ai-agent-book 精选快照（<2MB 代码与文档，来自 github.com/bojieli/ai-agent-book）

chapter7/elo-leaderboard/visualization.py

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+"""
+Visualization tools for Elo leaderboard analysis
+"""
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+from typing import List, Tuple, Optional
+import plotly.graph_objects as go
+import plotly.express as px
+
+
+def plot_leaderboard(leaderboard_data: list, top_n: int = 20, save_path: Optional[str] = None):
+    """
+    Plot static leaderboard bar chart.
+    
+    Args:
+        leaderboard_data: List of (model, rating, matches, wins) tuples
+        top_n: Number of top models to display
+        save_path: If provided, save figure to this path
+    """
+    # Convert to DataFrame and get top N
+    df = pd.DataFrame(leaderboard_data, columns=['model', 'rating', 'matches', 'wins'])
+    df = df.head(top_n)
+    
+    # Create figure
+    fig, ax = plt.subplots(figsize=(12, 8))
+    
+    # Create horizontal bar chart
+    bars = ax.barh(range(len(df)), df['rating'], color=plt.cm.viridis(np.linspace(0, 1, len(df))))
+    
+    # Customize
+    ax.set_yticks(range(len(df)))
+    ax.set_yticklabels(df['model'])
+    ax.set_xlabel('Elo Rating', fontsize=12)
+    ax.set_title(f'Model Leaderboard - Top {top_n} Models', fontsize=14, fontweight='bold')
+    ax.invert_yaxis()  # Highest rating at top
+    
+    # Add value labels on bars
+    for i, (rating, matches) in enumerate(zip(df['rating'], df['matches'])):
+        ax.text(rating + 10, i, f'{rating:.0f} ({matches} matches)', 
+                va='center', fontsize=9)
+    
+    ax.grid(axis='x', alpha=0.3)
+    plt.tight_layout()
+    
+    if save_path:
+        plt.savefig(save_path, dpi=300, bbox_inches='tight')
+        print(f"Saved leaderboard to {save_path}")
+    
+    plt.show()
+
+
+def plot_win_rate_matrix(win_rate_df: pd.DataFrame, 
+                         top_n: int = 15,
+                         save_path: Optional[str] = None):
+    """
+    Plot heatmap of win rate matrix.
+    
+    Args:
+        win_rate_df: DataFrame with win rates (rows beat columns)
+        top_n: Number of models to include
+        save_path: If provided, save figure to this path
+    """
+    # Get top N models by average win rate
+    avg_win_rates = win_rate_df.mean(axis=1).sort_values(ascending=False)
+    top_models = avg_win_rates.head(top_n).index.tolist()
+    
+    # Subset matrix
+    subset = win_rate_df.loc[top_models, top_models]
+    
+    # Create figure
+    fig, ax = plt.subplots(figsize=(14, 12))
+    
+    # Plot heatmap
+    sns.heatmap(subset, annot=True, fmt='.2f', cmap='RdYlGn', center=0.5,
+                vmin=0, vmax=1, square=True, linewidths=0.5,
+                cbar_kws={'label': 'Win Rate'}, ax=ax)
+    
+    ax.set_title(f'Win Rate Matrix - Top {top_n} Models\n(Row vs Column)', 
+                 fontsize=14, fontweight='bold')
+    ax.set_xlabel('Opponent (Column)', fontsize=12)
+    ax.set_ylabel('Model (Row)', fontsize=12)
+    
+    plt.tight_layout()
+    
+    if save_path:
+        plt.savefig(save_path, dpi=300, bbox_inches='tight')
+        print(f"Saved win rate matrix to {save_path}")
+    
+    plt.show()
+
+
+def plot_rating_history(history_df: pd.DataFrame, 
+                        models: Optional[List[str]] = None,
+                        top_n: int = 10,
+                        save_path: Optional[str] = None):
+    """
+    Plot rating evolution over time for selected models.
+    
+    Args:
+        history_df: DataFrame with columns: date, model, rating
+        models: List of specific models to plot (if None, plot top N)
+        top_n: If models not specified, plot top N models by final rating
+        save_path: If provided, save figure to this path
+    """
+    if models is None:
+        # Get top N models by final rating
+        final_date = history_df['date'].max()
+        final_ratings = history_df[history_df['date'] == final_date].nlargest(top_n, 'rating')
+        models = final_ratings['model'].tolist()
+    
+    # Filter data
+    plot_data = history_df[history_df['model'].isin(models)].copy()
+    
+    # Create figure
+    fig, ax = plt.subplots(figsize=(14, 8))
+    
+    # Plot each model
+    for model in models:
+        model_data = plot_data[plot_data['model'] == model].sort_values('date')
+        ax.plot(model_data['date'], model_data['rating'], marker='o', 
+                label=model, linewidth=2, markersize=4)
+    
+    ax.set_xlabel('Date', fontsize=12)
+    ax.set_ylabel('Elo Rating', fontsize=12)
+    ax.set_title('Model Rating Evolution Over Time', fontsize=14, fontweight='bold')
+    ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left', fontsize=9)
+    ax.grid(alpha=0.3)
+    
+    plt.xticks(rotation=45)
+    plt.tight_layout()
+    
+    if save_path:
+        plt.savefig(save_path, dpi=300, bbox_inches='tight')
+        print(f"Saved rating history to {save_path}")
+    
+    plt.show()
+
+
+def plot_rating_distribution(leaderboard_data: list, save_path: Optional[str] = None):
+    """
+    Plot distribution of ratings across all models.
