data_quality.py

"""
Data Quality Layer.

Filters and scores every collected text for trustworthiness before it
enters the sentiment pipeline. Three sub-systems:

  1. DUPLICATE DETECTION
     - Exact duplicate (same text across sources)
     - Near-duplicate (cosine similarity via character trigrams, >0.85 threshold)
     - Cross-model duplicate (same post attributed to multiple models)

  2. BOT / SPAM DETECTION
     - Repetitive posting pattern (same author, similar text, high frequency)
     - Generic content (text too short, no specifics, templated phrases)
     - Engagement anomaly (extremely high/low engagement vs author baseline)
     - New account signal (no engagement history → lower trust)

  3. SOURCE CREDIBILITY SCORING
     - Author history (repeat authors with consistent engagement = higher trust)
     - Platform weighting (SO/HN higher base credibility than Bluesky/Reddit)
     - Engagement ratio (high engagement = community-validated content)
     - Specificity score (mentions specific model features/versions vs generic)

Output: each text gets a `quality_score` (0-1) that multiplies its sentiment weight.
Texts scoring < 0.3 are flagged as low-quality and excluded from scoring.

Usage:
    python -m scoring.data_quality           — score all texts
    python -m scoring.data_quality --stats   — print quality distribution
"""

import re
import math
import json
import logging
import hashlib
from collections import defaultdict, Counter
from pathlib import Path
import sys

sys.path.insert(0, str(Path(__file__).parent.parent))
from db.schema import get_connection, db

logger = logging.getLogger(__name__)


# ═══════════════════════════════════════════════════════════════════════════════
# SCHEMA
# ═══════════════════════════════════════════════════════════════════════════════

def init_quality_tables():
    with db() as conn:
        conn.executescript("""
        CREATE TABLE IF NOT EXISTS data_quality (
            id              INTEGER PRIMARY KEY AUTOINCREMENT,
            source          TEXT NOT NULL,
            source_id       INTEGER NOT NULL,
            model_slug      TEXT NOT NULL,

            -- Quality sub-scores (0-1, higher = better quality)
            uniqueness       REAL,    -- 1.0 = totally unique, 0.0 = exact duplicate
            bot_score        REAL,    -- 1.0 = definitely human, 0.0 = likely bot
            credibility      REAL,    -- 1.0 = high-credibility source, 0.0 = low
            specificity      REAL,    -- 1.0 = very specific about model, 0.0 = generic

            -- Composite
            quality_score    REAL,    -- weighted average of sub-scores
            is_flagged       INTEGER DEFAULT 0,  -- 1 = excluded from scoring

            -- Metadata
            duplicate_of     INTEGER, -- source_id of the original if duplicate
            flag_reasons     TEXT,    -- JSON list of reasons

            computed_at      TEXT NOT NULL DEFAULT (datetime('now')),
            UNIQUE(source, source_id, model_slug)
        );
        CREATE INDEX IF NOT EXISTS idx_dq_model ON data_quality(model_slug);
        CREATE INDEX IF NOT EXISTS idx_dq_quality ON data_quality(quality_score);
        """)


# ═══════════════════════════════════════════════════════════════════════════════
# 1. DUPLICATE DETECTION
# ═══════════════════════════════════════════════════════════════════════════════

def _text_hash(text: str) -> str:
    """Normalize and hash text for exact duplicate detection."""
    normalized = re.sub(r'\s+', ' ', text.lower().strip())
    normalized = re.sub(r'https?://\S+', '', normalized)
    normalized = re.sub(r'@\w+', '', normalized)
    return hashlib.md5(normalized.encode()).hexdigest()


def _trigram_set(text: str) -> set:
    """Character trigrams for near-duplicate detection."""
    text = re.sub(r'\s+', ' ', text.lower().strip())
    if len(text) < 3:
        return set()
    return {text[i:i+3] for i in range(len(text) - 2)}


