使用 scikit-learn 进行鸢尾花分类

在这里，我们使用著名的鸢尾花物种数据集来说明 SHAP 如何解释多种不同模型类型的输出，从 K-最近邻到神经网络。这是一个非常小的数据集，只有 150 个样本。我们随机使用 130 个样本进行训练，20 个样本用于测试模型。由于这是一个特征很少的小数据集，我们使用整个训练数据集作为背景数据。在特征更多的问题中，我们可能只想传递训练数据集的中位数或加权的 K-中位数。虽然我们只有少量样本，但这个预测问题相当简单，所有方法都达到了完美的准确率。有趣的是，不同的方法有时会依赖于不同的特征集来进行预测。

加载数据

import time

import numpy as np
import sklearn
from sklearn.model_selection import train_test_split

import shap

X_train, X_test, Y_train, Y_test = train_test_split(*shap.datasets.iris(), test_size=0.2, random_state=0)

# rather than use the whole training set to estimate expected values, we could summarize with
# a set of weighted kmeans, each weighted by the number of points they represent. But this dataset
# is so small we don't worry about it
# X_train_summary = shap.kmeans(X_train, 50)


def print_accuracy(f):
    print(f"Accuracy = {100 * np.sum(f(X_test) == Y_test) / len(Y_test)}%")
    time.sleep(0.5)  # to let the print get out before any progress bars


shap.initjs()

K-最近邻

knn = sklearn.neighbors.KNeighborsClassifier()
knn.fit(X_train, Y_train)

print_accuracy(knn.predict)

Accuracy = 96.66666666666667%

解释测试集中的单个预测

explainer = shap.KernelExplainer(knn.predict_proba, X_train)
shap_values = explainer.shap_values(X_test.iloc[0, :])
shap.force_plot(explainer.expected_value[0], shap_values[:, 0], X_test.iloc[0, :])

Using 120 background data samples could cause slower run times. Consider using shap.sample(data, K) or shap.kmeans(data, K) to summarize the background as K samples.

可视化已省略，Javascript 库未加载！
你是否已在此 notebook 中运行 `initjs()`？如果此 notebook 来自其他用户，你还必须信任此 notebook (文件 -> 信任 notebook)。如果你在 github 上查看此 notebook，则 Javascript 已为安全起见被剥离。如果你正在使用 JupyterLab，此错误是因为尚未编写 JupyterLab 扩展。

解释测试集中的所有预测

shap_values = explainer.shap_values(X_test)
shap.force_plot(explainer.expected_value[0], shap_values[..., 0], X_test)

可视化已省略，Javascript 库未加载！
你是否已在此 notebook 中运行 `initjs()`？如果此 notebook 来自其他用户，你还必须信任此 notebook (文件 -> 信任 notebook)。如果你在 github 上查看此 notebook，则 Javascript 已为安全起见被剥离。如果你正在使用 JupyterLab，此错误是因为尚未编写 JupyterLab 扩展。

支持向量机（线性核）

svc_linear = sklearn.svm.SVC(kernel="linear", probability=True)
svc_linear.fit(X_train, Y_train)
print_accuracy(svc_linear.predict)

# explain all the predictions in the test set
explainer = shap.KernelExplainer(svc_linear.predict_proba, X_train)
shap_values = explainer.shap_values(X_test)
# this is multiclass so we only visualize the contributions to first class (hence index 0)
shap.force_plot(explainer.expected_value[0], shap_values[..., 0], X_test)

Accuracy = 100.0%

Using 120 background data samples could cause slower run times. Consider using shap.sample(data, K) or shap.kmeans(data, K) to summarize the background as K samples.

可视化已省略，Javascript 库未加载！
你是否已在此 notebook 中运行 `initjs()`？如果此 notebook 来自其他用户，你还必须信任此 notebook (文件 -> 信任 notebook)。如果你在 github 上查看此 notebook，则 Javascript 已为安全起见被剥离。如果你正在使用 JupyterLab，此错误是因为尚未编写 JupyterLab 扩展。

支持向量机（径向基函数核）

svc_linear = sklearn.svm.SVC(kernel="rbf", probability=True)
svc_linear.fit(X_train, Y_train)
print_accuracy(svc_linear.predict)

# explain all the predictions in the test set
explainer = shap.KernelExplainer(svc_linear.predict_proba, X_train)
shap_values = explainer.shap_values(X_test)
# this is multiclass so we only visualize the contributions to first class (hence index 0)
shap.force_plot(explainer.expected_value[0], shap_values[..., 0], X_test)

Accuracy = 100.0%

Using 120 background data samples could cause slower run times. Consider using shap.sample(data, K) or shap.kmeans(data, K) to summarize the background as K samples.

