| import os |
| import sys |
|
|
| |
| PROJECT_ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), "..")) |
| if PROJECT_ROOT not in sys.path: |
| sys.path.insert(0, PROJECT_ROOT) |
|
|
| import joblib |
| import numpy as np |
| import pandas as pd |
| from sklearn.model_selection import train_test_split |
| from sklearn.metrics import roc_auc_score, accuracy_score, mean_squared_error, silhouette_score |
|
|
| from ml.features.feature_engineering import CustomerFeatureEngineer, FEATURE_COLUMNS |
| from ml.models.segmentation.segmentation_model import CustomerSegmentationModel |
| from ml.models.churn.churn_model import ChurnPredictionModel |
| from ml.models.propensity.propensity_model import PurchasePropensityModel |
| from ml.models.clv.clv_model import CustomerLifetimeValueModel |
| from ml.explainability.shap_explainer import ShapExplainabilityEngine |
| from scripts.generate_dataset import generate_customer_dataset |
|
|
| ARTIFACTS_DIR = "ml/models/artifacts" |
|
|
| def train_pipeline(): |
| print("=== [1/5] Loading or Generating Training Data ===") |
| dataset_path = "ml/data/customer_signals.csv" |
| if not os.path.exists(dataset_path): |
| df_raw = generate_customer_dataset(500) |
| else: |
| df_raw = pd.read_csv(dataset_path) |
|
|
| print("=== [2/5] Engineering Features ===") |
| feature_engineer = CustomerFeatureEngineer() |
| X = feature_engineer.transform(df_raw) |
| y_churn = df_raw["churn_label"] |
| y_intent = df_raw["purchase_intent_label"] |
| y_clv = df_raw["clv"] |
|
|
| |
| X_train, X_test, y_churn_train, y_churn_test = train_test_split(X, y_churn, test_size=0.2, random_state=42) |
| _, _, y_intent_train, y_intent_test = train_test_split(X, y_intent, test_size=0.2, random_state=42) |
| _, _, y_clv_train, y_clv_test = train_test_split(X, y_clv, test_size=0.2, random_state=42) |
|
|
| os.makedirs(ARTIFACTS_DIR, exist_ok=True) |
|
|
| print("=== [3/5] Training Models ===") |
| |
| print(" -> Training Segmentation Model (K-Means)...") |
| seg_model = CustomerSegmentationModel(n_clusters=5) |
| seg_model.fit(X, df_raw) |
| segments = seg_model.predict(X_test) |
| sil_score = silhouette_score(seg_model.scaler.transform(X_test), seg_model.kmeans.predict(seg_model.scaler.transform(X_test))) |
| print(f" Segmentation Silhouette Score: {sil_score:.4f}") |
|
|
| |
| print(" -> Training Churn Prediction Model (XGBoost)...") |
| churn_model = ChurnPredictionModel(use_xgboost=True) |
| churn_model.fit(X_train, y_churn_train) |
| churn_preds_prob = churn_model.predict_proba(X_test) |
| churn_auc = roc_auc_score(y_churn_test, churn_preds_prob) |
| print(f" Churn Model ROC-AUC: {churn_auc:.4f}") |
|
|
| |
| print(" -> Training Purchase Propensity Model (XGBoost)...") |
| prop_model = PurchasePropensityModel(use_xgboost=True) |
| prop_model.fit(X_train, y_intent_train) |
| prop_preds_prob = prop_model.predict_proba(X_test) |
| prop_auc = roc_auc_score(y_intent_test, prop_preds_prob) |
| print(f" Purchase Propensity ROC-AUC: {prop_auc:.4f}") |
|
|
| |
| print(" -> Training Customer Lifetime Value Model...") |
| clv_model = CustomerLifetimeValueModel(use_xgboost=True) |
| clv_model.fit(X_train, y_clv_train) |
| clv_preds = clv_model.predict(X_test) |
| clv_rmse = np.sqrt(mean_squared_error(y_clv_test, clv_preds)) |
| print(f" CLV Model RMSE: Rs.{clv_rmse:,.2f}") |
|
|
| print("=== [4/5] Initializing SHAP Explainer ===") |
| shap_engine = ShapExplainabilityEngine(churn_model.model, FEATURE_COLUMNS) |
|
|
| print("=== [5/5] Serializing Artifacts ===") |
| joblib.dump(feature_engineer, os.path.join(ARTIFACTS_DIR, "feature_engineer.joblib")) |
| joblib.dump(seg_model, os.path.join(ARTIFACTS_DIR, "seg_model.joblib")) |
| joblib.dump(churn_model, os.path.join(ARTIFACTS_DIR, "churn_model.joblib")) |
| joblib.dump(prop_model, os.path.join(ARTIFACTS_DIR, "prop_model.joblib")) |
| joblib.dump(clv_model, os.path.join(ARTIFACTS_DIR, "clv_model.joblib")) |
| joblib.dump(shap_engine, os.path.join(ARTIFACTS_DIR, "shap_engine.joblib")) |
|
|
| print(f"\n[SUCCESS] All models trained & saved successfully to '{ARTIFACTS_DIR}'!") |
|
|
| if __name__ == "__main__": |
| train_pipeline() |
|
|
