Source code for utils.metrics

# Copyright 2020-present, Pietro Buzzega, Matteo Boschini, Angelo Porrello, Davide Abati, Simone Calderara.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

import numpy as np


[docs] def backward_transfer(results): """ Calculates the backward transfer metric. Args: results (list): A list of lists representing the results of all classes of all task. Returns: float: The mean backward transfer value. """ n_tasks = len(results) li = [] for i in range(n_tasks - 1): li.append(results[-1][i] - results[i][i]) return np.mean(li)
[docs] def forward_transfer(results, random_results): """ Calculates the forward transfer metric. Args: results (list): A list of lists representing the results of all classes of all task. random_results (list): A list of results from a random baseline. Returns: float: The mean forward transfer value. """ n_tasks = len(results) li = [] for i in range(1, n_tasks): li.append(results[i - 1][i] - random_results[i][0]) return np.mean(li)
[docs] def forgetting(results): """ Calculates the forgetting metric. Args: results (list): A list of lists representing the results of all classes of all task. Returns: float: The mean forgetting value. """ n_tasks = len(results) li = [] for i in range(n_tasks - 1): results[i] += [0.0] * (n_tasks - len(results[i])) np_res = np.array(results) maxx = np.max(np_res, axis=0) for i in range(n_tasks - 1): li.append(maxx[i] - results[-1][i]) return np.mean(li)