)
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# Initial flips
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for player in game.players:
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positions = GolfAI.choose_initial_flips(options.initial_flips)
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game.flip_initial_cards(player.id, positions)
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# Play game
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turn = 0
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max_turns = 200
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while game.phase in (GamePhase.PLAYING, GamePhase.FINAL_TURN) and turn < max_turns:
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current = game.current_player()
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if not current:
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break
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profile = player_profiles[current.id]
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# Draw
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discard_top = game.discard_top()
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take_discard = GolfAI.should_take_discard(discard_top, current, profile, game)
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source = "discard" if take_discard else "deck"
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drawn = game.draw_card(current.id, source)
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if not drawn:
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break
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# Swap or discard
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swap_pos = GolfAI.choose_swap_or_discard(drawn, current, profile, game)
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if swap_pos is None and game.drawn_from_discard:
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face_down = [i for i, c in enumerate(current.cards) if not c.face_up]
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if face_down:
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swap_pos = random.choice(face_down)
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else:
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worst_pos = 0
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worst_val = -999
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for i, c in enumerate(current.cards):
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card_val = get_ai_card_value(c, game.options)
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if card_val > worst_val:
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worst_val = card_val
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worst_pos = i
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swap_pos = worst_pos
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if swap_pos is not None:
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game.swap_card(current.id, swap_pos)
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else:
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game.discard_drawn(current.id)
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if game.flip_on_discard:
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flip_pos = GolfAI.choose_flip_after_discard(current, profile)
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game.flip_and_end_turn(current.id, flip_pos)
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turn += 1
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# Return scores
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return {p.name: p.total_score for p in game.players}
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def collect_scores(num_games: int = 100, num_players: int = 4) -> dict[str, list[int]]:
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"""Run multiple games and collect all scores by player."""
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all_scores: dict[str, list[int]] = defaultdict(list)
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print(f"Running {num_games} games with {num_players} players each...")
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for i in range(num_games):
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if (i + 1) % 20 == 0:
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print(f" {i + 1}/{num_games} games completed")
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scores = run_game_for_scores(num_players)
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for name, score in scores.items():
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all_scores[name].append(score)
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return dict(all_scores)
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def print_statistics(all_scores: dict[str, list[int]]):
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"""Print statistical summary."""
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print("\n" + "=" * 60)
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print("SCORE STATISTICS BY PLAYER")
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print("=" * 60)
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# Combine all scores
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combined = []
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for scores in all_scores.values():
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combined.extend(scores)
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combined.sort()
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def percentile(data, p):
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k = (len(data) - 1) * p / 100
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f = int(k)
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c = f + 1 if f + 1 < len(data) else f
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return data[f] + (k - f) * (data[c] - data[f])
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def stats(data):
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data = sorted(data)
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n = len(data)
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mean = sum(data) / n
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q1 = percentile(data, 25)
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median = percentile(data, 50)
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q3 = percentile(data, 75)
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return {
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'n': n,
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'min': min(data),
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'q1': q1,
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'median': median,
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'q3': q3,
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'max': max(data),
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'mean': mean,
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'iqr': q3 - q1
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}
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print(f"\n{'Player':<12} {'N':>5} {'Min':>6} {'Q1':>6} {'Med':>6} {'Q3':>6} {'Max':>6} {'Mean':>7}")
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print("-" * 60)
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for name in sorted(all_scores.keys()):
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s = stats(all_scores[name])
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print(f"{name:<12} {s['n']:>5} {s['min']:>6.0f} {s['q1']:>6.1f} {s['median']:>6.1f} {s['q3']:>6.1f} {s['max']:>6.0f} {s['mean']:>7.1f}")
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print("-" * 60)
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s = stats(combined)
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print(f"{'OVERALL':<12} {s['n']:>5} {s['min']:>6.0f} {s['q1']:>6.1f} {s['median']:>6.1f} {s['q3']:>6.1f} {s['max']:>6.0f} {s['mean']:>7.1f}")
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print(f"\nInterquartile Range (IQR): {s['iqr']:.1f}")
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print(f"Typical score range: {s['q1']:.0f} to {s['q3']:.0f}")
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# Score distribution buckets
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print("\n" + "=" * 60)
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print("SCORE DISTRIBUTION")
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print("=" * 60)
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buckets = defaultdict(int)
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for score in combined:
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if score < -5:
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bucket = "< -5"
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elif score < 0:
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bucket = "-5 to -1"
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elif score < 5:
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bucket = "0 to 4"
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elif score < 10:
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bucket = "5 to 9"
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elif score < 15:
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bucket = "10 to 14"
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elif score < 20:
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bucket = "15 to 19"
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elif score < 25:
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bucket = "20 to 24"
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else:
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bucket = "25+"
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buckets[bucket] += 1
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bucket_order = ["< -5", "-5 to -1", "0 to 4", "5 to 9", "10 to 14", "15 to 19", "20 to 24", "25+"]
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total = len(combined)
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for bucket in bucket_order:
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count = buckets.get(bucket, 0)
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pct = count / total * 100
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bar = "#" * int(pct / 2)
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print(f"{bucket:>10}: {count:>4} ({pct:>5.1f}%) {bar}")
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return stats(combined)
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def create_box_plot(all_scores: dict[str, list[int]], output_file: str = "score_distribution.png"):
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"""Create a box plot visualization."""
