We also offer a
ml4chem.visualization module to plot interesting
graphics about your model, features, or even monitor the progress of the loss
function and error minimization.
Two backends are supported to plot in ML4Chem: Seaborn and Plotly.
An example is shown below:
from ml4chem.visualization import plot_atomic_features fig = plot_atomic_features("latent_space.db", method="pca", dimensions=3, backend="plotly") fig.write_html("latent_example.html")
This will produce an interactive plot with plotly where dimensionality was reduced using PCA, and an html with the name latent_example.html is created.