可视化已省略，Javascript 库未加载！
你是否已在此 notebook 中运行 `initjs()`？如果此 notebook 来自其他用户，你还必须信任此 notebook (文件 -> 信任 notebook)。如果你在 github 上查看此 notebook，则 Javascript 已为安全起见被剥离。如果你正在使用 JupyterLab，此错误是因为尚未编写 JupyterLab 扩展。

逻辑回归

linear_lr = sklearn.linear_model.LogisticRegression(solver="newton-cg")
linear_lr.fit(X_train, Y_train)
print_accuracy(linear_lr.predict)

# explain all the predictions in the test set
explainer = shap.KernelExplainer(linear_lr.predict_proba, X_train)
shap_values = explainer.shap_values(X_test)
shap.force_plot(explainer.expected_value[0], shap_values[..., 0], X_test)

Accuracy = 100.0%

Using 120 background data samples could cause slower run times. Consider using shap.sample(data, K) or shap.kmeans(data, K) to summarize the background as K samples.

可视化已省略，Javascript 库未加载！
你是否已在此 notebook 中运行 `initjs()`？如果此 notebook 来自其他用户，你还必须信任此 notebook (文件 -> 信任 notebook)。如果你在 github 上查看此 notebook，则 Javascript 已为安全起见被剥离。如果你正在使用 JupyterLab，此错误是因为尚未编写 JupyterLab 扩展。

决策树

import sklearn.tree

dtree = sklearn.tree.DecisionTreeClassifier(min_samples_split=2)
dtree.fit(X_train, Y_train)
print_accuracy(dtree.predict)

# explain all the predictions in the test set
explainer = shap.KernelExplainer(dtree.predict_proba, X_train)
shap_values = explainer.shap_values(X_test)
shap.force_plot(explainer.expected_value[0], shap_values[..., 0], X_test)

Accuracy = 100.0%

Using 120 background data samples could cause slower run times. Consider using shap.sample(data, K) or shap.kmeans(data, K) to summarize the background as K samples.

可视化已省略，Javascript 库未加载！
你是否已在此 notebook 中运行 `initjs()`？如果此 notebook 来自其他用户，你还必须信任此 notebook (文件 -> 信任 notebook)。如果你在 github 上查看此 notebook，则 Javascript 已为安全起见被剥离。如果你正在使用 JupyterLab，此错误是因为尚未编写 JupyterLab 扩展。

随机森林

from sklearn.ensemble import RandomForestClassifier

rforest = RandomForestClassifier(n_estimators=100, max_depth=None, min_samples_split=2, random_state=0)
rforest.fit(X_train, Y_train)
print_accuracy(rforest.predict)

# explain all the predictions in the test set
explainer = shap.KernelExplainer(rforest.predict_proba, X_train)
shap_values = explainer.shap_values(X_test)
shap.force_plot(explainer.expected_value[0], shap_values[..., 0], X_test)

Accuracy = 100.0%

Using 120 background data samples could cause slower run times. Consider using shap.sample(data, K) or shap.kmeans(data, K) to summarize the background as K samples.

可视化已省略，Javascript 库未加载！
你是否已在此 notebook 中运行 `initjs()`？如果此 notebook 来自其他用户，你还必须信任此 notebook (文件 -> 信任 notebook)。如果你在 github 上查看此 notebook，则 Javascript 已为安全起见被剥离。如果你正在使用 JupyterLab，此错误是因为尚未编写 JupyterLab 扩展。

神经网络

from sklearn.neural_network import MLPClassifier

nn = MLPClassifier(solver="lbfgs", alpha=1e-1, hidden_layer_sizes=(5, 2), random_state=0)
nn.fit(X_train, Y_train)
print_accuracy(nn.predict)

# explain all the predictions in the test set
explainer = shap.KernelExplainer(nn.predict_proba, X_train)
shap_values = explainer.shap_values(X_test)
shap.force_plot(explainer.expected_value[0], shap_values[..., 0], X_test)

Accuracy = 96.66666666666667%

Using 120 background data samples could cause slower run times. Consider using shap.sample(data, K) or shap.kmeans(data, K) to summarize the background as K samples.

可视化已省略，Javascript 库未加载！
你是否已在此 notebook 中运行 `initjs()`？如果此 notebook 来自其他用户，你还必须信任此 notebook (文件 -> 信任 notebook)。如果你在 github 上查看此 notebook，则 Javascript 已为安全起见被剥离。如果你正在使用 JupyterLab，此错误是因为尚未编写 JupyterLab 扩展。