Better Interpretability Leads to Better Adoption. Is your highly-trained model easy to understand? The interpretability of a model is like the label on a drug bottle.
We need to make our effective pill transparent for easy adoption. How can we do that? I will demonstrate to you how the SHAP values increase model transparency.
This article also comes with Python code for you to produce nice results in your applications. What is the Shapley Value? After work they went to a local bar for a drink and I, a mathematician, came to join them. How to answer this question? I listed all the permutations and came up with the data in Table A.
Some of you already asked me how to come up with this table. See my note in the end of the article. When the ordering is A, B, C, the marginal contributions of the three are 4, 30, and 4 inches respectively.
The table shows the coalition of A,B or B,A is 34 inches, so the marginal contribution of C to this coalition is 4 inches. I will describe the calculation in the formal mathematical term in the end of this post.
The hammers are the predictors to attack the error log. How do we measure the contributions of the hammers predictors? The Shapley values! Inspired by several methods 1234567 on model interpretability, Lundberg and Lee proposed the SHAP value as a united approach to explain the output of any machine learning model.
Three benefits worth mentioning here. It is important to point out the SHAP values do not provide causality. Data Visualization and Model Explainability.
Data visualization and model explainability are two integral aspects in a data science project. They are the binoculars helping you to see the patterns in the data and the stories in your model. My goal in the data visualization articles is to assist you to produce data visualization exhibits and insights easily and proficiently.
Also, I choose the same data for both the data visualization and model explainability in all these articles so you can see how the two go hand in hand. If you would like to adopt all these data visualization codes or make your work more proficient, take a look of them. I am going to use the red wine quality data in Kaggle.
The target value of this dataset is the quality rating from low to high 0— The input variables are the content of each wine sample including fixed acidity, volatile acidity, citric acid, residual sugar, chlorides, free sulfur dioxide, total sulfur dioxide, density, pH, sulphates and alcohol. There are 1, wine samples. Yes, there is. It is called the KernelExplainer.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.
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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. Shap can be installed from either PyPI or conda-forge :.
While SHAP can explain the output of any machine learning model, we have developed a high-speed exact algorithm for tree ensemble methods see our Nature MI paper. The above explanation shows features each contributing to push the model output from the base value the average model output over the training dataset we passed to the model output. Features pushing the prediction higher are shown in red, those pushing the prediction lower are in blue these force plots are introduced in our Nature BME paper.
If we take many explanations such as the one shown above, rotate them 90 degrees, and then stack them horizontally, we can see explanations for an entire dataset in the notebook this plot is interactive :. To understand how a single feature effects the output of the model we can plot the SHAP value of that feature vs. Since SHAP values represent a feature's responsibility for a change in the model output, the plot below represents the change in predicted house price as RM the average number of rooms per house in an area changes.
Vertical dispersion at a single value of RM represents interaction effects with other features. In this case coloring by RAD index of accessibility to radial highways highlights that the average number of rooms per house has less impact on home price for areas with a high RAD value.
To get an overview of which features are most important for a model we can plot the SHAP values of every feature for every sample. The plot below sorts features by the sum of SHAP value magnitudes over all samples, and uses SHAP values to show the distribution of the impacts each feature has on the model output.
The color represents the feature value red high, blue low. We can also just take the mean absolute value of the SHAP values for each feature to get a standard bar plot produces stacked bars for multi-class outputs :. The implementation here differs from the original DeepLIFT by using a distribution of background samples instead of a single reference value, and using Shapley equations to linearize components such as max, softmax, products, divisions, etc. TensorFlow models and Keras models using the TensorFlow backend are supported there is also preliminary support for PyTorch :.
The plot above explains ten outputs digits for four different images. Red pixels increase the model's output while blue pixels decrease the output. The input images are shown on the left, and as nearly transparent grayscale backings behind each of the explanations. The sum of the SHAP values equals the difference between the expected model output averaged over the background dataset and the current model output. Note that for the 'zero' image the blank middle is important, while for the 'four' image the lack of a connection on top makes it a four instead of a nine.
