Probabalistic modeling of Adsorption capacity of Phosphate onto biochars
This project shows how to perform probabilistic modeling for tabular data. To this end, we employ one decision tree based approach ngboost and two neural network (NN) based approaches. The first NN based approach is aleoteric. This method qunatifies uncertainty in data generating process. To record aleoteric uncertainty in a NN, we have to modify the output layer. In this case, the output layer is a probability distribution instead of a fully connected layer. The parameters of this distribution are learned during model training. The other kind of uncertainty is epistemic uncertainty. This method quantifies uncertainty in our knowledge. It should be noted that recording epistemic uncertainty increases the learnable parameters of NN far more than vanilla or aleoteric NN. However, the good news is that this type of uncertainty can be reduced by collecting more data. Such NNs are also known as bayesian NNs. In such NNs, the weights (and biases) are distributions instead of scaler values. The parameter of these distributions are again learned during model training. We can also combine both aleoteric and epistemic uncertainties to make our NNs into a probablistic NN. Such a NNs have weights as distributions and output layer as distribution as well.
Data
The input data consists of eighteen parameters corresponding to removal efficiency of biochar for PO4 from wastewater using adsorption such as adsorption experimental conditions, elemental composition of adsorbent and physical characteristics of adsorbent. Four of the total 18 parameters were categorical in nature, which included adsorbent type, feedstock type, ion type and activation. Our target is adsorption capacity (mg/g). A comprehensive analysis of data is given in 1. Exploratory data analysis
Reproducibility
To replicate the experiments, you need to install all requirements given in requirements file . If your results are quite different from what are presented here, then make sure that you are using the exact versions of the libraries which were used at the time of running of these scripts. These versions are given printed at the start of each script. Download all the .py files in the scripts including utils.py (utils) file. The data is expected to be in the data folder under the scripts folder.
Some code in this project is inspired by following resoures