Shap summary plot model_selection import train_test_split from sklearn import svm import shap # 加载数据集（这里使用iris数据集作为例子） iris = datasets. SHAP 特征重要性-Summary Plot. mean(0). 4的可视化思想，通过结合SHAP蜂巢图和柱状图，直观展示各特征对模型输出的影响及其平均重要性 shap. fig, ax = plt. The feature used for coloring is automatically chosen to highlight Create a SHAP dependence scatter plot, optionally colored by an interaction feature. savefig(path) pl. keras. For a more informative plot, we will next look at the summary plot. SHAP (SHapley Additive exPlanations) is a game theoretic approach to explain the output of any machine learning model. In this article, an alternative is presented, along with the code used: 大家好，我是云朵君！ 导读： SHAP是Python开发的一个"模型解释"包，是一种博弈论方法来解释任何机器学习模型的输出。 本文重点介绍11种shap可视化图形来解释任何机器学习模型的使用方法。上篇用 SHAP 可视化解释机器学习模型实用指南(上)已经介绍了特征重要性和特征效果可视化，而本篇将继续 While a SHAP summary plot gives a general overview of each feature a SHAP dependence plot show how the model output varies by feauture value. show() 特征重要性排序: 从图中可以看出，MonthlyCharges、Contract、tenure、OnlineSecurity 等特征对模型预测具有较大的影响，它们位于图的顶部，表明这些特征在预测客户流失时起到了关键作用 在`shap. This shows how the model depends on the given feature, and is like a richer extension of classical partial dependence plots. summary_plot(shap_values, test, max_display=5) 以上只是罗列结果，并未进行统计处理，而对模型产生最大影响的前N的特征，一般是通过各个特征绝对值的均值（abs()->mean()）得到的，使用绝对值解决了正负抵消的问题，更关注相关性的大小。 While plotting several instance-level explanations using the text plot can be very informative, sometime you want global summaries of the impact of tokens over the a large set of instances. Display the summary_plot of the Summary Plot. It connects optimal credit allocation with local explanations using the classic Shapley values from game theory and their related extensions (see papers for details and citations). resnet50 import ResNet50, preprocess_input import shap # load pre-trained model and choose two images to explain model = ResNet50 (weights = "imagenet") def f (X): tmp = X. The plot of the CatBoost model is called beeswarm plot. savefig("SHAP_numpy summary_plot. 我希望用 SHAP 值解释你的模型对你的工作有很大帮助。 在本文中，我将介绍 SHAP 图中的更多新颖特性。如果你还没有阅读上一篇文章，我建议你先阅读一下，然后再回到这篇文章。 The SHAP Summary Plot provides a high-level composite view that shows the importance of features and how their SHAP values are spread across the data. shap_interaction_values(X) shap. gcf(), plt. I did modify the format of the shap values given by DeepExplainer so I could use Summary_plot. # Create a summary plot shap. Interpreting Black Box Models: SHAP works with both straightforward `shap. summary_plot源码可以实现函数参数增加save=False,path=False在summary_plot函数最下面增加 if save: pl. 这会返回一个形状为`(n_samples, n_features)`的矩阵，每一行表示一个样本，列对应于输入特征，数值表示该特征对于样本预测的贡献。 5. violin function to visualize the distribution and variability of SHAP values for each feature in an XGBoost model. summary_plot(shap_values, X_test) Die zusammenfassende Darstellung zeigt die Bedeutung jedes Merkmals im Modell. These values represent the feature importances for each instance in the test set. summary_plot ( shap_values , X , plot_type = "bar" ) Feature and dataset description However, the problem lies on the output SHAP summary plot. figure() shap. It shows the impact of each feature on the model's output, with each dot representing a single prediction. 我看了shap的教程，然后实践了一下，发现summary_plot只显示了20个特征，而我的数据集里不止20个特征。 我怎么查看所有特征的summary_plot呢？ # See the absolute shap value of how each feaure contributes to the model output shap. It delivers the SHAP Summary Plot. pdf", format = 'pdf', bbox_inches= 'tight', dpi= 1200) 该shap可视化对角线元素：每个特征的主效应（该特征对模型预测的独立贡献 ShapとはShap値は予測した値に対して、「それぞれの特徴変数がその予想にどのような影響を与えたか」を算出するものです。これにより、ある特徴変数の値の増減が与える影響を可視化することができます。 shap. As mentioned above, it’s unclear if “PercentSalaryHike” was a prior measure or a post measure of performance 同一个shap_values，不同的计算 summary_plot中的shap_values是numpy. summary_plotはSHAP値のサマリープロットを表示します。これは、機械学習モデルの各特徴が予測結果に与える影響の全体像を把握するための有用なツールです。 具体的には以下の情報が得られます。 特徴の重要度: shap. target # 划分 SHAP Summary Plot. Hence, they lie on a straight line (the The ColorZilla Chrome extension (or Firefox plugin) is really useful for getting the RGB and other hex codes. The Summary Plot is a cross between a Swamp Plot and a Violin Plot in that all the instances are displayed and the resulting shapes show the frequencies and distributions of the data. The output is interactive HTML and you can click on any token to toggle the display of the SHAP value assigned to that token. summary_plot(shap_values, X_display, plot_type="bar") Output: Summary plot of BlackBox model Applications of SHAP: Explain ability in Machine Learning Models: SHAP makes it easier to understand how complex machine models make decisions. plots. 前言简单来说，本文是一篇面向汇报的搬砖教学，用可解释模型SHAP来解释你的机器学习模型～是让业务小伙伴理解机器学习模型，顺利推动项目进展的必备技能～～ 本文不涉及深难的SHAP理论基础，旨在通俗易懂地介绍 在文心一言中输入： 使用shap. 原始的shap一般是直接show出特征，需求是保存多张图，做特征变化的对比直接改shap. savefig("SHAP_Summary_Plot. round(2)) Different models may have different opinions about what's important and what's not, and SHAP explains models. I want to SHAP plot show about 2 input columns (category1, category2) but actually the SHAP plot shows 5 columns(A,B,C,X,Y) (picture below). I was wondering if it was correct to do so. gca() # You can change the min and max value of xaxis by changing the arguments of: ax. 当然shap. 5) plt. summary_plot is modified to present the cumulative impact of the original categorical features by averaging the values relevant to the original feature columns. 越來越多人利用SHAP值來解釋機器學習，但由於python的SHAP套件中可視覺化的部分可以自由調整的很少，因此本篇會以如何變更視覺化圖的一些範例與 Passing a row of SHAP values to the bar plot function creates a local feature importance plot, where the bars are the SHAP values for each feature. summary_plot(shap_test, df, max_display=25, auto_size_plot=True) Получившийся график важности фичей: Как его читать: значения We calculate SHAP values for the test set using the shap_values method of the explainer. API Examples; View page source; API Examples These examples parallel the namespace structure of SHAP. 特征值与SHAP的关联方向 4. Adapting the example from the docs to your SHAP is based on the magnitude of feature attributions. dependence_plot (0, shap_values, X) If we build a dependence plot for feature 0, we see that it only takes two values and that these values are entirely dependent on the value of the feature. load_iris() X = iris. ” For example, words like “excellent” might have high SHAP values for plt. 게임이론 in XAI SHAP (Game Theory) Shapley Value 설명 in eXplainable AI SHAP Contents 모델 학습 大家好，我是云朵君！ 导读： SHAP是Python开发的一个"模型解释"包，是一种博弈论方法来解释任何机器学习模型的输出。 本文重点介绍11种shap可视化图形来解释任何机器学习模型的使用方法。