path: root/scripts/analyze_learning_trend.py

#!/usr/bin/env python3
"""
Analyze Learning Trend: Correlation and z_u Norm over Sessions

This script shows that:
1. User vector norms (||z_u||) grow over sessions (learning is happening)
2. Correlation between learned and ground-truth similarity increases over sessions

Usage:
    python scripts/analyze_learning_trend.py \
        --logs data/logs/pilot_v4_full-greedy_*.jsonl
"""

import argparse
import json
import numpy as np
from typing import Dict, List, Tuple
from collections import defaultdict
from dataclasses import dataclass
import os


# =============================================================================
# Persona Definitions (ground truth)
# =============================================================================

@dataclass
class StylePrefs:
    require_short: bool = False
    max_chars: int = 300
    require_bullets: bool = False
    lang: str = "en"


PERSONAS = {
    "user_A_short_bullets_en": StylePrefs(require_short=True, max_chars=200, require_bullets=True, lang="en"),
    "user_B_short_no_bullets_en": StylePrefs(require_short=True, max_chars=200, require_bullets=False, lang="en"),
    "user_C_long_bullets_en": StylePrefs(require_short=False, max_chars=800, require_bullets=True, lang="en"),
    "user_D_short_bullets_zh": StylePrefs(require_short=True, max_chars=200, require_bullets=True, lang="zh"),
    "user_E_long_no_bullets_zh": StylePrefs(require_short=False, max_chars=800, require_bullets=False, lang="zh"),
    "user_F_extreme_short_en": StylePrefs(require_short=True, max_chars=100, require_bullets=True, lang="en"),
}


# =============================================================================
# Data Loading
# =============================================================================

def load_logs(filepath: str) -> List[dict]:
    """Load turn logs from JSONL file."""
    logs = []
    with open(filepath, "r") as f:
        for line in f:
            if line.strip():
                logs.append(json.loads(line))
    return logs


def extract_z_norms_by_session(logs: List[dict]) -> Dict[str, Dict[int, Tuple[float, float]]]:
    """
    Extract z_long_norm and z_short_norm at the end of each session for each user.
    
    Returns:
        {user_id: {session_id: (z_long_norm, z_short_norm)}}
    """
    user_session_norms = defaultdict(dict)
    
    # Group by user and session, take the last turn's z_norm
    user_session_turns = defaultdict(lambda: defaultdict(list))
    for log in logs:
        user_id = log["user_id"]
        session_id = log["session_id"]
        user_session_turns[user_id][session_id].append(log)
    
    for user_id, sessions in user_session_turns.items():
        for session_id, turns in sessions.items():
            # Get the last turn of this session
            last_turn = max(turns, key=lambda x: x["turn_id"])
            z_long = last_turn.get("z_long_norm_after", 0.0)
            z_short = last_turn.get("z_short_norm_after", 0.0)
            user_session_norms[user_id][session_id] = (z_long, z_short)
    
    return dict(user_session_norms)


# =============================================================================
# Similarity Computation
# =============================================================================

def cosine_similarity(v1: np.ndarray, v2: np.ndarray) -> float:
    """Compute cosine similarity."""
    norm1 = np.linalg.norm(v1)
    norm2 = np.linalg.norm(v2)
    if norm1 < 1e-10 or norm2 < 1e-10:
        return 0.0
    return float(np.dot(v1, v2) / (norm1 * norm2))


def compute_ground_truth_similarity_matrix(user_order: List[str]) -> np.ndarray:
    """Compute ground truth similarity based on preference overlap."""
    n = len(user_order)
    sim_matrix = np.zeros((n, n))
    
    for i, u1 in enumerate(user_order):
        for j, u2 in enumerate(user_order):
            if u1 not in PERSONAS or u2 not in PERSONAS:
                sim_matrix[i, j] = 0.0 if i != j else 1.0
                continue
            
            p1 = PERSONAS[u1]
            p2 = PERSONAS[u2]
            