def _jaccard_similarity(set_a: set, set_b: set) -> float:
    if not set_a or not set_b:
        return 0.0
    intersection = len(set_a & set_b)
    union = len(set_a | set_b)
    return intersection / union if union > 0 else 0.0


def compute_uniqueness(texts: list[dict]) -> dict[tuple, float]:
    """
    Score uniqueness for each text.
    Returns {(source, source_id, model_slug): uniqueness_score}
    """
    # Build hash → first occurrence map
    hash_map: dict[str, tuple] = {}  # hash → (source, source_id, model_slug)
    trigram_map: dict[tuple, set] = {}
    results = {}

    for t in texts:
        key = (t["source"], t["source_id"], t["model_slug"])
        text = t["text"] or ""
        h = _text_hash(text)
        trigrams = _trigram_set(text)
        trigram_map[key] = trigrams

        if h in hash_map:
            # Exact duplicate
            results[key] = 0.0
        else:
            hash_map[h] = key
            results[key] = 1.0  # provisional — check near-duplicates below

    # Near-duplicate check (only for texts that passed exact duplicate check)
    # Sample-based for performance — check against last 500 unique texts
    unique_keys = [k for k, v in results.items() if v > 0]
    recent = unique_keys[-500:]

    for i, key in enumerate(unique_keys):
        if results[key] == 0.0:
            continue
        tri_a = trigram_map.get(key, set())
        if not tri_a:
            continue

        max_sim = 0
        for other_key in recent[max(0, i-50):i]:  # check 50 nearest
            if other_key == key:
                continue
            tri_b = trigram_map.get(other_key, set())
            sim = _jaccard_similarity(tri_a, tri_b)
            max_sim = max(max_sim, sim)

        if max_sim > 0.85:
            results[key] = 1.0 - max_sim  # near-duplicate: reduce score
        else:
            results[key] = 1.0

    return results


# ═══════════════════════════════════════════════════════════════════════════════
# 2. BOT / SPAM DETECTION
# ═══════════════════════════════════════════════════════════════════════════════

GENERIC_PATTERNS = [
    r"^.{0,15}$",                    # too short
    r"(check out|click here|visit|buy now|discount|promo)",  # spam
    r"^(yes|no|true|false|ok|thanks|same|agreed|this)\.?$",  # zero-content
    r"(.)\1{5,}",                    # repeated characters
    r"([\U0001F600-\U0001F9FF]){4,}",  # emoji spam
]

BOT_TEMPLATES = [
    r"i asked (chatgpt|claude|gemini) (to|about)",  # templated prompt sharing
    r"here'?s what .+ said",
    r"^thread:?\s*\d+/",              # automated thread numbering
]


def compute_bot_scores(texts: list[dict]) -> dict[tuple, float]:
    """Score how likely each text is from a real human (1.0) vs bot (0.0)."""
    # Track per-author posting frequency
    author_posts: dict[str, list] = defaultdict(list)
    for t in texts:
        author = t.get("author") or "anonymous"
        author_posts[author].append(t)

    results = {}
    for t in texts:
        key = (t["source"], t["source_id"], t["model_slug"])
        text = t["text"] or ""
        author = t.get("author") or "anonymous"
        score = 1.0
        reasons = []

        # Generic content check
        for pattern in GENERIC_PATTERNS:
            if re.search(pattern, text.lower()):
                score -= 0.3
                reasons.append("generic_content")
                break

        # Bot template check
        for pattern in BOT_TEMPLATES:
            if re.search(pattern, text.lower()):
                score -= 0.2
                reasons.append("bot_template")
                break

        # Author posting frequency (>20 posts in our data = suspicious)
        author_count = len(author_posts.get(author, []))
        if author_count > 50:
            score -= 0.4
            reasons.append("high_frequency_author")
        elif author_count > 20:
            score -= 0.2
            reasons.append("frequent_author")

