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xai2shiny's Issues

AUC in regressions models

I have tested the code with two regressions models: xgboost and glm. The results produce an AUC plot, which is meaningless for regressions.

Error running xai2shiny

Hi @Adamoso it's me again! I've updated your package to the most recent version and now I'm getting the following error:

Error in withSpinner(uiOutput("textPred"), hide.ui = FALSE) : 
  unused argument (hide.ui = FALSE)

Reproducible example:

library(xai2shiny) # devtools::install_github("ModelOriented/xai2shiny")
library(lares) # devtools::install_github("laresbernardo/lares")
ignore <- c("PassengerId","Ticket","Cabin")
model <- h2o_automl(dft, Survived, ignore = ignore, quiet = FALSE)
explainer <- h2o_explainer(model$datasets$test, model = model$model, y = "Survived", ignore = ignore)
xai2shiny(explainer)

more verbose xai2shiny::xai2shiny

after the dir is created there is no message where it was created and if the process was successful
be more verbose and let the user know that the process was OK and where is the outpur
(see verbose parameter in DALEX::explain)

finish TODOS

line 23

# TODO: create observation based on average data for each variable
chosen_observation <- data[1,-8]

Error rendering - missing comma

Error running xai2shiny function, something about a missing comma?

Load libraries

Data transformations

suppressMessages(library(tidyverse))
suppressMessages(library(data.table))

Saving data to disk

suppressMessages(library(feather))
suppressMessages(library(arrow))
suppressMessages(library(here))

Feature engineering

suppressMessages(library(recipes))
suppressMessages(library(yardstick))

Machine learning

suppressMessages(library(tidymodels))
suppressMessages(library(themis))

Explainer

suppressMessages(library(DALEX))

dir <- "/home/paulc/projects_Paul/31_user_journey"

Load models

lasso_model <- readRDS(paste0(dir, "/R/models/02_glmnet/", "final_model_smote.Rds"))
ranger_model <- readRDS(paste0(dir, "/R/models/03_randomForest/", "final_model_smote.Rds"))

Read data

path <- paste0(dir, "/R/data/processed/", "data_final.parquet")
df.data <- setDT(read_parquet(path))

Delete col_to_del

col_to_del <- c("username", "user_id", "start_activity",
"end_activity", "cohort", "min_to_purchase",
"token_bonus_ratio", "first_purchase")
df.data[, (col_to_del) := NULL]

Split the data into training and testing sets

set.seed(2020)
train_test_split <- df.data %>%
initial_split(prop = 0.8, strata = label_fct)

Set recipie

recipie_num <- training(train_test_split) %>%
recipe(label_fct ~. ) %>% # Fomula
step_mutate(label_fct = as.factor(label_fct)) %>%
step_normalize(all_predictors()) %>%
step_smote(label_fct) %>%
prep()

create the final data

df.train <- as.data.frame(juice(recipie_num))
df.test <- as.data.frame(bake(recipie_num, new_data = testing(train_test_split)))

binary variable for explainer

df.testing_original <- testing(train_test_split)
yTest <- as.integer(ifelse(df.testing_original$label_fct == "yes", 1, 0))

df.test <- df.test %>%
select(-label_fct)

custom_predict <- function(object, newdata) {pred <- predict(object, newdata, type = "prob")
response <- pred$.pred_yes
return(response)}

lasso_explainer <- DALEX::explain(model = lasso_model,
data = df.test,
y = yTest,
predict_function = custom_predict,
label = "Lasso",
colorize = FALSE)
#> Preparation of a new explainer is initiated
#> -> model label : Lasso
#> -> data : 20514 rows 5 cols
#> -> target variable : 20514 values
#> -> predict function : custom_predict
#> -> predicted values : numerical, min = 0.1982451 , mean = 0.35857 , max = 0.9535754
#> -> model_info : package parsnip , ver. 0.1.3 , task classification ( default )
#> -> residual function : difference between y and yhat ( default )
#> -> residuals : numerical, min = -0.9535754 , mean = -0.3346352 , max = 0.8017549
#> A new explainer has been created!

