Aarong chapter9 presentation (#13)

* ch 9 initial commit

* add video link and chat log

* Add DALEXtra to DESCRIPTION.

* Remove ~empty chat log.

---------

Co-authored-by: Jon Harmon <[email protected]>

09_local-interpretable-model-agnostic-explanations-lime.Rmd

@@ -1,24 +1,303 @@
 # Local Interpretable Model-agnostic Explanations (LIME)
 
-**Learning objectives:**
+**Sections:**
 
-- THESE ARE NICE TO HAVE BUT NOT ABSOLUTELY NECESSARY
+- Overview  
+- Intuition  
+- Method  
+- Titanic data  
+- Pros and Cons  
+- R Examples 
 
-## SLIDE 1 {-}
+## Overview {-}  
+
+* Recap of previous methods for instance-level explanation  
+  - Break-down plots (with and without interactions)  
+  - Shapley Additive Explanations (SHAP)  
+
+* Previous approaches may be problematic with large set of predictors
+  - Too complex for human comprehension
+  - Computational times  
+
+* Introducing LIME
+  - Recent method (first published in 2016)  
+  - Replace black-box model with glass-box model  
+  - Sparse explanations
 
-- ADD SLIDES AS SECTIONS (`##`).
-- TRY TO KEEP THEM RELATIVELY SLIDE-LIKE; THESE ARE NOTES, NOT THE BOOK ITSELF.
+## Intuition  
+
+**Binary classifier Example**  
 
-## Meeting Videos {-}
+*![Source: Figure 9.1](img/09-LIME/binary_classifier_example.png)*  
 
-### Cohort 1 {-}
+
+Plot description:  
+
+* Axes represent values for two separate features  
+* Colors represent binary predictions from black-box classifier  
+* Cross indicates instance of interest
+* Dots represent artificial generated points  
+* Dot size corresponds to distance to instance of interest  
+* Dashed line is linear glass-box model fit to artificial points  
+
+**Common Glass Box Models**  
+
+* LASSO  
+* Decision trees  
+
+## Method  
+
+**Loss Function Minimization**  
+
+$$
+\hat g = \arg \min_{g \in \mathcal{G}} L\{f, g, \nu(\underline{x}_*)\} + \Omega (g), 
+$$
+where  
+
+* $\mathcal{G}$ is class of simple models  
+* $f$ is black box model prediction  
+* $g$ is glass box model prediction  
+* $\nu(\underline{x}_*)$ is neighborhood around instance $\underline{x}_*$  
+* $\Omega (g)$ is penalty for complexity of $f$  
+
+In practice, exclude $\Omega (g)$ from optimization procedure. Instead, manually control complexity by specifying number of coefficients in glass-box model.  
+
+f() and g() operate on different spaces:  
+
+* f() works in $p$ dimensions, corresponds to $p$ predictors  
+* g() corresponds to $q$ dimensional spaces  
+* $q << p$  
+
+**Glass-box Fitting Procedure** 
+
+
+```  
+Specify:
+  - x* instance of interest
+  - N sample size of artificial points 
+  - K number of predictor variables for glass-box model
+  - similarity function
+  - glass box model
+    
+Steps:    
+1. Sample N artificial points around instance.   
+2. Fit black box model to points -- becomes target variable
+3. Calculate similarity between instance and each artifical point
+4. Fit glass box model:  
+    -use N artificial points as features
+    -use black-box predictions on N points as target
+    -limit to K nonzero coefficients
+    -Weight training of each point using similarity measure 
+
+```
+
+**Image Classifier Example**  
+
+Overview:  
+
+* Classify 244 x 244 color image into 1,000 potential categories  
+* Goal: Explain model classification. 
+* Issue: High dimensional problem: Each image comprises 178,608 dimensions (3 x 244 x 244)  
+
+Solution:  
+
+* Transform into 100 superpixels. Glass box applies to $\{0,1\}^{100}$ space
+* Sample around instance - (i.e.,randomly exclude some superpxiels)
+* Fit LASSO with K=15 nonzero coefficients
+
+*![Source: Figure 9.3](img/09-LIME/superpixel_image.png)*  
+*Figure 9.3: Left-original image; Middle-superpixels; Right-artificial data*  
+
+*![Figure 9.4](img/09-LIME/LASSO_horseduck.png)*  
+*Figure 9.4: 15 selected superpixel features*  
+
+**Sampling around instance**  
+
+* Can't always sample from existing points because data very sparse and "far" from each other in high dimensional space  
+* Usually instead create perturbations on instance of interest  
+  + continuous variables - multiple approaches
+      - adding Gaussian noise  
+      - perturb discretized versions of of variables
+  + binary variables - change some 0s to 1s and vice-versa  
+
+## Titanic Data Example  
+
+**Create Interpretable Data Space**  
+
+* Combine similar concepts  
+  + group *class* and *fare* into "wealth"   
+  + combine *age* and *gender* into "demographics"  
+* Simpler approach for this example - discretize every variable into two levels  
+  + *age* bucketed into "<= 15.36" and > 15.36
+  + *class* into "1st/2nd/deck crew" and "other"  
+
+**LIME Applied to Johnny D**  
+*![Figure 9.5](img/09-LIME/explain_johnnyd.png)*  
+*Figure 9.5: Johnny D LIME, LASSO, K=3*  
+
+
+## Pros and Cons  
+
+**Pros**  
+
+* Model agnostic  
+* Interpretable explanation  
+* Local fidelity  
+* Great for text and image analysis  
+
+**Cons**  
+
+* No consensus for best lower dimensional representations for continuous and category predictors
+* Multiple implementations, so may get different results based on package used  
+* Glass box model fit to black-box predictions, not actual data, so quality of local fit may be misleading  
+* Finding an appropriate neighborhood difficult with high dimensional data  
+
+
+## Code Examples in R  
+
+**Package options**  
+
+* `lime` is port of Python library, discretizes variables based on quartiles  
+* `localModel` uses ceteris-paribus profiles, 
+* `iml` works directly on continuous variables. One of package authors wrote popular interpretable ML book  
+
+**Notes:**  
+
+* `DALExtra` package needed for `predict_surrogate()` interface to multiple packages.  
+* Default method of`predict_surrogate()` is `localModel`.  
+* Examples below use K-LASSO for glass-box model  
+
+
+```{r load-data, warning=FALSE,message=FALSE}
+# core libraries
+library(randomForest)
+library(DALEX)
+library(DALEXtra)
+
+# load data nd models 
+titanic_imputed <- archivist::aread("pbiecek/models/27e5c")
+titanic_rf <- archivist:: aread("pbiecek/models/4e0fc")
+johnny_d <- archivist:: aread("pbiecek/models/e3596")  
+
+# explainer
+titanic_rf_exp <- DALEX::explain(model = titanic_rf,  
+                        data = titanic_imputed[, -9],
+                           y = titanic_imputed$survived == "yes", 
+                       label = "Random Forest")
+
+```  
+
+**Package: lime**  
+
+Fit model:  
+
+```{r lime-package,warning=FALSE, message=FALSE}
+library(lime)
+set.seed(1)
+
+# lime model
+model_type.dalex_explainer <- DALEXtra::model_type.dalex_explainer
+predict_model.dalex_explainer <- DALEXtra::predict_model.dalex_explainer
+
+lime_johnny <- predict_surrogate(explainer = titanic_rf_exp, 
+                  new_observation = johnny_d, 
+                  n_features = 3, 
+                  n_permutations = 1000,
+                  type = "lime")
+```
+
+```{r lime-out,echo=FALSE}
+# output
+lime_johnny |>
+  dplyr::mutate_if(is.numeric,round,3) |> 
+  knitr::kable() |> 
+  kableExtra::kable_styling(font_size=11)
+  
+```
+
+Interpretable equation:  
+
+$$
+\hat p_{lime} = 0.557 - 0.395 \cdot 1_{male} + 0.173 \cdot 1_{age <= 22}  + 0.146 \cdot 1_{class = 1st}=0.481,
+$$
+Plot `lime` model:
+
+```{r lime-plot}
+plot(lime_johnny)
+```
+
+**Package: localModel**  
+
+```{r localmodel, warning=FALSE, message=FALSE}
+library(localModel)
 
