通用基准测试调试工具

此 notebook 演示了调试模式，用于区分文本和图像解释在不同输出结果上的性能。在多输出 token / 类别的情况下，通常查看任何异常值的个体性能会很有用。

此 notebook 包含文本和图像的调试示例。

文本示例 (摘要)

请参考 “Abstractive Summarization Benchmark Demo.ipynb” 以获得关于此基准测试示例的更多描述。

import matplotlib.pyplot as plt
import nlp
import numpy as np
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

import shap
import shap.benchmark as benchmark

%matplotlib inline

tokenizer = AutoTokenizer.from_pretrained("sshleifer/distilbart-xsum-12-6")
model = AutoModelForSeq2SeqLM.from_pretrained("sshleifer/distilbart-xsum-12-6")

dataset = nlp.load_dataset("xsum", split="train")

Using custom data configuration default

s = dataset["document"][0:1]

explainer = shap.Explainer(model, tokenizer)

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

shap_values = explainer(s)

Partition explainer: 2it [00:19,  9.59s/it]

sort_order = "positive"
perturbation = "keep"

sequential_perturbation = benchmark.perturbation.SequentialPerturbation(
    explainer.model, explainer.masker, sort_order, perturbation
)
mask_vals, curves, aucs = sequential_perturbation.model_score(shap_values, s, debug_mode=True)

xs = np.linspace(0, 1, 100)
for index in range(len(curves)):
    plt.plot(xs, curves[index, :], label=f"{shap_values.output_names[0][index]}")
    plt.ylabel("Model Output")
    plt.xlabel("Percent Unmasked")
    plt.legend()
plt.show()

fig, axs = plt.subplots(6, 4, figsize=(15, 25))
xs = np.linspace(0, 1, 100)
for index in range(len(curves)):
    x = index // 4
    y = index % 4
    axs[x, y].plot(xs, curves[index, :])
    axs[x, y].set_title(f"{shap_values.output_names[0][index]}")
    axs[x, y].set_ylim(-10, 4)
plt.show()

图像示例 (多类别)

请参考 “Image Multiclass Classification Benchmark Demo.ipynb” 以获得关于此基准测试示例的更多描述。

import json

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

import shap
import shap.benchmark as benchmark

%matplotlib inline

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

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()]

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)

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

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

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

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 [01:01, 20.59s/it]

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])

# getting output labels
labels = np.array(class_names)
sliced_labels = np.array([labels[index_list[:num_of_outputs]] for index_list in sorted_indexes])

sort_order = "positive"
perturbation = "keep"

sequential_perturbation = benchmark.perturbation.SequentialPerturbation(
    explainer.model, explainer.masker, sort_order, perturbation
)
mask_vals, curves, aucs = sequential_perturbation.model_score(
    shap_values, X[1:2], indices=sliced_indexes[0], debug_mode=True
)

xs = np.linspace(0, 1, 100)
for index in range(len(curves)):
    plt.plot(xs, curves[index, :], label=f"{sliced_labels[0][index]}")
    plt.ylabel("Model Output")
    plt.xlabel("Percent Unmasked")
    plt.legend()
plt.show()

image = X[1:2][0] / 255
top_class = np.array(mask_vals)[0][0 : int(np.array(mask_vals)[0].shape[0] / num_of_outputs), :]
top_class = top_class.reshape(top_class.shape[0], *X[0].shape)

masked_image = image * top_class[0].astype(int)
fig, axs = plt.subplots(4, 4, figsize=(15, 15))
for percent in range(0, 80, 5):
    masked_image = image * top_class[percent].astype(int)
    x = percent // 5 // 4
    y = percent // 5 % 4
    axs[x, y].imshow(masked_image)
    axs[x, y].set_title(f"Unmasking ({percent}%), Output ({round(curves[0][percent], 3)})")
fig.suptitle("Unmask (ordered by most positive SHAP value to least)")
plt.show()