Seaborn Confusion Matrix (heatmap) 2 color schemes (correct diagonal vs wrong rest)

Question

Background

In a confusion matrix, the diagonal represents the cases that the predicted label matches the correct label. So the diagonal is good, while all other cel

Answer 1

You can use mask= in the call to heatmap() to choose which cells to show. Using two different masks for the diagonal and the off_diagonal cells, you can get the desired output:

import numpy as np
import seaborn as sns

cf_matrix = np.array([[50, 2, 38],
                      [7, 43, 32],
                      [9,  4, 76]])

vmin = np.min(cf_matrix)
vmax = np.max(cf_matrix)
off_diag_mask = np.eye(*cf_matrix.shape, dtype=bool)

fig = plt.figure()
sns.heatmap(cf_matrix, annot=True, mask=~off_diag_mask, cmap='Blues', vmin=vmin, vmax=vmax)
sns.heatmap(cf_matrix, annot=True, mask=off_diag_mask, cmap='OrRd', vmin=vmin, vmax=vmax, cbar_kws=dict(ticks=[]))

If you want to get fancy, you can create the axes using GridSpec to have a better layout:

import numpy as np import seaborn as sns

fig = plt.figure()
gs0 = matplotlib.gridspec.GridSpec(1,2, width_ratios=[20,2], hspace=0.05)
gs00 = matplotlib.gridspec.GridSpecFromSubplotSpec(1,2, subplot_spec=gs0[1], hspace=0)

ax = fig.add_subplot(gs0[0])
cax1 = fig.add_subplot(gs00[0])
cax2 = fig.add_subplot(gs00[1])

sns.heatmap(cf_matrix, annot=True, mask=~off_diag_mask, cmap='Blues', vmin=vmin, vmax=vmax, ax=ax, cbar_ax=cax2)
sns.heatmap(cf_matrix, annot=True, mask=off_diag_mask, cmap='OrRd', vmin=vmin, vmax=vmax, ax=ax, cbar_ax=cax1, cbar_kws=dict(ticks=[]))