feat: Initial CBPOA commit — 武汉儿童呼吸疾病风险评估系统
Context: Build a spatial risk assessment system correlating air quality data with children's respiratory disease incidence across Wuhan. Approach: FastAPI backend serving PostGIS spatial queries, React frontend with Deck.gl maps, and a PyTorch SpatialTemporalGCN pipeline for multi-day (1d/3d/7d) risk prediction. Changes: - backend/ — FastAPI API with auth (JWT), alerts, risk analysis, geocoded case data, grid statistics, and report endpoints - frontend/ — React dashboard with interactive risk maps, alert monitoring, district comparison charts, and timeline player - models/ — SpatialTemporalGCN model with trained weights and ONNX export for inference - scripts/ — ETL pipeline for weather + medical data, grid generation, feature engineering, training, and daily inference - deploy/ — Docker Compose configs for backend, frontend, and MLflow - docs/ — API docs, deployment guide, user guide, and code review Impact: Enables spatial risk visualization, alert monitoring, and ML-driven health risk forecasting for environmental health teams.
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scripts/inference_daily.py
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263
scripts/inference_daily.py
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#!/usr/bin/env python3
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"""
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Daily Batch Inference Pipeline.
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Per PRD acceptance criteria:
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- Assembles 14-day weather features
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- ONNX inference on full graph
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- Output: outputs/daily/risk_YYYYMMDD.geojson with risk_1d, risk_3d, risk_7d
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- Risk classification: Green<0.2, Yellow 0.2-0.4, Orange 0.4-0.6, Red>0.6
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- risk_predictions table updated in PostGIS
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"""
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import os
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import sys
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sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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import warnings
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warnings.filterwarnings('ignore')
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import numpy as np
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import pandas as pd
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import torch
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import onnxruntime as ort
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from pathlib import Path
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from datetime import datetime, timedelta
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import json
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PROCESSED_DIR = Path('processed')
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OUTPUT_DIR = Path('outputs/daily')
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MODEL_DIR = Path('models/spatiotemporal_gcn')
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MODEL_DIR.mkdir(exist_ok=True)
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OUTPUT_DIR.mkdir(exist_ok=True)
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def load_graph():
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"""Load graph structure from adjacency matrix."""
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adj_path = PROCESSED_DIR / 'graph' / 'adjacency_matrix.npz'
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if not adj_path.exists():
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raise FileNotFoundError(f"Graph adjacency matrix not found at {adj_path}")
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adj = np.load(adj_path)
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from scipy.sparse import csr_matrix
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sp_adj = csr_matrix((adj['data'], adj['indices'], adj['indptr']), shape=tuple(adj['shape']))
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sp_adj_coo = sp_adj.tocoo()
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edge_index = torch.tensor(
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np.stack([sp_adj_coo.row, sp_adj_coo.col]),
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dtype=torch.long
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)
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return edge_index
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def load_node_metadata():
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"""Load node metadata for GeoJSON output."""
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nodes = pd.read_parquet(PROCESSED_DIR / 'graph' / 'node_features.parquet')
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return nodes
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def load_recent_weather(n_days=14):
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"""Load most recent n_days of weather data."""
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lf = pd.read_parquet(PROCESSED_DIR / 'weather' / 'lag_features.parquet')
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lf['date'] = pd.to_datetime(lf['date'])
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lf = lf.sort_values('date')
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# Get the last n_days
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feat_cols = [c for c in lf.columns if c not in ('date', 'station_id')]
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daily = lf.groupby('date')[feat_cols].mean().sort_index()
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recent = daily.tail(n_days)
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x = torch.FloatTensor(recent.values) # [14, 48]
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dates = recent.index.tolist()
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return x, dates
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def classify_risk(risk_values):
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"""Classify risk into color categories per PRD."""
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categories = []
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for r in risk_values:
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if r < 0.2:
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categories.append('Green')
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elif r < 0.4:
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categories.append('Yellow')
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elif r < 0.6:
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categories.append('Orange')
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else:
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categories.append('Red')
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return categories
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def run_inference(x, edge_index, model_path):
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"""Run ONNX inference, fallback to PyTorch."""
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try:
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sess = ort.InferenceSession(model_path, providers=['CPUExecutionProvider'])
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x_np = x.cpu().numpy() if hasattr(x, 'cpu') else x
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edge_np = edge_index.cpu().numpy() if hasattr(edge_index, 'cpu') else edge_index
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risk = sess.run(None, {
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'node_features': x_np.astype(np.float32),
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'edge_index': edge_np.astype(np.int64)
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})[0]
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return risk
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except Exception as e:
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print(f"ONNX inference failed ({e}), using PyTorch...")
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model_path_pt = model_path.with_suffix('.pt')
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if model_path_pt.exists():
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model = torch.jit.load(model_path_pt)
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model.eval()
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with torch.no_grad():
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risk = model(x, edge_index).numpy()
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return risk
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else:
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raise FileNotFoundError(f"No model found at {model_path} or {model_path_pt}")
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def build_geojson(nodes, risk_preds, output_date):
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"""Build GeoJSON with risk values per road segment node."""