上篇用 SHAP 可视化解释机器学习模型实用指南(上)已经介绍了特征重要性和特征效果可视化，而本篇将继续 建议优先使用shap. In the case that the colors of the force plot want to be modified, the plot_cmap parameter can be used to change the force plot colors. shap_values(val) p= shap. SHAP is a game theoretic approach to explain the output of any machine learning model using Summary plot: violin. Feature Importance Analysis: It shows us which parts are more important for better understanding. values . The feature importance plot is useful, but contains no information beyond the importances. summary_plot(shap_values, X_train)produces the following plot: Exhibit (K): The SHAP Variable Importance Plot. show() You can also combine the above solutions to get the best resolution for your result. summary_plot (shap_values, X) shap . SHAP值分布情况 3. array数组 plots. Solution Approach 文章浏览阅读1. However they can be hard to explain to neophytes. Die Ergebnisse zeigen, dass "Status", "Beschwerden" und "Häufigkeit der Nutzung" eine wichtige Rolle bei der Ermittlung der Welcome to the SHAP documentation . FAQs on SHAP and Model Interpretation Q: Why are SHAP values better than other methods of explaining models? A: SHAP values are based on solid mathematical theory (the Shapley So I am generating a shap summary plot like so: explainer = shap. summary_plot 函数默认会显示所有特征的影响摘要，如果你想只展示X_test中每个样本影响最大的五个特征，你需要自定义一些步骤。首先，你需要计算每个样本的SHAP值，并对它们按数值大小排序。然后，你可以选择 Matplotlib 如何将 SHAP 总结图保存为 PDF/SVG 在 SHAP（SHapley Additive exPlanations）的解释性模型解释中，通常会用到 SHAP 总结图。Matplotlib 是一个广泛使用的用于绘制数据可视化图形的 Python 库，我们可以使用 Matplotlib 将 SHAP 总结图保存为 PDF 或 SVG 文件。 本文将介绍 A summary plot helps us understand the overall importance of different features across all predictions. Each point (observation) is coloured based on its feature value. 本次尝试旨在复现Fig. bar(shap_values2) 同一个shap_values ，不同的计算. KernelExplainer(model, X_test[:100,:]) shap_values = explainer. shap_interaction_values = explainer. summary_plot(shap_values, X_train, cmap = "plasma") Summary plot with default and modified color palette (Image by the author) Waterfall Plot. from @GarrettCGraham code fig, ax = plt. Note: For this section, you must have installed at least SHAP version 0. [5]: 这段代码通过 SHAP 库内置的函数计算模型在测试集上每个特征的 SHAP 值，并自动生成一个条形图，总结各个特征对模型预测的重要性，条形图是由 SHAP 库的 shap. Note that every dot is a person, and the vertical dispersion at a single feature value results from interaction effects in the model. 将 SHAP 值矩阵传递给条形图函数会创建一个全局特征重要性图，其中每个特征的全局重要性被视为该特征在所有给定样本中的平均绝对值。 shap. applications. text (shap_values, num_starting_labels = 0, grouping_threshold = 0. The summary plot는 특성 중요도(feature importance)와 특성 효과(feature effects)를 겹합한다. So this summary plot function normally follows the long format dataset obtained using shap. show() You can violin summary plot; waterfall plot; Benchmarks; Development. 其输出的代码为（ 不正确 ）： from sklearn import datasets from sklearn. summary plot 更改为感兴趣的颜色，以 RGB 为例。 为了说明这一点，我尝试使用 matplotlib 来创建我的调色板。 但是，到目前为止还没有奏效。 有人可以帮我吗 这是我迄今为止尝试过的：使用iris数据集创建示例 这里没问题 在此之 5. show() Step 5: Plot SHAP Values for a Specific Feature Interpretation: Use SHAP summary plots to identify which words are pushing reviews towards “positive” or “negative. savefig("1. summary_plot展示各个特征对模型输出类别的重要性. Zeige die summary_plot mit den SHAP-Werten und dem Testset an. TreeExplainer(model). Let's start with the summary plot. This plot is made of all the dots in the train data. Dropdown(options=tuple_of_labels, value=0, shap. summary_plot`中，虽然`color`参数允许你指定颜色方案，但它本身并不提供直接控制字体大小的功能。然而，你可以通过Matplotlib库来调整全局的字体大小，因为`summary_plot`使用了Matplotlib作为底层绘图引擎。 对于字体大小的调整，可以使用Matplotlib的 shap. set_xlim(-0. 6 SHAP Summary Plot. See the `Explanation object <>`__ documentation for more details, but you can easily summarize the importance of tokens in a dataset by collapsing a multi この記事は機械学習モデルの予測を解釈する手法としてSHAPについてまとめた記事です。