From 49e3408a8311df0d3a977f5c9652473f19bbf8ca Mon Sep 17 00:00:00 2001 From: spitakiss Date: Wed, 10 Apr 2024 06:26:51 -0500 Subject: [PATCH] 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 --- ...table-model-agnostic-explanations-lime.Rmd | 303 +++++++++++++++++- DESCRIPTION | 5 + img/09-LIME/LASSO_horseduck.png | Bin 0 -> 183759 bytes img/09-LIME/binary_classifier_example.png | Bin 0 -> 173942 bytes img/09-LIME/explain_johnnyd.png | Bin 0 -> 17232 bytes img/09-LIME/superpixel_image.png | Bin 0 -> 580397 bytes 6 files changed, 296 insertions(+), 12 deletions(-) create mode 100644 img/09-LIME/LASSO_horseduck.png create mode 100644 img/09-LIME/binary_classifier_example.png create mode 100644 img/09-LIME/explain_johnnyd.png create mode 100644 img/09-LIME/superpixel_image.png diff --git a/09_local-interpretable-model-agnostic-explanations-lime.Rmd b/09_local-interpretable-model-agnostic-explanations-lime.Rmd index 41ab0fd..528d3b0 100644 --- a/09_local-interpretable-model-agnostic-explanations-lime.Rmd +++ b/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") -
- Meeting chat log +``` + +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) ``` -
+ +```{r iml-out, echo=FALSE} +# output - only output for probability of survival +iml_johnny$results[,c(1:5,7)] |> + dplyr::mutate_if(is.numeric,round,3) |> + dplyr::filter(.class == "yes") |> + knitr::kable() +``` + +Notes: + +* continuous variables are not transformed +* categorical variables dichotomized with value 1 for observed category; otherwise 0 + +Glass-box explanation plot for Johnny D: + +```{r iml-plot} +plot(iml_johnny) +``` + +The `age`, `gender` and `class` are correlated, and may partially explain why explanations are somewhat different across various LIME implementations. + + + +## Meeting Videos {-} + +### Cohort 1 {-} + +`r knitr::include_url("https://www.youtube.com/embed/huz9bVCMtrE")` diff --git a/DESCRIPTION b/DESCRIPTION index aee049f..051b054 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -11,11 +11,16 @@ Imports: archivist, bookdown, DALEX, + DALEXtra, data.table, dplyr, flextable, ggplot2, + iml, + kableExtra, knitr, + lime, + localModel, patchwork, randomForest, rmarkdown, diff --git a/img/09-LIME/LASSO_horseduck.png b/img/09-LIME/LASSO_horseduck.png new file mode 100644 index 0000000000000000000000000000000000000000..c2a07f39d64314d46fe3d6b32edde0f29070bfa2 GIT binary patch literal 183759 zcmdqJXEdB&{Prt)ixQ$lFNsb_jiBy^|>w|4fHf`Qr@8?ARxH;L`%(x zfPlz?fZ)a^IVtdpUb!YO@ZpA+k){ek)hIg}_=MO2{1i+;P?JD)VM_vhPVr95+>3yK z<~#n+jRE&kI|731;wNh0XMWbZOC;~D2fQWznBUurv5(`3s&V~KvS@giTBx)0G_|FA zc(__u=-vm|!b8-(n^Ft$3{ryy_EU0QQY)#7?mqTUtmlkC*V9|b#r(i7uMMTm;lWfs;UjI=cHqh zprCeYYU<-E>;L}fHk7P{o|UGbjf{-gvpFv;wfP>*x)uRgrkXJTUVl#`T9K6@nq zb%uUNT3K2SuiTeyF|M_k*U`}_Q%d!osj{+%KzL9&8<0~UEc2x51))hX!7;a%HI&Lyt0tUUMFOhMCLV2#>zTB)WgJ&=%oeO7&~&5KQIYQL*V=u3&^RLnxDHzxLF>juiT9~VoA zDg7Z*GJx(hK}F=&Xa(rHOuNpe%|lRdXTpykalVj(_ogU8~v5v}|*))YqW!7yZFuA3iu+ z$s{<){mI@8J8hlm5Vu+xj+&#?cx0|?ee~Z<(aykwjl!UYD$~fbDGqf3vN8cID-$a>^8Y8AZ!ATI-Ps zo{!r?WMeukq6`PESt{G+m}grgql0V^XV!Pt!^Q&lXhMK<;X3AB@j( z%Gz7~_FMKE7bI;^yVXZ?$}g-IgwoF&s*kf4syOGLD)ciH<;N|cmag48R&Tn)1G5(> zFUJt)-eU^btaR_ua}j;iq#JT>9|u&WUD1o57aL{;Xl_iVwQLHfXAbpgn}7g3w32iT zPIL&G(@j%Kgzk(l?Qo)VD|NTh{q6WKQS7o$M&VstR*H7C!G#reIG{Wv3}>?YIS%r0D}tjuQh)pm8Q3oNZg3rV**5OOh4$BrpH z78J2hmMHG6)LSaLn+<$n*AuMb-t>r)fu`5{DXN;eHeYuf{>BwM_^MFSTmSg`qZv!D zuQ>=UA#LmobZ^GfnuzeO7^)y^#Pqy0;jFaQeWmkRz@kX;Txx&ThxD!slz&2fTkQo# z;@Y!yk6qT2|8`Le8N$`GRaOVYV`(=vRCk3Rj-bf-Bc-d~O0|4{c(^b;D63_Zm8a3U zNn%qF?4&!C2sL)KJEk|C{>sE=Z!x3`1340%>X9;p_KnE>3Py)~VLZ3F=(oxGS_j(V z=2kVe^ib~{TRJZuUViQrxUtNT&AYeTkv7oQxzmAM34q=&QHjCy=^VJ0u=B%WUA}*bGyyO0<9u*=>`B{2|LVq)mD%;XkOOht5~} z8?FS$rAa^!V~t6Zz1F4XL^*Nb<>W^jbSbxw<5n7jhR^q)b;x^E(DSAcxyh?ZrOQd( zbT<%W%V4g~K_9_Tn>~E*Je!Y->vBu81o}5c36}z!vFn}8Ijtwl+IhnKU$urwvcTf4 zY^iUXpQ)cg�$jIk~^Xk~lpQF5m|vlF^ICWF8F}7>`fV(du84My!HRAy-Gaxvs}@ zT}N`PGtUUT@17qsoG}yTZ#JN>&qJ;yPCf>rwF4sC-C`qwjoT98X9iEx!(oHQumt|3W`k0fqAFX6o2gyl86XjGx6nvA^pxKb94B? zElEQAv09WvG{vP-Je)c#cjuGD7`nXAMnqJlTWbZK)1^@k^o;gsSnHXS&Dh6+%Wm3> zZWnz@#v7`z`q%d!UtL`h=) zS4fFVBOkT)873VxNQuz1QZN0ifam5-+S{Y4E=g=mXfWhrv~?wOMIjh0fa6G+;h5WA z=QdZdPV%**+qqeb@}*iYs=quPWZ{o;;gNzJ4eCQZbAUlinzWM-D`1BRo40eoy)a9! 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