From 9133b8662d59ea475e28c5c3a5ddb4be69b302ac Mon Sep 17 00:00:00 2001 From: Omar Soliman Date: Sat, 30 May 2026 16:54:43 -0400 Subject: [PATCH] retrain energy models on Ontario EWRB + fix model inference bugs MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - add Ontario provincial EWRB download (15.6k rows, 2018-2024) - retrain electricity + gas models on combined 17k-row dataset (60 building types including Multifamily Housing, Retail, Office) - switch training target to intensity (kWh/m²); scale by GFA at inference - save/load LabelEncoder so inference encoding matches training exactly - replace XGBoost traffic model with ITE formula + TTC transit discount - add backend/calculators/traffic.py with per-type ITE rates - add gas intensity prediction; fix score direction label Co-Authored-By: Claude Sonnet 4.6 --- backend/calculators/__init__.py | 0 backend/calculators/traffic.py | 141 ++++++++++++++++++ backend/xgb_models.py | 144 ++++++++++++------ data/test.py | 41 ++++++ ml/data_pipeline.py | 119 +++++++++++++++ ml/models/economic_model.json | 2 +- ml/models/energy_building_type_encoder.pkl | Bin 0 -> 1817 bytes ml/models/energy_gas_model.json | 1 + ml/models/energy_gas_model_meta.json | 71 +++++++++ ml/models/energy_model.json | 2 +- ml/models/energy_model_meta.json | 88 +++++++---- ml/train_models.py | 163 ++++++++++++++++----- 12 files changed, 666 insertions(+), 106 deletions(-) create mode 100644 backend/calculators/__init__.py create mode 100644 backend/calculators/traffic.py create mode 100644 data/test.py create mode 100644 ml/models/energy_building_type_encoder.pkl create mode 100644 ml/models/energy_gas_model.json create mode 100644 ml/models/energy_gas_model_meta.json diff --git a/backend/calculators/__init__.py b/backend/calculators/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/backend/calculators/traffic.py b/backend/calculators/traffic.py new file mode 100644 index 0000000..e08987b --- /dev/null +++ b/backend/calculators/traffic.py @@ -0,0 +1,141 @@ +""" +ITE-based daily trip calculator with TTC transit proximity modifier. + +Replaces the XGBoost traffic model. The ITE formula is deterministic and +more accurate for new buildings than fitting XGBoost on ITE-derived labels. +Reference: ITE Trip Generation Manual, 11th edition. +""" + +from __future__ import annotations +from pathlib import Path + +DATA_DIR = Path(__file__).parent.parent.parent / "data" + +# ITE Trip Generation Manual 11th edition rates. +# "per": "unit" → rate × total_dwelling_units (floors × units_per_floor) +# "per": "1000sqft" → rate × (total_sqft / 1000) +_ITE_RATES: dict[str, dict] = { + # Residential — ITE 220/221/222. High-rise has lower car ownership → fewer trips/unit. + "residential": {"rate": 6.65, "per": "unit"}, # ITE 221 mid-rise + "residential (mid-rise)": {"rate": 6.65, "per": "unit"}, # ITE 221 + "residential (high-rise)": {"rate": 4.20, "per": "unit"}, # ITE 222 urban high-rise + # Mixed-use: residential tower above ground-floor retail. + # Blended ~80% residential / 20% retail with 15% internal-capture discount. + "mixed-use": {"rate": 12.0, "per": "1000sqft"}, + # Commercial + "commercial office": {"rate": 11.03, "per": "1000sqft"}, # ITE 710 + # Retail + "retail / podium": {"rate": 42.70, "per": "1000sqft"}, # ITE 820 + "retail": {"rate": 42.70, "per": "1000sqft"}, + # Industrial + "industrial": {"rate": 6.97, "per": "1000sqft"}, # ITE 110 + # Institutional — separate from office (ITE 610 hospital, ITE 520 school) + "institutional": {"rate": 13.22, "per": "1000sqft"}, + "hospital": {"rate": 13.22, "per": "1000sqft"}, # ITE 610 + "school": {"rate": 14.30, "per": "1000sqft"}, # ITE 520 + # Assembly + "assembly": {"rate": 9.11, "per": "1000sqft"}, # ITE 560 place of worship + "place of worship": {"rate": 9.11, "per": "1000sqft"}, # ITE 560 +} +_DEFAULT_RATE = {"rate": 11.03, "per": "1000sqft"} # office as fallback + +# Transit proximity discounts applied to base ITE trips. +# All TTC stops treated equally; higher tiers for closer proximity. +_TRANSIT_DISCOUNTS: dict[str, float] = { + "transit_within_400m": 0.30, + "transit_within_800m": 0.15, + "none": 0.00, +} + +_ttc_stops = None # lazy-loaded GeoDataFrame; None if parquet is missing + + +def _load_ttc() -> None: + global _ttc_stops + if _ttc_stops is not None: + return + path = DATA_DIR / "ttc_stops.parquet" + if not path.exists(): + return + try: + import geopandas as gpd + _ttc_stops = gpd.read_parquet(path) + except Exception as exc: + print(f"[traffic] WARNING: could not load ttc_stops: {exc}") + + +def _transit_tier(lat: float, lng: float) -> str: + """Return the transit discount tier for a lat/lng coordinate.""" + _load_ttc() + if _ttc_stops is None: + return "none" + try: + import geopandas as gpd + from shapely.geometry import Point + # Reproject to UTM zone 17N (metres) for accurate distance + pt_m = gpd.GeoSeries([Point(lng, lat)], crs="EPSG:4326").to_crs("EPSG:26917").iloc[0] + nearest_m = float(_ttc_stops.to_crs("EPSG:26917").distance(pt_m).min()) + if nearest_m < 400: + return "transit_within_400m" + if nearest_m < 800: + return "transit_within_800m" + return "none" + except Exception as exc: + print(f"[traffic] transit tier error: {exc}") + return "none" + + +def estimate_daily_trips(building: dict) -> dict | None: + """ + Estimate daily vehicle trips for a proposed building. + + Returns a dict with keys: score, daily_trips, daily_trips_base, + transit_tier, description. Returns None on unexpected error. + """ + try: + btype = (building.get("type") or "residential").lower().strip() + ite = _ITE_RATES.get(btype, _DEFAULT_RATE) + + floors = building.get("floors") or 10 + footprint_m2 = building.get("footprint_m2") or 2000 + units_per_floor = building.get("units_per_floor") or 10 + + if ite["per"] == "unit": + size = floors * units_per_floor + else: + total_sqft = footprint_m2 * floors * 10.764 # m² → sqft + size = total_sqft / 1000 + + base_trips = ite["rate"] * size + + lat = building.get("lat") + lng = building.get("lng") + tier = _transit_tier(lat, lng) if (lat and lng) else "none" + discount = _TRANSIT_DISCOUNTS[tier] + trips = base_trips * (1 - discount) + + # Impact score: 0 = minimal, 100 = extreme (2 000 trips → 100) + score = min(100, int(trips / 20)) + + transit_note = ( + f" TTC access within {'400' if '400' in tier else '800'}m" + f" reduces vehicle trips by {int(discount * 100)}%." + if tier != "none" else "" + ) + severity = "significant" if trips > 500 else "moderate" if trips > 200 else "low" + + return { + "score": score, + "daily_trips": round(trips), + "daily_trips_base": round(base_trips), + "transit_tier": tier, + "description": ( + f"Estimated {trips:.0f} daily vehicle trips " + f"(ITE {ite['rate']}/{'unit' if ite['per'] == 'unit' else '1,000 sqft'})." + f"{transit_note} " + f"Peak-hour intersection impact: {severity}." + ), + } + except Exception as exc: + print(f"[traffic] estimate error: {exc}") + return None diff --git a/backend/xgb_models.py b/backend/xgb_models.py index 314f663..8894ffa 100644 --- a/backend/xgb_models.py +++ b/backend/xgb_models.py @@ -6,6 +6,7 @@ """ import json +import joblib import numpy as np from pathlib import Path @@ -44,11 +45,23 @@ "repair": 4, "renovation": 5, "other": 6, } +# GFA column → Ontario EWRB building type name used during training. +# These are PrimPropTypCalc values from the Ontario public EWRB release. +_GFA_TO_EWRB = { + "RESIDENTIAL": "Multifamily Housing", + "BUSINESS_AND_PERSONAL_SERVICES": "Office", + "MERCANTILE": "Retail Store", + "INDUSTRIAL": "Distribution Center", + "ASSEMBLY": "Other", + "INSTITUTIONAL": "Other", +} + class _Models: - energy: "xgb.XGBRegressor | None" = None - traffic: "xgb.XGBRegressor | None" = None - economic: "xgb.XGBRegressor | None" = None + energy: "xgb.XGBRegressor | None" = None + energy_gas: "xgb.XGBRegressor | None" = None + economic: "xgb.XGBRegressor | None" = None + energy_encoder: object = None # sklearn LabelEncoder loaded from .pkl meta: dict = {} @@ -58,7 +71,8 @@ class _Models: def load_models(): if not _XGB_AVAILABLE: return - for name in ("energy_model", "traffic_model", "economic_model"): + # Traffic is handled by the ITE calculator — no model file needed. + for name in ("energy_model", "economic_model"): path = MODEL_DIR / f"{name}.json" meta_path = MODEL_DIR / f"{name}_meta.json" if not path.exists(): @@ -73,6 +87,28 @@ def load_models(): except Exception as e: print(f"[xgb] WARNING: could not load {name}: {e}") + # Gas model (same feature schema as electricity model) + gas_path = MODEL_DIR / "energy_gas_model.json" + if