            matches = 0
            if p1.require_short == p2.require_short:
                matches += 1
            if p1.require_bullets == p2.require_bullets:
                matches += 1
            if p1.lang == p2.lang:
                matches += 1
            
            sim_matrix[i, j] = matches / 3.0
    
    return sim_matrix


def compute_spearman_correlation(learned: np.ndarray, ground_truth: np.ndarray) -> float:
    """Compute Spearman correlation between similarity matrices."""
    from scipy.stats import spearmanr
    
    n = learned.shape[0]
    learned_flat = []
    gt_flat = []
    
    for i in range(n):
        for j in range(i + 1, n):
            learned_flat.append(learned[i, j])
            gt_flat.append(ground_truth[i, j])
    
    if len(learned_flat) < 2:
        return 0.0
    
    # Handle case where all values are the same
    if np.std(learned_flat) < 1e-10:
        return 0.0
    
    corr, _ = spearmanr(learned_flat, gt_flat)
    return float(corr) if not np.isnan(corr) else 0.0


def load_final_z_vectors(user_store_path: str) -> Dict[str, Tuple[np.ndarray, np.ndarray]]:
    """Load final z_u vectors from saved user store."""
    try:
        data = np.load(user_store_path, allow_pickle=True)
        user_vectors = {}
        
        # UserTensorStore saves in format: {uid}_long, {uid}_short
        user_ids = set()
        for key in data.files:
            if key.endswith("_long"):
                uid = key[:-5]
                user_ids.add(uid)
        
        for uid in user_ids:
            long_key = f"{uid}_long"
            short_key = f"{uid}_short"
            if long_key in data.files and short_key in data.files:
                user_vectors[uid] = (data[long_key], data[short_key])
        
        return user_vectors
    except Exception as e:
        print(f"[Warning] Could not load user store: {e}")
        return {}


# Global cache for final z vectors
_FINAL_Z_VECTORS = None


def get_z_vectors_at_session(
    logs: List[dict],
    user_order: List[str],
    up_to_session: int,
    final_z_vectors: Dict[str, Tuple[np.ndarray, np.ndarray]]
) -> Dict[str, np.ndarray]:
    """
    Estimate z_u vectors at a given session checkpoint.
    
    Method: Use the DIRECTION of the final z_u, scaled by the z_norm at session s.
    This assumes z_u direction is relatively stable but magnitude grows.
    
    z_u(s) ≈ (z_final / ||z_final||) * ||z(s)||
    """
    user_vectors = {}
    
    for user_id in user_order:
        # Get z_norm at the end of this session
        user_turns = [l for l in logs if l["user_id"] == user_id and l["session_id"] <= up_to_session]
        
        if not user_turns:
            user_vectors[user_id] = np.zeros(512)  # 256 + 256
            continue
        
        # Get the last turn's z_norm at this session
        last_turn = max(user_turns, key=lambda x: (x["session_id"], x["turn_id"]))
        z_long_norm_s = last_turn.get("z_long_norm_after", 0.0)
        z_short_norm_s = last_turn.get("z_short_norm_after", 0.0)
        
        # Get final z vectors (direction)
        if user_id in final_z_vectors:
            z_long_final, z_short_final = final_z_vectors[user_id]
            
            # Compute unit vectors (direction)
            z_long_final_norm = np.linalg.norm(z_long_final)
            z_short_final_norm = np.linalg.norm(z_short_final)
            
            if z_long_final_norm > 1e-10:
                z_long_unit = z_long_final / z_long_final_norm
            else:
                z_long_unit = np.zeros_like(z_long_final)
            
            if z_short_final_norm > 1e-10:
                z_short_unit = z_short_final / z_short_final_norm
            else:
                z_short_unit = np.zeros_like(z_short_final)
            
            # Scale by the norm at this session
            z_long_s = z_long_unit * z_long_norm_s
            z_short_s = z_short_unit * z_short_norm_s
            
