def discrimination_threshold(self) -> None:
visualizer = DiscriminationThreshold(self.trained_model)
visualizer.fit(self.X_test,
self.y_test) # Fit the data to the visualizer
save_dir = f"{self.plots_dir}/discrimination_plot_{self.model_id}.png"
visualizer.show(outpath=save_dir)
if not LOCAL:
upload_to_s3(save_dir,
f'plots/discrimination_plot_{self.model_id}.png',
bucket=S3_BUCKET_NAME)
plt.clf()
def discrimination_thresholding(xx,yy,estimatorss,**kwargs):
vz = DiscriminationThreshold(estimatorss, classes=['Reach, 1 Reach, or L/R Reach', 'Null, Multiple Reaches, Or Multiple Arms'],
cmap="YlGn", size=(600, 360), **kwargs)
vz.fit(xx,yy)
vz.score(xx,yy)
vz.show()