ramseshk
11182b47f8
Calibration overhaul: - Logistic regression mode for synthetic/bootstrap data (prevents LightGBM overfit) - 3-layer calibration stack: raw LR → Platt scaling → isotonic regression - Extreme probability smoothing: blend toward 0.5 when raw>0.95 or raw<0.05 - Platt preferred over isotonic (isotonic produces step functions with few points) - Continuous precipitation probability in bootstrap (beta distribution, not just 0/100) - Realistic NWP forecast errors: temp σ=2.0°C, rain calibration bias, diurnal-aware noise - Outlier injection: 10% of days have 2-3x larger errors (typhoon/low-pressure days) - LR model + StandardScaler saved as _lr.pkl alongside .lgb marker Results: - temp_gt_30c: AUC=0.987, Brier=0.049, predictions vary 20-85% per day - rain_gt_0mm: AUC=0.979, Brier=0.042, predictions vary 15-85% per day - temp_gt_35c: AUC=0.713 (realistic — extreme heat is hard to predict)
326 lines
15 KiB
Python
326 lines
15 KiB
Python
#!/usr/bin/env python3
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"""
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Train LightGBM models for HK weather prediction targets.
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The training data simulates the relationship between NWP model forecasts
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and actual observations. NWP models have systematic errors:
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- Temperature: RMSE ~1.5°C at 24h lead
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- Precipitation probability: poor calibration, often overconfident
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- Wind: RMSE 3-5 km/h at 24h lead
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The model learns to MAP noisy forecast features → binary outcome truth.
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"""
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import argparse
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import sys
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import json
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from datetime import datetime, timedelta
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from pathlib import Path
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from typing import Optional, Dict, Tuple
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import numpy as np
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import pandas as pd
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sys.path.insert(0, str(Path(__file__).parent.parent))
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from ml.features import FeatureEngine
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from ml.model import WeatherModel, TARGET_DEFINITIONS, MODEL_DIR
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from config import HK_COORDS
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def _add_nwp_forecast_error(
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daily_truth: pd.DataFrame,
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hourly_truth: pd.DataFrame,
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rng: np.random.RandomState,
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) -> Tuple[pd.DataFrame, pd.DataFrame]:
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"""
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Add realistic NWP forecast errors to truth data.
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Returns (daily_forecast, hourly_forecast) simulating:
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- Temperature: RMSE 1.5-2.5°C, warm bias in summer anticyclones
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- Precipitation: continuous calibrated probabilities (not just 0/100),
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systematic overforecasting of light rain, underforecasting of heavy
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- Wind: multiplicative errors 0.7-1.5x
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- Cloud cover: RMSE 15-20%
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"""
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n_days = len(daily_truth)
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# === TEMPERATURE: larger noise for wider training distribution ===
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temp_error_max = rng.normal(0.3, 2.0, n_days) # μ=0.3 bias, σ=2.0 RMSE
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temp_error_min = rng.normal(0.2, 1.8, n_days)
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# Random injection of larger errors (10% of days have outlier errors)
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outlier_mask = rng.random(n_days) < 0.10
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temp_error_max[outlier_mask] += rng.normal(0, 3.0, outlier_mask.sum())
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temp_error_min[outlier_mask] += rng.normal(0, 2.5, outlier_mask.sum())
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daily_fc = daily_truth.copy()
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if "temperature_2m_max" in daily_fc.columns:
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daily_fc["temperature_2m_max"] = np.clip(daily_truth["temperature_2m_max"] + temp_error_max, 5, 42)
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if "temperature_2m_min" in daily_fc.columns:
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daily_fc["temperature_2m_min"] = np.clip(daily_truth["temperature_2m_min"] + temp_error_min, 0, 33)
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# === PRECIPITATION: continuous calibrated probabilities + multiplicative rain error ===
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# NWP models output continuous probabilities, not 0/100
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# Base prob from truth, then add calibration noise
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true_prob = daily_truth["precipitation_probability_max"].values / 100.0
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# Systematic miscalibration: NWP overestimates low prob, underestimates high prob
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calibration_bias = 0.15 * (0.5 - true_prob) # +7.5% at prob=0, -7.5% at prob=1
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calibration_noise = rng.normal(0, 0.15, n_days)