            # Concatenate
            user_vectors[user_id] = np.concatenate([z_long_s, z_short_s])
        else:
            user_vectors[user_id] = np.zeros(512)
    
    return user_vectors


def compute_similarity_at_session(
    logs: List[dict],
    user_order: List[str],
    up_to_session: int,
    final_z_vectors: Dict[str, Tuple[np.ndarray, np.ndarray]] = None
) -> np.ndarray:
    """Compute learned similarity matrix at a given session using actual z vectors."""
    if final_z_vectors:
        user_vectors = get_z_vectors_at_session(logs, user_order, up_to_session, final_z_vectors)
    else:
        # Fallback to old method
        user_vectors = simulate_z_vectors_at_session_fallback(logs, user_order, up_to_session)
    
    n = len(user_order)
    sim_matrix = np.zeros((n, n))
    
    for i, u1 in enumerate(user_order):
        for j, u2 in enumerate(user_order):
            v1 = user_vectors.get(u1, np.zeros(512))
            v2 = user_vectors.get(u2, np.zeros(512))
            sim_matrix[i, j] = cosine_similarity(v1, v2)
    
    return sim_matrix


def simulate_z_vectors_at_session_fallback(
    logs: List[dict],
    user_order: List[str],
    up_to_session: int,
    dim: int = 256
) -> Dict[str, np.ndarray]:
    """Fallback: simulate z_u based on violation patterns (less accurate)."""
    user_vectors = {}
    
    for user_id in user_order:
        user_turns = [l for l in logs if l["user_id"] == user_id and l["session_id"] <= up_to_session]
        
        if not user_turns:
            user_vectors[user_id] = np.zeros(dim * 2)
            continue
        
        last_turn = max(user_turns, key=lambda x: (x["session_id"], x["turn_id"]))
        z_long_norm = last_turn.get("z_long_norm_after", 0.0)
        z_short_norm = last_turn.get("z_short_norm_after", 0.0)
        
        violation_counts = defaultdict(int)
        for turn in user_turns:
            for v in turn.get("violations", []):
                violation_counts[v] += 1
        
        feature_dim = 10
        features = np.zeros(feature_dim)
        features[0] = violation_counts.get("too_long", 0)
        features[1] = violation_counts.get("no_bullets", 0)
        features[2] = violation_counts.get("has_bullets", 0)
        features[3] = violation_counts.get("wrong_lang", 0)
        features[4] = z_long_norm * 100
        features[5] = z_short_norm * 100
        
        norm = np.linalg.norm(features)
        if norm > 1e-10:
            features = features / norm
        
        user_vectors[user_id] = features
    
    return user_vectors


def compute_similarity_at_session(
    logs: List[dict],
    user_order: List[str],
    up_to_session: int
) -> np.ndarray:
    """Compute learned similarity matrix at a given session."""
    user_vectors = simulate_z_vectors_at_session(logs, user_order, up_to_session)
    
    n = len(user_order)
    sim_matrix = np.zeros((n, n))
    
    for i, u1 in enumerate(user_order):
        for j, u2 in enumerate(user_order):
            v1 = user_vectors.get(u1, np.zeros(10))
            v2 = user_vectors.get(u2, np.zeros(10))
            sim_matrix[i, j] = cosine_similarity(v1, v2)
    
    return sim_matrix


# =============================================================================
# Main Analysis
# =============================================================================

def analyze_learning_trend(logs_path: str, output_dir: str = "data/analysis", 
                          user_store_path: str = "data/users/user_store_pilot_v4_full-greedy.npz"):
    """Analyze correlation and z_u norm trends over sessions."""
    os.makedirs(output_dir, exist_ok=True)
    
    print("=" * 70)
    print("LEARNING TREND ANALYSIS")
    print("=" * 70)
    
    # Load logs
    print(f"\n[1] Loading logs from: {logs_path}")
    logs = load_logs(logs_path)
    print(f"  Loaded {len(logs)} turns")
    