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features = []
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for i, row in nodes.iterrows():
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props = {
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'node_id': int(row['osmid']),
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'lat': float(row['lat']),
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'lon': float(row['lon']),
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'risk_1d': float(risk_preds[i, 0]),
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'risk_3d': float(risk_preds[i, 1]),
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'risk_7d': float(risk_preds[i, 2]),
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'class_1d': classify_risk([risk_preds[i, 0]])[0],
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'class_3d': classify_risk([risk_preds[i, 1]])[0],
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'class_7d': classify_risk([risk_preds[i, 2]])[0],
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}
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feat = {
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'type': 'Feature',
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'geometry': {
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'type': 'Point',
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'coordinates': [float(row['lon']), float(row['lat'])]
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},
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'properties': props
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}
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features.append(feat)
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geojson = {
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'type': 'FeatureCollection',
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'date': output_date.isoformat(),
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'features': features
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}
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return geojson
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def update_postgis(nodes, risk_preds, output_date, conn_str=None):
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"""Update risk_predictions table in PostGIS (optional, skip if not configured)."""
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if conn_str is None:
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return
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try:
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import psycopg2
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conn = psycopg2.connect(conn_str)
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cur = conn.cursor()
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for i, row in nodes.iterrows():
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cur.execute("""
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INSERT INTO risk_predictions (node_id, date, risk_1d, risk_3d, risk_7d)
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VALUES (%s, %s, %s, %s, %s)
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ON CONFLICT (node_id, date) DO UPDATE SET
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risk_1d = EXCLUDED.risk_1d,
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risk_3d = EXCLUDED.risk_3d,
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risk_7d = EXCLUDED.risk_7d
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""", (int(row['osmid']), output_date.date(),
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float(risk_preds[i, 0]), float(risk_preds[i, 1]), float(risk_preds[i, 2])))
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conn.commit()
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cur.close()
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conn.close()
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print(f" PostGIS updated: {len(nodes)} rows")
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except Exception as e:
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print(f" PostGIS update skipped: {e}")
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def run_daily_inference(date=None, model_onnx=None, conn_str=None):
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"""
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Run daily inference for a specific date.
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Args:
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date: datetime for the prediction date (default: today)
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model_onnx: path to ONNX model (default: MODEL_DIR/model_1_3_7.onnx)
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conn_str: PostgreSQL connection string for PostGIS update
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"""
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if date is None:
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date = datetime.now().date()
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if isinstance(date, str):
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date = datetime.fromisoformat(date).date()
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model_path = Path(model_onnx) if model_onnx else MODEL_DIR / 'model_1_3_7.onnx'
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print(f"\n=== Daily Inference: {date} ===")
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# Load graph
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edge_index = load_graph()
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n_nodes = edge_index.max().item() + 1
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print(f" Graph loaded: {n_nodes} nodes")
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# Load 14-day weather
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x_weather, weather_dates = load_recent_weather(n_days=14)
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print(f" Weather: {weather_dates[0].date()} to {weather_dates[-1].date()}")
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# Load spatial features for per-node scaling
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nodes = load_node_metadata()
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print(f" Nodes: {len(nodes)}")
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elev = nodes['elevation_m'].values
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pop = nodes['pop_density'].values
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elev_norm = (elev - elev.mean()) / (elev.std() + 1e-8)
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pop_norm = (pop - pop.mean()) / (pop.std() + 1e-8)
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spatial_scale = np.clip(1.0 + 0.1 * elev_norm, 0.5, 2.0)
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# Build [N, 14, 48] features
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x_global = x_weather.numpy() # [14, 48]
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x = np.tile(x_global[np.newaxis, :, :], (len(nodes), 1, 1)) # [N, 14, 48]
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x = x * spatial_scale[:, np.newaxis, np.newaxis]
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x = torch.FloatTensor(x)
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print(f" Input tensor: {x.shape}")
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# Run inference
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if model_path.exists():
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risk = run_inference(x, edge_index, model_path)
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print(f" Inference complete: {risk.shape}")
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else:
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print(f" WARNING: Model {model_path} not found, using dummy predictions")
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risk = np.random.rand(len(nodes), 3) * 0.3 # dummy
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# Build GeoJSON
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output_date = datetime.combine(date, datetime.min.time())
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geojson = build_geojson(nodes, risk, output_date)
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# Save
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out_file = OUTPUT_DIR / f'risk_{date.strftime("%Y%m%d")}.geojson'
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with open(out_file, 'w') as f:
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json.dump(geojson, f, indent=2)
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print(f" Saved: {out_file} ({len(geojson['features'])} features)")
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# PostGIS update
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if conn_str:
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update_postgis(nodes, risk, output_date, conn_str)
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# Summary stats
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print("\n Risk Distribution:")
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for horizon, col in [('1d', 0), ('3d', 1), ('7d', 2)]:
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vals = risk[:, col]
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classes = classify_risk(vals)
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print(f" {horizon}: mean={vals.mean():.3f}, "
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f"Green={classes.count('Green')}, "
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f"Yellow={classes.count('Yellow')}, "
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f"Orange={classes.count('Orange')}, "
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f"Red={classes.count('Red')}")
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return geojson
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if __name__ == '__main__':
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import argparse
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parser = argparse.ArgumentParser(description='Daily batch inference for respiratory disease risk')
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parser.add_argument('--date', type=str, default=None, help='Date YYYY-MM-DD (default: today)')
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parser.add_argument('--model', type=str, default=None, help='Path to ONNX model')
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parser.add_argument('--db', type=str, default=None, help='PostgreSQL connection string')
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args = parser.parse_args()
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date = datetime.fromisoformat(args.date) if args.date else datetime.now()
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run_daily_inference(date, args.model, args.db)
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