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)