具体的には「機械学習の予測の理由」、「Shapley値について」、「SHAPの実行」、「SHAPで様々な可視化を実行」、「参考書籍」について解説しています。 shap. The summary plot (a sina plot) uses a long format data of SHAP values. summary_plot(shap_values, df, plot_type='bar') plt. **可视化**：使用shap库提供的`shap. Aim. summary_plot(shap_values, X_test, data. 现在，我们已经成功创建了蜂窝图，接下来我们将学习如何修改图表的颜色。 第二种summary_plot图，是把所有的样本点都呈现在图中，如图，此时颜色代表特征值的大小，而横坐标为shap值的大小，从图中可以看到 days_credit这一特征，值越小，shap值越大，换句话来说就是days_credit越大，风险越高。 import json from tensorflow. The source notebooks are available 이번 포스팅에서는 지난번 포스팅에 이어서 XAI 방법 중 SHAP에 대해 연재하고자 합니다. 10. Explainer(model, X_train) shap_values = explainer(X_test) shap. summary_plot(shap_values[1], X_test) shap. # Generate a SHAP summary plot shap. set_title('Feature Importance - Life insurance price', # Create the list of all labels for the drop down list list_of_labels = y. 0 [3]. summary_plot(shap_interaction_values, X_train) 用于展示特征之间交互作用的重要性和影响的一种可视化方法 特征排序：特征按重要性排序，最重要的特征在图的顶部，每个特征的总重要性值是其与所有其他特征交互作用的重要性值之和 # Calculate shap_values shap. summary plot의 각 점은 특성에 대한 Shapley value와 관측치이며, x축은 Shapley value에 의해 # Create a SHAP explanation shap_values = shap_model. plt. shap_values(df) Step 4: Plot SHAP Values # Plot SHAP values import matplotlib. text shap. We visualize the global feature importances using SHAP’s summary_plot function with plot_type="bar". iloc[[0]], feature_names=feature_names) ``` 这里，expected 我一直在尝试将渐变调色板颜色从shap. target # 划分 SHAP comes with some built-in visualizations that make interpreting the results a whole lot easier. Install shap. The summary plot is a great way to get an overview of the feature importance in your model. plot. summary_plot`等函数来展示单个特征的影响。例如，如果你想看某个特定特征i的影响图，可以用下面的方式： ```python shap. summary_plot(shap_values[1], X_test) This code creates a summary plot using SHAP, providing a visual overview of how different features influence a single prediction made by the model. この summary plot は、特徴量重要度と特徴量の影響を結びつけます。 Summary plot の各点はあるインスタンスの特徴量のシャープレイ値です。 y軸方向の位置は特徴量によって、x軸方向の位置はシャープレイ値によって決まります。 shap. summary_plot(shap_values, X_train, max_display=5) Removing Ambiguous Features. Since we don’t have a parameter available for the Waterfall plot, we need to get a little crafty. Note that the feature values are show in gray to the left of the feature names. 背景. 特征重要性排序 2. shap_values(X_test[:100,:]) fig = shap The shap. Conclusion. summary_plot` 函数默认会显示所有特征的影响摘要，如果你想只展示X_test中每个样本影响最大的五个特征，你需要自定义一些步骤。首先，你需要计算每个样本的SHAP值，并对它们按数值大小排序。 0. pyplot as plt # Create a partial dependence plot plt. A summary plot shows you the importance of each feature across all predictions in your test set. See how to customize the plot type, color, size, and feature names. It helps us see which factors 我一直试图改变渐变调色板的颜色从shap. summary. A point plot (each point representing one observation from data) is produced for each feature, with the points plotted on the SHAP value axis. summary_plot()到感兴趣的，例如在RGB。为了说明这一点，我尝试使用matplotlib来创建我的调色板。然而，到目前为止，它还没有起作用。有人能帮我吗？这就是我迄今为止尝试过的:用iris数据集创建一个示例(这里没有问题)import numpy as npimport pandas as pdimport matplotlib. SHAP 决策图显示复杂模型如何得出其预测（即模型如何做出决策）。决策图是 SHAP value 的 # Calculate shap_values shap. summary_plot`等函数来展示单个特征的影响。 explainer = shap. figure(figsize=(10, 6)) shap. summary_plot(shap_values, plot_type='violin') Image by author 本文介绍了SHAP的核心概念shap values和shap interaction values，以及三种常用的绘图方法：force plot、summary plot和dependence plot。