"""
EDA trên sampled_1m_reviews.jsonl và sampled_meta.jsonl
"""
import json, time, math
from collections import Counter, defaultdict
from pathlib import Path

REVIEWS_PATH = Path("/workspace/amazon/sampled_1m_reviews.jsonl")
META_PATH    = Path("/workspace/amazon/sampled_meta.jsonl")

def hist(values, bins, total=None):
    """ASCII histogram."""
    total = total or len(values)
    for lo, hi in bins:
        cnt = sum(1 for v in values if lo <= v <= hi)
        bar = "#" * int(cnt / total * 40)
        print(f"  {lo:>5}–{hi:<6} {cnt:>8,}  ({cnt/total*100:5.1f}%)  {bar}")

def percentiles(vals, ps=(10,25,50,75,90,95,99)):
    s = sorted(vals)
    n = len(s)
    parts = {f"p{p}": s[min(int(n*p/100), n-1)] for p in ps}
    parts["min"] = s[0]; parts["max"] = s[-1]; parts["mean"] = sum(s)/n
    return parts

# ═══════════════════════════════════════════════════════════════════════════
# 1. REVIEWS EDA
# ═══════════════════════════════════════════════════════════════════════════
print("\n" + "="*60)
print("1. REVIEWS EDA  (sampled_1m_reviews.jsonl)")
print("="*60)
t0 = time.time()

user_reviews   = defaultdict(list)   # uid -> [ts, ...]
user_stars     = defaultdict(list)   # uid -> [stars, ...]
item_counts    = Counter()
star_dist      = Counter()
year_dist      = Counter()
verified_cnt   = 0
text_lens      = []
total_reviews  = 0

import datetime

with open(REVIEWS_PATH, "r", encoding="utf-8", errors="replace") as f:
    for line in f:
        line = line.strip()
        if not line: continue
        try: rec = json.loads(line)
        except: continue
        total_reviews += 1

        uid  = rec.get("reviewerID", "")
        asin = rec.get("asin", "")
        ts   = rec.get("unixReviewTime", 0) or 0
        star = int(rec.get("overall", 0))

        user_reviews[uid].append(ts)
        user_stars[uid].append(star)
        item_counts[asin] += 1
        star_dist[star]   += 1

        if ts:
            year_dist[datetime.datetime.fromtimestamp(ts).year] += 1

        txt = rec.get("reviewText", "") or ""
        if txt.strip():
            text_lens.append(len(txt.split()))

print(f"Total reviews     : {total_reviews:,}")
print(f"Unique users      : {len(user_reviews):,}")
print(f"Unique items      : {len(item_counts):,}")

# Rating distribution
print("\nRating distribution:")
for s in range(1, 6):
    cnt = star_dist[s]
    bar = "#" * int(cnt / total_reviews * 40)
    print(f"  {s}★  {cnt:>8,}  ({cnt/total_reviews*100:5.1f}%)  {bar}")

# Year distribution
print("\nYear distribution:")
for yr in sorted(year_dist):
    cnt = year_dist[yr]
    bar = "#" * int(cnt / total_reviews * 40)
    print(f"  {yr}  {cnt:>8,}  ({cnt/total_reviews*100:5.1f}%)  {bar}")

# Review text length
if text_lens:
    p = percentiles(text_lens)
    print(f"\nReview text length (words):")
    print(f"  min={p['min']}  p25={p['p25']}  p50={p['p50']}  p75={p['p75']}  p90={p['p90']}  p99={p['p99']}  max={p['max']}  mean={p['mean']:.1f}")

# Per-user interaction count
user_counts_list = [len(v) for v in user_reviews.values()]
p = percentiles(user_counts_list)
print(f"\nInteractions per user:")
print(f"  min={p['min']}  p25={p['p25']}  p50={p['p50']}  p75={p['p75']}  p90={p['p90']}  p99={p['p99']}  max={p['max']}  mean={p['mean']:.1f}")
print("  Buckets:")
hist(user_counts_list, [(5,9),(10,19),(20,49),(50,99),(100,499),(500,9999)], len(user_counts_list))

