英文:
How to plot a matrix with description for each column and row
问题
以下是您要翻译的内容：
- 要绘制
dt_accuracy_mw和lstm_accuracy_mw，以便可视化每个sigma和knots：
sigma/knots 4 5 6 7
0.2
0.35 实际矩阵包含上述的准确性
0.5
0.65
- 上述的综合版本，每个条目包括
dt_accuracy (ref_dt_accuracy - dt_accuracy)/lstm_accuracy (ref_lstm_accuracy - lstm_accuracy)，因此每个条目包括dt_accuracy与参考值的差异以及相同的lstm_accuracy。然后，模型的每个准确性得分由/分隔。
要使用开源库如matplotlib、seaborn等实现这两个图形，您可以参考以下Python代码：
import matplotlib.pyplot as plt
import numpy as np
# 1. 绘制第一个矩阵
plt.figure(figsize=(8, 6))
plt.imshow(dt_accuracy_mw, cmap='viridis', origin='lower')
plt.colorbar()
plt.xticks(np.arange(len(knots)), knots)
plt.yticks(np.arange(len(sigma)), sigma)
plt.xlabel('knots')
plt.ylabel('sigma')
plt.title('dt_accuracy_mw')
# 2. 绘制第二个矩阵
combined_matrix = np.zeros_like(dt_accuracy_mw, dtype=float)
for i in range(len(sigma)):
for j in range(len(knots)):
combined_matrix[i, j] = f'{dt_accuracy_mw[i][j] - ref_dt_accuracy:.2f} / {lstm_accuracy_mw[i][j] - ref_lstm_accuracy:.2f}'
plt.figure(figsize=(8, 6))
plt.imshow(combined_matrix, cmap='coolwarm', origin='lower')
plt.colorbar()
plt.xticks(np.arange(len(knots)), knots)
plt.yticks(np.arange(len(sigma)), sigma)
plt.xlabel('knots')
plt.ylabel('sigma')
plt.title('Combined Matrix')
plt.show()
这段代码将帮助您绘制所需的两个矩阵。您可以根据需要进一步调整图形的样式和标签。
英文:
I have a data set I need to augment. Therefore, I have implemented an augmentation method called magnitude warping that has two hyperparameters to tune, namely sigma and knots. To assess the quality, I have two models that I train using the augmented data and test it on part of the real data. To compare the accuracy I also trained the models on only the real data. Lets assume the following Python code:
# test accuracy trained on real data only
ref_dt_accuracy = 0.86
ref_lstm_accuracy = 0.85
# test accuracy for each pair of hyperparameters
sigma = [0.2, 0.35, 0.5, 0.65]
knots = [4,5,6,7]
dt_accuracy_mw = [
[0.82, 0.85, 0.83, 0.84],
[0.8, 0.79, 0.81, 0.79],
[0.78,0.77, 0.74, 0.76],
[0.74, 0.72, 0.78, 0.70]
]
lstm_accuracy_mw = [
[0.80, 0.83, 0.81, 0.82],
[0.78, 0.77, 0.79, 0.77],
[0.76,0.75, 0.72, 0.74],
[0.72, 0.7, 0.76, 0.68]
]
Now, I want to plot two (three if the last option is possible) matrices:
- Plotting
dt_accuracy_mwandlstm_accuracy_mwsuch that eachsigmaandknotsare visualized:
sigma/knots 4 5 6 7
0.2
0.35 Actual matrix consisting of aforementioned accuracies
0.5
0.65
- A combined version of above such that each entry consists of
dt_accuracy (ref_dt_accuracy - dt_accuracy)/lstm_accuracy (ref_lstm_accuracy - lstm_accuracy), so each entry consists of thedt_accuracythe difference to the ref and the same for thelstm_accuracy. Each accuracy score of the models are then seperated by the/
How would one achieve this using any open source libraries such as matplotlib, seaborn etc.
