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import os
import sys
import json
import numpy as np
from dataclasses import dataclass
import optuna
from optuna.samplers import TPESampler
optuna.logging.set_verbosity(optuna.logging.WARNING)
import mlflow
import mlflow.sklearn
import matplotlib
matplotlib.use('Agg')
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, BaggingClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.feature_selection import RFECV
from sklearn.metrics import (
recall_score, roc_auc_score, classification_report
)
from lightgbm import LGBMClassifier
from xgboost import XGBClassifier
from src.exception import CustomException
from src.logger import logging
from src.utils import save_object, evaluate_models, evaluate_final_model, save_best_params, load_best_params
@dataclass
class ModelTrainerConfig:
trained_model_file_path: str = os.path.join("artifacts", "model.pkl")
eval_report_path: str = os.path.join("artifacts", "eval_report.png")
def tune_lightgbm(X_train, y_train, X_val, y_val, n_trials=20, params_path="artifacts/params/lightgbm_best_params.json"):
if os.path.exists(params_path):
logging.info("LightGBM: found saved params, skipping Tuning.")
return LGBMClassifier(**load_best_params(params_path), random_state=42, verbosity=-1)
def objective(trial):
params = {
'n_estimators': trial.suggest_int('n_estimators', 200, 1000),
'num_leaves': trial.suggest_int('num_leaves', 20, 80),
'max_depth': trial.suggest_int('max_depth', 5, 9),
'learning_rate': trial.suggest_float('learning_rate', 0.01, 0.1, log=True),
'min_child_samples': trial.suggest_int('min_child_samples', 20, 100),
'reg_lambda': trial.suggest_float('reg_lambda', 0.0, 10.0),
'colsample_bytree': trial.suggest_float('colsample_bytree', 0.6, 1.0),
}
model = LGBMClassifier(**params, random_state=42, verbosity=-1)
model.fit(X_train, y_train)
return roc_auc_score(y_val, model.predict_proba(X_val)[:, 1])
study = optuna.create_study(direction="maximize", sampler=TPESampler(seed=42))
study.optimize(objective, n_trials=n_trials, show_progress_bar=True)
logging.info(f"LightGBM best params: {study.best_params} | AUC: {study.best_value: .4f}")
save_best_params(study.best_params, params_path)
return LGBMClassifier(**study.best_params, random_state=42, verbosity=-1)
def tune_xgboost(X_train, y_train, X_val, y_val,
n_trials=20,
params_path="artifacts/params/xgboost_best_params.json"):
if os.path.exists(params_path):
logging.info("XGBoost: found saved params, skipping Optuna.")
return XGBClassifier(**load_best_params(params_path), random_state=42, eval_metric='logloss', verbosity=0)
def objective(trial):
params = {
'n_estimators': trial.suggest_int('n_estimators', 200, 1000),
'max_depth': trial.suggest_int('max_depth', 3, 8),
'learning_rate': trial.suggest_float('learning_rate', 0.01, 0.1, log=True),
'subsample': trial.suggest_float('subsample', 0.6, 1.0),
'colsample_bytree': trial.suggest_float('colsample_bytree', 0.6, 1.0),
'reg_lambda': trial.suggest_float('reg_lambda', 0.0, 5.0),
'min_child_weight': trial.suggest_int('min_child_weight', 1, 10),
}
model = XGBClassifier(**params, random_state=42, eval_metric='logloss', verbosity=0)
model.fit(X_train, y_train)
return roc_auc_score(y_val, model.predict_proba(X_val)[:, 1])
study = optuna.create_study(direction='maximize', sampler=TPESampler(seed=42))
study.optimize(objective, n_trials=n_trials, show_progress_bar=True)
logging.info(f"XGBoost best params: {study.best_params} | AUC: {study.best_value:.4f}")
save_best_params(study.best_params, params_path)
return XGBClassifier(**study.best_params, random_state=42, eval_metric='logloss', verbosity=0)
def tune_random_forest(X_train, y_train, X_val, y_val,
n_trials=20,
params_path="artifacts/params/rf_best_params.json"):
if os.path.exists(params_path):
logging.info("Random Forest: found saved params, skipping Optuna.")
return RandomForestClassifier(**load_best_params(params_path), random_state=42)
def objective(trial):
params = {
'n_estimators': trial.suggest_int('n_estimators', 100, 500),
'max_depth': trial.suggest_int('max_depth', 5, 15),
'min_samples_leaf': trial.suggest_int('min_samples_leaf', 5, 30),
'max_features': trial.suggest_categorical('max_features', ['sqrt', 'log2', 0.5]),
}
model = RandomForestClassifier(**params, random_state=42)
model.fit(X_train, y_train)
return roc_auc_score(y_val, model.predict_proba(X_val)[:, 1])
study = optuna.create_study(direction='maximize', sampler=TPESampler(seed=42))
study.optimize(objective, n_trials=n_trials, show_progress_bar=True)
logging.info(f"Random Forest best params: {study.best_params} | AUC: {study.best_value:.4f}")
save_best_params(study.best_params, params_path)
return RandomForestClassifier(**study.best_params, random_state=42)
TUNING_FUNCTIONS = {
'LightGBM': tune_lightgbm,
'XGBoost': tune_xgboost,
'Random Forest': tune_random_forest,
}
mlflow.set_experiment("Threat_Forecaster")
class ModelTrainer:
def __init__(self):
self.model_trainer_config = ModelTrainerConfig()
def initiate_model_trainer(self, train_array, val_array):
try:
logging.info("Splitting train and val arrays into features and target.")