# Per-user avg rating
user_avg_stars = [sum(v)/len(v) for v in user_stars.values()]
p = percentiles(user_avg_stars)
print(f"\nAvg rating per user:")
print(f"  min={p['min']:.2f}  p25={p['p25']:.2f}  p50={p['p50']:.2f}  p75={p['p75']:.2f}  mean={p['mean']:.2f}")

# Item popularity (long tail)
pop = sorted(item_counts.values(), reverse=True)
n_items = len(pop)
top1_pct  = sum(pop[:max(1,n_items//100)])
top10_pct = sum(pop[:max(1,n_items//10)])
print(f"\nItem popularity (long tail):")
print(f"  Top 1% items  cover {top1_pct/total_reviews*100:.1f}% of reviews")
print(f"  Top 10% items cover {top10_pct/total_reviews*100:.1f}% of reviews")
print(f"  Items with 1 review: {sum(1 for v in pop if v==1):,}")
print(f"  Items with >=10:     {sum(1 for v in pop if v>=10):,}")
print(f"  Items with >=100:    {sum(1 for v in pop if v>=100):,}")

# Temporal span per user
user_spans = []
for uid, tss in user_reviews.items():
    if len(tss) >= 2:
        user_spans.append((max(tss) - min(tss)) / (3600*24))  # days
p = percentiles(user_spans)
print(f"\nUser history span (days, users with ≥2 reviews):")
print(f"  p25={p['p25']:.0f}d  p50={p['p50']:.0f}d  p75={p['p75']:.0f}d  p90={p['p90']:.0f}d  max={p['max']:.0f}d  mean={p['mean']:.1f}d")

print(f"\n(Reviews EDA: {time.time()-t0:.1f}s)")

# ═══════════════════════════════════════════════════════════════════════════
# 2. META EDA
# ═══════════════════════════════════════════════════════════════════════════
print("\n" + "="*60)
print("2. META EDA  (sampled_meta.jsonl)")
print("="*60)
t1 = time.time()

cat_top      = Counter()
brand_top    = Counter()
price_vals   = []
also_buy_len = []
also_view_len= []
desc_lens    = []
title_lens   = []
total_meta   = 0
has_fields   = Counter()

with open(META_PATH, "r", encoding="utf-8", errors="replace") as f:
    for line in f:
        line = line.strip()
        if not line: continue
        try: rec = json.loads(line)
        except: continue
        total_meta += 1

        # categories
        cats = rec.get("category") or []
        if len(cats) > 0:
            has_fields["category"] += 1
            cat_top[cats[0]] += 1

        # brand
        brand = (rec.get("brand") or "").strip()
        if brand:
            has_fields["brand"] += 1
            brand_top[brand] += 1

        # price
        p_raw = str(rec.get("price") or "").replace("$","").strip()
        if p_raw and p_raw.lower() not in ("", "none"):
            try:
                pv = float(p_raw.split("-")[0].replace(",","").strip())
                if 0 < pv < 100000:
                    price_vals.append(pv)
                    has_fields["price"] += 1
            except: pass

        # also_buy / also_view
        ab = rec.get("also_buy") or []
        av = rec.get("also_view") or []
        if ab: also_buy_len.append(len(ab)); has_fields["also_buy"] += 1
        if av: also_view_len.append(len(av)); has_fields["also_view"] += 1

        # description length
        desc = rec.get("description") or []
        if isinstance(desc, list): desc = " ".join(desc)
        desc = str(desc).strip()
        if desc:
            has_fields["description"] += 1
            desc_lens.append(len(desc.split()))

        # title length
        title = (rec.get("title") or "").strip()
        if title:
            has_fields["title"] += 1
            title_lens.append(len(title.split()))

print(f"Total meta records: {total_meta:,}")
print(f"\nField coverage:")
for field in ["title","brand","category","price","description","also_buy","also_view"]:
    cnt = has_fields[field]
    print(f"  {field:<14} {cnt:>8,}  ({cnt/total_meta*100:.1f}%)")