        # Text length — very short texts have less signal
        if len(text) < 30:
            score -= 0.15
            reasons.append("very_short")

        results[key] = max(score, 0.0)

    return results


# ═══════════════════════════════════════════════════════════════════════════════
# 3. SOURCE CREDIBILITY SCORING
# ═══════════════════════════════════════════════════════════════════════════════

# Base credibility by platform (higher = more rigorous community)
PLATFORM_CREDIBILITY = {
    "stackoverflow": 0.90,   # heavily moderated, technical
    "hn": 0.85,              # curated, technical community
    "github_disc": 0.85,     # developer context
    "devto": 0.70,           # developer blogs, some SEO spam
    "mastodon": 0.65,        # smaller but genuine community
    "lemmy": 0.65,           # niche, genuine
    "v2ex": 0.70,            # chinese dev community, active moderation
    "reddit": 0.60,          # large, noisy, some bots
    "bluesky": 0.55,         # social media, high noise
}


def compute_credibility(texts: list[dict]) -> dict[tuple, float]:
    """Score source credibility per text."""
    results = {}
    for t in texts:
        key = (t["source"], t["source_id"], t["model_slug"])
        base = PLATFORM_CREDIBILITY.get(t["source"], 0.5)

        # Engagement bonus: highly engaged content is community-validated
        engagement = t.get("engagement", 0)
        if engagement > 5:
            base = min(base + 0.1, 1.0)
        elif engagement > 20:
            base = min(base + 0.2, 1.0)

        results[key] = base

    return results


# ═══════════════════════════════════════════════════════════════════════════════
# 4. SPECIFICITY SCORING
# ═══════════════════════════════════════════════════════════════════════════════

SPECIFIC_TERMS = [
    # Model-specific technical terms
    r"(context window|token|latency|throughput|ttft|tps)",
    r"(api|endpoint|rate limit|pricing|cost per|million tokens)",
    r"(benchmark|eval|score|elo|mmlu|humaneval|arena)",
    r"(hallucin|accuracy|quality|performance|speed|slow|fast)",
    r"(fine-?tun|rlhf|dpo|rag|function call|tool use)",
    r"(version|update|release|v\d|model card)",
    r"(parameter|weight|quantiz|gguf|fp16|int8)",
    r"\b\d+[bB]\b",  # model sizes like "70B"
    r"\$[\d.]+",      # pricing mentions
]


def compute_specificity(texts: list[dict]) -> dict[tuple, float]:
    """Score how specific each text is about LLM details (vs generic chatter)."""
    results = {}
    for t in texts:
        key = (t["source"], t["source_id"], t["model_slug"])
        text = t["text"] or ""
        text_lower = text.lower()

        matches = sum(1 for p in SPECIFIC_TERMS if re.search(p, text_lower))
        # 0 matches = 0.3 (generic), 3+ matches = 1.0 (very specific)
        score = min(0.3 + matches * 0.25, 1.0)
        results[key] = score

    return results


# ═══════════════════════════════════════════════════════════════════════════════
# MAIN: Score all texts
# ═══════════════════════════════════════════════════════════════════════════════

def run_quality_scoring() -> dict:
    conn = get_connection()
    init_quality_tables()

    # Load all scored texts
    rows = conn.execute("""
        SELECT source, source_id, model_slug, text_preview, engagement_weight
        FROM sentiment_scores
    """).fetchall()

    texts = [{"source": r[0], "source_id": r[1], "model_slug": r[2],
              "text": r[3], "engagement": r[4] or 0} for r in rows]

    if not texts:
        conn.close()
        return {"total": 0}

    logger.info("[quality] scoring %d texts...", len(texts))

    # Compute all sub-scores
    uniqueness = compute_uniqueness(texts)
    bot_scores = compute_bot_scores(texts)
    credibility = compute_credibility(texts)
    specificity = compute_specificity(texts)

