Created
July 13, 2020 19:42
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| import numpy as np | |
| def compute_log_loss(predicted, actual, eps=1e-14): | |
| """ Computes the logarithmic loss between predicted and | |
| actual when these are 1D arrays. | |
| :param predicted: The predicted probabilities as floats between 0-1 | |
| :param actual: The actual binary labels. Either 0 or 1. | |
| :param eps (optional): log(0) is inf, so we need to offset our | |
| predicted values slightly by eps from 0 or 1. | |
| """ | |
| predicted = np.clip(predicted, eps, 1 - eps) | |
| loss = -1 * np.mean(actual * np.log(predicted) | |
| + (1 - actual) | |
| * np.log(1 - predicted)) | |
| return loss |
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