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Improved Model Training Script
Better parameters for higher accuracy
"""
import pandas as pd
import numpy as np
import xgboost as xgb
from sklearn.model_selection import TimeSeriesSplit
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
import joblib
import os
import warnings
warnings.filterwarnings('ignore')
# Improved XGBoost parameters
IMPROVED_PARAMS = {
'objective': 'reg:squarederror',
'max_depth': 8,
'learning_rate': 0.05,
'n_estimators': 300,
'min_child_weight': 3,
'subsample': 0.8,
'colsample_bytree': 0.8,
'reg_alpha': 0.1,
'reg_lambda': 1.0,
'random_state': 42,
'n_jobs': -1
}
def load_data():
"""Load and prepare data"""
script_dir = os.path.dirname(os.path.abspath(__file__))
data_path = os.path.join(script_dir, '..', 'data', 'hospital_data_ml.csv')
if not os.path.exists(data_path):
data_path = os.path.join(script_dir, '..', 'hospital_data_ml.csv')
df = pd.read_csv(data_path)
print(f"Loaded {len(df)} records")
return df
def train_icu_model(df):
"""Train improved ICU demand model"""
print("\n" + "="*50)
print("Training ICU Demand Model")
print("="*50)
feature_cols = [
'hour', 'day_of_week', 'month', 'is_weekend',
'temperature', 'flu_season_index', 'air_quality_index',
'emergency_admissions_lag_1h', 'emergency_admissions_lag_7h',
'emergency_admissions_rolling_3h', 'emergency_admissions_rolling_7h',
'icu_demand_lag_1h', 'icu_demand_lag_7h'
]
X = df[feature_cols].copy()
y = df['icu_demand'].copy()
# Time series cross-validation
tscv = TimeSeriesSplit(n_splits=5)
mae_scores = []
r2_scores = []
print("\nCross-validation results:")
for fold, (train_idx, val_idx) in enumerate(tscv.split(X)):
X_train, X_val = X.iloc[train_idx], X.iloc[val_idx]
y_train, y_val = y.iloc[train_idx], y.iloc[val_idx]
model = xgb.XGBRegressor(**IMPROVED_PARAMS, verbosity=0)
model.fit(X_train, y_train,
eval_set=[(X_val, y_val)],
verbose=False)
pred = model.predict(X_val)
mae = mean_absolute_error(y_val, pred)
r2 = r2_score(y_val, pred)
mae_scores.append(mae)
r2_scores.append(r2)
print(f" Fold {fold+1}: MAE={mae:.4f}, R²={r2:.4f}")
print(f"\nAverage: MAE={np.mean(mae_scores):.4f} (±{np.std(mae_scores):.4f})")
print(f"Average: R²={np.mean(r2_scores):.4f} (±{np.std(r2_scores):.4f})")
# Train final model on all data
print("\nTraining final model on all data...")
final_model = xgb.XGBRegressor(**IMPROVED_PARAMS, verbosity=0)
final_model.fit(X, y)
# Feature importance
importance = pd.DataFrame({
'feature': feature_cols,
'importance': final_model.feature_importances_
}).sort_values('importance', ascending=False)
print("\nTop features:")
for _, row in importance.head(5).iterrows():
print(f" {row['feature']}: {row['importance']:.4f}")
return final_model, feature_cols
def train_staff_model(df):
"""Train improved staff workload model"""
print("\n" + "="*50)
print("Training Staff Workload Model")
print("="*50)
feature_cols = [
'hour', 'day_of_week', 'month', 'is_weekend',
'temperature', 'flu_season_index', 'air_quality_index',
'emergency_admissions_lag_1h', 'emergency_admissions_lag_7h',
'emergency_admissions_rolling_3h', 'emergency_admissions_rolling_7h',
'icu_demand_lag_1h',
'bed_occupancy'
]
X = df[feature_cols].copy()
y = df['staff_workload'].copy()
# Time series cross-validation
tscv = TimeSeriesSplit(n_splits=5)
mae_scores = []
r2_scores = []
print("\nCross-validation results:")
for fold, (train_idx, val_idx) in enumerate(tscv.split(X)):
X_train, X_val = X.iloc[train_idx], X.iloc[val_idx]
y_train, y_val = y.iloc[train_idx], y.iloc[val_idx]
model = xgb.XGBRegressor(**IMPROVED_PARAMS, verbosity=0)
model.fit(X_train, y_train,
eval_set=[(X_val, y_val)],
verbose=False)
pred = model.predict(X_val)
mae = mean_absolute_error(y_val, pred)
r2 = r2_score(y_val, pred)
mae_scores.append(mae)
r2_scores.append(r2)
print(f" Fold {fold+1}: MAE={mae:.4f}, R²={r2:.4f}")
print(f"\nAverage: MAE={np.mean(mae_scores):.4f} (±{np.std(mae_scores):.4f})")
print(f"Average: R²={np.mean(r2_scores):.4f} (±{np.std(r2_scores):.4f})")
# Train final model on all data
print("\nTraining final model on all data...")
final_model = xgb.XGBRegressor(**IMPROVED_PARAMS, verbosity=0)
final_model.fit(X, y)
# Feature importance
importance = pd.DataFrame({
'feature': feature_cols,
'importance': final_model.feature_importances_
}).sort_values('importance', ascending=False)
print("\nTop features:")
for _, row in importance.head(5).iterrows():
print(f" {row['feature']}: {row['importance']:.4f}")
return final_model, feature_cols
def main():
"""Main training pipeline"""
print("="*60)
print("HOSPITAL PREDICTION - IMPROVED MODEL TRAINING")
print("="*60)
# Load data
df = load_data()
# Train models
icu_model, icu_features = train_icu_model(df)
staff_model, staff_features = train_staff_model(df)
# Save models
script_dir = os.path.dirname(os.path.abspath(__file__))
models_dir = os.path.join(script_dir, '..', 'models')
os.makedirs(models_dir, exist_ok=True)
icu_path = os.path.join(models_dir, 'icu_demand_model.pkl')
staff_path = os.path.join(models_dir, 'staff_workload_model.pkl')
joblib.dump(icu_model, icu_path)
joblib.dump(staff_model, staff_path)
print("\n" + "="*60)
print("MODELS SAVED SUCCESSFULLY")
print("="*60)
print(f"ICU Model: {icu_path}")
print(f"Staff Model: {staff_path}")
# Verify
print("\nVerifying models...")
icu_loaded = joblib.load(icu_path)
staff_loaded = joblib.load(staff_path)
# Test prediction
test_sample = df[icu_features].tail(1)
icu_pred = icu_loaded.predict(test_sample)
print(f"ICU test prediction: {icu_pred[0]:.2f}")
test_sample = df[staff_features].tail(1)
staff_pred = staff_loaded.predict(test_sample)
print(f"Staff test prediction: {staff_pred[0]:.2f}")
print("\n✅ Training complete!")
if __name__ == "__main__":
main()
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