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fc_prob = np.clip(true_prob + calibration_bias + calibration_noise, 0.01, 0.99)
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if "precipitation_probability_max" in daily_fc.columns:
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daily_fc["precipitation_probability_max"] = fc_prob * 100.0
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# Rain amount: multiplicative error, more noise on heavy rain
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rain_mult_error = np.where(
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daily_truth["precipitation_sum"] > 5,
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rng.lognormal(0, 0.4, n_days), # High variance for heavy rain
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rng.lognormal(0, 0.25, n_days), # Lower variance for light rain
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)
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if "precipitation_sum" in daily_fc.columns:
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daily_fc["precipitation_sum"] = daily_truth["precipitation_sum"] * rain_mult_error
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# === WIND: multiplicative with 10% outlier days ===
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wind_mult = rng.lognormal(0, 0.20, n_days)
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gust_mult = rng.lognormal(0, 0.30, n_days)
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outlier_wind = rng.random(n_days) < 0.10
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wind_mult[outlier_wind] *= rng.uniform(1.3, 2.0, outlier_wind.sum())
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gust_mult[outlier_wind] *= rng.uniform(1.3, 2.5, outlier_wind.sum())
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for col, mult in [("wind_speed_10m_max", wind_mult), ("wind_gusts_10m_max", gust_mult)]:
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if col in daily_fc.columns:
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daily_fc[col] = np.clip(daily_truth[col] * mult, 0, 200)
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# === CLOUD COVER: systematic bias (underestimate in convective conditions) ===
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if "weather_code" in daily_fc.columns:
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daily_fc["weather_code"] = daily_truth["weather_code"]
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# === HOURLY: larger noise ranges ===
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hourly_fc = hourly_truth.copy()
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n_hours = len(hourly_fc)
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# Temperature: diurnal-cycle-aware errors (larger at night)
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hour_of_day = np.array([i % 24 for i in range(n_hours)])
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t_noise_scale = 1.2 + 0.8 * np.sin(2 * np.pi * (hour_of_day - 14) / 24) # Peak error at night
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if "temperature_2m" in hourly_fc.columns:
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hourly_fc["temperature_2m"] = np.clip(
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hourly_truth["temperature_2m"] + rng.normal(0, 2.0, n_hours) * t_noise_scale,
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-5, 45,
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)
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# Humidity: large errors (NWP struggles with boundary layer moisture)
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if "relative_humidity_2m" in hourly_fc.columns:
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rh_err = rng.normal(-3, 12, n_hours)
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# More error during convective hours
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convective_mask = (hour_of_day > 11) & (hour_of_day < 19)
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rh_err[convective_mask] *= 1.5
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hourly_fc["relative_humidity_2m"] = np.clip(hourly_truth["relative_humidity_2m"] + rh_err, 15, 100)
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# Cloud cover: large RMSE
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if "cloud_cover" in hourly_fc.columns:
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hourly_fc["cloud_cover"] = np.clip(hourly_truth["cloud_cover"] + rng.normal(0, 20, n_hours), 0, 100)
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for level in ["cloud_cover_low", "cloud_cover_mid", "cloud_cover_high"]:
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if level in hourly_fc.columns:
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hourly_fc[level] = np.clip(hourly_truth[level] + rng.normal(0, 15, n_hours), 0, 100)
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# Pressure: typical errors
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if "surface_pressure" in hourly_fc.columns:
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hourly_fc["surface_pressure"] = hourly_truth["surface_pressure"] + rng.normal(0, 3.0, n_hours)
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# Wind: multiplicative
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for col in ["wind_speed_10m", "wind_speed_100m", "wind_gusts_10m"]:
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if col in hourly_fc.columns:
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mult = rng.lognormal(0, 0.25, n_hours)
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hourly_fc[col] = hourly_truth[col] * mult
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return daily_fc, hourly_fc
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def bootstrap_training_data(n_samples: int = 8000) -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame, pd.DataFrame]:
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"""
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Generate NWP forecast + observation training pairs.
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Returns (daily_forecast, hourly_forecast, daily_truth, hourly_truth)
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where forecast has realistic NWP errors and truth is the actual observation.
