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# Source code for tllib.ranking.leep

"""
@author: Yong Liu
@contact: liuyong1095556447@163.com
"""

import numpy as np

__all__ = ['log_expected_empirical_prediction']

[docs]def log_expected_empirical_prediction(predictions: np.ndarray, labels: np.ndarray):
r"""
Log Expected Empirical Prediction in LEEP: A New Measure to
Evaluate Transferability of Learned Representations (ICML 2020)
<http://proceedings.mlr.press/v119/nguyen20b/nguyen20b.pdf>_.

The LEEP :math:\mathcal{T} can be described as:

.. math::
\mathcal{T}=\mathbb{E}\log \left(\sum_{z \in \mathcal{C}_s} \hat{P}\left(y \mid z\right) \theta\left(y \right)_{z}\right)

where :math:\theta\left(y\right)_{z} is the predictions of pre-trained model on source category, :math:\hat{P}\left(y \mid z\right) is the empirical conditional distribution estimated by prediction and ground-truth label.

Args:
predictions (np.ndarray): predictions of pre-trained model.
labels (np.ndarray): groud-truth labels.

Shape:
- predictions: (N, :math:C_s), with number of samples N and source class number :math:C_s.
- labels: (N, ) elements in [0, :math:C_t), with target class number :math:C_t.
- score: scalar
"""
N, C_s = predictions.shape
labels = labels.reshape(-1)
C_t = int(np.max(labels) + 1)

normalized_prob = predictions / float(N)
joint = np.zeros((C_t, C_s), dtype=float)  # placeholder for joint distribution over (y, z)

for i in range(C_t):
this_class = normalized_prob[labels == i]
row = np.sum(this_class, axis=0)
joint[i] = row

p_target_given_source = (joint / joint.sum(axis=0, keepdims=True)).T  # P(y | z)
empirical_prediction = predictions @ p_target_given_source
empirical_prob = np.array([predict[label] for predict, label in zip(empirical_prediction, labels)])
score = np.mean(np.log(empirical_prob))

return score


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