    # Composite quality score
    WEIGHTS = {"uniqueness": 0.30, "bot": 0.25, "credibility": 0.25, "specificity": 0.20}
    flagged = 0
    total = 0

    with db() as wconn:
        batch = []
        for t in texts:
            key = (t["source"], t["source_id"], t["model_slug"])
            u = uniqueness.get(key, 1.0)
            b = bot_scores.get(key, 1.0)
            c = credibility.get(key, 0.5)
            s = specificity.get(key, 0.5)

            quality = (u * WEIGHTS["uniqueness"] +
                       b * WEIGHTS["bot"] +
                       c * WEIGHTS["credibility"] +
                       s * WEIGHTS["specificity"])

            is_flagged = 1 if quality < 0.3 else 0
            if is_flagged:
                flagged += 1

            reasons = []
            if u < 0.3: reasons.append("duplicate")
            if b < 0.5: reasons.append("bot_suspected")
            if s < 0.4: reasons.append("too_generic")

            batch.append((
                t["source"], t["source_id"], t["model_slug"],
                u, b, c, s, quality, is_flagged,
                json.dumps(reasons) if reasons else None,
            ))
            total += 1

            if len(batch) >= 200:
                wconn.executemany("""
                    INSERT OR REPLACE INTO data_quality
                        (source, source_id, model_slug,
                         uniqueness, bot_score, credibility, specificity,
                         quality_score, is_flagged, flag_reasons)
                    VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
                """, batch)
                batch = []

        if batch:
            wconn.executemany("""
                INSERT OR REPLACE INTO data_quality
                    (source, source_id, model_slug,
                     uniqueness, bot_score, credibility, specificity,
                     quality_score, is_flagged, flag_reasons)
                VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
            """, batch)

    conn.close()
    logger.info("[quality] scored %d texts, flagged %d (%.1f%%) as low-quality",
                total, flagged, flagged / max(total, 1) * 100)

    return {"total": total, "flagged": flagged, "flagged_pct": flagged / max(total, 1) * 100}


if __name__ == "__main__":
    logging.basicConfig(level=logging.INFO,
                        format="%(asctime)s [%(levelname)s] %(name)s — %(message)s")

    import argparse
    parser = argparse.ArgumentParser()
    parser.add_argument("--stats", action="store_true")
    args = parser.parse_args()

    result = run_quality_scoring()
    print(f"\nScored {result['total']} texts, flagged {result['flagged']} ({result['flagged_pct']:.1f}%)")

    if args.stats:
        conn = get_connection()
        print("\n=== Quality Distribution ===")
        for bucket in ["0.0-0.3 (flagged)", "0.3-0.5 (low)", "0.5-0.7 (medium)", "0.7-0.9 (good)", "0.9-1.0 (excellent)"]:
            lo, hi = float(bucket.split("-")[0]), float(bucket.split("-")[1].split(" ")[0])
            n = conn.execute("SELECT COUNT(*) FROM data_quality WHERE quality_score >= ? AND quality_score < ?", (lo, hi)).fetchone()[0]
            print(f"  {bucket}: {n}")

        print("\n=== Flagged by reason ===")
        for r in conn.execute("""
            SELECT flag_reasons, COUNT(*) FROM data_quality
            WHERE is_flagged = 1 AND flag_reasons IS NOT NULL
            GROUP BY flag_reasons ORDER BY COUNT(*) DESC LIMIT 10
        """).fetchall():
            print(f"  {r[0]}: {r[1]}")

        print("\n=== Quality by source ===")
        for r in conn.execute("""
            SELECT source, COUNT(*), ROUND(AVG(quality_score),3),
                   SUM(is_flagged), ROUND(SUM(is_flagged)*100.0/COUNT(*),1)
            FROM data_quality GROUP BY source ORDER BY AVG(quality_score) DESC
        """).fetchall():
            print(f"  {r[0]:<20} n={r[1]:<5} avg_quality={r[2]}  flagged={r[3]} ({r[4]}%)")