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shap-post.Rmd
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---
title: "SHAP values in R"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
Hi there! During the first meetup of [argentinaR.org](https://argentinar.org/) -an R user group- [Daniel Quelali](https://www.linkedin.com/in/danielquelali/) introduced us to a new model validation technique called **SHAP values**.
This novel approach allows us to dig a little bit more in the complexity of the predictive model results, while it allows us to explore the relationships between variables for predicted case.
<img src="https://blog.datascienceheroes.com/content/images/2019/03/simpsons.gif" width="300px">
I've been using this it with "real" data, cross-validating the results, and let me tell you it works.
This post is a gentle introduction to it, hope you enjoy it!
_Find me on [Twitter](https://twitter.com/pabloc_ds) and [Linkedin](https://www.linkedin.com/in/pcasas/)._
**Clone [this github repository](https://github.com/pablo14/shap-values)** to reproduce the plots.
## Introduction
Complex predictive models are not easy to interpret. By complex I mean: random forest, xgboost, deep learning, etc.
In other words, given a certain prediction, like having a _likelihood of buying= 90%_, what was the influence of each input variable in order to get that score?
A recent technique to interpret black-box models has stood out among others: [SHAP](https://github.com/slundberg/shap) (**SH**apley **A**dditive ex**P**lanations) developed by Scott M. Lundberg.
Imagine a sales score model. A customer living in zip code "A1" with "10 purchases" arrives and its score is 95%, while other from zip code "A2" and "7 purchases" has a score of 60%.
Each variable had its contribution to the final score. Maybe a slight change in the number of purchases changes the score _a lot_, while changing the zip code only contributes a tiny amount on that specific customer.
SHAP measures the impact of variables taking into account the interaction with other variables.
> Shapley values calculate the importance of a feature by comparing what a model predicts with and without the feature. However, since the order in which a model sees features can affect its predictions, this is done in every possible order, so that the features are fairly compared.
[Source](https://medium.com/@gabrieltseng/interpreting-complex-models-with-shap-values-1c187db6ec83)
## SHAP values in data
If the original data has 200 rows and 10 variables, the shap value table will **have the same dimension** (200 x 10).
The original values from the input data are replaced by its SHAP values. However it is not the same replacement for all the columns. Maybe a value of `10 purchases` is replaced by the value `0.3` in customer 1, but in customer 2 it is replaced by `0.6`. This change is due to how the variable for that customer interacts with other variables. Variables work in groups and describe a whole.
Shap values can be obtained by doing:
`shap_values=predict(xgboost_model, input_data, predcontrib = TRUE, approxcontrib = F)`
## Example in R
After creating an xgboost model, we can plot the shap summary for a rental bike dataset. The target variable is the count of rents for that particular day.
Function `plot.shap.summary` (from the [github repo](https://github.com/pablo14/shap-values)) gives us:
<img src="https://blog.datascienceheroes.com/content/images/2019/03/shap_summary_bike.png" alt="Shap summary" width="600px">
### How to interpret the shap summary plot?
* The y-axis indicates the variable name, in order of importance from top to bottom. The value next to them is the mean SHAP value.
* On the x-axis is the SHAP value. Indicates how much is the change in log-odds. From this number we can extract the probability of success.
* Gradient color indicates the original value for that variable. In booleans, it will take two colors, but in number it can contain the whole spectrum.
* Each point represents a row from the original dataset.
Going back to the bike dataset, most of the variables are boolean.
We can see that having a high humidity is associated with **high and negative** values on the target. Where _high_ comes from the color and _negative_ from the x value.
In other words, people rent fewer bikes if humidity is high.
When `season.WINTER` is high (or true) then shap value is high. People rent more bikes in winter, this is nice since it sounds counter-intuitive. Note the point dispersion in `season.WINTER` is less than in `hum`.
Doing a simple violin plot for variable `season` confirms the pattern:
<img src="https://blog.datascienceheroes.com/content/images/2019/03/bike_season.png" alt="Season variable distribution" width="500px">
As expected, rainy, snowy or stormy days are associated with less renting. However, if the value is `0`, it doesn't affect much the bike renting. Look at the yellow points around the 0 value. We can check the original variable and see the difference:
<img src="https://blog.datascienceheroes.com/content/images/2019/03/bike_warhersit.png" alt="Analysis of warhersit" width="500px">
What conclusion can you draw by looking at variables `weekday.SAT` and `weekday.MON`?
### Shap summary from xgboost package
Function `xgb.plot.shap` from xgboost package provides these plots:
<img src="https://blog.datascienceheroes.com/content/images/2019/03/shap_value_all.png" alt="Shap value for all variables" width="600px">
* y-axis: shap value.
* x-axis: original variable value.
Each blue dot is a row (a _day_ in this case).
Looking at `temp` variable, we can see how lower temperatures are associated with a big decrease in shap values. Interesting to note that around the value 22-23 the curve starts to decrease again. A perfect non-linear relationship.
Taking `mnth.SEP` we can observe that dispersion around 0 is almost 0, while on the other hand, the value 1 is associated mainly with a shap increase around 200, but it also has certain days where it can push the shap value to more than 400.
`mnth.SEP` is a good case of **interaction** with other variables, since in presence of the same value (`1`), the shap value can differ a lot. What are the effects with other variables that explain this variance in the output? A topic for another post.
## R packages with SHAP
**[Interpretable Machine Learning](https://cran.r-project.org/web/packages/iml/vignettes/intro.html)** by Christoph Molnar.
<img src="https://blog.datascienceheroes.com/content/images/2019/03/iml_shap_R_package.png" alt="iml R package" width="500px">
**[xgboostExplainer](https://medium.com/applied-data-science/new-r-package-the-xgboost-explainer-51dd7d1aa211)**
Altough it's not SHAP, the idea is really similar. It calculates the contribution for each value in every case, by accessing at the trees structure used in model.
<img src="https://blog.datascienceheroes.com/content/images/2019/03/xgboostExplainer.png" alt="iml R package" width="500px">
## Recommended literature about SHAP values `r emo::ji("books")`
There is a vast literature around this technique, check the online book _Interpretable Machine Learning_ by Christoph Molnar. It addresses in a nicely way [Model-Agnostic Methods](https://christophm.github.io/interpretable-ml-book/agnostic.html) and one of its particular cases [Shapley values](https://christophm.github.io/interpretable-ml-book/shapley.html). An outstanding work.
From classical variable, ranking approaches like _weight_ and _gain_, to shap values: [Interpretable Machine Learning with XGBoost](https://towardsdatascience.com/interpretable-machine-learning-with-xgboost-9ec80d148d27) by Scott Lundberg.
A permutation perspective with examples: [One Feature Attribution Method to (Supposedly) Rule Them All: Shapley Values](https://towardsdatascience.com/one-feature-attribution-method-to-supposedly-rule-them-all-shapley-values-f3e04534983d).
--
Thanks for reading! `r emo::ji('rocket')`
Other readings you might like:
- [New discretization method: Recursive information gain ratio maximization](https://blog.datascienceheroes.com/discretization-recursive-gain-ratio-maximization/)
- [Feature Selection using Genetic Algorithms in R](https://blog.datascienceheroes.com/feature-selection-using-genetic-algorithms-in-r/)
- `r emo::ji('green_book')`[Data Science Live Book](http://livebook.datascienceheroes.com/)
[Twitter](https://twitter.com/pabloc_ds) and [Linkedin](https://www.linkedin.com/in/pcasas/).