-`r knitr::include_url("https://www.youtube.com/embed/URL")`
+# localModel build
+locMod_johnny <- predict_surrogate(explainer = titanic_rf_exp, 
+                  new_observation = johnny_d, 
+                  size = 1000,
+                  seed = 1,
+                  type = "localModel")
+```  
+
+```{r localmodel-out, echo=FALSE}
+# output 
+locMod_johnny[,1:3] |> 
+  dplyr::mutate_if(is.numeric,round,3) |>  
+  knitr::kable()
+```
+
+Plot to explain how continuous `age` variable was dichotomized:  
+
+```{r localModel-age-discrete}
+# plot
+plot_interpretable_feature(locMod_johnny, "age")
 
-<details>
-<summary> Meeting chat log </summary>
+```  
+
+Glass-box explanation plot for Johnny D:  
 
+```{r localmodel-plot}
+plot(locMod_johnny)
 ```
-LOG
+
+**Package: iml**  
+
+```{r iml, warning=FALSE,message=FALSE}
+library(iml)
+
+# model using iml pacakge
+iml_johnny <- predict_surrogate(explainer = titanic_rf_exp, 
+                  new_observation = johnny_d, 
+                  k = 3, 
+                  type = "iml",
+                  seed=1)
 ```
-</details>
+
+```{r iml-out, echo=FALSE}
+# output - only output for probability of survival
+iml_johnny$results[,c(1:5,7)] |> 
+  dplyr::mutate_if(is.numeric,round,3) |>  
+  dplyr::filter(.class == "yes") |> 
+  knitr::kable()
+```  
+
+Notes:  
+
+* continuous variables are not transformed    
+* categorical variables dichotomized with value 1 for observed category; otherwise 0  
+
+Glass-box explanation plot for Johnny D:  
+
+```{r iml-plot}
+plot(iml_johnny)
+```
+
+The `age`, `gender` and `class` are correlated, and may partially explain why explanations are somewhat different across various LIME implementations.  
+
+
+
+## Meeting Videos {-}
+
+### Cohort 1 {-}
+
+`r knitr::include_url("https://www.youtube.com/embed/huz9bVCMtrE")`

DESCRIPTION

@@ -11,11 +11,16 @@ Imports:
     archivist,
     bookdown,
     DALEX,
+    DALEXtra,
     data.table,
     dplyr,
     flextable,
     ggplot2,
+    iml,
+    kableExtra,
     knitr,
+    lime,
+    localModel,
     patchwork,
     randomForest,
     rmarkdown,