X_train = train_array[:, :-1]
y_train = train_array[:, -1]
X_val = val_array[:, :-1]
y_val = val_array[:, -1]
models = {
'Logistic Regression': LogisticRegression(random_state=1, max_iter=1000),
'Random Forest': RandomForestClassifier(random_state=1),
'LightGBM': LGBMClassifier(random_state=1, verbosity=-1),
'XGBoost': XGBClassifier(random_state=1, verbosity=0, eval_metric='logloss'),
'Decision Tree': DecisionTreeClassifier(random_state=1),
'AdaBoost': AdaBoostClassifier(random_state=1),
'Bagging': BaggingClassifier(random_state=1),
}
model_report = evaluate_models(
X_train=X_train, y_train=y_train,
X_val=X_val, y_val=y_val,
models=models
)
logging.info(f"Baseline model report: {model_report}")
for name, model in models.items():
baseline_metrics = evaluate_final_model(
model = model,
X_val = X_val,
y_val = y_val,
selected = None,
name = f"Baseline_{name}",
report_dir = "artifacts/eval/baseline"
)
with mlflow.start_run(run_name=f"Baseline_{name}"):
mlflow.set_tag("stage", "baseline")
mlflow.log_metric("val_roc_auc", baseline_metrics['auc'])
mlflow.log_metric("val_recall", baseline_metrics['recall'])
mlflow.log_metric("val_precision", baseline_metrics['precision'])
mlflow.log_metric("val_f1", baseline_metrics['f1'])
mlflow.log_metric("val_ap", baseline_metrics['ap'])
top_3_names = sorted(
model_report.keys(),
key=lambda k: model_report[k]['val_roc_auc'],
reverse=True
)[:3]
logging.info(f"Top 3 models by AUC: {top_3_names}")
print(f"\nTop 3 selected for tuning: {top_3_names}")
tuned_results = {}
for name in top_3_names:
if name not in TUNING_FUNCTIONS:
logging.warning(f"No tuning function for {name} — using baseline model as-is.")
tuned_model = models[name]
else:
tune_fn = TUNING_FUNCTIONS[name]
params_path = f"artifacts/params/{name.replace(' ', '_').lower()}_best_params.json"
tuned_model = tune_fn(X_train, y_train, X_val, y_val, params_path=params_path)
tuned_model.fit(X_train, y_train)
tuned_metrics = evaluate_final_model(
model = tuned_model,
X_val = X_val,
y_val = y_val,
selected = None,
name = f"Tuned_{name}",
report_dir = "artifacts/eval/tuned"
)
logging.info(f"Tuned {name} → AUC: {tuned_metrics['auc']:.4f} | Recall: {tuned_metrics['recall']:.4f}")
tuned_results[name] = {
'model': tuned_model,
'val_roc_auc': tuned_metrics['auc'],
'recall': tuned_metrics['recall'],
}
with mlflow.start_run(run_name=f"Tuned_{name}"):
mlflow.set_tag("stage", "tuning")
mlflow.log_params(tuned_model.get_params())
mlflow.log_metric("val_roc_auc", tuned_metrics['auc'])
mlflow.log_metric("val_recall", tuned_metrics['recall'])
mlflow.log_metric("val_precision", tuned_metrics['precision'])
mlflow.log_metric("val_f1", tuned_metrics['f1'])
mlflow.log_metric("val_ap", tuned_metrics['ap'])
best_name = max(tuned_results, key=lambda k: tuned_results[k]['val_roc_auc'])
best = tuned_results[best_name]
if best['val_roc_auc'] < 0.5:
raise CustomException("No best model found — AUC below 0.5.", sys)
logging.info(f"Best model: {best_name} | AUC: {best['val_roc_auc']:.4f}")
print(f"\nBest model: {best_name} — running RFECV for feature selection.")
rfecv = RFECV(
estimator = best['model'],
step = 2,
cv = 5,
scoring = 'roc_auc',
n_jobs = -1
)
rfecv.fit(X_train, y_train)
selected = rfecv.support_
logging.info(f"RFECV selected {selected.sum()} / {len(selected)} features.")
X_train_sel = X_train[:, selected]
X_val_sel = X_val[:, selected]
best['model'].fit(X_train_sel, y_train)
final_metrics = evaluate_final_model(
model = best['model'],
X_val = X_val,
y_val = y_val,
selected = selected,
name = f"Final_{best_name}",
report_dir = "artifacts/eval/final"
)
with mlflow.start_run(run_name=f"Final_{best_name}"):
mlflow.set_tag("stage", "final")
mlflow.log_params(best['model'].get_params())
mlflow.log_metric("val_roc_auc", final_metrics['auc'])
mlflow.log_metric("val_recall", final_metrics['recall'])
mlflow.log_metric("val_precision", final_metrics['precision'])
mlflow.log_metric("val_f1", final_metrics['f1'])
mlflow.log_metric("val_ap", final_metrics['ap'])
mlflow.log_metric("features_selected", int(selected.sum()))
mlflow.sklearn.log_model(
best['model'],
artifact_path="model",
registered_model_name="Threat_Forecaster"
)
save_object(
file_path = self.model_trainer_config.trained_model_file_path,
obj = {
'model': best['model'],
'selected': selected,
'name': best_name
}
)
logging.info("Best model saved to artifacts/.")
return best['val_roc_auc']
except Exception as e:
raise CustomException(e, sys)
|