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import pandas as pd
import numpy as np
import joblib
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
from sklearn.metrics import mean_squared_error, accuracy_score, classification_report
from sklearn.cluster import KMeans
from sklearn.preprocessing import LabelEncoder
from sklearn.decomposition import PCA
# Paths
DATA_PATH = "data/processed/california_wildfire.csv"
MODEL_DIR = "models"
def train_all_tasks():
print("🚀 Loading Processed Data...")
df = pd.read_csv(DATA_PATH)
# Ensure we have data
if df.empty:
print("❌ Error: Dataset is empty. Run preprocess.py first.")
return
# ==========================================
# TASK 1: REGRESSION (Predict Fire Intensity)
# Target: 'bi' (Burning Index)
# Features: Weather metrics (Temp, Humidity, Wind, Rain)
# ==========================================
print("\n🔥 Training Task 1: Regression (Predict Burning Index)...")
# Features: Temp Min/Max, Humidity Min/Max, Wind Speed, Precipitation, Energy Release Component
reg_features = ['tmmn', 'tmmx', 'rmin', 'rmax', 'vs', 'pr', 'erc']
target_reg = 'bi'
X_reg = df[reg_features]
y_reg = df[target_reg]
X_train_r, X_test_r, y_train_r, y_test_r = train_test_split(X_reg, y_reg, test_size=0.2, random_state=42)
# Train Random Forest Regressor
reg_model = RandomForestRegressor(n_estimators=50, max_depth=10, n_jobs=-1, random_state=42)
reg_model.fit(X_train_r, y_train_r)
rmse = np.sqrt(mean_squared_error(y_test_r, reg_model.predict(X_test_r)))
print(f"✅ Regression RMSE: {rmse:.4f}")
joblib.dump(reg_model, f"{MODEL_DIR}/regression_model.pkl")
# ==========================================
# TASK 2: CLASSIFICATION (Predict Risk Level)
# Target: Custom 'Risk_Level' based on Burning Index
# Logic: 0-40 Low, 40-80 Medium, >80 High
# ==========================================
print("\n⚠️ Training Task 2: Classification (Fire Risk Level)...")
def get_risk_level(bi_val):
if bi_val < 40: return 'Low'
elif bi_val < 80: return 'Medium'
else: return 'High'
df['risk_level'] = df['bi'].apply(get_risk_level)
# Encode Target (Low=0, Medium=1, High=2)
le = LabelEncoder()
y_clf = le.fit_transform(df['risk_level'])
# Use same weather features for classification
X_clf = df[reg_features]
X_train_c, X_test_c, y_train_c, y_test_c = train_test_split(X_clf, y_clf, test_size=0.2, random_state=42)
clf_model = RandomForestClassifier(n_estimators=50, max_depth=10, n_jobs=-1, random_state=42)
clf_model.fit(X_train_c, y_train_c)
acc = accuracy_score(y_test_c, clf_model.predict(X_test_c))
print(f"✅ Classification Accuracy: {acc:.4f}")
# Save Model + Encoder (needed to decode predictions later)
joblib.dump(clf_model, f"{MODEL_DIR}/classification_model.pkl")
joblib.dump(le, f"{MODEL_DIR}/label_encoder.pkl")
# ==========================================
# TASK 3: CLUSTERING (Recovery Zones)
# Group by Location (Lat/Lon) and Fire Intensity (bi)
# ==========================================
print("\n🌍 Training Task 3: Clustering (Recovery Zones)...")
X_cluster = df[['latitude', 'longitude', 'bi']]
kmeans = KMeans(n_clusters=5, random_state=42, n_init=10)
kmeans.fit(X_cluster)
joblib.dump(kmeans, f"{MODEL_DIR}/clustering_model.pkl")
print("✅ Clustering Model Saved.")
# ==========================================
# TASK 4: DIMENSIONALITY REDUCTION (PCA)
# Reduce weather features to 2D for visualization
# ==========================================
print("\n🧩 Training Task 4: Dimensionality Reduction (PCA)...")
# Fit PCA on the weather features to reduce to 2D
pca = PCA(n_components=2)
pca.fit(df[reg_features])
# Save the PCA model
joblib.dump(pca, f"{MODEL_DIR}/pca_model.pkl")
print("✅ PCA Model Saved.")
# ==========================================
# TASK 5: TIME SERIES (SEASONALITY)
# Calculate monthly average burning index trends
# ==========================================
print("\n📈 Training Task 5: Time Series (Seasonality)...")
# Ensure datetime is correct
if 'datetime' in df.columns:
df['datetime'] = pd.to_datetime(df['datetime'])
df['month'] = df['datetime'].dt.month
# Calculate average Burning Index (BI) per month
seasonal_trend = df.groupby('month')['bi'].mean().to_dict()
# Save this dictionary (Month -> Avg BI)
joblib.dump(seasonal_trend, f"{MODEL_DIR}/seasonal_model.pkl")
print("✅ Seasonal Model Saved.")
else:
print("⚠️ Skipping Seasonality Task: 'datetime' column not found")
print("\n🎉 All Systems Go! Models are ready in 'models/'")
print("📁 Models created:")
print(" - regression_model.pkl")
print(" - classification_model.pkl")
print(" - label_encoder.pkl")
print(" - clustering_model.pkl")
print(" - pca_model.pkl")
print(" - seasonal_model.pkl")
if __name__ == "__main__":
train_all_tasks() |