From fd2dc93d1568a7b8354aba1e5b20bd17d9a62c15 Mon Sep 17 00:00:00 2001 From: Michael Mayer Date: Sun, 8 Sep 2024 12:05:28 +0200 Subject: [PATCH] Update basic_use vignette and README --- NEWS.md | 4 +- README.md | 28 +++-- man/figures/README-bee.png | Bin 0 -> 245780 bytes man/figures/README-dep.png | Bin 69187 -> 844712 bytes man/figures/README-imp.svg | 227 +++++++++++++++++++------------------ vignettes/basic_use.Rmd | 23 ++-- 6 files changed, 147 insertions(+), 135 deletions(-) create mode 100644 man/figures/README-bee.png diff --git a/NEWS.md b/NEWS.md index 33cc639..42215b0 100644 --- a/NEWS.md +++ b/NEWS.md @@ -2,7 +2,9 @@ ### Documentation -Add vignette for Tidymodels. +- Add vignette for Tidymodels. +- Update "basic_use" vignette. +- Update README. # shapviz 0.9.4 diff --git a/README.md b/README.md index 0d4adab..6beda54 100644 --- a/README.md +++ b/README.md @@ -15,17 +15,19 @@ {shapviz} provides typical SHAP plots: -- `sv_importance()`: Importance plots (bar plots and/or beeswarm plots). +- `sv_importance()`: Importance plot (bar/beeswarm). - `sv_dependence()` and `sv_dependence2D()`: Dependence plots to study feature effects and interactions. -- `sv_interaction()`: Interaction plots. -- `sv_waterfall()`: Waterfall plots to study single predictions. -- `sv_force()`: Force plots as alternative to waterfall plots. +- `sv_interaction()`: Interaction plot (beeswarm). +- `sv_waterfall()`: Waterfall plot to study single or average predictions. +- `sv_force()`: Force plot as alternative to waterfall plot. SHAP and feature values are stored in a "shapviz" object that is built from: -1. Models that know how to calculate SHAP values: XGBoost, LightGBM, h2o, or -2. SHAP crunchers like {fastshap}, {kernelshap}, {treeshap}, {fastr}, {DALEX}, or simply from a -3. SHAP matrix and its corresponding feature values. +1. Models that know how to calculate SHAP values: XGBoost, LightGBM, H2O (boosted trees). +2. SHAP crunchers like {fastshap}, {kernelshap}, {treeshap}, {fastr}, and {DALEX}. +3. SHAP matrix and corresponding feature values. + +We use {patchwork} to glue together multiple plots with (potentially) inconsistent x and/or color scale. ## Installation @@ -59,12 +61,16 @@ dia_2000 <- diamonds[sample(nrow(diamonds), 2000), x] shp <- shapviz(fit, X_pred = data.matrix(dia_2000), X = dia_2000) sv_importance(shp, show_numbers = TRUE) -sv_dependence(shp, v = x) +sv_importance(shp, kind = "bee") +sv_dependence(shp, v = x) # patchwork ``` ![](man/figures/README-imp.svg) -![](man/figures/README-dep.png) +beeswarm + +scatter + Decompositions of individual predictions can be visualized as waterfall or force plot: @@ -81,10 +87,10 @@ sv_force(shp, row_id = 1) Check-out the vignettes for topics like: -- How to work with other SHAP packages like {fastshap}, {kernelshap} or {treeshap}? -- SHAP interactions. +- Basic use (includes working with other packages and SHAP interactions). - Multiple models, multi-output models, and subgroup analyses. - Plotting geographic effects. +- Working with Tidymodels. ## References diff --git a/man/figures/README-bee.png b/man/figures/README-bee.png new file mode 100644 index 0000000000000000000000000000000000000000..a924634c7dd270d366707d4953ccc6d4c864915c GIT binary patch literal 245780 zcmeFYhc}z=|39wdRb8rz+SSIZwyG_Nv?VBNgcz~+ruLRj)U3U!y{TQBw)U#MM-iKp z*pcLWYx_Ru{rUU_zwP6t4*_ZF_fPYtv6=bDJi040_8grw7cdosZ({?5y(Um#>y+9Q$^^}CbjIS<+I_|?6Sv$JAAVqB3;Pgy^iaP7{2|Jt%k1Lo z_)VN7!%g$~r`$-A*S9ae`_Y8KS*An?M#>0eYJ1n!ZzLb#GQGUK@^?yny>fPN8wxum zVynJs1sfZYU?@bn8%zou>i2_jGl)g+KQBl^-h8_GpBE-iRj&M9zbd=We(CSJMeBC- zg}-Yn>l{-m&G-h1?IxWnAsVZ2(?QWg#22OaN8 zK86@j3T7D{D&eWW*MyE>BHx**_nqGwv6Z$J_1pVyk?L_umkT+{9)O&H+3Xt8gW|k( zxYsr{5=|!