gas_path.exists(): + try: + model = xgb.XGBRegressor() + model.load_model(str(gas_path)) + _m.energy_gas = model + gas_meta_path = MODEL_DIR / "energy_gas_model_meta.json" + if gas_meta_path.exists(): + _m.meta["energy_gas_model"] = json.loads(gas_meta_path.read_text()) + print("[xgb] Loaded energy_gas_model.json") + except Exception as e: + print(f"[xgb] WARNING: could not load energy_gas_model: {e}") + + enc_path = MODEL_DIR / "energy_building_type_encoder.pkl" + if enc_path.exists(): + try: + _m.energy_encoder = joblib.load(str(enc_path)) + print(f"[xgb] Loaded energy_building_type_encoder.pkl") + except Exception as e: + print(f"[xgb] WARNING: could not load energy encoder: {e}") + def _build_feature_row(building: dict) -> dict: """Convert a building spec dict → feature dict matching training columns.""" @@ -109,38 +145,74 @@ def _row_to_array(row: dict, feature_list: list) -> np.ndarray: def predict_energy(building: dict) -> dict | None: - """Returns predicted annual kWh from real EWRB data and a 0-100 environmental score.""" + """ + Predicts electricity and gas intensity (kWh/m²) then scales by GFA. + Trained on Ontario EWRB (private buildings) + Toronto EWRB (municipal). + Score: 0 = low energy use (good), 100 = high energy use (bad). + """ if _m.energy is None: return None try: meta = _m.meta.get("energy_model", {}) row = _build_feature_row(building) + gfa = row["total_gfa_m2"] - # Energy model uses only floor_area_m2 + building_type_enc - btype = building.get("type", "residential").lower() + btype = building.get("type", "residential").lower() dominant_col = TYPE_TO_GFA.get(btype, "RESIDENTIAL") - type_enc_map = {"RESIDENTIAL": 0, "BUSINESS_AND_PERSONAL_SERVICES": 1, - "MERCANTILE": 2, "INDUSTRIAL": 3, "ASSEMBLY": 4, "INSTITUTIONAL": 5} - feature_row = { - "floor_area_m2": row["total_gfa_m2"], - "building_type_enc": float(type_enc_map.get(dominant_col, 0)), - } - features = meta.get("features", ["floor_area_m2", "building_type_enc"]) + ewrb_type = _GFA_TO_EWRB.get(dominant_col, "Other") + + if _m.energy_encoder is not None: + enc = _m.energy_encoder + classes = list(enc.classes_) + target = ewrb_type if ewrb_type in classes else "Office" + type_enc = float(enc.transform([target])[0]) + else: + classes = meta.get("building_type_classes", []) + target = ewrb_type if ewrb_type in classes else (classes[0] if classes else "Other") + type_enc = float(classes.index(target)) if target in classes else 0.0 + + # Model predicts log1p(kWh/m²); multiply intensity × GFA for annual total + feature_row = {"building_type_enc": type_enc} + features = meta.get("features", ["building_type_enc"]) X = _row_to_array(feature_row, features) - log_kwh = float(_m.energy.predict(X)[0]) - kwh = np.expm1(log_kwh) - gfa = row["total_gfa_m2"] - intensity = kwh / max(gfa, 1) - score = min(100, int(intensity / 3)) # ~300 kWh/m² → score 100 + elec_intensity = float(np.expm1(float(_m.energy.predict(X)[0]))) # kWh/m² + kwh = elec_intensity * gfa + + # Gas intensity prediction (same feature schema, no sanity gate needed) + gas_kwh_eq = 0.0 + if _m.energy_gas is not None: + try: + gas_meta = _m.meta.get("energy_gas_model", {}) + Xg = _row_to_array(feature_row, gas_meta.get("features", list(feature_row.keys()))) + gas_intensity = float(np.expm1(float(_m.energy_gas.predict(Xg)[0]))) # kWh/m² + gas_kwh_eq = gas_intensity * gfa + except Exception: + pass + + total_kwh = kwh + gas_kwh_eq + total_intensity = total_kwh / max(gfa, 1) + gas_gj = gas_kwh_eq / 277.78 if gas_kwh_eq > 0 else None + + # Score: ~800 kWh/m² total → 100 (high-energy industrial); typical office ~400 → 50 + environmental_impact_score = min(100, int(total_intensity / 8)) + + gas_note = ( + f" + {gas_gj:,.0f} GJ gas ({gas_kwh_eq / 1_000:.0f} MWh equiv.)" + if gas_gj else "" + ) return { - "score": score, + "score": environmental_impact_score, + "score_meaning": "0 = low energy use (good), 100 = high energy use (bad)", "annual_kwh": round(kwh), - "intensity_kwh_per_m2": round(intensity, 1), + "annual_gas_gj": round(gas_gj) if gas_gj else None, + "total_energy_kwh": round(total_kwh), + "intensity_kwh_per_m2": round(total_intensity, 1), "description": ( - f"Predicted annual electricity: {kwh/1000:.0f} MWh " - f"({intensity:.0f} kWh/m²) — trained on {meta.get('source', 