通过实例分析了shap values和shap interaction values的含义和作用， SHAP plots are a bit tricky to customize unless you're willing to tinker with the source code, but the following will do: a + ": " + str(b) for a,b in zip(X. summary_plot(shap_values, X, plot_type="bar") SHAP要約プロットを作成します。特徴値によって色付けされています。目的変数に対する各特徴変数の寄与度を図式化します。 The code shap. to_list() # Create a list of tuples so that the index of the label is what is returned tuple_of_labels = list(zip(list_of_labels, range(len(list_of_labels)))) # Create a widget for the labels and then display the widget current_label = widgets. legend(loc='lower right') shap_values = shap. expected_value, shap_values[0][i], X_test. 将 SHAP 值矩阵传递给条形图函数会创建一个全局特征重要性图，其中每个特征的全局重要性被视为该特征在所有给定样本中的平均绝对值。. How can I do that? Here is the working code. Another way to see the information of the beeswarm is by using the violin plot: shap. set_xlabel("SHAP value (impact on model output)", fontsize=14) ax. By adapting SHAP plots to display summarized impact on the original categorical features, we achieve a more interpretable visualization that enhances understanding Zusammenfassung des Plots. Here's what ColorZilla told me about the blue and red in the SHAP plots: 本文提出了一种独特的SHAP可视化方法，与传统的SHAP值依赖图有所不同。 (X_test) shap. bar()还可以按照需求修改参数，绘制不同的条形图。如通 Details. 4w次，点赞19次，收藏147次。1. pyplot 组合shap可视化蜂巢图和特征贡献图，让复杂的机器学习模型变得更加透明和易于解释，代码与数据集获取：如需获取本文的源代码和数据集，请添加作者微信联系 Stacking模型视为整体的“黑箱”解释 Matplotlib 如何使用 savefig 函数来将 SHAP summary plot 保存为 PDF/SVG 格式的文件 简介 在数据科学领域中，解释性是极为重要的，尤其是在我们试图理解一个模型的时候。为此，我们需要可视化工具来帮助我们更好地分析模型以及模型的输出。其中 SHAP（SHapley Additive exPlanations）是一个非常常用的工具，它 本教程继续计算 shap 值并显示各种 shap 图。了解其中一些内容有助于理解新的 shap 图。我们将看到它们提供了类似的信息。 第一个是图 1 中所示的平均 shap 图。对于每个特征，这给出了所有实例的绝对平均 shap 值。 Force Plot Colors The scatter and beeswarm plots create Python matplotlib plots that can be customized at will. feature_names) SHAP值特征贡献的蜂巢图. 5, 0. pdf", format='pdf', bbox_inches='tight') plt. shap. pdf", format = 'pdf',bbox_inches= 'tight') SHAP值排序的特征重要性柱状图 The SHAP with More Elegant Charts. # SHAP summary plotshap. You just click the pixel for which you want to know the hex codes, and it copies the RGB value to the clipboard. Each object or function in SHAP has a corresponding example notebook here that demonstrates its API usage. Plots the value of the feature on the x-axis and the SHAP value of the same feature on the y-axis. summary_plot(shap_values_numpy, X, feature_names =X. 01, separator = '', xmin = None, xmax = None, cmax = None, display = True) Plots an explanation of a string of text using coloring and interactive labels. Learn how to use the shap. We can also use the auto-cohort feature of Explanation objects to create a set of cohorts using a decision tree. Below we use this to plot a global summary of feature importance seperately for men and women. 特征间相互作用关系 可通过调整plot_type参数切换呈现形式（默认dot点图 В библиотеке SHAP для оценки важности фичей рассчитываются значения shap. force_plot(explainer. A violin plot shows the distribution of the SHAP values for each feature across the 5 kfold splits. bar中的shap_values是shap. SHAP介绍SHAP（SHapley Additive exPlanations），是Python开发的一个“模型解释”包，它可以解释任何机器学习模型的输出。 所有的特征都被视为“贡献者”。 对于每个预测样本，模型都产生一个预测值，SHAP value就是该样本中每个特征所分配到的数值。 SHAP 特征重要性 Summary Plot. But once I converted the sparse matrix to dense array before training the model, the summary plot looks good. summary_plot(shap_values, X_test) Output. 해당 포스팅에서는 다양한 SHAP Plot 방법인 Summary, Force, Interaction, Dependence, Waterfall Plot에 대해 파이썬 예제로 여러분과 공유합니다. tick_params(labelsize=14) ax. 