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Yet another ML method comparison

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# yamc
Yet another ML method comparison, a comparison of 
* Gaussian Process Regression (GP)
* XGBoost (XGB)
* FeedForward Neural Network (FFNN)
* ChemProp (CP)

We are only comparing algorithms for GP, XGB and FFNN. The same descriptors (RDKit Morgan2) are used for all three.

Datasets are from [https://pubs.acs.org/doi/10.1021/acs.jcim.8b00542](https://pubs.acs.org/doi/10.1021/acs.jcim.8b00542)

## Procedure

1. fixed_comparison.py - runs GP, XGB, and FFNN, writes output to comparison.csv
2. run_chemprop.py - runs ChemProp (this takes several days, I could probably make it faster)
3. analyze_chemprop.py - gathers ouptut from ChemProp and writes output to cp_comparison.csv
4. analysis.ipynb - reads comparison.csv and cp_comparison.csv and generates plots

## Random Splits

![](https://raw.githubusercontent.com/PatWalters/yamc/main/Random_Split_r2.png)

![](https://raw.githubusercontent.com/PatWalters/yamc/main/Random_Split_rmse.png)

## Scaffold Splits

![](https://raw.githubusercontent.com/PatWalters/yamc/main/Scaffold_Split_r2.png)

![](https://raw.githubusercontent.com/PatWalters/yamc/main/Scaffold_Split_rmse.png)

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