How change the color of boxes in confusion matrix using sklearn?

Question

Here is my code snippet to produce confusion matrix: I am wondering how can I change the color of boxes in confusion matrix for those boxes which are not located in diagonal same as heatmap using sklearn.

nb_classes = 15    
confusion_matrix = torch.zeros(nb_classes, nb_classes)

with torch.no_grad():
    for i, (inputs, target, classes, im_path) in enumerate(dataLoaders['test']):
        
        inputs = inputs.to(device)
        target = target.to(device)

        outputs = model(inputs)
        _, preds = torch.max(outputs, 1)

        for t, p in zip(target.view(-1), preds.view(-1)):
                confusion_matrix[t.long(), p.long()] += 1
                
num_classes = 15
class_names = ['A2CH', 'A3CH', 'A4CH_LV', 'A4CH_RV', 'A5CH', 'Apical_MV_LA_IAS',
                 'OTHER', 'PLAX_TV', 'PLAX_full', 'PLAX_valves', 'PSAX_AV', 'PSAX_LV',
                 'Subcostal_IVC', 'Subcostal_heart', 'Suprasternal']                
    
plt.figure()
plt.imshow(confusion_matrix, interpolation='nearest', cmap=plt.cm.Blues)

tick_marks = numpy.arange(num_classes)
classNames = class_names
   
thresh = confusion_matrix.max() / 2.
for i in range(confusion_matrix.shape[0]):
    for j in range(confusion_matrix.shape[1]):
        plt.text(j, i, format(confusion_matrix[i, j]),
                ha="center", va="center",
                color="white" if  confusion_matrix[i, j] == 0 or confusion_matrix[i, j] > thresh else "black") 
plt.tight_layout()
plt.colorbar()
return plt
plt.show()

what are you try to get any image? Normally heatmaps/ confusion metrix use color map for boxes, higher the value, darker the color. So, color is laready different as there is huge difference in values outside the diagonal — M_S_N, Jul 25 '19 at 09:14
@– M_S_N thank you. yes you are right. One more question. I am wondering how can I remove the .0 from values? I tried to convert them to int, but still returns float. — AI_NA, Aug 12 '19 at 11:42
try this and tell me if it worked confusion_matrix = torch.zeros(nb_classes, nb_classes, dtype=torch.int64) — M_S_N, Aug 12 '19 at 18:03
When I read *"change the color of boxes in confusion matrix for those boxes which are not located in diagonal"*, I expected the question was about 2 colormaps (e.g. `cmap='Blues'` and `'OrRd'`). For those people landing here looking for that, check this question: https://stackoverflow.com/questions/64800003/seaborn-confusion-matrix-heatmap-2-color-schemes-correct-diagonal-vs-wrong-re — NumesSanguis, Nov 12 '20 at 08:07

score 11 · Answer 1 · answered Oct 05 '19 at 07:16

Use heatmap to plot confusion matrix

import seaborn as sn
import pandas as pd
import matplotlib.pyplot as plt
array = [[33,2,0,0,0,0,0,0,0,1,3], 
    [3,31,0,0,0,0,0,0,0,0,0], 
    [0,4,41,0,0,0,0,0,0,0,1], 
    [0,1,0,30,0,6,0,0,0,0,1], 
    [0,0,0,0,38,10,0,0,0,0,0], 
    [0,0,0,3,1,39,0,0,0,0,4], 
    [0,2,2,0,4,1,31,0,0,0,2],
    [0,1,0,0,0,0,0,36,0,2,0], 
    [0,0,0,0,0,0,1,5,37,5,1], 
    [3,0,0,0,0,0,0,0,0,39,0], 
    [0,0,0,0,0,0,0,0,0,0]]
df_cm = pd.DataFrame(array, index = [i for i in "ABCDEFGHIJK"],
              columns = [i for i in "ABCDEFGHIJK"])
plt.figure(figsize = (10,7))
sn.heatmap(df_cm, annot=True,cmap="OrRd")

heatmap accept an extra argument cmap to change the color of matrix. These are some possible values for camp.

cmap = [Accent, Accent_r, Blues, Blues_r, BrBG, BrBG_r, BuGn, BuGn_r, BuPu, 
BuPu_r, CMRmap, CMRmap_r, Dark2, Dark2_r, GnBu, GnBu_r, Greens, Greens_r, 
Greys, Greys_r, OrRd, OrRd_r, Oranges, Oranges_r, PRGn, PRGn_r, Paired, Paired_r, 
Pastel1, Pastel1_r, Pastel2, Pastel2_r, PiYG, PiYG_r, PuBu, PuBuGn, PuBuGn_r, 
PuBu_r, PuOr, PuOr_r, PuRd, PuRd_r, Purples, Purples_r, RdBu, RdBu_r, RdGy, RdGy_r, 
RdPu, RdPu_r, RdYlBu, RdYlBu_r, RdYlGn, RdYlGn_r, Reds, Reds_r, Set1, Set1_r, 
Set2, Set2_r, Set3, Set3_r, Spectral, Spectral_r, Wistia, Wistia_r, YlGn, YlGnBu, 
YlGnBu_r, YlGn_r, YlOrBr, YlOrBr_r, YlOrRd, YlOrRd_r, afmhot, afmhot_r, autumn,
autumn_r, binary, binary_r, bone, bone_r, brg, brg_r, bwr, bwr_r, cividis, 
cividis_r, cool, cool_r, coolwarm, coolwarm_r, copper, copper_r, cubehelix, 
cubehelix_r, flag, flag_r, gist_earth, gist_earth_r, gist_gray, gist_gray_r,
gist_heat, gist_heat_r, gist_ncar, gist_ncar_r, gist_rainbow, gist_rainbow_r, 
gist_stern, gist_stern_r, gist_yarg, gist_yarg_r, gnuplot, gnuplot2, gnuplot2_r, 
gnuplot_r, gray, gray_r, hot, hot_r, hsv, hsv_r, icefire, icefire_r, inferno, 
inferno_r, jet, jet_r, magma, magma_r, mako, mako_r, nipy_spectral, nipy_spectral_r,
ocean, ocean_r, pink, pink_r, plasma, plasma_r, prism, prism_r, rainbow, rainbow_r, 
rocket, rocket_r, seismic, seismic_r, spring, spring_r, summer, summer_r, tab10, 
tab10_r, tab20, tab20_r, tab20b, tab20b_r, tab20c, tab20c_r, terrain, terrain_r, 
viridis, viridis_r, vlag, vlag_r, winter, winter_r]

cmap = "OrRd"

cmap = "Greens_r" cmap = "OrRd_r"

score 1 · Answer 2 · edited Mar 22 '22 at 14:54

def plot_confusion_matrix(y_true, y_pred, classes,
                      normalize=False,
                      title=None,
                      cmap=plt.cm.Blues):

you can change a name in cmap=plt.cm.Blues as the color you want such as green, red, orange, etc. Don't forget to add s in every word of colors. In addition, there are two default forms of each confusion matrix color. For example, it is green.

Greens. it is for green color in diagonal line.
Greens_r. It is for green color outside of diagonal line.

How change the color of boxes in confusion matrix using sklearn?

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