Explainable AI: understand the black box of predictive models with chemical engineering applications
This project will focus on the explainability of machine learning predictive models for drop coalescence conducted in microfludics device and use initial experimental conditions as input. In the first stage, the student will construct different ML predictive models, including random forest, XGboost and deep neural networks. The second stage consists of extensive analysis of model results/parameters. SHAP and LIME will be used for model interpretbility.
Programming language: Python 3.8.16
First of all, please set up a conda environment
conda env create -n XAI
conda activate XAI
pip install numpy pandas scikit-learn xgboost matplotlib seaborn shap tensorflow lime
| Package Name | Version |
|---|---|
| _py-xgboost-mutex | 2.0 |
| libxgboost | 1.7.4 |
| lime | 0.2.0.1 |
| matplotlib-base | 3.7.1 |
| matplotlib-inline | 0.1.6 |
| numpy | 1.24.3 |
| pandas | 2.0.1 |
| py-xgboost | 1.7.4 |
| scikit-learn | 1.2.2 |
| seaborn | 0.12.2 |
| shap | 0.41.0 |
| tensorflow | 2.13.0rc1 |
| tensorflow-estimator | 2.13.0rc0 |
| tensorflow-macos | 2.13.0rc1 |
| xgboost | 1.7.4 |
We use min-max normlazation to rescale the raw dataset, and split it into two for training and testing repectively. Validation step is evaluated by using 5-fold cross validation. The experimental data can be available upon reasonable request to Dr. Nina Kovalchuk (n.kovalchuk@bham.ac.uk)
| Coalescence | Non-Coalescence | Total | |
|---|---|---|---|
| Total Dataset | 782 | 719 | 1501 |
| Training Dataset | 625 | 575 | 1200 |
| Testing Dataset | 157 | 144 | 301 |
We trains random forest, XGBoost and MLP classifier to get predictions. The corresponding implementation code are shown in code folder.
We use SHAP and LIME to get model interpetbility. Feature ablation testing is also used to analyse feature importance. The feature ablation results is shown in code/ablation_test.ipynb