| import os | |
| os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE" | |
| os.environ["TOKENIZERS_PARALLELISM"] = "false" | |
| import pandas as pd | |
| import numpy as np | |
| from pipeline.a_data_preparation import dataset_cleaning | |
| from pipeline.b_model_training import model_training | |
| from pipeline.c_explainability import captum_explainability | |
| from pipeline.d_llm_explanation import generate_llm_explication | |
| # Configuration of settings that can be changed according to the user | |
| sample_df = pd.read_csv("sample/gld_price_data.csv") | |
| N = 30 | |
| K = 20 | |
| # Pipeline execution | |
| df, data = dataset_cleaning(sample_df) | |
| model, final_predictions, X_test_t, dataframes = model_training(df, N, K) | |
| importance_map = captum_explainability(model, X_test_t, dataframes) | |
| explanation = generate_llm_explication(importance_map, final_predictions, data) | |