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import sys
import mlflow
import dagshub
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import seaborn as sns
from networksecurity.exception.exception import NetworkSecurityException
from networksecurity.logging.logger import logging
from networksecurity.entity.config_entity import ModelTrainerConfig
from networksecurity.entity.artifact_entity import (
DataTransformationArtifact,
ModelTrainerArtifact,
)
from networksecurity.utils.main_utils.utils import (
save_object,
load_object,
load_numpy_array_data,
evaluate_models,
)
from networksecurity.utils.ml_utils.metric.classfication_metric import (
get_classification_score,
)
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import (
RandomForestClassifier,
GradientBoostingClassifier,
AdaBoostClassifier,
)
from sklearn.metrics import (
confusion_matrix,
roc_curve,
precision_recall_curve,
)
import os
# ---------------- Dagshub + MLflow ----------------
if os.getenv("MLFLOW_TRACKING_URI"):
print("info: MLflow tracking URI is already set, skipping DagsHub init")
elif os.getenv("MLFLOW_TRACKING_USERNAME") and os.getenv("MLFLOW_TRACKING_PASSWORD"):
dagshub.init(repo_owner="Inder-26", repo_name="NetworkSecurity", mlflow=True)
else:
print("Warning: DagsHub credentials not found. Tracking might rely on local configs or fail.")
# ---------------- Helper: log visual artifacts ----------------
def log_classification_artifacts(y_true, y_pred, y_proba):
# Confusion Matrix
cm = confusion_matrix(y_true, y_pred)
plt.figure(figsize=(4, 4))
sns.heatmap(cm, annot=True, fmt="d", cmap="Blues")
plt.xlabel("Predicted")
plt.ylabel("Actual")
plt.title("Confusion Matrix")
plt.tight_layout()
plt.savefig("confusion_matrix.png")
mlflow.log_artifact("confusion_matrix.png")
plt.close()
# ROC Curve
fpr, tpr, _ = roc_curve(y_true, y_proba)
plt.figure()
plt.plot(fpr, tpr)
plt.plot([0, 1], [0, 1], "--")
plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")
plt.title("ROC Curve")
plt.tight_layout()
plt.savefig("roc_curve.png")
mlflow.log_artifact("roc_curve.png")
plt.close()
# Precision-Recall Curve
precision, recall, _ = precision_recall_curve(y_true, y_proba)
plt.figure()
plt.plot(recall, precision)
plt.xlabel("Recall")
plt.ylabel("Precision")
plt.title("Precision-Recall Curve")
plt.tight_layout()
plt.savefig("precision_recall_curve.png")
mlflow.log_artifact("precision_recall_curve.png")
plt.close()
# ---------------- Model Trainer ----------------
class ModelTrainer:
def __init__(
self,
model_trainer_config: ModelTrainerConfig,
data_transformation_artifact: DataTransformationArtifact,
):
try:
logging.info(f"{'>>'*20} Model Trainer {'<<'*20}")
self.model_trainer_config = model_trainer_config
self.data_transformation_artifact = data_transformation_artifact
except Exception as e:
raise NetworkSecurityException(e, sys)
def train_model(self, X_train, X_test, y_train, y_test):
models = {
"Logistic Regression": LogisticRegression(),
"Decision Tree": DecisionTreeClassifier(),
"Random Forest": RandomForestClassifier(),
"Gradient Boosting": GradientBoostingClassifier(),
"AdaBoost": AdaBoostClassifier(),
}
# ---------- MLflow logging ----------
best_f1 = -1
best_model = None
best_model_name = None
best_run_id = None
for model_name, model in models.items():
with mlflow.start_run(run_name=model_name) as run:
model.fit(X_train, y_train)
y_train_pred = model.predict(X_train)
y_test_pred = model.predict(X_test)
y_test_proba = model.predict_proba(X_test)[:, 1]
train_metric = get_classification_score(y_train, y_train_pred)
test_metric = get_classification_score(y_test, y_test_pred)
# Params & tags
mlflow.log_params(model.get_params())
mlflow.set_tag("model_name", model_name)
mlflow.set_tag("stage", "experiment")
# Metrics (decision metric = test_f1)
mlflow.log_metric("train_f1", train_metric.f1_score)
mlflow.log_metric("test_f1", test_metric.f1_score)
mlflow.log_metric("train_precision", train_metric.precision_score)
mlflow.log_metric("test_precision", test_metric.precision_score)
mlflow.log_metric("train_recall", train_metric.recall_score)
mlflow.log_metric("test_recall", test_metric.recall_score)
# Visual evaluation (artifacts)
log_classification_artifacts(
y_true=y_test,
y_pred=y_test_pred,
y_proba=y_test_proba,
)
if test_metric.f1_score > best_f1:
best_f1 = test_metric.f1_score
best_model = model
best_model_name = model_name
best_run_id = run.info.run_id
logging.info(
f"Best Model: {best_model_name} | "
f"Test F1: {best_f1}"
)
with mlflow.start_run(run_id=best_run_id):
mlflow.set_tag("best_model", "true")
# ---------- Save final model for deployment ----------
preprocessor = load_object(
self.data_transformation_artifact.transformed_object_file_path
)
final_model_dir = os.path.join(os.getcwd(), "final_model")
os.makedirs(final_model_dir, exist_ok=True)
save_object(
os.path.join(final_model_dir, "model.pkl"),
best_model,
)
save_object(
os.path.join(final_model_dir, "preprocessor.pkl"),
preprocessor,
)
logging.info(f"Final model and preprocessor saved in final_model")
y_train_pred = best_model.predict(X_train)
y_test_pred = best_model.predict(X_test)
best_train_metric = get_classification_score(y_train, y_train_pred)
best_test_metric = get_classification_score(y_test, y_test_pred)
return ModelTrainerArtifact(
trained_model_file_path=os.path.join(final_model_dir, "model.pkl"),
train_metric_artifact=best_train_metric,
test_metric_artifact=best_test_metric,
)
def initiate_model_trainer(self) -> ModelTrainerArtifact:
try:
train_array = load_numpy_array_data(
self.data_transformation_artifact.transformed_train_file_path
)
test_array = load_numpy_array_data(
self.data_transformation_artifact.transformed_test_file_path
)
X_train, y_train = train_array[:, :-1], train_array[:, -1]
X_test, y_test = test_array[:, :-1], test_array[:, -1]
return self.train_model(X_train, X_test, y_train, y_test)
except Exception as e:
raise NetworkSecurityException(e, sys)
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