    # Get user order
    user_order = [u for u in PERSONAS.keys() if any(l["user_id"] == u for l in logs)]
    print(f"  Users: {user_order}")
    
    # Get max session
    max_session = max(l["session_id"] for l in logs)
    print(f"  Sessions: 1 to {max_session}")
    
    # Extract z_norms by session
    print("\n[2] Extracting z_u norms by session...")
    z_norms_by_session = extract_z_norms_by_session(logs)
    
    # Load final z vectors from user store
    print(f"\n[2.5] Loading final z vectors from: {user_store_path}")
    final_z_vectors = load_final_z_vectors(user_store_path)
    if final_z_vectors:
        print(f"  Loaded final z vectors for {len(final_z_vectors)} users")
    else:
        print("  [Warning] No final z vectors found, using fallback method")
    
    # Compute ground truth similarity (constant)
    gt_sim = compute_ground_truth_similarity_matrix(user_order)
    
    # Compute CUMULATIVE correlation and avg z_norm
    # At session N, we use all data from session 1 to N
    print("\n[3] Computing CUMULATIVE correlation trend (S1→S1-2→S1-3→...→S1-N)...")
    sessions = list(range(1, max_session + 1))
    correlations = []
    avg_z_norms = []
    
    for s in sessions:
        # Compute similarity using z_u at end of session s (cumulative learning)
        learned_sim = compute_similarity_at_session(logs, user_order, s, final_z_vectors)
        corr = compute_spearman_correlation(learned_sim, gt_sim)
        correlations.append(corr)
        
        # Compute average z_norm at the END of session s (this is already cumulative)
        z_norms = []
        for user_id in user_order:
            if user_id in z_norms_by_session and s in z_norms_by_session[user_id]:
                zl, zs = z_norms_by_session[user_id][s]
                z_norms.append(np.sqrt(zl**2 + zs**2))  # Combined norm
        
        avg_z = np.mean(z_norms) if z_norms else 0.0
        avg_z_norms.append(avg_z)
    
    # Print results
    print("\n[4] Results:")
    print("-" * 60)
    print(f"{'Session':<10} {'Correlation':<15} {'Avg ||z_u||':<15}")
    print("-" * 60)
    for s, corr, z_norm in zip(sessions, correlations, avg_z_norms):
        print(f"{s:<10} {corr:<15.4f} {z_norm:<15.6f}")
    
    # Summary statistics
    print("\n[5] Trend Summary:")
    print("-" * 60)
    
    # Linear regression for correlation trend
    from scipy.stats import linregress
    slope_corr, intercept_corr, r_corr, p_corr, _ = linregress(sessions, correlations)
    print(f"  Correlation trend: slope={slope_corr:.4f}, R²={r_corr**2:.4f}, p={p_corr:.4f}")
    
    # Linear regression for z_norm trend
    slope_z, intercept_z, r_z, p_z, _ = linregress(sessions, avg_z_norms)
    print(f"  ||z_u|| trend:     slope={slope_z:.6f}, R²={r_z**2:.4f}, p={p_z:.4f}")
    
    # Correlation between the two trends
    trend_corr, _ = spearmanr(correlations, avg_z_norms) if len(correlations) > 2 else (0, 1)
    print(f"  Correlation between trends: {trend_corr:.4f}")
    
    # Save data
    results = {
        "sessions": np.array(sessions),
        "correlations": np.array(correlations),
        "avg_z_norms": np.array(avg_z_norms),
        "slope_corr": slope_corr,
        "slope_z": slope_z,
        "trend_corr": trend_corr,
    }
    results_path = os.path.join(output_dir, "learning_trend_results.npz")
    np.savez(results_path, **results)
    print(f"\n[Results] Saved to: {results_path}")
    
