bot-backend
9e5086890f
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sat_factor = 1 + ((sold_pct-50)/100)*0.30 was computed in 09_macro_and_trend.py, written to district_economics.sat_factor, and fetched in server.py and 10_score_v2.py — but never multiplied into any score. The live market sub-score uses a separate sat_score = min(100, sold_pct*100/70) directly, so sat_factor was dead code that would double-count absorption if ever wired in. - 09_macro_and_trend.py: remove sat_factor computation, ALTER TABLE column, UPDATE binding, and debug print column - 10_score_v2.py: remove sat_factor from SELECT and unpacking - server.py: remove sat_factor variable assignment and from macro_factors response - static/index.html: remove sat_factor documentation row - data/sql/162_drop_district_economics_sat_factor.sql: DROP COLUMN IF EXISTS
191 lines
9.3 KiB
Python
191 lines
9.3 KiB
Python
"""Scoring v2 — uses prod-style macro factors + per-flat metrics from Objective.
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Components (all 0..100):
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education 20% школы (×1) + садики (×1) + ВУЗы (×0.3) — distance score
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health 10% аптеки (×1) + поликлиники (×1) + больницы (×0.5)
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retail 13% max(big, 0.7×med) — distance score
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transit 15% max(metro, 0.85×tram, 0.7×bus) — distance score
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leisure 9% парки + площадки + спорт
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economic 30% real_*: price_position 50% + velocity_real 25% + liquidity 25%
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velocity adjusted by trend_factor (clamp 0.7..2.0, prod-style)
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market 3% competitive density (jk_count_1km) + saturation factor
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(informational; small weight)
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Macro context shown alongside but not in score:
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mortgage_rate_sverdl, city_avg_poi_1km, city_med_price_m2.
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"""
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import sqlite3, pathlib, math
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DB = pathlib.Path(__file__).parent / "analysis.db"
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EDU = [("kindergarten", 300, 1000, 1.0),
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("school", 400, 1500, 1.0),
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("university", 1000, 5000, 0.3)]
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HEALTH = [("pharmacy", 300, 1000, 1.0),
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("clinic", 500, 2000, 1.0),
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("hospital", 1500, 5000, 0.5)]
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RETAIL_BIG = [("shop_big", 500, 2000, 1.0)]
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RETAIL_MED = [("shop_med", 300, 1000, 1.0)]
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TRANSIT_M = [("metro", 1000, 3000, 1.0)]
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TRANSIT_T = [("tram_stop", 400, 1500, 1.0)]
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TRANSIT_B = [("bus_stop", 200, 800, 1.0)]
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LEISURE = [("park", 500, 2000, 1.0),
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("playground", 200, 700, 1.0),
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("sports", 500, 2000, 0.7)]
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def dist_score(d_m, ideal, mx):
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if d_m is None: return 0.0
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if d_m <= ideal: return 100.0
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if d_m >= mx: return 0.0
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return 100.0 * (mx - d_m) / (mx - ideal)
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def comp(conn, sid, cats):
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tw = sum(c[3] for c in cats); s = 0
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for cat, ideal, mx, w in cats:
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d = conn.execute("SELECT MIN(distance_m) FROM pois WHERE site_id=? AND category=?",
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(sid, cat)).fetchone()[0]
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s += w * dist_score(d, ideal, mx)
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return s / tw if tw else 0
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def hav(la1, lo1, la2, lo2):
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R = 6371000; p1, p2 = math.radians(la1), math.radians(la2)
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dp = math.radians(la2-la1); dl = math.radians(lo2-lo1)
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a = math.sin(dp/2)**2 + math.cos(p1)*math.cos(p2)*math.sin(dl/2)**2
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return 2*R*math.asin(math.sqrt(a))
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def main():
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conn = sqlite3.connect(DB)
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weights = {r[0]: r[1] for r in conn.execute("SELECT component, weight FROM scoring_weights").fetchall()}
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if not weights:
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weights = {"education":0.18,"health":0.10,"retail":0.13,"transit":0.15,
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"leisure":0.09,"economic":0.30,"market":0.05}
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print("Weights:", weights)
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sites = conn.execute("SELECT site_id, lat, lon FROM sites").fetchall()
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site_coords = {sid: (la, lo) for sid, la, lo in sites}
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# Bounds for normalization (computed once)
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prices = sorted([r[0] for r in conn.execute(
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"SELECT real_median_price_m2 FROM district_economics WHERE real_median_price_m2 IS NOT NULL").fetchall()])
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pmin, pmax = (prices[0], prices[-1]) if prices else (100, 200)
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if pmax <= pmin: pmax = pmin + 1
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vels = sorted([r[0] for r in conn.execute(
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"SELECT real_velocity_6mo FROM district_economics WHERE real_velocity_6mo IS NOT NULL").fetchall()])
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vmax = vels[int(len(vels) * 0.9)] if vels else 8
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# Citywide median jk_count_1km — for market component
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counts = sorted([r[0] for r in conn.execute(
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"SELECT count(*) FROM sites s2 JOIN sites s ON s2.site_id != s.site_id "
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"WHERE 1=0 GROUP BY s.site_id").fetchall()]) # noop, computed below
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conn.execute("DELETE FROM features")
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conn.execute("DELETE FROM scores")
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conn.execute("DELETE FROM scores_total")
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for sid, lat, lon in sites:
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# POI features
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for cat in ["kindergarten","school","university","pharmacy","clinic","hospital",
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"shop_big","shop_med","shop_small","bus_stop","tram_stop","metro",
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"park","playground","sports"]:
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n = conn.execute("SELECT MIN(distance_m) FROM pois WHERE site_id=? AND category=?",
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(sid, cat)).fetchone()[0]
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c500 = conn.execute("SELECT count(*) FROM pois WHERE site_id=? AND category=? AND distance_m<=500",