+    
+    Args:
+        leaderboard_data: List of (model, rating, matches, wins) tuples
+        save_path: If provided, save figure to this path
+    """
+    df = pd.DataFrame(leaderboard_data, columns=['model', 'rating', 'matches', 'wins'])
+    
+    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 5))
+    
+    # Histogram
+    ax1.hist(df['rating'], bins=30, color='steelblue', edgecolor='black', alpha=0.7)
+    ax1.axvline(df['rating'].mean(), color='red', linestyle='--', 
+                linewidth=2, label=f'Mean: {df["rating"].mean():.1f}')
+    ax1.axvline(df['rating'].median(), color='green', linestyle='--', 
+                linewidth=2, label=f'Median: {df["rating"].median():.1f}')
+    ax1.set_xlabel('Elo Rating', fontsize=12)
+    ax1.set_ylabel('Count', fontsize=12)
+    ax1.set_title('Rating Distribution', fontsize=13, fontweight='bold')
+    ax1.legend()
+    ax1.grid(alpha=0.3)
+    
+    # Box plot
+    ax2.boxplot(df['rating'], vert=True)
+    ax2.set_ylabel('Elo Rating', fontsize=12)
+    ax2.set_title('Rating Box Plot', fontsize=13, fontweight='bold')
+    ax2.grid(alpha=0.3)
+    
+    plt.tight_layout()
+    
+    if save_path:
+        plt.savefig(save_path, dpi=300, bbox_inches='tight')
+        print(f"Saved rating distribution to {save_path}")
+    
+    plt.show()
+
+
+def create_interactive_leaderboard(history_df: pd.DataFrame, top_n: int = 15) -> go.Figure:
+    """
+    Create interactive Plotly visualization of ranking evolution.
+    
+    Args:
+        history_df: DataFrame with columns: date, model, rating, rank
+        top_n: Number of top models to include
+        
+    Returns:
+        Plotly Figure object
+    """
+    # Get top N models by final rating
+    final_date = history_df['date'].max()
+    final_ratings = history_df[history_df['date'] == final_date].nlargest(top_n, 'rating')
+    top_models = final_ratings['model'].tolist()
+    
+    # Filter data
+    plot_data = history_df[history_df['model'].isin(top_models)].copy()
+    
+    # Create figure
+    fig = go.Figure()
+    
+    for model in top_models:
+        model_data = plot_data[plot_data['model'] == model].sort_values('date')
+        
+        fig.add_trace(go.Scatter(
+            x=model_data['date'],
+            y=model_data['rating'],
+            mode='lines+markers',
+            name=model,
+            hovertemplate='<b>%{fullData.name}</b><br>' +
+                         'Date: %{x}<br>' +
+                         'Rating: %{y:.0f}<br>' +
+                         '<extra></extra>'
+        ))
+    
+    fig.update_layout(
+        title='Interactive Model Rating Evolution',
+        xaxis_title='Date',
+        yaxis_title='Elo Rating',
+        hovermode='closest',
+        height=600,
+        legend=dict(
+            yanchor="top",
+            y=0.99,
+            xanchor="left",
+            x=0.01
+        )
+    )
+    
+    return fig
+
+
+def create_rank_evolution_chart(history_df: pd.DataFrame, top_n: int = 15) -> go.Figure:
+    """
+    Create interactive rank evolution chart (lower rank number is better).
+    
+    Args:
+        history_df: DataFrame with columns: date, model, rating, rank
+        top_n: Number of models to track
+        
+    Returns:
+        Plotly Figure object
+    """
+    # Get models that were ever in top N
+    models_in_top = history_df[history_df['rank'] <= top_n]['model'].unique()
+    
+    # Filter data
+    plot_data = history_df[history_df['model'].isin(models_in_top)].copy()
+    
+    # Create figure
+    fig = go.Figure()
+    
+    for model in models_in_top:
+        model_data = plot_data[plot_data['model'] == model].sort_values('date')
+        
+        fig.add_trace(go.Scatter(
+            x=model_data['date'],
+            y=model_data['rank'],
+            mode='lines+markers',
+            name=model,
+            hovertemplate='<b>%{fullData.name}</b><br>' +
+                         'Date: %{x}<br>' +
+                         'Rank: #%{y}<br>' +
+                         '<extra></extra>'
+        ))
+    
+    fig.update_layout(
+        title=f'Model Rank Evolution (Top {top_n})',
+        xaxis_title='Date',
+        yaxis_title='Rank',
+        yaxis=dict(autorange='reversed'),  # Lower rank at top
+        hovermode='closest',
+        height=600,
+        legend=dict(
+            yanchor="top",
+            y=0.99,
+            xanchor="right",
+            x=0.99
+        )
+    )
+    
+    return fig
+