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import argparse
import os
from data_loader import load_and_process_data, CATEGORICAL_COLUMNS
from model_trainer import train_models
from model_manager import save_models, load_models
from model_predictor import predict
from config import MODEL_DIR, CATBOOST_PARAMS, XGB_PARAMS, RF_PARAMS
import wandb
from sklearn.metrics import accuracy_score, balanced_accuracy_score, classification_report
import pandas as pd
## ===========================
# MAIN FUNCTION
# ===========================
def main(train=True, retrain=False):
""" Main entry point to train, retrain or predict """
# Create model directory if it doesn't exist
if not os.path.exists(MODEL_DIR):
os.makedirs(MODEL_DIR)
print("\n🚀 Loading data...")
X_train, X_val, y_train, y_val, test_df = load_and_process_data()
if train or retrain:
print("\n🚀 Training models...")
models = train_models(X_train, y_train, CATEGORICAL_COLUMNS)
save_models(models)
else:
print("\n🚀 Loading existing models...")
models = load_models()
# add wandb, validation set scoring
param_grid = {"CATBOOST_PARAMS": CATBOOST_PARAMS,
"XGB_PARAMS": XGB_PARAMS,
"RF_PARAMS": RF_PARAMS}
os.getenv("WANDB_API_KEY")
run = wandb.init(project="is_click_predictor", config=param_grid)
print("\n🔍 Makings predictions for validation set...")
predictions_val = predict(models, X_val)
accuracy_val = accuracy_score(y_val, predictions_val["is_click_predicted"])
balanced_accuracy_val = balanced_accuracy_score(y_val, predictions_val["is_click_predicted"])
classification_report_val = classification_report(y_val, predictions_val["is_click_predicted"], output_dict=True)
classification_report_val = pd.DataFrame(classification_report_val).transpose()
predictions_val_table = wandb.Table(dataframe=predictions_val)
classification_report_val_table = wandb.Table(dataframe=classification_report_val)
print("\n🔍 Making predictions for test set...")
predictions = predict(models, test_df)
# wandb logging
run.log({"param_grid": param_grid,
"accuracy_val": accuracy_val,
"balanced_accuracy_val": balanced_accuracy_val,
"classification_report_val_table": classification_report_val_table,
"predictions_val_table": predictions_val_table,
"y_val": y_val.tolist()})
run.finish()
# Save final predictions
predictions.to_csv("final_predictions.csv", index=False)
print("\n✅ Predictions saved successfully as 'final_predictions.csv'!")
# ===========================
# COMMAND-LINE EXECUTION
# ===========================
if __name__ == "__main__":
# parser = argparse.ArgumentParser(description="Train, retrain or make predictions")
# parser.add_argument("--train", action="store_true", help="Train new models")
# parser.add_argument("--retrain", action="store_true", help="Retrain models with updated data")
#
# args = parser.parse_args()
main(train=True, retrain=False) |