dataset_stats.py

"""
Knowledge Drift Dataset Statistics
====================================
Produces comprehensive statistics for the clean dataset.
Outputs both console tables and a structured JSON report.

Usage:
    python dataset_stats.py --dataset data/knowledge_drift_clean.json
    python dataset_stats.py --dataset data/knowledge_drift_clean.json --output data/stats/
"""

import json
import argparse
import os
import logging
from collections import Counter, defaultdict
from itertools import groupby

logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)


def load_dataset(path):
    with open(path, 'r', encoding='utf-8') as f:
        return json.load(f)


def print_section(title, width=90):
    print(f"\n{'='*width}")
    print(f"  {title}")
    print(f"{'='*width}")


def print_table(headers, rows, col_widths=None):
    """Print a formatted table."""
    if col_widths is None:
        col_widths = []
        for i, h in enumerate(headers):
            max_w = len(str(h))
            for row in rows:
                if i < len(row):
                    max_w = max(max_w, len(str(row[i])))
            col_widths.append(max_w + 2)
    
    # Header
    header_line = ""
    for i, h in enumerate(headers):
        if i == 0:
            header_line += f"  {str(h):<{col_widths[i]}}"
        else:
            header_line += f"{str(h):>{col_widths[i]}}"
    print(header_line)
    print("  " + "-" * (sum(col_widths) + len(col_widths)))
    
    # Rows
    for row in rows:
        line = ""
        for i, val in enumerate(row):
            if i == 0:
                line += f"  {str(val):<{col_widths[i]}}"
            else:
                line += f"{str(val):>{col_widths[i]}}"
        print(line)


def compute_stats(dataset):
    """Compute all statistics from the dataset."""
    samples = dataset["samples"]
    metadata = dataset.get("metadata", {})
    total = len(samples)
    
    stats = {"total_samples": total, "metadata": metadata}
    
    # ============================================================
    # 1. CATEGORY DISTRIBUTION
    # ============================================================
    cat_counts = Counter(s["category"] for s in samples)
    stats["categories"] = {k: {"count": v, "pct": round(100*v/total, 1)} 
                           for k, v in sorted(cat_counts.items())}
    
    # ============================================================
    # 2. TEMPORAL ZONE DISTRIBUTION
    # ============================================================
    zone_counts = Counter(s["temporal_zone"] for s in samples)
    stats["temporal_zones"] = {k: {"count": v, "pct": round(100*v/total, 1)} 
                               for k, v in sorted(zone_counts.items())}
    
    # ============================================================
    # 3. CATEGORY × TEMPORAL ZONE CROSS-TAB
    # ============================================================
    cross = defaultdict(lambda: defaultdict(int))
    for s in samples:
        cross[s["category"]][s["temporal_zone"]] += 1
    stats["category_x_zone"] = {cat: dict(zones) for cat, zones in sorted(cross.items())}
    
    # ============================================================
    # 4. DRIFTED vs NON-DRIFTED BREAKDOWN
    # ============================================================
    drifted = [s for s in samples if s.get("is_drifted_query")]
    not_drifted = [s for s in samples if not s.get("is_drifted_query")]
    post_cutoff = [s for s in samples if s["temporal_zone"] == "post_cutoff"]
    drifted_post = [s for s in drifted if s["temporal_zone"] == "post_cutoff"]
    not_drifted_post = [s for s in not_drifted if s["temporal_zone"] == "post_cutoff"]
    
    stats["drift_breakdown"] = {
        "total_drifted": len(drifted),
        "total_not_drifted": len(not_drifted),
        "post_cutoff_total": len(post_cutoff),
        "post_cutoff_drifted": len(drifted_post),
        "post_cutoff_not_drifted": len(not_drifted_post),
        "drift_ratio_post_cutoff": round(len(drifted_post) / max(len(post_cutoff), 1), 3),
    }
    
    # ============================================================
    # 5. KNOWLEDGE TYPE DISTRIBUTION
    # ============================================================
    kt_counts = Counter(s["knowledge_type"] for s in samples)
    stats["knowledge_types"] = {k: {"count": v, "pct": round(100*v/total, 1)} 
                                 for k, v in sorted(kt_counts.items())}
    
