8892479082

Committed 30 Apr 2024 09:27AM UTC coverage: 51.805% (+0.05%) from 51.751%

Build # 8892479082

Build Type

Pull #1633

github

Committed by

web-flow

Commit Message

Merge 876140c8f into 6e5e3b278

Pull Request Pull Request #1633: Updatable objects

Run Details

248 of 436 new or added lines in 20 files covered. (56.88%)

9 existing lines in 1 file now uncovered.

15081 of 29111 relevant lines covered (51.81%)

0.52 hits per line

Source File
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59.26

/avalanche/evaluation/plot_utils.py

################################################################################
# Copyright (c) 2021 ContinualAI.                                              #
# Copyrights licensed under the MIT License.                                   #
# See the accompanying LICENSE file for terms.                                 #
#                                                                              #
# Date: 24/07/2021                                                             #
# Author(s): Antonio Carta                                                     #
# E-mail: contact@continualai.org                                              #
# Website: avalanche.continualai.org                                           #
################################################################################
import numpy as np
from matplotlib import pyplot as plt


def learning_curves_plot(all_metrics: dict):
    """Creates a plot with separate learning curves for each experience.

    :param all_metrics: Dictionary of metrics as returned by
        EvaluationPlugin.get_all_metrics
    :return: matplotlib figure
    """
    accs_keys = list(filter(lambda x: "Top1_Acc_Exp" in x, all_metrics.keys()))
    fig, ax = plt.subplots()
    for ak in accs_keys:
        k = ak.split("/")[-1]
        x, y = all_metrics[ak]
        plt.plot(x, y, label=k)
    ax.legend()
    ax.set_xlabel("Iterations")
    ax.set_ylabel("Experience Accuracy")
    return fig


def plot_metric_matrix(metric_matrix, title, *, ax=None, text_values=True):
    """Plot a matrix of metrics (e.g. forgetting over time).

    :param metric_matrix: 2D accuracy matrix with shape <time, experiences>
    :param title: plot title
    :param ax: axes to use for figure
    :param text_values: (bool) whether to add the value as text in each cell.

    :return: a matplotlib.Figure
    """
    if ax is None:
        fig, ax = plt.subplots()
    else:
        fig = ax.figure

    metric_matrix = np.array(metric_matrix).T
    ax.matshow(metric_matrix)
    ax.set_ylabel("Experience")
    ax.set_xlabel("Time")
    ax.set_title(title)

    if text_values:
        for i in range(len(metric_matrix)):
            for j in range(len(metric_matrix[0])):
                ax.text(
                    j,
                    i,
                    f"{metric_matrix[i][j]:.3f}",
                    ha="center",
                    va="center",
                    color="w",
                )
    return fig

1	################################################################################
2	# Copyright (c) 2021 ContinualAI. #
3	# Copyrights licensed under the MIT License. #
4	# See the accompanying LICENSE file for terms. #
5	# #
6	# Date: 24/07/2021 #
7	# Author(s): Antonio Carta #
8	# E-mail: contact@continualai.org #
9	# Website: avalanche.continualai.org #
10	################################################################################
11	import numpy as np	1✔
12	from matplotlib import pyplot as plt	1✔
13
14
15	def learning_curves_plot(all_metrics: dict):	1✔
16	"""Creates a plot with separate learning curves for each experience.
17
18	:param all_metrics: Dictionary of metrics as returned by
19	EvaluationPlugin.get_all_metrics
20	:return: matplotlib figure
21	"""
22	accs_keys = list(filter(lambda x: "Top1_Acc_Exp" in x, all_metrics.keys()))	×
23	fig, ax = plt.subplots()	×
24	for ak in accs_keys:	×
25	k = ak.split("/")[-1]	×
26	x, y = all_metrics[ak]	×
27	plt.plot(x, y, label=k)	×
28	ax.legend()	×
29	ax.set_xlabel("Iterations")	×
30	ax.set_ylabel("Experience Accuracy")	×
31	return fig	×
32
33
34	def plot_metric_matrix(metric_matrix, title, *, ax=None, text_values=True):	1✔
35	"""Plot a matrix of metrics (e.g. forgetting over time).
36
37	:param metric_matrix: 2D accuracy matrix with shape <time, experiences>
38	:param title: plot title
39	:param ax: axes to use for figure
40	:param text_values: (bool) whether to add the value as text in each cell.
41
42	:return: a matplotlib.Figure
43	"""
44	if ax is None:	1✔
45	fig, ax = plt.subplots()	1✔
46	else:
NEW 47	fig = ax.figure	×
48
49	metric_matrix = np.array(metric_matrix).T	1✔
50	ax.matshow(metric_matrix)	1✔
51	ax.set_ylabel("Experience")	1✔
52	ax.set_xlabel("Time")	1✔
53	ax.set_title(title)	1✔
54
55	if text_values:	1✔
56	for i in range(len(metric_matrix)):	1✔
57	for j in range(len(metric_matrix[0])):	1✔
58	ax.text(	1✔
59	j,
60	i,
61	f"{metric_matrix[i][j]:.3f}",
62	ha="center",
63	va="center",
64	color="w",
65	)
66	return fig	1✔

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