train.py

"""
Bioweather Model Training v2.0
EmpedocLabs © 2025

Trains:
  1. Risk regressor (0-100 score)
  2. Advice classifier (15 weather conditions)

Both use HistGradientBoosting (sklearn) — no XGBoost dependency needed.
"""

import os
import pickle
import json
import numpy as np
import pandas as pd
from datetime import datetime

from sklearn.ensemble import HistGradientBoostingRegressor, HistGradientBoostingClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import (
    mean_absolute_error, mean_squared_error, r2_score,
    classification_report, accuracy_score, f1_score,
)

from generate_data import generate_production_data

FEATURE_COLS = [
    "temp_c", "pressure_hpa", "humidity", "wind_kph",
    "uv_index", "pressure_drop", "temp_change",
]

CONDITION_NAMES = {
    0: "Clear Skies", 1: "Rapid Pressure Drop", 2: "Pressure Squeeze",
    3: "Sauna Effect", 4: "High Wind", 5: "High UV Glare",
    6: "Bitter Cold", 7: "Drastic Temp Drop", 8: "Heat Shock",
    9: "Heavy Dampness", 10: "Mild Pressure Dip", 11: "Mild Pressure Rise",
    12: "Breezy Pollen", 13: "Dry Air", 14: "Stagnant & Gloomy",
}


def main():
    print("=" * 60)
    print("  BIOWEATHER v2.0 — Production Training")
    print("  EmpedocLabs")
    print("=" * 60)

    # ── 1. Generate data ─────────────────────────────────────────
    print("\n📊 Generating training data...")
    df = generate_production_data(n=25000, seed=42)

    X = df[FEATURE_COLS].values
    y_risk = df["risk_score"].values
    y_advice = df["advice_label"].values

    # ── 2. Split ─────────────────────────────────────────────────
    X_train, X_test, yr_train, yr_test, ya_train, ya_test = train_test_split(
        X, y_risk, y_advice, test_size=0.15, random_state=42,
    )
    print(f"\n📂 Split: Train={len(X_train):,}  Test={len(X_test):,}")

    # ── 3. Train risk regressor ──────────────────────────────────
    print("\n🚀 Training risk regressor...")
    risk_model = HistGradientBoostingRegressor(
        max_iter=400,
        max_depth=6,
        learning_rate=0.05,
        min_samples_leaf=15,
        l2_regularization=0.5,
        early_stopping=True,
        validation_fraction=0.1,
        n_iter_no_change=30,
        random_state=42,
    )
    risk_model.fit(X_train, yr_train)
    print(f"   Iterations: {risk_model.n_iter_}")

    yr_pred = risk_model.predict(X_test)
    yr_pred = np.clip(yr_pred, 0, 100)
    mae = mean_absolute_error(yr_test, yr_pred)
    rmse = np.sqrt(mean_squared_error(yr_test, yr_pred))
    r2 = r2_score(yr_test, yr_pred)
    print(f"   MAE:  {mae:.2f}")
    print(f"   RMSE: {rmse:.2f}")
    print(f"   R²:   {r2:.4f}")

    # ── 4. Train advice classifier ───────────────────────────────
    print("\n🚀 Training advice classifier (15 conditions)...")
    advice_model = HistGradientBoostingClassifier(
        max_iter=400,
        max_depth=6,
        learning_rate=0.05,
        min_samples_leaf=10,
        l2_regularization=0.3,
        early_stopping=True,
        validation_fraction=0.1,
        n_iter_no_change=30,
        random_state=42,
    )
    advice_model.fit(X_train, ya_train)
    print(f"   Iterations: {advice_model.n_iter_}")

    ya_pred = advice_model.predict(X_test)
    acc = accuracy_score(ya_test, ya_pred)
    f1_macro = f1_score(ya_test, ya_pred, average="macro", zero_division=0)
    print(f"   Accuracy: {acc:.4f}")
    print(f"   F1 macro: {f1_macro:.4f}")

    print("\n   Per-condition report:")
    target_names = [CONDITION_NAMES.get(i, f"Cond_{i}") for i in sorted(set(ya_test) | set(ya_pred))]
    print(classification_report(ya_test, ya_pred, target_names=target_names, zero_division=0))

    # ── 5. Save models ───────────────────────────────────────────
    os.makedirs("model", exist_ok=True)

    with open("model/risk_model.pkl", "wb") as f:
        pickle.dump(risk_model, f)
    with open("model/advice_model.pkl", "wb") as f:
        pickle.dump(advice_model, f)

    metadata = {
        "version": "2.0.0",
        "trained_at": datetime.now().isoformat(),
        "training_samples": len(X_train),
        "features": FEATURE_COLS,
        "num_conditions": 15,
        "risk_metrics": {"mae": round(mae, 2), "rmse": round(rmse, 2), "r2": round(r2, 4)},
        "advice_metrics": {"accuracy": round(acc, 4), "f1_macro": round(f1_macro, 4)},
    }
    with open("model/metadata.json", "w") as f:
        json.dump(metadata, f, indent=2)

    print(f"\n💾 model/risk_model.pkl ({os.path.getsize('model/risk_model.pkl') // 1024} KB)")
    print(f"💾 model/advice_model.pkl ({os.path.getsize('model/advice_model.pkl') // 1024} KB)")
    print(f"📋 model/metadata.json")

    print(f"\n{'=' * 60}")
    print(f"  ✅ BIOWEATHER v2.0 READY")
    print(f"  Risk:   MAE={mae:.2f}, R²={r2:.4f}")
    print(f"  Advice: Acc={acc:.4f}, F1={f1_macro:.4f}")
    print(f"{'=' * 60}")


if __name__ == "__main__":
    main()