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try:
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import matplotlib.pyplot as plt
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import matplotlib
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matplotlib.use('Agg') # Non-interactive backend
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except ImportError:
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print("\nMatplotlib not installed. Install with: pip install matplotlib")
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print("Skipping box plot generation.")
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return False
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# Prepare data
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names = sorted(all_scores.keys())
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data = [all_scores[name] for name in names]
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# Also add combined data
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combined = []
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for scores in all_scores.values():
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combined.extend(scores)
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names.append("ALL")
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data.append(combined)
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# Create figure
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fig, ax = plt.subplots(figsize=(12, 6))
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# Box plot
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bp = ax.boxplot(data, labels=names, patch_artist=True)
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# Color boxes
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colors = ['#FF9999', '#99FF99', '#9999FF', '#FFFF99',
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'#FF99FF', '#99FFFF', '#FFB366', '#B366FF', '#CCCCCC']
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for patch, color in zip(bp['boxes'], colors[:len(bp['boxes'])]):
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patch.set_facecolor(color)
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patch.set_alpha(0.7)
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# Labels
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ax.set_xlabel('Player (AI Personality)', fontsize=12)
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ax.set_ylabel('Round Score (lower is better)', fontsize=12)
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ax.set_title('6-Card Golf AI Score Distribution', fontsize=14)
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# Add horizontal line at 0
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ax.axhline(y=0, color='green', linestyle='--', alpha=0.5, label='Zero (par)')
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# Add reference lines
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ax.axhline(y=10, color='orange', linestyle=':', alpha=0.5, label='Good (10)')
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ax.axhline(y=20, color='red', linestyle=':', alpha=0.5, label='Poor (20)')
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ax.legend(loc='upper right')
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ax.grid(axis='y', alpha=0.3)
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# Save
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plt.tight_layout()
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plt.savefig(output_file, dpi=150)
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print(f"\nBox plot saved to: {output_file}")
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return True
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def create_ascii_box_plot(all_scores: dict[str, list[int]]):
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"""Create an ASCII box plot for terminal display."""
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print("\n" + "=" * 70)
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print("ASCII BOX PLOT (Score Distribution)")
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print("=" * 70)
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def percentile(data, p):
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data = sorted(data)
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k = (len(data) - 1) * p / 100
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f = int(k)
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c = f + 1 if f + 1 < len(data) else f
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return data[f] + (k - f) * (data[c] - data[f])
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# Find global min/max for scaling
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all_vals = []
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for scores in all_scores.values():
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all_vals.extend(scores)
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global_min = min(all_vals)
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global_max = max(all_vals)
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# Scale to 50 characters
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width = 50
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def scale(val):
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if global_max == global_min:
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return width // 2
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return int((val - global_min) / (global_max - global_min) * (width - 1))
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# Print scale
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print(f"\n{' ' * 12} {global_min:<6} {'':^{width-12}} {global_max:>6}")
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print(f"{' ' * 12} |{'-' * (width - 2)}|")
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# Add combined
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combined = list(all_vals)
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scores_to_plot = dict(all_scores)
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scores_to_plot["COMBINED"] = combined
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for name in sorted(scores_to_plot.keys()):
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scores = scores_to_plot[name]
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q1 = percentile(scores, 25)
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med = percentile(scores, 50)
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q3 = percentile(scores, 75)
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min_val = min(scores)
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max_val = max(scores)
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# Build the line
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line = [' '] * width
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# Whiskers
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min_pos = scale(min_val)
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max_pos = scale(max_val)
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q1_pos = scale(q1)
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q3_pos = scale(q3)
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med_pos = scale(med)
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# Left whisker
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line[min_pos] = '|'
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for i in range(min_pos + 1, q1_pos):
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line[i] = '-'
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# Box
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for i in range(q1_pos, q3_pos + 1):
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line[i] = '='
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# Median
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line[med_pos] = '|'
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# Right whisker
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for i in range(q3_pos + 1, max_pos):
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line[i] = '-'
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line[max_pos] = '|'
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print(f"{name:>11} {''.join(line)}")
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print(f"\n Legend: |---[===|===]---| = min--Q1--median--Q3--max")
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print(f" Lower scores are better (left side of plot)")
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if __name__ == "__main__":
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num_games = int(sys.argv[1]) if len(sys.argv) > 1 else 100
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num_players = int(sys.argv[2]) if len(sys.argv) > 2 else 4
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# Collect scores
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all_scores = collect_scores(num_games, num_players)
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# Print statistics
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print_statistics(all_scores)
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# ASCII box plot (always works)
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create_ascii_box_plot(all_scores)
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# Try matplotlib box plot
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create_box_plot(all_scores)
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