`R%~lu=0^|Z4?xf%ni3dqzvcJyIForE!N1{zdsPfXa!Dg*v~P9ZZ#SSs zmxEaVlP`Ou>)zitp5l!4Ut(q5tZIL~UOALV0j4(7F0NGX_go#*6R+8+Bg`a;FhWm) zPzUAX#1-5!QO`8xMMDYD*uL9Abe@I@2q#B(4+MfSXKmY#EZC_=k0nr%G$$*yi zBUaS-;!FCKpL6+CRU&)#nIaR0lkNOYp((Dzo;9~DA%8AeutY%5PpX~>d0dYtu%&r< 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zGE6+a-1QIrw8?A*&W?we!2N6JkF*)td}m*@Iv`c22!@rTWEpaiMEqEVWW=-xJiulO zuw&7KgFzD$cRxfr2N|jsy!0vUQ)}Hkc&0V+Nbv~yo6KF}0}}cFi#$e`T6@{>Pj+tW zZWeDDBJ0>JPvaO^JY-x|)L07uL$Leij^$3gdz4hhDn>LQRDcOJk>IPpg8%}878Sv) zMK`|L3qvt`XIzh814%}FHC%pK23%{be+0VO6J - + - + - + - + - + - + + + + @@ -108,187 +111,187 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - - - + + + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - + + + + - - - - + + + + - - - + + + - - - + + + - - - + + + - - - + + + - - - - - + + + + + - - - - - - - + + + + + + + - - - - - - - - - - - - - - + + + + + + + + + + + + + + - + - - - - + + + + - - - - + + + + - - - - + + + + - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + diff --git a/vignettes/basic_use.Rmd b/vignettes/basic_use.Rmd index d3346af..e3ee69f 100644 --- a/vignettes/basic_use.Rmd +++ b/vignettes/basic_use.Rmd @@ -27,19 +27,19 @@ SHAP (SHapley Additive exPlanations, see @lundberg2017) is an ingenious way to s In particular, the following plots are available: -- `sv_importance()`: Importance plots (bar plots and/or beeswarm plots). +- `sv_importance()`: Importance plot (bar/beeswarm). - `sv_dependence()` and `sv_dependence2D()`: Dependence plots to study feature effects and interactions. -- `sv_interaction()`: Interaction plots. -- `sv_waterfall()`: Waterfall plots to study single predictions. -- `sv_force()`: Force plots as alternative to waterfall plots. +- `sv_interaction()`: Interaction plot (beeswarm). +- `sv_waterfall()`: Waterfall plot to study single or average predictions. +- `sv_force()`: Force plot as alternative to waterfall plot. SHAP and feature values are stored in a "shapviz" object that is built from: -1. Models that know how to calculate SHAP values: XGBoost, LightGBM, h2o, or -2. SHAP crunchers like {fastshap}, {kernelshap}, {treeshap}, {fastr}, {DALEX}, or simply from a -3. SHAP matrix and its corresponding feature values. +1. Models that know how to calculate SHAP values: XGBoost, LightGBM, h2o (boosted trees). +2. SHAP crunchers like {fastshap}, {kernelshap}, {treeshap}, {fastr}, and {DALEX}. +3. SHAP matrix and corresponding feature values. -See vignette "Multiple shapviz objects" for how to deal with multiple models or multiclass models. +We use {patchwork} to glue together multiple plots with (potentially) inconsistent x and/or color scale. ## Installation @@ -81,7 +81,7 @@ sv_importance(shp, show_numbers = TRUE) sv_importance(shp, kind = "beeswarm") # kind = "both" combines bar and bee ``` ```{r, fig.width=8.5, fig.height=5.5} -sv_dependence(shp, v = x) +sv_dependence(shp, v = x) # patchwork object ``` ### Decompose single predictions @@ -110,7 +110,7 @@ If SHAP interaction values have been computed (via {xgboost} or {treeshap}), we Note that SHAP interaction values are multiplied by two (except main effects). -```{r} +```{r, fig.width=8.5, fig.height=5.5} shp_i <- shapviz( fit, X_pred = data.matrix(dia_2000[x]), X = dia_2000, interactions = TRUE ) @@ -232,8 +232,9 @@ library(kernelshap) set.seed(1) fit <- lm(Sepal.Length ~ . + Species:Petal.Width, data = iris) -ks <- permshap(fit, iris[-1], bg_X = iris) +ks <- permshap(fit, iris[-1]) shp <- shapviz(ks) + sv_importance(shp) sv_dependence(shp, colnames(iris[-1])) ```