| from scipy.stats import norm |
| import numpy as np |
| import os |
|
|
|
|
| def score(outputs): |
| |
| weight = 0.03 |
| binding_size_dist = np.load(os.path.join(os.path.dirname(__file__), "../params/binding_size_train_dist.npy")) |
|
|
|
|
| mean = np.mean(binding_size_dist) |
|
|
| std = np.std(binding_size_dist) |
|
|
| dist = norm(mean, std) |
| |
| |
| max_score = 0 |
| |
| |
| |
| scores = np.exp(outputs[0])[:, 1] |
| |
| indices = np.argsort(-1*scores) |
| |
| for j in range(1, len(indices)): |
| |
| |
| |
| score = (1-weight)*np.mean(scores[indices[:j]]) + weight*(dist.pdf(j/len(indices))) |
| |
| |
| if score > max_score: |
| |
| max_score = score |
| |
| |
| return max_score |
| |