    # Knowledge type × category
    kt_cat = defaultdict(lambda: defaultdict(int))
    for s in samples:
        kt_cat[s["knowledge_type"]][s["category"]] += 1
    stats["knowledge_type_x_category"] = {kt: dict(cats) for kt, cats in sorted(kt_cat.items())}
    
    # ============================================================
    # 6. ENTITY ANALYSIS
    # ============================================================
    all_entities = set(s["entity"] for s in samples)
    drifted_entities = set(s["entity"] for s in drifted)
    unchanged_entities = set(s["entity"] for s in samples 
                            if s["category"] == "no_drift" and s["knowledge_type"] == "entity_role")
    stable_entities = set(s["entity"] for s in samples if s["category"] == "stable")
    
    stats["entities"] = {
        "total_unique": len(all_entities),
        "drifted_unique": len(drifted_entities),
        "unchanged_unique": len(unchanged_entities),
        "stable_unique": len(stable_entities),
    }
    
    # Per-entity detail for drifted
    entity_detail = {}
    for s in drifted:
        ent = s["entity"]
        if ent not in entity_detail:
            entity_detail[ent] = {
                "relation": s.get("relation", ""),
                "old_answer": s.get("model_likely_answer", ""),
                "new_answer": s.get("expected_answer", ""),
                "change_date": s.get("change_date", ""),
                "knowledge_type": s.get("knowledge_type", ""),
                "num_queries": 0,
            }
        entity_detail[ent]["num_queries"] += 1
    stats["drifted_entity_details"] = entity_detail
    
    # Per-entity detail for unchanged
    unchanged_detail = {}
    for s in samples:
        if s["category"] == "no_drift" and s["knowledge_type"] == "entity_role":
            ent = s["entity"]
            if ent not in unchanged_detail:
                unchanged_detail[ent] = {
                    "relation": s.get("relation", ""),
                    "answer": s.get("expected_answer", ""),
                    "num_queries": 0,
                }
            unchanged_detail[ent]["num_queries"] += 1
    stats["unchanged_entity_details"] = unchanged_detail
    
    # ============================================================
    # 7. YEAR DISTRIBUTION
    # ============================================================
    year_counts = Counter(s["year"] for s in samples)
    stats["year_distribution"] = {str(k): v for k, v in sorted(year_counts.items())}
    
    # Year × drift status
    year_drift = defaultdict(lambda: {"drifted": 0, "not_drifted": 0})
    for s in samples:
        key = "drifted" if s.get("is_drifted_query") else "not_drifted"
        year_drift[s["year"]][key] += 1
    stats["year_x_drift"] = {str(k): dict(v) for k, v in sorted(year_drift.items())}
    
    # ============================================================
    # 8. TEMPLATE / QUERY ANALYSIS
    # ============================================================
    # Count unique query templates (strip year)
    queries = [s["query"] for s in samples]
    unique_queries = len(set(queries))
    
    # Identify question vs cloze format
    question_format = [s for s in samples if "?" in s["query"] or s["query"].lower().startswith("who") 
                       or s["query"].lower().startswith("what") or s["query"].lower().startswith("which")]
    cloze_format = [s for s in samples if "___" in s["query"] or s["query"].endswith(".")]
    other_format = [s for s in samples if s not in question_format and s not in cloze_format]
    
    stats["query_formats"] = {
        "unique_queries": unique_queries,
        "question_format": len(question_format),
        "cloze_format": len(cloze_format),
        "other_format": len(other_format),
    }
    
    # Question vs cloze × drifted
    q_drifted = len([s for s in question_format if s.get("is_drifted_query")])
    c_drifted = len([s for s in cloze_format if s.get("is_drifted_query")])
    stats["query_format_x_drift"] = {
        "question_drifted": q_drifted,
        "question_not_drifted": len(question_format) - q_drifted,
        "cloze_drifted": c_drifted,
        "cloze_not_drifted": len(cloze_format) - c_drifted,
    }
    
    # ============================================================
    # 9. SOURCE DISTRIBUTION
    # ============================================================
    src_counts = Counter(s.get("source", "unknown") for s in samples)
    stats["sources"] = dict(sorted(src_counts.items()))
    