ranger_explainer <- DALEX::explain(model = ranger_model,
data = df.test,
y = yTest,
predict_function = custom_predict,
label = "Random Forest",
colorize = FALSE)
#> Preparation of a new explainer is initiated
#> -> model label : Random Forest
#> -> data : 20514 rows 5 cols
#> -> target variable : 20514 values
#> -> predict function : custom_predict
#> -> predicted values : numerical, min = 0.2152264 , mean = 0.3666207 , max = 0.9012534
#> -> model_info : package parsnip , ver. 0.1.3 , task classification ( default )
#> -> residual function : difference between y and yhat ( default )
#> -> residuals : numerical, min = -0.9012534 , mean = -0.3426858 , max = 0.7847736
#> A new explainer has been created!
library(xai2shiny)
xai2shiny(lasso_explainer, ranger_explainer)
#> Loading required package: shiny
#> Error in parse(file, keep.source = FALSE, srcfile = src, encoding = enc) :
#> /tmp/RtmpdRkELf/xai2shiny/app.R:11:1: unexpected ','
#> 10: library(parsnip)
#> 11: ,
#> ^
#> Possible missing comma at:
#> 30: if(!is.null(header)) tags$li(class="header",header),
#> ^
#> Possible extra comma at:
#> 127: column(width = 3, uiOutput("pdpvariable"),),
#> ^
#> Possible missing comma at:
#> 153: nulls <- sapply(obs, function(x) length(x) == 0)
#> ^
#> Error in sourceUTF8(fullpath, envir = new.env(parent = sharedEnv)): Error sourcing /tmp/RtmpdRkELf/xai2shiny/app.R
sessionInfo()
#> R version 4.0.2 (2020-06-22)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: CentOS Linux 7 (Core)
#>
#> Matrix products: default
#> BLAS/LAPACK: /usr/lib64/libopenblasp-r0.3.3.so
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
#> [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
#> [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
#> [9] LC_ADDRESS=C LC_TELEPHONE=C
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
#>
#> attached base packages:
#> [1] stats graphics grDevices datasets utils methods base
#>
#> other attached packages:
#> [1] shiny_1.5.0 xai2shiny_0.1.0 DALEX_2.0
#> [4] themis_0.1.2 workflows_0.2.0 tune_0.1.1
#> [7] rsample_0.0.8 parsnip_0.1.3 modeldata_0.0.2
#> [10] infer_0.5.3 dials_0.0.9 scales_1.1.1
#> [13] broom_0.7.0 tidymodels_0.1.1.9000 yardstick_0.0.7
#> [16] recipes_0.1.13 here_0.1 arrow_1.0.0.20200728
#> [19] feather_0.3.5 data.table_1.12.8 forcats_0.5.0
#> [22] stringr_1.4.0 dplyr_1.0.2 purrr_0.3.4
#> [25] readr_1.4.0 tidyr_1.1.2 tibble_3.0.4
#> [28] ggplot2_3.3.2 tidyverse_1.3.0
#>
#> loaded via a namespace (and not attached):
#> [1] readxl_1.3.1 mlr_2.17.1 backports_1.1.10
#> [4] fastmatch_1.1-0 plyr_1.8.6 shinydashboard_0.7.1
#> [7] splines_4.0.2 listenv_0.8.0 digest_0.6.26
#> [10] foreach_1.5.0 htmltools_0.5.0 fansi_0.4.1
#> [13] magrittr_1.5 checkmate_2.0.0 BBmisc_1.11
#> [16] unbalanced_2.0 doParallel_1.0.15 globals_0.13.0
#> [19] modelr_0.1.8 gower_0.2.2 colorspace_1.4-1
#> [22] blob_1.2.1 rvest_0.3.5 haven_2.3.1
#> [25] xfun_0.17 crayon_1.3.4 jsonlite_1.7.1
#> [28] survival_3.1-12 iterators_1.0.12 glue_1.4.2
#> [31] gtable_0.3.0 ipred_0.9-9 shape_1.4.4
#> [34] DBI_1.1.0 Rcpp_1.0.5 xtable_1.8-4
#> [37] GPfit_1.0-8 bit_1.1-15.2 lava_1.6.8
#> [40] prodlim_2019.11.13 glmnet_4.0-2 httr_1.4.1
#> [43] sourcetools_0.1.7 FNN_1.1.3 ellipsis_0.3.1
#> [46] pkgconfig_2.0.3 ParamHelpers_1.14 nnet_7.3-14
#> [49] dbplyr_1.4.4 tidyselect_1.1.0 rlang_0.4.8
#> [52] DiceDesign_1.8-1 later_1.1.0.1 munsell_0.5.0
#> [55] cellranger_1.1.0 tools_4.0.2 cli_2.1.0
#> [58] generics_0.0.2 ranger_0.12.1 evaluate_0.14
#> [61] fastmap_1.0.1 yaml_2.2.1 knitr_1.30
#> [64] bit64_0.9-7 fs_1.4.2 shinycssloaders_1.0.0
#> [67] RANN_2.6.1 future_1.19.1 whisker_0.4
#> [70] mime_0.9 xml2_1.3.2 compiler_4.0.2
#> [73] rstudioapi_0.11 reprex_0.3.0 lhs_1.0.2
#> [76] stringi_1.5.3 highr_0.8 lattice_0.20-41
#> [79] Matrix_1.2-18 shinyjs_2.0.0 vctrs_0.3.4
#> [82] pillar_1.4.6 lifecycle_0.2.0 furrr_0.1.0
#> [85] httpuv_1.5.4 R6_2.4.1 promises_1.1.1
#> [88] renv_0.12.0-12 codetools_0.2-16 MASS_7.3-51.6
#> [91] assertthat_0.2.1 rprojroot_1.3-2 shinyWidgets_0.5.4
#> [94] ROSE_0.0-3 withr_2.3.0 parallel_4.0.2
#> [97] hms_0.5.3 grid_4.0.2 rpart_4.1-15
#> [100] timeDate_3043.102 class_7.3-17 rmarkdown_2.3
#> [103] parallelMap_1.5.0 pROC_1.16.2 lubridate_1.7.9
Created on 2020-10-22 by the reprex package (v0.3.0)

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