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(sid, cat)).fetchone()[0]
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c1k = conn.execute("SELECT count(*) FROM pois WHERE site_id=? AND category=? AND distance_m<=1000",
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(sid, cat)).fetchone()[0]
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for k, v in (("nearest_m", n), ("count_500m", c500), ("count_1km", c1k)):
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conn.execute("INSERT INTO features(site_id,feature,value) VALUES (?,?,?)",
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(sid, f"{cat}_{k}", float(v) if v is not None else None))
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# Locational components
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edu = comp(conn, sid, EDU)
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health = comp(conn, sid, HEALTH)
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retail = max(comp(conn, sid, RETAIL_BIG), 0.7 * comp(conn, sid, RETAIL_MED))
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transit = max(comp(conn, sid, TRANSIT_M),
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0.85 * comp(conn, sid, TRANSIT_T),
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0.7 * comp(conn, sid, TRANSIT_B))
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leisure = comp(conn, sid, LEISURE)
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# Economic + market
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econ_row = conn.execute("""SELECT de.real_median_price_m2, de.real_velocity_6mo,
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de.real_trend_ratio, de.months_to_sellout,
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de.real_sold_pct, de.real_n_lots
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FROM site_district sd
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JOIN district_economics de USING (district)
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WHERE sd.site_id=?""", (sid,)).fetchone()
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if econ_row:
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price, v_rec, trend, mts, sold_pct, n_lots = econ_row
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# price_score: linear by district median price percentile
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p_score = max(0, min(100, ((price or 0) - pmin) * 100 / (pmax - pmin)))
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# velocity_score: real recent (6mo), capped at p90
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v_score = max(0, min(100, (v_rec or 0) * 100 / vmax)) if vmax else 0
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# trend modifier (prod-style): clamp 0.7..2.0 → 0.5..1.0 multiplier on velocity
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tf = max(0.7, min(2.0, trend or 1.0))
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v_score = v_score * (0.5 + 0.5 * (tf / 2.0)) # 0.7→0.675, 1.0→0.75, 2.0→1.0
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# liquidity_score from months_to_sellout
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liq_score = max(0, 100 - min(mts or 24, 24) * 100 / 24) if mts else 50
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economic = 0.50 * p_score + 0.25 * v_score + 0.25 * liq_score
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else:
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economic = 0; trend = None; sold_pct = None; n_lots = None
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# Competitive density: number of OTHER ЖК within 1km
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n_jk_1km = sum(
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1 for sid2, (la2, lo2) in site_coords.items()
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if sid2 != sid and sid2.startswith("jk:") and hav(lat, lon, la2, lo2) <= 1000
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)
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# market component: 50/50 saturation × density penalty
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# density: 0..15 jks → 100..0 (linearly capped)
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density_score = max(0, 100 - n_jk_1km * 100 / 15)
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# saturation: optimal 50% sold (proven absorption, room to grow). 0% raw, 100% over-saturated
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sat_score = 100 - abs((sold_pct or 50) - 50) * 2 if sold_pct is not None else 50
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market = 0.5 * density_score + 0.5 * sat_score
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conn.execute("INSERT INTO features(site_id,feature,value) VALUES (?,?,?)",
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(sid, "jk_count_1km", n_jk_1km))
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comps = {"education": edu, "health": health, "retail": retail,
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"transit": transit, "leisure": leisure, "economic": economic,
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"market": market}
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for c, val in comps.items():
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conn.execute("INSERT INTO scores(site_id,component,score_0_100) VALUES (?,?,?)",
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(sid, c, val))
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weighted = sum(weights.get(k, 0) * v for k, v in comps.items())
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conn.execute("INSERT INTO scores_total(site_id,weighted) VALUES (?,?)", (sid, weighted))
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conn.commit()
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# Ranks
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rows = conn.execute("""SELECT s.site_id, st.weighted FROM sites s
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JOIN scores_total st USING (site_id)
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ORDER BY st.weighted DESC""").fetchall()
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for rank, (sid, _) in enumerate(rows, 1):
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conn.execute("UPDATE scores_total SET rank_overall=? WHERE site_id=?", (rank, sid))
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# district rank
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by_dist = {}
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for sid, _ in rows:
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d = conn.execute("SELECT district FROM site_district WHERE site_id=?", (sid,)).fetchone()
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by_dist.setdefault(d[0] if d else "—", []).append(sid)
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for d, sids in by_dist.items():
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for rank, sid in enumerate(sids, 1):
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conn.execute("UPDATE scores_total SET rank_district=? WHERE site_id=?", (rank, sid))
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conn.commit()
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# Print parcel summary
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pid = "parcel:66:41:0204016:10"
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p = conn.execute("SELECT weighted, rank_overall, rank_district FROM scores_total WHERE site_id=?",
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(pid,)).fetchone()
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print(f"\n=== Parcel ===")
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print(f" total: {p[0]:.2f}/100 overall #{p[1]} district #{p[2]}")
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print(f" components:")
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for r in conn.execute("SELECT component, score_0_100 FROM scores WHERE site_id=? ORDER BY component", (pid,)).fetchall():
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wt = weights.get(r[0], 0)
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print(f" {r[0]:<10} {r[1]:>6.1f} × {wt*100:>4.0f}% = {r[1]*wt:>5.2f}")
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n_jk = conn.execute("SELECT count(*) FROM sites WHERE kind='jk'").fetchone()[0]
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print(f" vs {n_jk} строящихся ЖК Ekb")
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conn.close()
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if __name__ == "__main__":
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main()
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