PyEDCR is a Python implementation of the f-EDR (Focused Error Detection Rules) paradigm. The goal of EDR is to use a set of conditions to learn when a machine learning model makes an incorrect prediction, as first introduced in:
The package was tested for Python >= 3.9.
To demonstrate the use of the package, consider the following running example using the 'simulate_for_values' function from NeuralPyEDCR.py
import os
from NeuralPyEDCR import simulate_for_values
data_str = 'imagenet'
main_model_name = binary_model_name = 'dinov2_vits14'
secondary_model_name = 'dinov2_vitl14'
main_lr = secondary_lr = binary_lr = 0.000001
original_num_epochs = 8
secondary_num_epochs = 2
binary_num_epochs = 5
number_of_fine_classes = 42
binary_l_strs = list({f.split(f'e{binary_num_epochs - 1}_')[-1].replace('.npy', '')
for f in os.listdir('binary_results')
if f.startswith(f'{data_str}_{binary_model_name}')})
simulate_for_values(total_number_of_points=1,
min_value=0.1,
max_value=0.1,
binary_l_strs=binary_l_strs,
binary_lr=binary_lr,
binary_num_epochs=binary_num_epochs,
multi_processing=True,
secondary_model_name=secondary_model_name,
secondary_model_loss='BCE',
secondary_num_epochs=secondary_num_epochs,
secondary_lr=secondary_lr,
maximize_ratio=True,
lists_of_fine_labels_to_take_out=[[]],
negated_conditions=False)