    # ============================================================
    # 10. CONFIDENCE DISTRIBUTION
    # ============================================================
    conf_counts = Counter(s.get("confidence", "unknown") for s in samples)
    stats["confidence"] = dict(sorted(conf_counts.items()))
    
    return stats


def print_report(stats):
    """Print a formatted report to console."""
    
    print("\n" + "█" * 90)
    print("█" + " " * 30 + "DATASET STATISTICS REPORT" + " " * 34 + "█")
    print("█" * 90)
    
    # ---- OVERVIEW ----
    print_section("1. OVERVIEW")
    print(f"  Total samples:        {stats['total_samples']}")
    print(f"  Model:                {stats['metadata'].get('model', 'N/A')}")
    print(f"  Model cutoff:         {stats['metadata'].get('model_cutoff', 'N/A')}")
    print(f"  Unique entities:      {stats['entities']['total_unique']}")
    print(f"  Unique queries:       {stats['query_formats']['unique_queries']}")
    
    # ---- CATEGORY DISTRIBUTION ----
    print_section("2. CATEGORY DISTRIBUTION")
    rows = []
    for cat, info in stats["categories"].items():
        desc = {
            "stable": "Timeless facts (capitals, science, math)",
            "no_drift": "Could change but didn't (same leader/CEO)",
            "unknown_drift": "Changed after cutoff (model doesn't know)",
        }.get(cat, "")
        rows.append([cat, info["count"], f"{info['pct']}%", desc])
    print_table(["Category", "Count", "%", "Description"], rows, [22, 8, 8, 48])
    
    # ---- TEMPORAL ZONE ----
    print_section("3. TEMPORAL ZONE DISTRIBUTION")
    rows = [[z, info["count"], f"{info['pct']}%"] for z, info in stats["temporal_zones"].items()]
    print_table(["Zone", "Count", "%"], rows, [18, 8, 8])
    
    # ---- CROSS-TAB: Category × Zone ----
    print_section("4. CATEGORY × TEMPORAL ZONE")
    zones = sorted(set(z for zones in stats["category_x_zone"].values() for z in zones))
    headers = ["Category"] + zones + ["Total"]
    rows = []
    for cat, zone_counts in sorted(stats["category_x_zone"].items()):
        row = [cat] + [zone_counts.get(z, 0) for z in zones]
        row.append(sum(zone_counts.values()))
        rows.append(row)
    print_table(headers, rows, [22] + [14]*len(zones) + [8])
    
    # ---- DRIFT BREAKDOWN ----
    print_section("5. DRIFT ANALYSIS (KEY EXPERIMENTAL GROUPS)")
    db = stats["drift_breakdown"]
    print(f"\n  POST-CUTOFF SAMPLES (the experimental comparison):")
    print(f"    Total post-cutoff:        {db['post_cutoff_total']}")
    print(f"    ├── DRIFTED:              {db['post_cutoff_drifted']}  ← answer changed, model doesn't know")
    print(f"    └── NOT DRIFTED:          {db['post_cutoff_not_drifted']}  ← answer unchanged, model correct")
    print(f"    Drift ratio:              {db['drift_ratio_post_cutoff']:.1%}")
    print(f"\n  ALL SAMPLES:")
    print(f"    Drifted queries:          {db['total_drifted']}")
    print(f"    Non-drifted queries:      {db['total_not_drifted']}")
    
    # ---- DRIFTED ENTITIES ----
    print_section("6. DRIFTED ENTITIES (Post-Cutoff Changes)")
    rows = []
    for ent, info in sorted(stats["drifted_entity_details"].items()):
        rows.append([
            ent, info["relation"], info["old_answer"], 
            info["new_answer"], info["change_date"], info["num_queries"]
        ])
    print_table(
        ["Entity", "Relation", "Old (pre-cutoff)", "New (post-cutoff)", "Changed", "#Q"],
        rows, [18, 14, 28, 28, 12, 5]
    )
    
    # ---- UNCHANGED ENTITIES ----
    print_section("7. UNCHANGED ENTITIES (Same Type, No Drift — Controls)")
    rows = []
    for ent, info in sorted(stats["unchanged_entity_details"].items()):
        rows.append([ent, info["relation"], info["answer"], info["num_queries"]])
    print_table(["Entity", "Relation", "Answer (stable)", "#Q"], rows, [18, 16, 32, 5])
    
