Cleaning up

Testing Machine Learning Models

-------------------------------

Testing the correctness of a machine learning model can be quite tricky to do

in practice. When adding a new machine learning model to DeepChem, you should

add at least a few basic types of unit tests:

- Overfitting test: Create a small synthetic dataset and test that your model

  can learn this datasest with high accuracy. For regression and classification

  task, this should correspond to low training error on the dataset. For

  generative tasks, this should correspond to low training loss on the dataset.

- Reloading test: Check that a trained model can be saved to disk and reloaded

  correctly. This should involve checking that predictions from the saved and

  reloaded models

  matching exactly.

Note that unit tests are not sufficient to gauge the real performance of a

model. You should benchmark your model on larger datasets as well and report

your benchmarking tests in the PR comments.

Testing the correctness of a machine learning model can be quite

tricky to do in practice. When adding a new machine learning model to

DeepChem, you should add at least a few basic types of unit tests:

- Overfitting test: Create a small synthetic dataset and test that

your model can learn this datasest with high accuracy. For regression

and classification task, this should correspond to low training error

on the dataset. For generative tasks, this should correspond to low

training loss on the dataset.

- Reloading test: Check that a trained model can be saved to disk and

reloaded correctly. This should involve checking that predictions from

the saved and reloaded models matching exactly.

Note that unit tests are not sufficient to gauge the real performance

of a model. You should benchmark your model on larger datasets as well

and report your benchmarking tests in the PR comments.

Type Annotations

----------------