# Category distribution
print(f"\nTop-20 categories (level-1):")
for cat, cnt in cat_top.most_common(20):
    bar = "#" * int(cnt / total_meta * 30)
    print(f"  {cat[:50]:<52} {cnt:>7,}  ({cnt/total_meta*100:.1f}%)  {bar}")

# Brand distribution
print(f"\nTop-15 brands:")
for brand, cnt in brand_top.most_common(15):
    print(f"  {brand[:40]:<42} {cnt:>7,}")
print(f"  ... ({len(brand_top):,} unique brands total)")

# Price
if price_vals:
    p = percentiles(price_vals)
    print(f"\nPrice distribution (USD, records with valid price):")
    print(f"  mean=${p['mean']:.2f}  p25=${p['p25']:.2f}  p50=${p['p50']:.2f}  p75=${p['p75']:.2f}  p90=${p['p90']:.2f}  p99=${p['p99']:.2f}  max=${p['max']:.2f}")
    print("  Buckets:")
    hist(price_vals, [(0,9),(10,24),(25,49),(50,99),(100,499),(500,99999)], len(price_vals))

# also_buy / also_view
if also_buy_len:
    p = percentiles(also_buy_len)
    print(f"\nalso_buy per item (when present):  mean={p['mean']:.1f}  p50={p['p50']}  p90={p['p90']}  max={p['max']}")
if also_view_len:
    p = percentiles(also_view_len)
    print(f"also_view per item (when present): mean={p['mean']:.1f}  p50={p['p50']}  p90={p['p90']}  max={p['max']}")

# Co-purchase graph density
total_edges = sum(also_buy_len) + sum(also_view_len)
print(f"\nCo-purchase graph:")
print(f"  also_buy  edges: {sum(also_buy_len):,}")
print(f"  also_view edges: {sum(also_view_len):,}")
print(f"  total edges     : {total_edges:,}")
print(f"  avg degree      : {total_edges/total_meta:.1f}")

# Description & title length
if desc_lens:
    p = percentiles(desc_lens)
    print(f"\nDescription length (words): p25={p['p25']}  p50={p['p50']}  p75={p['p75']}  p90={p['p90']}  mean={p['mean']:.1f}")
if title_lens:
    p = percentiles(title_lens)
    print(f"Title length (words):       p25={p['p25']}  p50={p['p50']}  p75={p['p75']}  p90={p['p90']}  mean={p['mean']:.1f}")

print(f"\n(Meta EDA: {time.time()-t1:.1f}s)")

# ═══════════════════════════════════════════════════════════════════════════
# 3. CROSS-ANALYSIS
# ═══════════════════════════════════════════════════════════════════════════
print("\n" + "="*60)
print("3. CROSS-ANALYSIS")
print("="*60)

# Category distribution of reviewed items (via meta lookup)
meta_asin_cat = {}
with open(META_PATH, "r", encoding="utf-8", errors="replace") as f:
    for line in f:
        line = line.strip()
        if not line: continue
        try: rec = json.loads(line)
        except: continue
        asin = rec.get("asin","")
        cats = rec.get("category") or []
        if asin and cats:
            meta_asin_cat[asin] = cats[0]

# Map reviews to categories
review_cat_dist = Counter()
n_mapped = 0
with open(REVIEWS_PATH, "r", encoding="utf-8", errors="replace") as f:
    for line in f:
        line = line.strip()
        if not line: continue
        try: rec = json.loads(line)
        except: continue
        asin = rec.get("asin","")
        cat  = meta_asin_cat.get(asin)
        if cat:
            review_cat_dist[cat] += 1
            n_mapped += 1

print(f"\nCategory distribution of reviews (top-15):")
for cat, cnt in review_cat_dist.most_common(15):
    bar = "#" * int(cnt / total_reviews * 30)
    print(f"  {cat[:50]:<52} {cnt:>7,}  ({cnt/total_reviews*100:.1f}%)  {bar}")

print(f"\nDone. Total EDA time: {time.time()-t0:.1f}s")