"ValueError: max_evals=500 is too low for the Permutation explainer" shap answers me do I have to give more data (photos)? - shap

Question

I want to test the explainability of a multiclass semantic segmentation model, deeplab_v3plus with shap to know which features contribute the most to semantic classification. However I have a ValueError: max_evals=500 is too low when running my file, and I struggle to understand the reason.
import glob
from PIL import Image
import torch
from torchvision import transforms
from torchvision.utils import make_grid
import torchvision.transforms.functional as tf
from deeplab import deeplab_v3plus
import shap
def test(args):
# make a video prez
model = deeplab_v3plus('resnet101', num_classes=args.nclass, output_stride=16, pretrained_backbone=True)
model.load_state_dict(torch.load(args.seg_file,map_location=torch.device('cpu'))) # because no gpu available on sandbox environnement
model = model.to(args.device)
model.eval()
explainer = shap.Explainer(model)
with torch.no_grad():
for i, file in enumerate(args.img_folder):
img = img2tensor(file, args)
pred = model(img)
print(explainer(img))
if __name__ == '__main__':
class Arguments:
def __init__(self):
self.device = torch.device("cuda:1" if torch.cuda.is_available() else "cpu")
self.seg_file = "Model_Woodscape.pth"
self.img_folder = glob.glob("test_img/*.png")
self.mean = [0.485, 0.456, 0.406]
self.std = [0.229, 0.224, 0.225]
self.h, self.w = 483, 640
self.nclass = 10
self.cmap = {
1: [128, 64, 128], # "road",
2: [69, 76, 11], # "lanemarks",
3: [0, 255, 0], # "curb",
4: [220, 20, 60], # "person",
5: [255, 0, 0], # "rider",
6: [0, 0, 142], # "vehicles",
7: [119, 11, 32], # "bicycle",
8: [0, 0, 230], # "motorcycle",
9: [220, 220, 0], # "traffic_sign",
0: [0, 0, 0] # "void"
}
args = Arguments()
test(args)
But it returns:
(dee_env) jovyan#jupyter:~/use-cases/Scene_understanding/Code_Woodscape/deeplab_v3+$ python test_shap.py
BILINEAR is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BILINEAR instead.
Traceback (most recent call last):
File "/home/jovyan/use-cases/Scene_understanding/Code_Woodscape/deeplab_v3+/test_shap.py", line 85, in <module>
test(args)
File "/home/jovyan/use-cases/Scene_understanding/Code_Woodscape/deeplab_v3+/test_shap.py", line 37, in test
print(explainer(img))
File "/home/jovyan/use-cases/Scene_understanding/Code_Woodscape/deeplab_v3+/dee_env/lib/python3.9/site-packages/shap/explainers/_permutation.py", line 82, in __call__
return super().__call__(
File "/home/jovyan/use-cases/Scene_understanding/Code_Woodscape/deeplab_v3+/dee_env/lib/python3.9/site-packages/shap/explainers/_explainer.py", line 266, in __call__
row_result = self.explain_row(
File "/home/jovyan/use-cases/Scene_understanding/Code_Woodscape/deeplab_v3+/dee_env/lib/python3.9/site-packages/shap/explainers/_permutation.py", line 164, in explain_row
raise ValueError(f"max_evals={max_evals} is too low for the Permutation explainer, it must be at least 2 * num_features + 1 = {2 * len(inds) + 1}!")
ValueError: max_evals=500 is too low for the Permutation explainer, it must be at least 2 * num_features + 1 = 1854721!
In the source code it looks like it's because I don't give enough arguments. I only have three images in my test_img/* folder, is that why?

Answer 1

I have the same issue. A possible solution I found which seems to be working for my case is to replace this line
explainer = shap.Explainer(model)
With this line
explainer = shap.explainers.Permutation(model, max_evals = 1854721)
shap.Explainer by default has algorithm='auto'. From the documentation: shape.Explainer
By default the “auto” options attempts to make the best choice given
the passed model and masker, but this choice can always be overriden
by passing the name of a specific algorithm.
Since 'permutation' has been selected you can directly use shap.explainers.Permutation and set max_evals to the value suggested in the error message above.
Given the high number of your use case, this might take a really long time. I would suggest to use an easier model just for testing the above solution.