Visualizing Models and Data#

Let’s revisit the starter snippet from the homepage of our documentation.

Line by line, we’ll walk through how Visualizing your Models and Datasets works!

First, let’s import the necessary libraries:

from olorenchemengine.visualizations import *
from olorenchemengine import *
import olorenchemengine as oce

Second, we’ll use a pandas dataframe of csv data given by the line

df = oce.ExampleDataset

:: to train the model, but visualizations do not allow us to pass in a pandas dataframe. Instead, it must be of type ‘BaseDataset’

with this line,

test_dataset = oce.BaseDataset(name='purple', data=df.to_csv(), structure_col='Smiles', property_col='pChEMBL Value')

See how it all comes together below.

## Loading in a dataset

# df is a Pandas Dataframe with the following columns:
# "Smiles" (structure)
# "pChEMBL Value" (property to be predicted)
df = oce.ExampleDataset
test = oce.BaseDataset(name='purple', data=df.to_csv(), structure_col='Smiles', property_col='pChEMBL Value')

Next, we’ll train, fit, save, and load our model in the same as before:

model = oce.BaseBoosting([
            oce.BaseTorchGeometricModel(oce.TLFromCheckpoint("default"), preinitialized=True),

## Training the model["Smiles"], df["pChEMBL Value"])

## Predicting property of new compounds
y_pred = model.predict(["CC(=O)OC1=CC=CC=C1C(=O)O"])

## Saving the model, "model.oce")

## Loading the model
loaded_model = oce.load("model.oce")

Now, we can use the loaded_model to Visualize our with VisualizeModelSim, a method that “ Visualizes a model’s predicted vs true plot on given dataset, where each point is colored by a compound’s similarity to the train set”

Note: notice how we use test_dataset, which is oce.ExampleDataset converted into a BaseDataset object.

vis =  oce.VisualizeModelSim(model=loaded_model,dataset=test_dataset)

Finally, every Visualization has a render_ipynb method that can be used to render the visualization. So, we can do the following:


:: And your visualization is viewable in a jupyter notebook.