This allows an entire dataset to be used as the background distribution as opposed to a single reference value and allows local smoothing. If we approximate the model with a linear function between each background data sample and the current input to be explained, and we assume the input features are independent then expected gradients will compute approximate SHAP values. In the example below we have explained how the 7th intermediate layer of the VGG16 ImageNet model impacts the output probabilities.
Predictions for two input images are explained in the plot above. Red pixels represent positive SHAP values that increase the probability of the class, while blue pixels represent negative SHAP values the reduce the probability of the class. Below is a simple example for explaining a multi-class SVM on the classic iris dataset. The above explanation shows four features each contributing to push the model output from the base value the average model output over the training dataset we passed towards zero.
If there were any features pushing the class label higher they would be shown in red.Description Usage Arguments Value Examples. This function by default makes a simple dependence plot with feature values on the x-axis and SHAP values on the y-axis, optional to color by another feature. It is optional to use a different variable for SHAP values on the y-axis, and color the points by the feature value of a designated variable. Dependence plot is easy to make if you have the SHAP values dataset from predict.
It is not necessary to start with the long format data, but since that is used for the summary plot, we just continue to use it here. Booster or shap. As long as dilute! But notice the plot after adding histogram is a ggExtraPlot object instead of ggplot2 so cannot add geom to that anymore.
Turn the histogram off if you wish to add more ggplot2 geoms. For more information on customizing the embed code, read Embedding Snippets. Man pages API Source code 8.
R Description This function by default makes a simple dependence plot with feature values on the x-axis and SHAP values on the y-axis, optional to color by another feature. Width" 3. Width" 4. Width" Optional to add hist or remove smooth line, optional to plot fewer data make plot quicker shap. Related to shap. SHAPforxgboost index. R Package Documentation rdrr. We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that. Tweet to rdrrHQ.Tree SHAP is a fast and exact method to estimate SHAP values for tree models and ensembles of trees, under several different possible assumptions about feature dependence.
For models with a single output this returns a tensor of SHAP values samples x features x features. Each row of this matrix sums to the SHAP value for that feature for that sample.
For models with vector outputs this returns a list of tensors, one for each output. For models with a single output this returns a matrix of SHAP values samples x features.
SHAP and LIME Python Libraries: Part 2 – Using SHAP and LIME
For models with vector outputs this returns a list of such matrices, one for each output. Expected gradients an extension of the integrated gradients method Sundararajan et al. Integrated gradients values are a bit different from SHAP values, and require a single reference value to integrate from. As an adaptation to make them approximate SHAP values, expected gradients reformulates the integral as an expectation and combines that expectation with sampling reference values from the background dataset.
This leads to a single combined expectation of gradients that converges to attributions that sum to the difference between the expected model output and the current output. For a model with multiple outputs this returns a list of SHAP value tensors, each of which are the same shape as X.
By integrating over many backgound samples DeepExplainer estimates approximate SHAP values such that they sum up to the difference between the expected model output on the passed background samples and the current model output f x - E[f x ]. Kernel SHAP is a method that uses a special weighted linear regression to compute the importance of each feature.
The computed importance values are Shapley values from game theory and also coefficents from a local linear regression. It is a good alternative to KernelExplainer when you want to use a large background set as opposed to a single reference value for example. SHAP latest. Explainers Plots. DataFrame The background dataset to use for integrating out features.
Anywhere from to random background samples are good sizes to use. This approach does not require a background dataset and so is used by default when no background dataset is provided. This is helpful for breaking down model performance by feature. X : numpy. DataFrame or catboost. Used when explaining loss functions not yet supported. By default None means no use the limit of the original model, and -1 means no limit.
Used when explaining loss functions. This runs a method previously proposed by Saabas which only considers a single feature ordering.
Take care since this does not have the consistency guarantees of Shapley values and places too much weight on lower splits in the tree. This check takes only a small amount of time, and will catch potential unforeseen errors. Note that this check only runs right now when explaining the margin of the model. Model User supplied function that takes a matrix of samples samples x features and computes a the output of the model for those samples. The output can be a vector samples or a matrix samples x model outputs.
