In this PR, predictions and leave-one-image-out probabilities are derived for the single-class models.
For predictions, we simply load in the model for a particular phenotypic class, model type (final or shuffled baseline), and feature type (CP, DP, CP_and_DP) then derive model predictions for cells from both data subsets (train and test). Predictions are saved to predictions/.
For each image in MitoCheck labeled cell dataset (as specified by the Metadata_DNA field):
Train a logistic regression model with optimal hyperparameters (C and l1_ratio, saved with model data in models/) on every cell that is not in the specific image.
Predict probabilities on every cell that is in the specific image.
This PR is ready for review!
In this PR, predictions and leave-one-image-out probabilities are derived for the single-class models.
For predictions, we simply load in the model for a particular phenotypic class, model type (final or shuffled baseline), and feature type (CP, DP, CP_and_DP) then derive model predictions for cells from both data subsets (train and test). Predictions are saved to
predictions/
.The LOIO evaluation procedure is as follows:
Metadata_DNA
field):C
andl1_ratio
, saved with model data in models/) on every cell that is not in the specific image.The probabilities are saved to
LOIO_probas/
.