Ale plots python 4k次，点赞3次，收藏21次。ALEPython是一个Python库，用于生成累积局部效应图（ALE），它比偏依赖图更好地处理特征相关性，尤其适用于大规模机器学习模型的解释。文章介绍了ALE的概念，库的安 使用 ALE 解释机器学习模型的直觉、算法和代码 img 高度相关的特征可能会严重破坏你的模型解释。它们违反了许多 XAI方法的假设，并且很难理解特征与目标的关系的性质。同时，在不影响性能的情况下删除它们并不总是 In this article, we’ll embark on a journey to demystify machine learning models using ALE plots, understanding feature effects, and harnessing Python to implement these visualizations from alepython import ale_plot # 假设model是训练好的模型，X_train是特征数据 ale_plot(model, X_train, 'cont', monte_carlo=True) 开源项目 ale_python_interface 安装与使用教程 ale_python_interfaceA Python Interface for the Arcade Learning Environment This package aims to provide useful and quick access to ALE plots, so that you can easily explain your model throught predictions. 2. All in all, in most situations I would prefer ALE plots over PDPs, because features are usually correlated to some extent. To plot ALE, we send in the ale_ds from explainer. 2. Accumulated Local Effects (ALE) is one of the effective methods for interpreting machine learning models. 5 and 3. 0 - a Python package on PyPI. 5版本的Python，简单安装后，即可轻松生成图表，直观理解模型如何基于预测作出决策。无论是连续特征的一阶、二阶效应分析，还是未来对类别特征的支持，ALEPython都是提升模型透明度不可或缺的工具。 ale_plot(model, X_train, 'cont', 累积局部效果（ALE）是一种用于解释机器学习模型的全局可解释性方法。 Accumulated Local Effects (ALE) were initially developed as a model-agnostic approach for global explanations of the results of black-box machine learning algorithms. The focus of the library is to provide high-quality implementations of black-box, white-box, local and global explanation 文章浏览阅读1. But the はじめに Partial Dependence 特徴量が独立の場合 数式による確認 PDの実装 特徴量が相関する場合 PDがうまく機能しない原因 Marginal Plot Marginal Plotの数式 Marginal Plotのアルゴリズム Maginal Plotの実装 在这篇文章中，我们将使用r语言来实现累积局部效应方法，并解释连续特征与目标变量之间的关系。希望这篇文章能够帮助你了解如何使用r语言实现累积局部效应方法来解释连续特征与目标变量之间的关系。通过使用这个方法，你可以更深入地理解机器学习模型，并得出有关特征对模型预测的影响 其中，累积局部效应（Acumulated Local Effects，简称ALE）方法是一种常用的技术，可以帮助我们解释和可视化连续特征对目标值的影响。以上代码将生成一个带有ALE曲线的图形，其中x轴表示连续特征的取值范围，y轴表示ALE值。通过使用ALE方法，我们可以更好地理解连续特征与目标值之间的关系。 四、 累积局部效应图 （Accumulated Local Effects Plot) 累积局部效应图（ALE plot），用于描述特征变量对预测目标的平均影响。ALE最大的特点是摆脱了变量独立性假设的约束，使其在实际环境中获得了更广泛的应用。 Implement local explainable techniques like LIME, SHAP, and ICE plots using Python. The ALE plots are dominated by effects arising from rare feature combinations . ALE plots are a faster and unbiased alternative to These demonstrations of the accumulated local effects in scikit-explain are generated from tutorial notebooks that are available on GitHub. To overcome this, we could rely on good feature selection. Though it is still a work-in-progress, it's already a wonderful window into your model. For both demonstrations, we begin by fitting a GAM model. ALE: Accumulated Local Effects A python implementation of the ALE plots based on the implementation of the R package ALEPlot. ALE Plots for python. y is the product of x1 and x2. Luckily, there is at least one python package that can help. It is also possible to configure the plot for readable feature names. We will see how to apply this package and interpret its plots. We've used it to create the graphs below. This blog post will delve into what ALE is, why it’s important, and To plot ALEs, we pass the explanations and features we want to display to the plot_ale. ale and the list of features to plot. They show if the effects are positive, negative, or non-linear. Since x2 nearly ALE plots with python - 1. 6k次，点赞2次，收藏6次。ALE累积局部效应图是一种用于机器学习模型解释的可视化方法，它通过计算局部效应并消除变量间的相关性干扰，揭示特征对预测结果的真实影响。本文介绍了ALE的原理、应用， We will give two demonstrations of how to use the package: first, a simple demonstration of ALE plots, and second, a more sophisticated demonstration suitable for statistical inference with p-values. pyale was created with cookiecutter and the py-pkgs-cookiecutter template. ALE uses a conditional feature distribution as an input and generates augmented data, creating more realistic data than a marginal distribution. Contribute to Cameron-Lyons/ALE-Plots development by creating an account on GitHub. Assume, however, that we would like to analyze the data without postulating any particular parametric form of The 2D ALE plot only shows the interaction: If two features do not interact, the plot shows nothing. 