How to plot a matrix with description for each column and row
Question:
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_mw
and lstm_accuracy_mw
such that each sigma
and knots
are 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 the dt_accuracy
the difference to the ref and the same for the lstm_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.
Answers:
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()
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_mw
andlstm_accuracy_mw
such that eachsigma
andknots
are 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_accuracy
the 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.
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()