    # ---- KNOWLEDGE TYPE ----
    print_section("8. KNOWLEDGE TYPE DISTRIBUTION")
    rows = []
    for kt, info in stats["knowledge_types"].items():
        rows.append([kt, info["count"], f"{info['pct']}%"])
    print_table(["Type", "Count", "%"], rows, [18, 8, 8])
    
    # Knowledge type × category
    print("\n  Knowledge Type × Category:")
    for kt, cats in sorted(stats["knowledge_type_x_category"].items()):
        parts = ", ".join(f"{c}={n}" for c, n in sorted(cats.items()))
        print(f"    {kt:<18} {parts}")
    
    # ---- YEAR DISTRIBUTION ----
    print_section("9. YEAR DISTRIBUTION")
    rows = []
    for year, count in sorted(stats["year_distribution"].items()):
        drift_info = stats["year_x_drift"].get(year, {})
        d = drift_info.get("drifted", 0)
        nd = drift_info.get("not_drifted", 0)
        rows.append([year, count, d, nd])
    print_table(["Year", "Total", "Drifted", "Not Drifted"], rows, [8, 8, 10, 14])
    
    # ---- QUERY FORMAT ----
    print_section("10. QUERY FORMAT ANALYSIS")
    qf = stats["query_formats"]
    print(f"  Question format (Who is...?):   {qf['question_format']}")
    print(f"  Cloze format (X was ___.):      {qf['cloze_format']}")
    print(f"  Other:                          {qf['other_format']}")
    
    qfd = stats["query_format_x_drift"]
    print(f"\n  Format × Drift status:")
    print(f"    Question — drifted: {qfd['question_drifted']}, not drifted: {qfd['question_not_drifted']}")
    print(f"    Cloze    — drifted: {qfd['cloze_drifted']}, not drifted: {qfd['cloze_not_drifted']}")
    
    # ---- DATA QUALITY ----
    print_section("11. DATA QUALITY")
    src = stats["sources"]
    conf = stats["confidence"]
    print(f"  Sources:")
    for s, c in src.items():
        print(f"    {s}: {c}")
    print(f"  Confidence levels:")
    for c, n in conf.items():
        print(f"    {c}: {n}")
    
    # ---- PAPER-READY SUMMARY ----
    print_section("12. PAPER-READY SUMMARY")
    db = stats["drift_breakdown"]
    ent = stats["entities"]
    print(f"""
  "We construct a temporally-scoped factual knowledge dataset comprising
   {stats['total_samples']} query instances across {ent['total_unique']} unique entities.
   
   The dataset spans three categories:
   - {stats['categories'].get('unknown_drift', {}).get('count', 0)} queries about {ent['drifted_unique']} entities whose facts 
     changed after the model's knowledge cutoff (August 2024),
   - {stats['categories'].get('no_drift', {}).get('count', 0)} queries about {ent['unchanged_unique']} entities of the same 
     type that did NOT change (controls), and
   - {stats['categories'].get('stable', {}).get('count', 0)} queries about timeless facts (geography, science, math).
   
   Each query is instantiated across {len(stats['year_distribution'])} time points 
   ({', '.join(sorted(stats['year_distribution'].keys()))}), yielding 
   {db['post_cutoff_drifted']} drifted and {db['post_cutoff_not_drifted']} non-drifted 
   post-cutoff instances for the primary experimental comparison."
""")
    
    print("=" * 90)


def main():
    parser = argparse.ArgumentParser(description="Dataset Statistics")
    parser.add_argument("--dataset", default="data/knowledge_drift_clean.json")
    parser.add_argument("--output", default=None, help="Output directory for JSON stats")
    args = parser.parse_args()
    
    dataset = load_dataset(args.dataset)
    stats = compute_stats(dataset)
    print_report(stats)
    
    # Save JSON
    if args.output:
        os.makedirs(args.output, exist_ok=True)
        out_path = os.path.join(args.output, "dataset_statistics.json")
        with open(out_path, 'w') as f:
            json.dump(stats, f, indent=2, ensure_ascii=False)
        logger.info(f"Statistics saved to {out_path}")


if __name__ == "__main__":
    main()