From c54b77f8157028042df39d8e0cfbdeeeb94cb3e1 Mon Sep 17 00:00:00 2001 From: krzyzinskim Date: Fri, 29 Sep 2023 20:07:25 +0200 Subject: [PATCH] less computationally expensive examples --- R/plot_contribution.R | 5 ++--- R/plot_feature_dependence.R | 5 ++--- R/plot_feature_importance.R | 5 ++--- R/plot_interaction.R | 2 -- R/predict.R | 19 ++++++++----------- R/set_reference_dataset.R | 15 ++++++--------- R/unify_gbm.R | 4 +--- R/unify_lightgbm.R | 5 ++--- R/unify_xgboost.R | 2 -- man/gbm.unify.Rd | 4 +--- man/lightgbm.unify.Rd | 5 ++--- man/plot_contribution.Rd | 5 ++--- man/plot_feature_dependence.Rd | 5 ++--- man/plot_feature_importance.Rd | 5 ++--- man/plot_interaction.Rd | 2 -- man/predict.model_unified.Rd | 19 ++++++++----------- man/set_reference_dataset.Rd | 15 ++++++--------- man/treeshap.Rd | 6 ++---- man/xgboost.unify.Rd | 2 -- src/RcppExports.o | Bin 666408 -> 726384 bytes src/treeshap.o | Bin 750640 -> 703568 bytes 21 files changed, 48 insertions(+), 82 deletions(-) diff --git a/R/plot_contribution.R b/R/plot_contribution.R index e2894fd..060286a 100644 --- a/R/plot_contribution.R +++ b/R/plot_contribution.R @@ -26,17 +26,16 @@ #' #' #' @examples -#' \dontrun{ #' library(xgboost) #' data <- fifa20$data[colnames(fifa20$data) != 'work_rate'] #' target <- fifa20$target #' param <- list(objective = "reg:squarederror", max_depth = 3) -#' xgb_model <- xgboost::xgboost(as.matrix(data), params = param, label = target, nrounds = 200) +#' xgb_model <- xgboost::xgboost(as.matrix(data), params = param, label = target, +#' nrounds = 20, verbose = FALSE) #' unified_model <- xgboost.unify(xgb_model, as.matrix(data)) #' x <- head(data, 1) #' shap <- treeshap(unified_model, x) #' plot_contribution(shap, 1, min_max = c(0, 120000000)) -#' } plot_contribution <- function(treeshap, obs = 1, max_vars = 5, diff --git a/R/plot_feature_dependence.R b/R/plot_feature_dependence.R index 5d68ceb..0ae0651 100644 --- a/R/plot_feature_dependence.R +++ b/R/plot_feature_dependence.R @@ -20,17 +20,16 @@ #' #' #' @examples -#' \dontrun{ #' library(xgboost) #' data <- fifa20$data[colnames(fifa20$data) != 'work_rate'] #' target <- fifa20$target #' param <- list(objective = "reg:squarederror", max_depth = 3) -#' xgb_model <- xgboost::xgboost(as.matrix(data), params = param, label = target, nrounds = 200) +#' xgb_model <- xgboost::xgboost(as.matrix(data), params = param, label = target, +#' nrounds = 20, verbose = FALSE) #' unified_model <- xgboost.unify(xgb_model, as.matrix(data)) #' x <- head(data, 100) #' shaps <- treeshap(unified_model, x) #' plot_feature_dependence(shaps, variable = "overall") -#' } plot_feature_dependence <- function(treeshap, variable, title = "Feature Dependence", subtitle = NULL) { diff --git a/R/plot_feature_importance.R b/R/plot_feature_importance.R index 88d46c5..9e88440 100644 --- a/R/plot_feature_importance.R +++ b/R/plot_feature_importance.R @@ -24,16 +24,15 @@ #' #' #' @examples -#' \dontrun{ #' library(xgboost) #' data <- fifa20$data[colnames(fifa20$data) != 'work_rate'] #' target <- fifa20$target #' param <- list(objective = "reg:squarederror", max_depth = 3) -#' xgb_model <- xgboost::xgboost(as.matrix(data), params = param, label = target, nrounds = 200) +#' xgb_model <- xgboost::xgboost(as.matrix(data), params = param, label = target, +#' nrounds = 20, verbose = FALSE) #' unified_model <- xgboost.unify(xgb_model, as.matrix(data)) #' shaps <- treeshap(unified_model, as.matrix(head(data, 3))) #' plot_feature_importance(shaps, max_vars = 4) -#' } plot_feature_importance <- function(treeshap, desc_sorting = TRUE, max_vars = ncol(shaps), diff --git a/R/plot_interaction.R