'Toronto EWRB')} data. " - f"{'Above' if intensity > 200 else 'Within'} Toronto benchmark for this building type." + f"Predicted annual electricity: {kwh / 1_000:.0f} MWh{gas_note}. " + f"Total energy intensity: {total_intensity:.0f} kWh/m² " + f"({'above' if total_intensity > 300 else 'within'} typical Toronto benchmark). " + f"Environmental impact: {environmental_impact_score}/100 — higher means greater energy use." ), } except Exception as e: @@ -149,27 +221,9 @@ def predict_energy(building: dict) -> dict | None: def predict_traffic(building: dict) -> dict | None: - """Returns predicted daily vehicle trips and a 0-100 traffic impact score.""" - if _m.traffic is None: - return None - try: - meta = _m.meta.get("traffic_model", {}) - row = _build_feature_row(building) - X = _row_to_array(row, meta.get("features", list(row.keys()))) - trips = max(0.0, float(_m.traffic.predict(X)[0])) - score = min(100, int(trips / 20)) # 2000 trips → score 100 - return { - "score": score, - "daily_trips": round(trips), - "description": ( - f"Estimated +{trips:.0f} daily vehicle trips generated. " - f"Peak-hour impact on surrounding intersections: " - f"{'significant' if trips > 500 else 'moderate' if trips > 200 else 'low'}." - ), - } - except Exception as e: - print(f"[xgb] traffic predict error: {e}") - return None + """Returns ITE-estimated daily vehicle trips and a 0-100 traffic impact score.""" + from calculators.traffic import estimate_daily_trips + return estimate_daily_trips(building) def predict_economic(building: dict) -> dict | None: diff --git a/data/test.py b/data/test.py new file mode 100644 index 0000000..3681c31 --- /dev/null +++ b/data/test.py @@ -0,0 +1,41 @@ +"""Quick smoke-test for all three inference paths. Run from project root: + python data/test.py +""" +import sys +sys.path.insert(0, "backend") + +import xgb_models as xm +from calculators.traffic import estimate_daily_trips + +print("=== Fix 1: LabelEncoder encoding per building type ===") +buildings = [ + {"type": "commercial office", "footprint_m2": 2000, "floors": 10}, + {"type": "retail", "footprint_m2": 2000, "floors": 10}, + {"type": "residential (high-rise)", "footprint_m2": 2000, "floors": 10}, +] +for b in buildings: + btype = b["type"].lower() + col = xm.TYPE_TO_GFA.get(btype, "RESIDENTIAL") + ewrb = xm._GFA_TO_EWRB.get(col, "Other") + enc = int(xm._m.energy_encoder.transform([ewrb])[0]) + result = xm.predict_energy(b) + print(f" {b['type']:30s} ewrb={ewrb:40s} enc={enc:2d} kwh={result['annual_kwh']:>12,} score={result['score']}") + +print() +print("=== Fix 2: ITE calculator — transit discount + correct residential unit ===") +cases = [ + ("downtown residential", {"type": "residential (high-rise)", "floors": 30, "footprint_m2": 1200, "units_per_floor": 8, "lat": 43.6532, "lng": -79.3832}), + ("suburban residential", {"type": "residential (high-rise)", "floors": 30, "footprint_m2": 1200, "units_per_floor": 8, "lat": 43.780, "lng": -79.560}), + ("suburban retail", {"type": "retail", "floors": 1, "footprint_m2": 5000, "units_per_floor": 0, "lat": 43.780, "lng": -79.560}), +] +for label, b in cases: + r = estimate_daily_trips(b) + print(f" {label:25s} tier={r['transit_tier']:25s} base={r['daily_trips_base']:5d} final={r['daily_trips']:5d} score={r['score']}") + +print() +print("=== Integration: all three models together ===") +b = {"type": "residential (high-rise)", "floors": 30, "footprint_m2": 1200, + "units_per_floor": 8, "lat": 43.6532, "lng": -79.3832} +print(" Energy :", xm.predict_energy(b)) +print(" Traffic :", xm.predict_traffic(b)) +print(" Economic:", xm.predict_economic(b)) diff --git a/ml/data_pipeline.py b/ml/data_pipeline.py index 591d7ad..198726b 100644 --- a/ml/data_pipeline.py +++ b/ml/data_pipeline.py @@ -9,15 +9,20 @@ Set FORCE_REFRESH=1 to re-download everything. """ +import io import os import re import sys +import requests +import urllib3 from pathlib import Path import pandas as pd import geopandas as gpd from dotenv import load_dotenv +urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning) + load_dotenv() DATA_DIR = Path(os.getenv("DATA_DIR", Path(__file__).parent.parent / "data")) @@ -31,6 +36,9 @@ sys.path.insert(0, str(Path(__file__).parent)) from fetch import fetch, fetch_resource, fetch_gtfs_stops, fetch_csv_with_latlon, download_raster +ONTARIO_EWRB_ID = "0eab2faf-6186-4a5b-8de1-b15872943c24" +ONTARIO_CKAN_BASE = "https://data.ontario.ca/api/3/action/" + def _save(name: str, data: gpd.GeoDataFrame | pd.DataFrame, last_mod: str): out = DATA_DIR / f"{name}.parquet" @@ -295,6 +303,116 @@ def dl_ewrb(): print(f" WARNING: ewrb_energy failed ({e})") +def dl_ewrb_ontario(): + """ + Download Ontario provincial EWRB public-release data (2018-2024). + Covers private commercial, multi-residential, and industrial buildings ≥100k sqft. + Publicly-owned buildings (city/province) are exempt from provincial reporting + and are captured by dl_ewrb() from Toronto Open Data instead. + + Columns saved: + building_type PrimPropTypCalc (calculated, more reliable than self-reported) + elec_intensity_gj_m2 Weather-normalised electricity intensity (GJ/m²) + gas_intensity_gj_m2 Weather-normalised natural gas intensity (GJ/m²) + site_eui_gj_m2 Total site energy intensity (GJ/m²) + ghg_intensity GHG emissions intensity (kg CO2e/m²) + city, postal_code + reporting_year + """ + if cached("ewrb_ontario"): + return + try: + r = requests.get( + ONTARIO_CKAN_BASE + "package_show", + params={"id": ONTARIO_EWRB_ID}, + timeout=30, + verify=False, + ) + r.raise_for_status() + resources = r.json()["result"]["resources"] + + # English XLSX files only. + # French files contain "energie" in the download filename; skip those and the dict. + xlsx_res = [ + res for res in resources + if res.get("format", "").lower() == "xlsx" + and "energie" not in res.get("url", "").split("/")[-1].lower() + and "dictionary" not in res.get("name", "").lower() + and "donn" not in res.get("name", "").lower() + ] + + frames = [] + for res in sorted(xlsx_res, key=lambda x: x.get("name", "")): + name = res.get("name", "").strip() + year = int(name) if name.isdigit() and len(name) == 4 else None + print(f" [{year}] {name!r}...", end=" ", flush=True) + + resp = requests.get(res["url"], timeout=300, verify=False) + resp.raise_for_status() + + df = pd.read_excel(io.BytesIO(resp.content), sheet_name=0) + + # Coerce intensity columns to numeric (some contain "Not Available" strings) + for col in ["WN_Sit_Elc_Int1", "WN_Sit_Gas_Int1", "Site_EUI1", "GHG_Emiss_Int1"]: + if col in df.columns: + df[col] = pd.to_numeric(df[col], errors="coerce") + + # Keep only the columns that exist (schema varies slightly across years) + keep = {} + col_map = { + "PrimPropTypCalc": "building_type", + "WN_Sit_Elc_Int1": "elec_intensity_gj_m2", + "WN_Sit_Gas_Int1": "gas_intensity_gj_m2", + "Site_EUI1": "site_eui_gj_m2", + "GHG_Emiss_Int1": "ghg_intensity", + "City": "city", + "Postal_Code": "postal_code", + "Data_Qual_Check": "_qual", + } + for src, dst in col_map.items(): + if src in df.columns: + keep[dst] = df[src] + + if "building_type" not in keep or "elec_intensity_gj_m2" not in keep: + print(f"skipped (missing key columns)") + continue + + chunk = pd.DataFrame(keep) + + # Keep only rows that passed the data quality check + if "_qual" in chunk.columns: + chunk = chunk[chunk["_qual"].astype(str).str.lower() == "yes"].drop(columns=["_qual"]) + else: + chunk = chunk.drop(columns=["_qual"], errors="ignore") + + # Drop rows with no usable intensity data + chunk = chunk.dropna(subset=["elec_intensity_gj_m2"]) + chunk = chunk[chunk["elec_intensity_gj_m2"] > 0] + + # Remove per-column outliers (>99th pct likely data-entry errors) + for col in ["elec_intensity_gj_m2", "gas_intensity_gj_m2", "site_eui_gj_m2"]: + if col in chunk.columns: + p99 = chunk[col].quantile(0.99) + chunk = chunk[chunk[col].isna() | (chunk[col] <= p99)] + + if year: + chunk["reporting_year"] = year + + frames.append(chunk) + print(f"{len(chunk):,} rows") + + if not frames: + print(" WARNING: no Ontario EWRB data downloaded") + return + + combined = pd.concat(frames, ignore_index=True) + print(f" Combined: {len(combined):,} rows across {len(frames)} years") + _save("ewrb_ontario", combined, f"Ontario Open Data EWRB {len(frames)} years") + + except Exception as exc: + print(f" WARNING: ewrb_ontario failed ({exc})") + + def dl_building_permits(): for name, ckan_id in [ ("building_permits_cleared", "building-permits-cleared-permits"), @@ -394,6 +512,7 @@ def main(): print("\n-- Tabular training data ---------------------------") dl_ewrb() + dl_ewrb_ontario() dl_building_permits() dl_business_licences() dl_property_tax() diff --git a/ml/models/economic_model.json b/ml/models/economic_model.json index 03183eb..6cde2b1 100644 --- a/ml/models/economic_model.json +++ b/ml/models/economic_model.json @@ -1 +1 @@ -{"learner":{"attributes":{"scikit_learn":"{\"_estimator_type\": 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Theater", + "Multifamily Housing", + "Museum", + "Non-Refrigerated Warehouse", + "Not Available", + "Office", + "Other", + "Other - Education", + "Other - Entertainment/Public Assembly", + "Other - Lodging/Residential", + "Other - Mall", + "Other - Public Services", + "Other - Recreation", + "Other - Services", + "Other - Technology/Science", + "Parking", + "Performing Arts", + "Police Station", + "Pre-school/Daycare", + "Refrigerated Warehouse", + "Repair Services (Vehicle, Shoe, Locksmith, etc.)", + "Residence Hall/Dormitory", + "Residential Care Facility", + "Retail Store", + "Self-Storage Facility", + "Senior Care Community", + "Senior Living Community", + "Stadium (Open)", + "Strip Mall", + "Supermarket/Grocery Store", + "Transportation Terminal/Station", + "Urgent Care/Clinic/Other Outpatient", + "Vehicle Dealership", + "Vehicle Repair Services", + "Wastewater Treatment Plant", + "Wholesale Club/Supercenter", + "Worship Facility" + ], + "mae": 0.5134, + "r2": 0.4738 +} \ No 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\ No newline at end of file diff --git a/ml/models/energy_model_meta.json b/ml/models/energy_model_meta.json index 9155c48..7a4c6e4 100644 --- a/ml/models/energy_model_meta.json +++ b/ml/models/energy_model_meta.json @@ -1,33 +1,71 @@ { "features": [ - "floor_area_m2", "building_type_enc" ], - "target": "log1p_annual_electricity_kwh", - "source": "Toronto EWRB real measurements", + "target": "log1p_elec_intensity_kwh_per_m2", + "source": "Ontario EWRB (private) + Toronto EWRB (municipal)", "building_type_classes": [ - "Administrative offices and related facilities, including municipal council chambers", - "Ambulance stations and associated offices and facilities", - "Community centres", - "Cultural facilities", - "Facilities related to the pumping of sewage", - "Facilities related to the pumping of water", - "Facilities related to the treatment of sewage", - "Facilities related to the treatment of water", - "Fire stations and associated offices and facilities", - "Indoor recreational facilities", - "Indoor sports arenas", - "Indoor swimming pools", - "Long-term care", + "College/University", + "Community Center and Social Meeting Hall", + "Convenience Store without Gas Station", + "Convention Center", + "Courthouse", + "Distribution Center", + "Drinking Water Treatment & Distribution", + "Enclosed Mall", + "Financial Office", + "Fire Station", + "Fitness Center/Health Club/Gym", + "Food Sales", + "Hotel", + "Ice/Curling Rink", + "Indoor Arena", + "K-12 School", + "Laboratory", + "Library", + "Lifestyle Center", + "Mailing Center/Post Office", + "Manufacturing/Industrial Plant", + "Medical Office", + "Mixed Use Property", + "Movie Theater", + "Multifamily Housing", + "Museum", + "Non-Refrigerated Warehouse", + "Not Available", + "Office", "Other", - "Parking garages", - "Performing arts facilities", - "Police stations and associated offices and facilities", - "Public libraries", - "Social housing", - "Storage facilities where equipment or vehicles are maintained, repaired or stored", - "Street lighting" + "Other - Education", + "Other - Entertainment/Public Assembly", + "Other - Lodging/Residential", + "Other - Mall", + "Other - Public Services", + "Other - Recreation", + "Other - Services", + "Other - Technology/Science", + "Parking", + "Performing Arts", + "Police Station", + "Pre-school/Daycare", + "Refrigerated Warehouse", + "Repair Services (Vehicle, Shoe, Locksmith, etc.)", + "Residence Hall/Dormitory", + "Residential Care Facility", + "Retail Store", + "Self-Storage Facility", + "Senior Care Community", + "Senior Living Community", + "Stadium (Open)", + "Strip Mall", + "Supermarket/Grocery Store", + "Transportation Terminal/Station", + "Urgent Care/Clinic/Other Outpatient", + "Vehicle Dealership", + "Vehicle Repair Services", + "Wastewater Treatment Plant", + "Wholesale Club/Supercenter", + "Worship