DataFrame or shap. DenseData or any scipy. So if the background dataset is a simple sample of all zeros, then we would approximate a feature being missing by setting it to zero. For small problems this background dataset can be the whole training set, but for larger problems consider using a single reference value or using the kmeans function to summarize the dataset.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.
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Branch: master. Find file Copy path. Azanov, Ivan Added string categorical features for dependence plot. Issue: 8bf2c8d Mar 28, Raw Blame History. Plots the value of the feature on the x-axis and the SHAP value of the same feature on the y-axis.
This shows how the model depends on the given feature, and is like a richer extenstion of the classical parital dependence plots. Vertical dispersion of the data points represents interaction effects. Grey ticks along the y-axis are data points where the feature's value was NaN. Parameters ind : int or string If this is an int it is the index of the feature to plot. If this is a string it is either the name of the feature to plot, or it can have the form "rank int " to specify the feature with that rank ordered by mean absolute SHAP value over all the samples.
DataFrame Matrix of feature values samples x features.
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DataFrame Matrix of feature values for visual display such as strings instead of coded values. The name of a feature can also be passed as a string. If "auto" then shap. May increase plot readability when feature is discrete. This can be useful to the show density of the data points when using a large dataset. It can be a string of the format "percentile float " to denote that percentile of the feature's value used on the x-axis.
In this case we do not create a Figure, otherwise we do. BoundaryNorm boundscmap. You signed in with another tab or window.This chapter is currently only available in this web version. This chapter explains both the new estimation approaches and the global interpretation methods. The goal of SHAP is to explain the prediction of an instance x by computing the contribution of each feature to the prediction.
The feature values of a data instance act as players in a coalition. A player can be an individual feature value, e.
A player can also be a group of feature values. For example to explain an image, pixels can be grouped to super pixels and the prediction distributed among them. One innovation that SHAP brings to the table is that the Shapley value explanation is represented as an additive feature attribution method, a linear model. SHAP specifies the explanation as:. I think this name was chosen, because for e.
This should sound familiar to you if you know about Shapley values. The formula simplifies to:. You can find this formula in similar notation in the Shapley value chapter. More about the actual estimation comes later. Shapley values are the only solution that satisfies properties of Efficiency, Symmetry, Dummy and Additivity. SHAP also satisfies these, since it computes Shapley values. SHAP describes the following three desirable properties:.
Only with a different name and using the coalition vector.
Explain Your Model with the SHAP Values
Missingness says that a missing feature gets an attribution of zero. The presence of a 0 would mean that the feature value is missing for the instance of interest.This notebook is designed to demonstrate and so document how to use the shap.
It uses an XGBoost model trained on the classic UCI adult income dataset which is classification task to predict if people made over 50k in the 90s. A dependence plot is a scatter plot that shows the effect a single feature has on the predictions made by the model. In this example the log-odds of making over 50k increases significantly between age 20 and Documentation by example for shap.
Each dot is a single prediction row from the dataset. The x-axis is the value of the feature from the X matrix. The y-axis is the SHAP value for that feature, which represents how much knowing that feature's value changes the output of the model for that sample's prediction.
For this model the units are log-odds of making over 50k annually. The color corresponds to a second feature that may have an interaction effect with the feature we are plotting by default this second feature is chosen automatically. If an interaction effect is present between this other feature and the feature we are plotting it will show up as a distinct vertical pattern of coloring. For the example below year-olds with a high level of education are less likely make over 50k than year-olds with a low level of education.AI Simplified: SHAP Values in Machine Learning
This suggests an interaction effect between Education-Num and Age. The first argument is the index of the feature we want to plot The second argument is the matrix of SHAP values it is the same shape as the data matrix The third argument is the data matrix a pandas dataframe or numpy array shap. If we pass a numpy array instead of a data frame then we need pass the feature names in separately shap.
We can pass a feature name instead of an index shap. We can also use the special "rank i " systax to specify the i'th most important feature to the model. As measured by: np.