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"""
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rng = np.random.RandomState(42)
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rng_noise = np.random.RandomState(99)
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start_date = datetime(2015, 1, 1)
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dates = [start_date + timedelta(days=i) for i in range(n_samples)]
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doy = np.array([d.timetuple().tm_yday for d in dates])
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# === Generate OBSERVATION TRUTH (clean, no NWP error) ===
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tmax_base = 26.0 + 7.0 * np.sin(2 * np.pi * (doy - 200) / 365.25)
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tmax = np.clip(tmax_base + rng_noise.normal(0, 2.0, n_samples), 8, 38)
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tmin = np.clip(tmax - (7.0 + rng_noise.exponential(2.0, n_samples)), 4, 30)
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tmean = (tmax + tmin) / 2
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rain_seasonal = 4.0 + 10.0 * np.maximum(0, np.sin(2 * np.pi * (doy - 172) / 365.25))
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rain_day_mask_prob = 0.3 + 0.4 * np.maximum(0, np.sin(2 * np.pi * (doy - 172) / 365.25))
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rain_day = rng_noise.random(n_samples) < rain_day_mask_prob
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rain_sum = np.where(rain_day, rng_noise.exponential(rain_seasonal, n_samples), 0)
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rain_sum = np.where(rain_sum < 0.1, 0, rain_sum)
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# Continuous precipitation probability: beta distribution centered on actual prob
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from scipy.stats import beta as beta_dist
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precip_prob = np.zeros(n_samples)
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for i in range(n_samples):
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# Center the beta around the climatological rain prob
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p = rain_day_mask_prob[i]
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a = max(0.5, p * 8)
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b = max(0.5, (1 - p) * 8)
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precip_prob[i] = rng_noise.beta(a, b) * 100.0
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precip_prob = np.clip(precip_prob, 0.5, 99.5)
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wind_base = 15 + 8 * np.maximum(0, np.sin(2 * np.pi * (doy - 180) / 365.25))
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wind_max = np.clip(wind_base + rng_noise.exponential(5, n_samples), 3, 120)
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gusts_max = np.clip(wind_max * (1.0 + rng_noise.exponential(0.5, n_samples)), wind_max, 200)
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wind_dir = rng_noise.uniform(0, 360, n_samples)
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cloud = np.clip(30 + rng_noise.beta(2, 3, n_samples) * 70 * (0.5 + 0.5 * (rain_sum > 0)), 0, 100)
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pressure = np.clip(1013 - 5 * np.sin(2 * np.pi * (doy - 172) / 365.25) + rng_noise.normal(0, 3, n_samples), 980, 1035)
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sw_rad = np.clip(5.0 + 10.0 * np.sin(2 * np.pi * (doy - 172) / 365.25) * (1 - cloud / 100) + rng_noise.normal(0, 2, n_samples), 0, 30)
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daily_truth = pd.DataFrame({
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"temperature_2m_max": tmax,
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"temperature_2m_min": tmin,
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"temperature_2m_mean": tmean,
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"precipitation_sum": rain_sum,
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"precipitation_probability_max": precip_prob,
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"rain_sum": rain_sum,
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"wind_speed_10m_max": wind_max,
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"wind_gusts_10m_max": gusts_max,
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"wind_direction_10m_dominant": wind_dir,
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"shortwave_radiation_sum": sw_rad,
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"et0_fao_evapotranspiration": sw_rad * 0.4,
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"weather_code": np.where(rain_sum > 0, np.where(rain_sum > 10, 63, 61), 0),
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}, index=pd.to_datetime(dates))
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# Hourly truth
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hours_per_day = 24
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total_hours = n_samples * hours_per_day
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hour_of_day = np.tile(np.arange(24), n_samples)
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t_range = np.repeat(tmax - tmin, hours_per_day)
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t_phase = 2 * np.pi * (hour_of_day - 14) / 24
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daily_rh = np.clip(78 + 12 * np.sin(2 * np.pi * (doy - 172) / 365.25) + rng_noise.normal(0, 6, n_samples), 45, 98)
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dewpoint = tmean - ((100 - daily_rh) / 5.0) + rng_noise.normal(0, 0.5, n_samples)
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t_hourly = np.repeat(tmin, hours_per_day) + t_range * (0.5 + 0.5 * np.cos(t_phase)) + rng_noise.normal(0, 0.5, total_hours)
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rh_hourly = np.clip(np.repeat(daily_rh, hours_per_day) - 5 * np.cos(t_phase) + rng_noise.normal(0, 3, total_hours), 20, 100)
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hour_timestamps = [start_date + timedelta(hours=i) for i in range(total_hours)]
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hourly_truth = pd.DataFrame({
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"temperature_2m": t_hourly,
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"relative_humidity_2m": rh_hourly,
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"dew_point_2m": np.repeat(dewpoint, hours_per_day) + rng_noise.normal(0, 0.5, total_hours),
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"apparent_temperature": t_hourly + rng_noise.exponential(2.0, total_hours),
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"precipitation_probability": np.clip(np.repeat(precip_prob, hours_per_day) / 24 + rng_noise.normal(0, 1, total_hours), 0, 100),
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"precipitation": np.repeat(rain_sum, hours_per_day) / 24 * rng_noise.uniform(0.5, 1.5, total_hours),
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"rain": np.repeat(rain_sum, hours_per_day) / 24,
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"cloud_cover": np.clip(np.repeat(cloud, hours_per_day) + rng_noise.normal(0, 5, total_hours), 0, 100),
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"cloud_cover_low": np.clip(np.repeat(cloud * 0.6, hours_per_day), 0, 100),
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"cloud_cover_mid": np.clip(np.repeat(cloud * 0.3, hours_per_day), 0, 100),
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"cloud_cover_high": np.clip(np.repeat(cloud * 0.2, hours_per_day), 0, 100),
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"wind_speed_10m": np.repeat(wind_max, hours_per_day) * 0.5 * (0.5 + 0.5 * np.cos(t_phase)),
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"wind_speed_100m": np.repeat(wind_max, hours_per_day) * 1.3 * 0.6,
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"wind_gusts_10m": np.repeat(gusts_max, hours_per_day) * (0.3 + 0.7 * rng_noise.beta(2, 5, total_hours)),
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"wind_direction_10m": np.repeat(wind_dir, hours_per_day) + rng_noise.normal(0, 10, total_hours),
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"surface_pressure": np.repeat(pressure, hours_per_day) + rng_noise.normal(0, 0.5, total_hours),
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"visibility": np.clip(15000 - np.repeat(rain_sum, hours_per_day) * 500 + rng_noise.normal(0, 2000, total_hours), 500, 25000),
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}, index=pd.to_datetime(hour_timestamps))
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# === Add NWP forecast errors ===
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daily_fc, hourly_fc = _add_nwp_forecast_error(daily_truth.copy(), hourly_truth.copy(), rng)
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return daily_fc, hourly_fc, daily_truth, hourly_truth
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def train_targets(target_names=None, n_bootstrap=8000, test_split=0.2):
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"""Train models with proper NWP forecast → observation mapping."""
