Comments (6)
Hm, that's weird ... Do you have a example snippet to reproduce this issue?
from mlxtend.
cmt = confusion_matrix
of 1000x1000 <type 'numpy.ndarray'>
and here the part where I am trying to save plot:
fig, ax = plot_confusion_matrix(conf_mat=cmt, figsize=(50,50))
fig.savefig('results.jpg')
from mlxtend.
50 x 50 is pretty large. Technically, it should work, but maybe that's why it's eating up your memory. For debugging purposes, could you try figsize=(10,10) or so?
from mlxtend.
Same thing.. segmentation fault with (10,10) and or with default values of (2.5, 2.5)!
from mlxtend.
Hm, I believe that's due to the many text labels in the plot (1000 * 1000 = 1,000,000); it's probably more than matplotlib can handle on a desktop computer. E.g., if I am just trying to execute the following, I get a similar memory prob:
import numpy as np
import matplotlib.pyplot as plt
ary = (np.random.sample((1000, 1000)) * 1000).astype(int)
plt.table(cellText=ary, loc='center')
plt.show()
I don't have a good solution for that in mind, unfortunately.
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Aha.. I see. Anyway, thank you for the quick replies.
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