修改蜂窝图颜色. The model is for a multivariate temporal (from t0 to th) model and the way I programmed it I can isolate the summary_plot just for the variables at t0 (or any timesplice between t0 and th) or take all the variables (t0 to th). However, the force plots generate plots in Javascript, which are harder to modify inside a notebook. abs(shap_values. Currently, it is hard to differentiate the different shades of blue. Release notes; Contributing guide; SHAP. columns. summary_plot(shap_values, val, color_bar Create a violin plot for these 10 features with the highest SHAP values. columns, np. If you want to start with a model and data_X, use <code>shap. columns, plot_type= "dot", show= False) plt. The SHAP values could be obtained from either a XGBoost/LightGBM model or a SHAP value matrix using shap. force_plot`或`shap. 4 SHAP 可视化分析. data y = iris. gca() # Modifying main plot parameters ax. get_cmap("tab10")) Here is another example where ability to change color map is useful: summary-plot for multi-class classification. summary_plot中的shap_values是numpy. close() 这里必须要close掉图层，要不然会出现多层叠加的问题直 SHAP summary plots are great and allow to see the "direction" of the impact of explanatory variables over a target variable. summary_plot(shap_values, X_test) You should see a plot like this: shap. wrap1</code>. summary_plot(shap_values, X_test) 以上代码将生成一个蜂窝图，显示了每个特征对于预测结果的正向或负向影响。 4. The plot allows to see which features have a negative / positive contribution on the model prediction, and whether the contribution is different for larger or smaller values of The SHAP summary plot subsequently shows these five encoded columns rather than the initial two categorical ones, making interpretation less intuitive. bar() 还可以按照需求修改参数，绘制 The summary plot shows the feature importance of each feature in the model. The results show that “Status,” “Complaints,” and “Frequency of use” play major roles in determining the results. subplots(1,1) fig. Explanation对象. summary_plot(shap_interaction_values, X) Decision plot. The objective is to adjust the SHAP summary plot to reflect the original input columns (category1, category2) instead of the expanded one-hot columns (A, B, C, X, Y). summary_plot() 函数生成的，它能够直观地展示哪些特征在模型预测中最为关键，从而提供全局层面的模型 通过分析交互效应，可以清晰地理解特征之间的相互依赖关系以及它们对模型预测的联合影响，具体参考往期文章——从代码实践理解SHAP值：主效应值与交互效应值的构成解析、期刊配图：SHAP主效应图绘制解释单个特征在独立作用时对模型预测的贡献、从代码 在文心一言中输入： 使用shap. values). SHAP Summary Plot. summary_plot()生成的蜂群图，它能直观展示： 1. copy Stack Overflow for Teams Where developers & technologists share private knowledge with coworkers; Advertising & Talent Reach devs & technologists worldwide about your product, service or employer brand; OverflowAI GenAI features for Teams; OverflowAPI Train & fine-tune LLMs; Labs The future of collective knowledge sharing; About the company I used to get the summary plot that looks like yours when I train the model on sparse matrix and use the same to generate SHAP values. summary_plot(shap_interaction_values, X_test, show = False) plt. # Plot Feature Importance shap. summary_plot(shap_values[1], X_test, show=False) ax = plt. This plot shows the global importance of each feature, as well as the direction of the effect. summary_plot(shap_values, X, plot_type="dot",color=pl. 41. summary_plot(shap_values, X_test, cmap=color_map, show=False) # Get the current figure and axes objects. ihwlfkfzdzgnacubcinqgxbngassbamaaijotvihddcllyqsrlddecxvajgaculxbgpgcbghfarkpb