    # Plot
    print("\n[6] Generating plots...")
    plot_learning_trend(sessions, correlations, avg_z_norms, output_dir)
    
    print("\n" + "=" * 70)
    print("ANALYSIS COMPLETE")
    print("=" * 70)
    
    return results


def plot_learning_trend(sessions, correlations, avg_z_norms, output_dir):
    """Generate plots for learning trend."""
    try:
        import matplotlib.pyplot as plt
        import matplotlib
        matplotlib.use('Agg')  # Non-interactive backend
    except ImportError:
        print("[Warning] matplotlib not available, skipping plots")
        # Save as text instead
        with open(os.path.join(output_dir, "learning_trend.txt"), "w") as f:
            f.write("Session,Correlation,Avg_Z_Norm\n")
            for s, c, z in zip(sessions, correlations, avg_z_norms):
                f.write(f"{s},{c:.4f},{z:.6f}\n")
        print(f"[Data] Saved to: {os.path.join(output_dir, 'learning_trend.txt')}")
        return
    
    fig, axes = plt.subplots(1, 2, figsize=(12, 5))
    
    # Plot 1: Correlation vs Session
    ax1 = axes[0]
    ax1.plot(sessions, correlations, 'o-', color='#2ecc71', linewidth=2, markersize=8)
    ax1.axhline(y=0, color='gray', linestyle='--', alpha=0.5)
    
    # Add trend line
    from scipy.stats import linregress
    slope, intercept, _, _, _ = linregress(sessions, correlations)
    trend_line = [slope * s + intercept for s in sessions]
    ax1.plot(sessions, trend_line, '--', color='#27ae60', alpha=0.7, label=f'Trend (slope={slope:.3f})')
    
    ax1.set_xlabel('Sessions (Cumulative: 1→N)', fontsize=12)
    ax1.set_ylabel('Spearman Correlation', fontsize=12)
    ax1.set_title('Learned vs Ground-Truth Similarity\nCorrelation with Cumulative Data', fontsize=14)
    ax1.set_xticks(sessions)
    ax1.legend()
    ax1.grid(True, alpha=0.3)
    ax1.set_ylim(-0.5, 1.0)
    
    # Plot 2: z_u norm vs Session
    ax2 = axes[1]
    ax2.plot(sessions, avg_z_norms, 's-', color='#3498db', linewidth=2, markersize=8)
    
    # Add trend line
    slope_z, intercept_z, _, _, _ = linregress(sessions, avg_z_norms)
    trend_line_z = [slope_z * s + intercept_z for s in sessions]
    ax2.plot(sessions, trend_line_z, '--', color='#2980b9', alpha=0.7, label=f'Trend (slope={slope_z:.5f})')
    
    ax2.set_xlabel('Session (End of)', fontsize=12)
    ax2.set_ylabel('Average ||z_u||', fontsize=12)
    ax2.set_title('User Vector Norm\n(Cumulative Learning)', fontsize=14)
    ax2.set_xticks(sessions)
    ax2.legend()
    ax2.grid(True, alpha=0.3)
    
    plt.tight_layout()
    
    output_path = os.path.join(output_dir, "learning_trend.png")
    plt.savefig(output_path, dpi=150, bbox_inches='tight')
    print(f"[Plot] Saved to: {output_path}")
    
    # Also save as PDF for paper
    pdf_path = os.path.join(output_dir, "learning_trend.pdf")
    plt.savefig(pdf_path, bbox_inches='tight')
    print(f"[Plot] Saved to: {pdf_path}")


# Need this import at top level for trend calculation
from scipy.stats import spearmanr


def main():
    parser = argparse.ArgumentParser(description="Analyze Learning Trend")
    parser.add_argument("--logs", type=str, required=True, help="Path to log file")
    parser.add_argument("--user-store", type=str, default="data/users/user_store_pilot_v4_full-greedy.npz",
                        help="Path to user store with final z vectors")
    parser.add_argument("--output-dir", type=str, default="data/analysis", help="Output directory")
    args = parser.parse_args()
    
    analyze_learning_trend(args.logs, args.output_dir, args.user_store)


if __name__ == "__main__":
    main()