答案1
得分: 3
你可以按照以下方式创建一个Seaborn热图：
from matplotlib import pyplot as plt
import seaborn as sns
sigma = [0.2, 0.35, 0.5, 0.65]
knots = [4, 5, 6, 7]
dt_accuracy_mw = [[0.82, 0.85, 0.83, 0.84],
[0.8, 0.79, 0.81, 0.79],
[0.78, 0.77, 0.74, 0.76],
[0.74, 0.72, 0.78, 0.70]]
ax = sns.heatmap(data=dt_accuracy_mw, xticklabels=knots, yticklabels=sigma,
linewidths=1, linecolor='blue', clip_on=False, annot=True, cbar=False,
cmap=sns.color_palette(['white'], as_cmap=True))
ax.set_xlabel('knots')
ax set_ylabel('sigma')
plt.tight_layout()
plt.show()
如果我正确理解第二个问题，一个注释矩阵可以完成任务（data可以是任何具有正确宽度和高度的内容）：
from matplotlib import pyplot as plt
import seaborn as sns
ref_dt_accuracy = 0.86
ref_lstm_accuracy = 0.85
sigma = [0.2, 0.35, 0.5, 0.65]
knots = [4, 5, 6, 7]
dt_accuracy_mw = [[0.82, 0.85, 0.83, 0.84],
[0.8, 0.79, 0.81, 0.79],
[0.78, 0.77, 0.74, 0.76],
[0.74, 0.72, 0.78, 0.70]]
lstm_accuracy_mw = [[0.80, 0.83, 0.81, 0.82],
[0.78, 0.77, 0.79, 0.77],
[0.76, 0.75, 0.72, 0.74],
[0.72, 0.7, 0.76, 0.68]]
annot_matrix = [[f'{ref_dt_accuracy - dt:.2f} / {ref_lstm_accuracy - lstm:.2f}'
for dt, lstm in zip(dt_row, lstm_row)]
for dt_row, lstm_row in zip(dt_accuracy_mw, lstm_accuracy_mw)]
ax = sns.heatmap(data=dt_accuracy_mw, xticklabels=knots, yticklabels=sigma,
annot=annot_matrix, fmt='',
linewidths=2, linecolor='crimson', clip_on=False, cbar=False,
cmap=sns.color_palette(['aliceblue'], as_cmap=True))
ax.set_xlabel('knots')
ax set_ylabel('sigma')
plt.tight_layout()
plt.show()
英文:
You can create a Seaborn heatmap as follows:
from matplotlib import pyplot as plt
import seaborn as sns
sigma = [0.2, 0.35, 0.5, 0.65]
knots = [4, 5, 6, 7]
dt_accuracy_mw = [[0.82, 0.85, 0.83, 0.84],
[0.8, 0.79, 0.81, 0.79],
[0.78, 0.77, 0.74, 0.76],
[0.74, 0.72, 0.78, 0.70]]
ax = sns.heatmap(data=dt_accuracy_mw, xticklabels=knots, yticklabels=sigma,
linewidths=1, linecolor='blue', clip_on=False, annot=True, cbar=False,
cmap=sns.color_palette(['white'], as_cmap=True))
ax.set_xlabel('knots')
ax.set_ylabel('sigma')
plt.tight_layout()
plt.show()
If I understand the second question correctly, a matrix of annotations would do the job (the data can be anything with the correct width and height):
from matplotlib import pyplot as plt
import seaborn as sns
ref_dt_accuracy = 0.86
ref_lstm_accuracy = 0.85
sigma = [0.2, 0.35, 0.5, 0.65]
knots = [4, 5, 6, 7]
dt_accuracy_mw = [[0.82, 0.85, 0.83, 0.84],
[0.8, 0.79, 0.81, 0.79],
[0.78, 0.77, 0.74, 0.76],
[0.74, 0.72, 0.78, 0.70]]
lstm_accuracy_mw = [[0.80, 0.83, 0.81, 0.82],
[0.78, 0.77, 0.79, 0.77],
[0.76, 0.75, 0.72, 0.74],
[0.72, 0.7, 0.76, 0.68]]
annot_matrix = [[f'{ref_dt_accuracy - dt:.2f} / {ref_lstm_accuracy - lstm:.2f}'
for dt, lstm in zip(dt_row, lstm_row)]
for dt_row, lstm_row in zip(dt_accuracy_mw, lstm_accuracy_mw)]
ax = sns.heatmap(data=dt_accuracy_mw, xticklabels=knots, yticklabels=sigma,
annot=annot_matrix, fmt='',
linewidths=2, linecolor='crimson', clip_on=False, cbar=False,
cmap=sns.color_palette(['aliceblue'], as_cmap=True))
ax.set_xlabel('knots')
ax.set_ylabel('sigma')
plt.tight_layout()
plt.show()
通过集体智慧和协作来改善编程学习和解决问题的方式。致力于成为全球开发者共同参与的知识库，让每个人都能够通过互相帮助和分享经验来进步。
评论