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if target_names is None:
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target_names = list(TARGET_DEFINITIONS.keys())
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print(f"Training {len(target_names)} models with realistic NWP errors")
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print(f" Samples: {n_bootstrap} (test: {test_split:.0%})")
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print()
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daily_fc, hourly_fc, daily_truth, hourly_truth = bootstrap_training_data(n_bootstrap)
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engine = FeatureEngine()
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# Features from FORECAST (noisy NWP output)
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X = engine.transform(daily_fc, hourly_fc)
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print(f"Features: {X.shape[1]} from NWP forecast output")
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split_idx = int(len(daily_fc) * (1 - test_split))
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X_train, X_test = X[:split_idx], X[split_idx:]
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daily_truth_train, daily_truth_test = daily_truth.iloc[:split_idx], daily_truth.iloc[split_idx:]
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results = {}
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for target_name in target_names:
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tdef = TARGET_DEFINITIONS[target_name]
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print(f"\n{'='*60}")
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print(f" {target_name} — {tdef['description']}")
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print(f"{'='*60}")
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# Targets from TRUTH (actual observation)
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y_train = WeatherModel.build_target(daily_truth_train, tdef["variable"], tdef["threshold"], tdef["op"])
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y_test = WeatherModel.build_target(daily_truth_test, tdef["variable"], tdef["threshold"], tdef["op"])
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p_yes = y_train.mean() * 100
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print(f" Class balance: {p_yes:.1f}% YES / {100-p_yes:.1f}% NO")
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model = WeatherModel(target_name, mode="lr")
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model.train(X_train, y_train, X_test, y_test)
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metrics = model.evaluate(X_test, y_test)
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model.save()
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results[target_name] = metrics
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print(f" Pre-calibration Brier: — ")
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print(f" Post-calibration:")
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print(f" Brier: {metrics['brier_score']:.4f} AUC: {metrics['roc_auc']:.3f}")
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print(f" Predicted mean: {metrics['p_yes_predicted']:.1f}% Actual: {metrics['p_yes_actual']:.1f}%")
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print(f" Calibration: {metrics['calibration_method']}")
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print(f" Top 8 features:")
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for feat, imp in list(model.top_features(8).items()):
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print(f" {feat:30s} {imp:>10.1f}")
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print(f"\n{'='*60}")
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print("TRAINING SUMMARY")
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print(f"{'='*60}")
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print(f"{'Target':<25s} {'Brier':>8s} {'AUC':>8s} {'Cal Err%':>9s} {'Cal':>10s}")
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print("-" * 65)
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for name, m in results.items():
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cal_err = abs(m["p_yes_predicted"] - m["p_yes_actual"])
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print(f"{name:<25s} {m['brier_score']:>8.4f} {m['roc_auc']:>8.3f} {cal_err:>9.1f} {m['calibration_method']:>10s}")
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print(f"\nModels saved to: {MODEL_DIR}")
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return results
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def main():
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parser = argparse.ArgumentParser(description="Train HK weather prediction models")
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parser.add_argument("--target", type=str, default=None)
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parser.add_argument("--samples", type=int, default=8000)
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parser.add_argument("--all", action="store_true", default=False)
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args = parser.parse_args()
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targets = [args.target] if args.target else list(TARGET_DEFINITIONS.keys())
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results = train_targets(targets, n_bootstrap=args.samples)
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MODEL_DIR.mkdir(parents=True, exist_ok=True)
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with open(MODEL_DIR / "training_summary.json", "w") as f:
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json.dump({
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"training_date": datetime.now().isoformat(),
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"n_samples": args.samples,
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"results": results,
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}, f, indent=2, default=str)
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if __name__ == "__main__":
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main()
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