Image Data Explanation Benchmarking: Image Multiclass Classification

This notebook demonstrates how to use the benchmark utility to benchmark the performance of an explainer for image data. In this demo, we showcase explanation performance for partition explainer on an Image Multiclass Classification model. The metrics used to evaluate are “keep positive” and “keep negative”. The masker used is Image Masker with Inpaint Telea.

The new benchmark utility uses the new API with MaskedModel as wrapper around user-imported model and evaluates masked values of inputs.

import json

import numpy as np
from tensorflow.keras.applications.resnet50 import ResNet50, preprocess_input

import shap
import shap.benchmark as benchmark

Load Data and Model

model = ResNet50(weights="imagenet")
X, y = shap.datasets.imagenet50()

Class Label Mapping

url = "https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json"
with open(shap.datasets.cache(url)) as file:
    class_names = [v[1] for v in json.load(file).values()]

Define Score Function

def f(x):
    tmp = x.copy()
    if len(tmp.shape) == 2:
        tmp = tmp.reshape(tmp.shape[0], *X[0].shape)
    preprocess_input(tmp)
    return model(tmp)

Define Image Masker

masker = shap.maskers.Image("inpaint_telea", X[0].shape)

Create Explainer Object

explainer = shap.Explainer(f, masker, output_names=class_names)

explainers.Partition is still in an alpha state, so use with caution...

Run SHAP Explanation

shap_values = explainer(
    X[1:3], max_evals=500, batch_size=50, outputs=shap.Explanation.argsort.flip[:4]
)

Partition explainer:  50%|█████████████████                 | 1/2 [00:00<?, ?it/s]

Partition explainer: 3it [03:15, 65.24s/it]

Plot SHAP Explanation

shap.image_plot(shap_values)

Get Output Class Indices

output = f(X[1:3]).numpy()
num_of_outputs = 4
sorted_indexes = np.argsort(-output, axis=1)
sliced_indexes = np.array(
    [index_list[:num_of_outputs] for index_list in sorted_indexes]
)

Define Metrics (Sort Order & Perturbation Method)

sort_order = "positive"
perturbation = "keep"

Benchmark Explainer

sequential_perturbation = benchmark.perturbation.SequentialPerturbation(
    explainer.model, explainer.masker, sort_order, perturbation
)
xs, ys, auc = sequential_perturbation.model_score(
    shap_values, X[1:2], indices=sliced_indexes[0]
)
sequential_perturbation.plot(xs, ys, auc)