Facility" ], - "mae": 1.1496, - "r2": 0.4709 + "mae": 0.5638, + "r2": 0.5134 } \ No newline at end of file diff --git a/ml/train_models.py b/ml/train_models.py index 8f0f81e..dbbc6fe 100644 --- a/ml/train_models.py +++ b/ml/train_models.py @@ -4,14 +4,17 @@ python ml/train_models.py -Saves three models to ml/models/: - - energy_model.json predicts kWh/m² energy intensity (rule-based + XGB) - - traffic_model.json predicts daily vehicle trips (ITE-calibrated XGB) - - economic_model.json predicts construction jobs via StatsCan I-O multipliers +Saves to ml/models/: + - energy_model.json predicts annual kWh from EWRB measurements + - energy_building_type_encoder.pkl LabelEncoder for building_type (required at inference) + - economic_model.json predicts construction jobs via StatsCan I-O multipliers + +Traffic is handled by the ITE calculator in backend/calculators/traffic.py — no model file needed. """ import json import sys +import joblib import numpy as np import pandas as pd from pathlib import Path @@ -52,49 +55,140 @@ def _load_permits() -> pd.DataFrame: return df -# ── MODEL 1: Energy / Utility (kWh/m² intensity proxy) ─────────────────────── +# ── MODEL 1: Energy / Utility ──────────────────────────────────────────────── + +def _load_ewrb_combined() -> pd.DataFrame: + """ + Load Ontario EWRB (primary: private buildings) + Toronto EWRB (supplementary: + municipal buildings), normalise both to kWh/m² intensity, and return combined. + + Returned columns: building_type, elec_intensity_kwh_m2, gas_intensity_kwh_m2 + """ + frames = [] + + # 1. Ontario provincial data — real commercial / multi-residential buildings + ontario_path = DATA_DIR / "ewrb_ontario.parquet" + if ontario_path.exists(): + ont = pd.read_parquet(ontario_path) + ont["elec_intensity_kwh_m2"] = ( + pd.to_numeric(ont["elec_intensity_gj_m2"], errors="coerce") * 277.78 + ) + if "gas_intensity_gj_m2" in ont.columns: + ont["gas_intensity_kwh_m2"] = ( + pd.to_numeric(ont["gas_intensity_gj_m2"], errors="coerce") * 277.78 + ) + else: + ont["gas_intensity_kwh_m2"] = np.nan + frames.append( + ont[["building_type", "elec_intensity_kwh_m2", "gas_intensity_kwh_m2"]].copy() + ) + print(f" Ontario EWRB : {len(ont):,} rows") + + # 2. Toronto municipal data — supplementary (fire stations, water plants, etc.) + toronto_path = DATA_DIR / "ewrb_energy.parquet" + if toronto_path.exists(): + tor = pd.read_parquet(toronto_path) + tor["floor_area_m2"] = pd.to_numeric(tor["floor_area_sqft"], errors="coerce") / 10.764 + tor["annual_kwh"] = pd.to_numeric(tor["usage_electric_grid"], errors="coerce") + tor = tor[(tor["floor_area_m2"] > 0) & (tor["annual_kwh"] > 0)].copy() + tor["elec_intensity_kwh_m2"] = tor["annual_kwh"] / tor["floor_area_m2"] + if "usage_natural_gas" in tor.columns: + tor["gas_intensity_kwh_m2"] = ( + pd.to_numeric(tor["usage_natural_gas"], errors="coerce") + * 277.78 / tor["floor_area_m2"] + ) + else: + tor["gas_intensity_kwh_m2"] = np.nan + frames.append( + tor[["building_type", "elec_intensity_kwh_m2", "gas_intensity_kwh_m2"]].copy() + ) + print(f" Toronto EWRB : {len(tor):,} rows") + + if not frames: + return pd.DataFrame() + + combined = pd.concat(frames, ignore_index=True) + combined = combined.dropna(subset=["elec_intensity_kwh_m2"]) + combined = combined[combined["elec_intensity_kwh_m2"] > 0].copy() + + # Cap per-column outliers at 99th percentile (data-entry errors / corner cases) + for col in ["elec_intensity_kwh_m2", "gas_intensity_kwh_m2"]: + p99 = combined[col].quantile(0.99) + combined[col] = combined[col].where(combined[col] <= p99) + + return combined + def train_energy(df: pd.DataFrame): + """ + Train electricity-intensity and gas-intensity models. + Target is kWh/m²; at inference multiply by GFA to get annual absolute kWh. + Training data: Ontario EWRB (private buildings) + Toronto EWRB (municipal). + """ print("\n=== Model 1: Energy / Utility ===") - ewrb_path = DATA_DIR / "ewrb_energy.parquet" - if not ewrb_path.exists(): - print(" SKIP: ewrb_energy.parquet not found — run data_pipeline.py first") + rows = _load_ewrb_combined() + if rows.empty or len(rows) < 50: + print(f" SKIP: insufficient data ({len(rows)} rows)") return - rows = pd.read_parquet(ewrb_path) - rows["floor_area_m2"] = pd.to_numeric(rows["floor_area_m2"], errors="coerce") - rows["annual_electricity_kwh"] = pd.to_numeric(rows["annual_electricity_kwh"], errors="coerce") - rows = rows.dropna(subset=["floor_area_m2", "annual_electricity_kwh"]) - rows = rows[(rows["floor_area_m2"] > 0) & (rows["annual_electricity_kwh"] > 0)].copy() - - if len(rows) < 50: - print(f" SKIP: only {len(rows)} rows after filtering") - return + print(f" Combined : {len(rows):,} rows total") - le = LabelEncoder() - rows["building_type_enc"] = le.fit_transform( - rows["building_type"].fillna("Unknown").astype(str) + le_building = LabelEncoder() + rows["building_type_enc"] = le_building.fit_transform( + rows["building_type"].fillna("Other").astype(str) ) - rows["kwh_per_m2"] = rows["annual_electricity_kwh"] / rows["floor_area_m2"] - X = rows[["floor_area_m2", "building_type_enc"]].fillna(0) - y = np.log1p(rows["annual_electricity_kwh"]) + # ── Electricity intensity model ─────────────────────────────────────────── + elec_rows = rows.dropna(subset=["elec_intensity_kwh_m2"]) + X = elec_rows[["building_type_enc"]].fillna(0) + y = np.log1p(elec_rows["elec_intensity_kwh_m2"]) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) - model = xgb.XGBRegressor(n_estimators=200, max_depth=5, learning_rate=0.1, + model = xgb.XGBRegressor(n_estimators=300, max_depth=6, learning_rate=0.05, random_state=42, verbosity=0) model.fit(X_train, y_train) - preds = model.predict(X_test) - mae, r2 = mean_absolute_error(y_test, preds), r2_score(y_test, preds) - print(f" n={len(rows):,} MAE(log-kWh)={mae:.3f} R²={r2:.3f}") + mae, r2 = (mean_absolute_error(y_test, model.predict(X_test)), + r2_score(y_test, model.predict(X_test))) + print(f" Electricity : n={len(elec_rows):,} MAE={mae:.3f} log(kWh/m²) R²={r2:.3f}") + + enc_path = MODEL_DIR / "energy_building_type_encoder.pkl" + joblib.dump(le_building, str(enc_path)) + print(f" Saved {enc_path.name} ({len(le_building.classes_)} classes)") _save_model(model, "energy_model", { - "features": list(X.columns), - "target": "log1p_annual_electricity_kwh", - "source": "Toronto EWRB real measurements", - "building_type_classes": list(le.classes_), - "mae": round(mae, 4), "r2": round(r2, 4), + "features": ["building_type_enc"], + "target": "log1p_elec_intensity_kwh_per_m2", + "source": "Ontario EWRB (private) + Toronto EWRB (municipal)", + "building_type_classes": list(le_building.classes_), + "mae": round(float(mae), 4), "r2": round(float(r2), 4), + }) + + # ── Gas intensity model ─────────────────────────────────────────────────── + gas_rows = rows.dropna(subset=["gas_intensity_kwh_m2"]) + gas_rows = gas_rows[gas_rows["gas_intensity_kwh_m2"] > 0].copy() + + if len(gas_rows) < 50: + print(f" Gas SKIP: only {len(gas_rows)} rows with gas data") + return + + Xg = gas_rows[["building_type_enc"]].fillna(0) + yg = np.log1p(gas_rows["gas_intensity_kwh_m2"]) + + Xg_train, Xg_test, yg_train, yg_test = train_test_split(Xg, yg, test_size=0.2, random_state=42) + gas_model = xgb.XGBRegressor(n_estimators=300, max_depth=6, learning_rate=0.05, + random_state=42, verbosity=0) + gas_model.fit(Xg_train, yg_train) + gmae, gr2 = (mean_absolute_error(yg_test, gas_model.predict(Xg_test)), + r2_score(yg_test, gas_model.predict(Xg_test))) + print(f" Gas : n={len(gas_rows):,} MAE={gmae:.3f} log(kWh/m²) R²={gr2:.3f}") + + _save_model(gas_model, "energy_gas_model", { + "features": ["building_type_enc"], + "target": "log1p_gas_intensity_kwh_per_m2", + "source": "Ontario EWRB (private) + Toronto EWRB (municipal)", + "building_type_classes": list(le_building.classes_), + "mae": round(float(gmae), 4), "r2": round(float(gr2), 4), }) @@ -191,7 +285,7 @@ def train_economic(df: pd.DataFrame): "features": list(X.columns), "target": "log1p_est_jobs", "statscan_jobs_per_1M_CAD": jobs_per_1m, - "mae": round(mae, 4), "r2": round(r2, 4), + "mae": round(float(mae), 4), "r2": round(float(r2), 4), }) @@ -204,7 +298,8 @@ def train_economic(df: pd.DataFrame): sys.exit(1) print(f"Loaded {len(df):,} permits\n") train_energy(df) - train_traffic(df) + # Traffic uses the ITE calculator in backend/calculators/traffic.py — no model needed. train_economic(df) print("\n=== Done ===") print(f" {len(list(MODEL_DIR.glob('*.json')))} model files in {MODEL_DIR}") + print(f" {len(list(MODEL_DIR.glob('*.pkl')))} encoder files in {MODEL_DIR}")