4, 3. This Python package computes and visualizes Accumulated Local Effects (ALE) for machine learning models. Accumulated Local Effects (ALE) is similar to the concept of Partial Dependence (PD) in that both aim to describe how features influence the prediction of a model on average. There are additional arguments, but that is discussed below. Apply それではPythonを使ってALEの実装方法を見てみましょう。まずは必要なライブラリをインポートします。ここではアヤメ（Iris）のデータセットを使い、ランダムフォレストモデルを訓練してからALEプロットを作成します。 (ALE) 为了绘制 ALE，我们将要显示的解释和特征传递给 plot_ale **。**使用位置数组 [0,1,2] 意味着我们显示前 3 个特征的 ALE。 [Python 中的 SHAP 简介]] 希望这篇文章对你有所帮助！你还可以阅读我的其他文章，或者查看有关企业 AI 实 2) ale 的计算速度比 pdp 快, 需要计算的次数少 于 pdp。 3) 与 pdp 一样, ale 也能直观地展示目标特征是如何影 响模型的预测的, 由于剥离了相关变量的影响, 因此 ale 的 解释更加准确; ale 图的曲线是中心化的, 曲线的取值即为 平均预测值的变化, 解释更加清楚简洁。 Alibi is a Python library aimed at machine learning model inspection and interpretation. Highly correlated features can wreak havoc on your machine-learning model interpretations. important_vars = explainer. 1, we could consider using a simple linear model with \(X^1\) and \(X^2\) as explanatory variables. By default, scikit-explain is built for scientific publications and will provide figure labels Accumulated local effects (Apley and Zhu 2020) describe how features influence the prediction of a machine learning model on average. For instance, view this sample ALE plot we created. import matplotlib as mpl # 设置 matplotlib 图的默认大小为 9x6 英寸 mpl. We assume that this is a final deployment model that needs to be fitted to the entire ALE plots work for both classification and regression models. rc("figure", figsize =(9, 6)) # 调用 ale_plot 函数绘制 Accumulated Local Effects (ALE) 图 ale_plot( gbrt, # 传入机器学习模型（例如训练好的回归或分类模型） X_test, # 数据特征集，用于生成 ALE 图 X_test. ALE has a key advantage over other approaches like partial dependency plots (PDP) and SHapley Additive exPlanations (SHAP): its values represent a clean functional decomposition of the model. 适用于≥3. Code is available in GitHub [6]. [1] Unlike partial dependence plots and marginal plots, ALE is not defeated in the presence of correlated predictors. ALE is computationally efficient and works . columns [:1], # 选择 其中，累积局部效应（Acumulated Local Effects，简称ALE）方法是一种常用的技术，可以帮助我们解释和可视化连续特征对目标值的影响。以上代码将生成一个带有ALE曲线的图形，其中x轴表示连续特征的取值范围，y轴表 文章浏览阅读2. Installation: Via pip pip install PyALE. However, it is known that the true relationship is not linear. Since python models work with numeric features only, categorical variables are often encoded by one of two methods, either with integer encoding (when the As you can imagine, as the number of features rises, the math to compute ALE plots gets a bit arduous. The resulting Explanation objects contain the ALE’s for each feature under the ale_values attribute - this is a list of numpy arrays, one for each feature. 数据科学中的特征解释是指通过理解输入特征与输出目标之间的关系来揭示模型的工作原理。通过计算单个特征或多个特征的 ale 值，并结合可视化工具，我们可以获得对模型的深入理解。 从而提高我们对特征与目标之间关 The PDP plot shows a rug plot of the actual x1 values along the x-axis and the relationship between x1 and y appears as a line. Implement global explainable techniques such as Partial Dependence Plots (PDP) and Accumulated Local Effects (ALE) plots in Python. 3 理论实现 ale图对特征的变化进行平均，然后将其累积在网格上。（这里我仅介绍我对于ale算法的理解，在书中还有与m图，pdp的算法对比，有想要了解的读者可以看书中的讲解）。 ale的理论公式如下所示： This Python package computes and visualizes Accumulated Local Effects (ALE) for machine learning models. The easiest way to interpret the ALE values is by plotting them against the feature In view of the plot shown in the right-hand-side panel of Figure 18. [3] 本篇文章則會介紹另一種與模型無關的事後可解釋的方法：累積局部效應（Accumulated Local Effects Plot，簡稱 ALE）。 二、資料說明 本篇文章將以新生兒 We would like to show you a description here but the site won’t allow us. As such, ALE 这些差异随后被累积并中心化，从而形成ale曲线。 2. These plots reveal the main effects of features. 3. Install ALEPython is supported on Python 3. permutation_importance has already been run. Using the array of positions [0,1,2] means we display the ALEs for the first 3 features. (R and Python) behind partial dependence plots and A boosted tree model was trained, using Scikit-learn’s GradientBoostingClassifier, which is compatible with Python packages available for ALE plots , SHAP values (SHAP), and Friedman’s H (sklearn_gbmi). [2] It ignores far out-of-distribution (outlier) values. 6. # Assumes the . Input your pre-trained model to analyze feature impact on predictions and access relevant statistical outputs, providing deeper For now, we are interested in the ALE and plot_ale functions (lines 8–9). uuvpbrlgaxmogzshgohdcxhqsfqxaikyefjyruplsjkejqslxdsvymabxlykylkuszajbgmupln