b/R/plot_interaction.R index b374a71..31c675d 100644 --- a/R/plot_interaction.R +++ b/R/plot_interaction.R @@ -22,7 +22,6 @@ #' #' #' @examples -#' \donttest{ #' data <- fifa20$data[colnames(fifa20$data) != 'work_rate'] #' target <- fifa20$target #' param2 <- list(objective = "reg:squarederror", max_depth = 5) @@ -30,7 +29,6 @@ #' unified_model2 <- xgboost.unify(xgb_model2, data) #' inters <- treeshap(unified_model2, as.matrix(data[1:50, ]), interactions = TRUE) #' plot_interaction(inters, "dribbling", "defending") -#' } plot_interaction <- function(treeshap, var1, var2, title = "SHAP Interaction Value Plot", subtitle = "") { diff --git a/R/predict.R b/R/predict.R index 85fe33e..c49185f 100644 --- a/R/predict.R +++ b/R/predict.R @@ -11,21 +11,18 @@ #' @export #' #' @examples -#' \dontrun{ #' library(gbm) #' data <- fifa20$data[colnames(fifa20$data) != 'work_rate'] #' data['value_eur'] <- fifa20$target #' gbm_model <- gbm::gbm( -#' formula = value_eur ~ ., -#' data = data, -#' distribution = "laplace", -#' n.trees = 1000, -#' cv.folds = 2, -#' interaction.depth = 2, -#' n.cores = 1) -#' unified <- gbm.unify(gbm_model, data) -#' predict(unified, data[3:7, ]) -#'} +#' formula = value_eur ~ ., +#' data = data, +#' distribution = "laplace", +#' n.trees = 20, +#' interaction.depth = 4, +#' n.cores = 1) +#' unified <- gbm.unify(gbm_model, data) +#' predict(unified, data[2001:2005, ]) predict.model_unified <- function(object, x, ...) { unified_model <- object model <- unified_model$model diff --git a/R/set_reference_dataset.R b/R/set_reference_dataset.R index 4ef00b9..629d28d 100644 --- a/R/set_reference_dataset.R +++ b/R/set_reference_dataset.R @@ -26,21 +26,18 @@ #' \code{\link{randomForest.unify}} for \code{\link[randomForest:randomForest]{randomForest models}} #' #' @examples -#' \dontrun{ #' library(gbm) #' data <- fifa20$data[colnames(fifa20$data) != 'work_rate'] #' data['value_eur'] <- fifa20$target #' gbm_model <- gbm::gbm( -#' formula = value_eur ~ ., -#' data = data, -#' distribution = "laplace", -#' n.trees = 1000, -#' cv.folds = 2, -#' interaction.depth = 2, -#' n.cores = 1) +#' formula = value_eur ~ ., +#' data = data, +#' distribution = "laplace", +#' n.trees = 20, +#' interaction.depth = 4, +#' n.cores = 1) #' unified <- gbm.unify(gbm_model, data) #' set_reference_dataset(unified, data[200:700, ]) -#'} set_reference_dataset <- function(unified_model, x) { model <- unified_model$model data <- x diff --git a/R/unify_gbm.R b/R/unify_gbm.R index acfe8cc..9d4f773 100644 --- a/R/unify_gbm.R +++ b/R/unify_gbm.R @@ -21,7 +21,6 @@ #' \code{\link{randomForest.unify}} for \code{\link[randomForest:randomForest]{randomForest models}} #' #' @examples -#'\donttest{ #' library(gbm) #' data <- fifa20$data[colnames(fifa20$data) != 'work_rate'] #' data['value_eur'] <- fifa20$target @@ -29,13 +28,12 @@ #' formula = value_eur ~ ., #' data = data, #' distribution = "gaussian", -#' n.trees = 50, +#' n.trees = 20, #' interaction.depth = 4, #' n.cores = 1) #' unified_model <- gbm.unify(gbm_model, data) #' shaps <- treeshap(unified_model, data[1:2,]) #' plot_contribution(shaps, obs = 1) -#' } gbm.unify <- function(gbm_model, data) { if(!inherits(gbm_model,'gbm')) { stop('Object gbm_model was not of class "gbm"') diff --git a/R/unify_lightgbm.R b/R/unify_lightgbm.R index 4a20ac4..184cbf3 100644 --- a/R/unify_lightgbm.R +++ b/R/unify_lightgbm.R @@ -25,9 +25,9 @@ #' \code{\link{randomForest.unify}} for \code{\link[randomForest:randomForest]{randomForest models}} #' #' @examples -#' \donttest{ #' library(lightgbm) -#' param_lgbm <- list(objective = "regression", max_depth = 2, force_row_wise = TRUE) +#' param_lgbm <- list(objective = "regression", max_depth = 2, +#' force_row_wise = TRUE, num_iterations = 20) #' data_fifa <- fifa20$data[!colnames(fifa20$data) %in% #' c('work_rate', 'value_eur', 'gk_diving', 'gk_handling', #' 'gk_kicking', 'gk_reflexes', 'gk_speed', 'gk_positioning')] @@ -40,7 +40,6 @@ #' unified_model <- lightgbm.unify(lgb_model, sparse_data) #' shaps <- treeshap(unified_model, data[1:2, ]) #' plot_contribution(shaps, obs = 1) -#' } lightgbm.unify <- function(lgb_model, data, recalculate = FALSE) { if (!requireNamespace("lightgbm", quietly = TRUE)) { stop("Package \"lightgbm\" needed for this function to work. Please install it.", diff --git a/R/unify_xgboost.R b/R/unify_xgboost.R index 0b5fd4d..ee1d95a 100644 --- a/R/unify_xgboost.R +++ b/R/unify_xgboost.R @@ -21,7 +21,6 @@ #' \code{\link{randomForest.unify}} for \code{\link[randomForest:randomForest]{randomForest models}} #' #' @examples -#' \donttest{ #' library(xgboost) #' data <- fifa20$data[colnames(fifa20$data) != 'work_rate'] #' target <- fifa20$target @@ -31,7 +30,6 @@ #' unified_model <- xgboost.unify(xgb_model, as.matrix(data)) #' shaps <- treeshap(unified_model, data[1:2,]) #' plot_contribution(shaps, obs = 1) -#' } #' xgboost.unify <- function(xgb_model, data, recalculate = FALSE) { if (!requireNamespace("xgboost", quietly = TRUE)) { diff --git a/man/gbm.unify.Rd b/man/gbm.unify.Rd index bc672e4..0d896e3 100644 --- a/man/gbm.unify.Rd +++ b/man/gbm.unify.Rd @@ -20,7 +20,6 @@ Convert your GBM model into a standardized representation. The returned representation is easy to be interpreted by the user and ready to be used as an argument in \code{treeshap()} function. } \examples{ -\donttest{ library(gbm) data <- fifa20$data[colnames(fifa20$data) != 'work_rate'] data['value_eur'] <- fifa20$target @@ -28,14 +27,13 @@ gbm_model <- gbm::gbm( formula = value_eur ~ ., data = data, distribution = "gaussian", - n.trees = 50, + n.trees = 20, interaction.depth = 4, n.cores = 1) unified_model <- gbm.unify(gbm_model, data) shaps <- treeshap(unified_model, data[1:2,]) plot_contribution(shaps, obs = 1) } -} \seealso{ \code{\link{lightgbm.unify}} for \code{\link[lightgbm:lightgbm]{LightGBM models}} diff --git a/man/lightgbm.unify.Rd b/man/lightgbm.unify.Rd index 65b0317..2adc3d1 100644 --- a/man/lightgbm.unify.Rd +++ b/man/lightgbm.unify.Rd @@ -21,9 +21,9 @@ Convert your LightGBM model into a standardized representation. The returned representation is easy to be interpreted by the user and ready to be used as an argument in \code{treeshap()} function. } \examples{ -\donttest{ library(lightgbm) -param_lgbm <- list(objective = "regression", max_depth = 2, force_row_wise = TRUE) +param_lgbm <- list(objective = "regression", max_depth = 2, + force_row_wise = TRUE, num_iterations = 20) data_fifa <- fifa20$data[!colnames(fifa20$data) \%in\% c('work_rate', 'value_eur', 'gk_diving', 'gk_handling', 'gk_kicking', 'gk_reflexes', 'gk_speed', 'gk_positioning')] @@ -37,7 +37,6 @@ unified_model <- lightgbm.unify(lgb_model, sparse_data) shaps <- treeshap(unified_model, data[1:2, ]) plot_contribution(shaps, obs = 1) } -} \seealso{ \code{\link{gbm.unify}} for \code{\link[gbm:gbm]{GBM models}} diff --git a/man/plot_contribution.Rd b/man/plot_contribution.Rd index 31a5c47..5133f11 100644 --- a/man/plot_contribution.Rd +++ b/man/plot_contribution.Rd @@ -41,18 +41,17 @@ a \code{ggplot2} object This function plots contributions of features into the prediction for a single observation. } \examples{ -\dontrun{ library(xgboost) data <- fifa20$data[colnames(fifa20$data) != 'work_rate'] target <- fifa20$target param <- list(objective = "reg:squarederror", max_depth = 3) -xgb_model <- xgboost::xgboost(as.matrix(data), params = param, label = target, nrounds = 200) +xgb_model <- xgboost::xgboost(as.matrix(data), params = param, label = target, + nrounds = 20, verbose = FALSE) unified_model <- xgboost.unify(xgb_model, as.matrix(data)) x <- head(data, 1) shap <- treeshap(unified_model, x) plot_contribution(shap, 1, min_max = c(0, 120000000)) } -} \seealso{ \code{\link{treeshap}} for calculation of SHAP values diff --git a/man/plot_feature_dependence.Rd b/man/plot_feature_dependence.Rd index dbe2cf4..688e55a 100644 --- a/man/plot_feature_dependence.Rd +++ b/man/plot_feature_dependence.Rd @@ -27,18 +27,17 @@ a \code{ggplot2} object Depending on the value of a variable: how does it contribute into the prediction? } \examples{ -\dontrun{ library(xgboost) data <- fifa20$data[colnames(fifa20$data) != 'work_rate'] target <- fifa20$target param <- list(objective = "reg:squarederror", max_depth = 3) -xgb_model <- xgboost::xgboost(as.matrix(data), params = param, label = target, nrounds = 200) +xgb_model <- xgboost::xgboost(as.matrix(data), params = param, label = target, + nrounds = 20, verbose = FALSE) unified_model <- xgboost.unify(xgb_model, as.matrix(data)) x <- head(data, 100) shaps <- treeshap(unified_model, x) plot_feature_dependence(shaps, variable = "overall") } -} \seealso{ \code{\link{treeshap}} for calculation of SHAP values diff --git a/man/plot_feature_importance.Rd b/man/plot_feature_importance.Rd index 4935019..fef3aec 100644 --- a/man/plot_feature_importance.Rd +++ b/man/plot_feature_importance.Rd @@ -30,17 +30,16 @@ a \code{ggplot2} object This function plots feature importance calculated as means of absolute values of SHAP values of variables (average impact on model output magnitude). } \examples{ -\dontrun{ library(xgboost) data <- fifa20$data[colnames(fifa20$data) != 'work_rate'] target <- fifa20$target param <- list(objective = "reg:squarederror", max_depth = 3) -xgb_model <- xgboost::xgboost(as.matrix(data), params = param, label = target, nrounds = 200) +xgb_model <- xgboost::xgboost(as.matrix(data), params = param, label = target, + nrounds = 20, verbose = FALSE) unified_model <- xgboost.unify(xgb_model, as.matrix(data)) shaps <- treeshap(unified_model, as.matrix(head(data, 3))) plot_feature_importance(shaps, max_vars = 4) } -} \seealso{ \code{\link{treeshap}} for calculation of SHAP values diff --git a/man/plot_interaction.Rd b/man/plot_interaction.Rd index 30b22f5..20a271e 100644 --- a/man/plot_interaction.Rd +++ b/man/plot_interaction.Rd @@ -31,7 +31,6 @@ This function plots SHAP Interaction value for two variables depending on the va Value of the second variable is marked with the color. } \examples{ -\donttest{ data <- fifa20$data[colnames(fifa20$data) != 'work_rate'] target <- fifa20$target param2 <- list(objective = "reg:squarederror", max_depth = 5) @@ -40,7 +39,6 @@ unified_model2 <- xgboost.unify(xgb_model2, data) inters <- treeshap(unified_model2, as.matrix(data[1:50, ]), interactions = TRUE) plot_interaction(inters, "dribbling", "defending") } -} \seealso{ \code{\link{treeshap}} for calculation of SHAP Interaction values diff --git a/man/predict.model_unified.Rd b/man/predict.model_unified.Rd index 1f764ac..e2dca99 100644 --- a/man/predict.model_unified.Rd +++ b/man/predict.model_unified.Rd @@ -20,19 +20,16 @@ a vector of predictions. Predict using unified_model representation. } \examples{ -\dontrun{ library(gbm) data <- fifa20$data[colnames(fifa20$data) != 'work_rate'] data['value_eur'] <- fifa20$target gbm_model <- gbm::gbm( - formula = value_eur ~ ., - data = data, - distribution = "laplace", - n.trees = 1000, - cv.folds = 2, - interaction.depth = 2, - n.cores = 1) -unified <- gbm.unify(gbm_model, data) -predict(unified, data[3:7, ]) -} + formula = value_eur ~ ., + data = data, + distribution = "laplace", + n.trees = 20, + interaction.depth = 4, + n.cores = 1) + unified <- gbm.unify(gbm_model, data) + predict(unified, data[2001:2005, ]) } diff --git a/man/set_reference_dataset.Rd b/man/set_reference_dataset.Rd index f1a46ad..79b6085 100644 --- a/man/set_reference_dataset.Rd +++ b/man/set_reference_dataset.Rd @@ -22,22 +22,19 @@ Usually reference dataset is dataset used to train the model. Important property of reference dataset is that SHAP values for each observation add up to its deviation from mean prediction for a reference dataset. } \examples{ -\dontrun{ library(gbm) data <- fifa20$data[colnames(fifa20$data) != 'work_rate'] data['value_eur'] <- fifa20$target gbm_model <- gbm::gbm( - formula = value_eur ~ ., - data = data, - distribution = "laplace", - n.trees = 1000, - cv.folds = 2, - interaction.depth = 2, - n.cores = 1) +formula = value_eur ~ ., + data = data, + distribution = "laplace", + n.trees = 20, + interaction.depth = 4, + n.cores = 1) unified <- gbm.unify(gbm_model, data) set_reference_dataset(unified, data[200:700, ]) } -} \seealso{ \code{\link{lightgbm.unify}} for \code{\link[lightgbm:lightgbm]{LightGBM models}} diff --git a/man/treeshap.Rd b/man/treeshap.Rd index b8a4b46..021b31e 100644 --- a/man/treeshap.Rd +++ b/man/treeshap.Rd @@ -22,15 +22,14 @@ A \code{\link{treeshap.object}} object. SHAP values can be accessed with \code{$ Calculate SHAP values and optionally SHAP Interaction values. } \examples{ -\dontrun{ library(xgboost) data <- fifa20$data[colnames(fifa20$data) != 'work_rate'] target <- fifa20$target # calculating simple SHAP values param <- list(objective = "reg:squarederror", max_depth = 3) -xgb_model <- xgboost::xgboost(as.matrix(data), params = param, label = target, nrounds = 200, - verbose = 0) +xgb_model <- xgboost::xgboost(as.matrix(data), params = param, label = target, + nrounds = 20, verbose = FALSE) unified_model <- xgboost.unify(xgb_model, as.matrix(data)) treeshap1 <- treeshap(unified_model, head(data, 3)) plot_contribution(treeshap1, obs = 1) @@ -49,7 +48,6 @@ unified_model2 <- xgboost.unify(xgb_model2, as.matrix(data)) treeshap2 <- treeshap(unified_model2, head(data, 3), interactions = TRUE) treeshap2$interactions } -} \seealso{ \code{\link{xgboost.unify}} for \code{XGBoost models} \code{\link{lightgbm.unify}} for \code{LightGBM models} diff --git a/man/xgboost.unify.Rd b/man/xgboost.unify.Rd index 15b5acf..09dfd8e 100644 --- a/man/xgboost.unify.Rd +++ b/man/xgboost.unify.Rd @@ -21,7 +21,6 @@ Convert your XGBoost model into a standardized representation. The returned representation is easy to be interpreted by the user and ready to be used as an argument in \code{treeshap()} function. } \examples{ -\donttest{ library(xgboost) data <- fifa20$data[colnames(fifa20$data) != 'work_rate'] target <- fifa20$target @@ -31,7 +30,6 @@ xgb_model <- xgboost::xgboost(as.matrix(data), params = param, label = target, unified_model <- xgboost.unify(xgb_model, as.matrix(data)) shaps <- treeshap(unified_model, data[1:2,]) plot_contribution(shaps, obs = 1) -} } \seealso{ diff --git a/src/RcppExports.o b/src/RcppExports.o index dc2920f838215f06aa31a9ddd58292d88c074fd7..08325277778555609e1324d443eb8f07348ece0d 100644 GIT binary patch literal 726384 zcmeFa4|rAAbtk&dy#if{3z6*$$e2Xe0XM?N2~LPD5>g9;8#!TGA!J)lX(U+^NVbdw z$Sod|A_v;1)JiMWnyG5@%~fj6SBcVjq(i3iD{m|p<~?=7%!roB)0^QPKs;EiO=Nmxf5* zY=AxAC{S|>0pzJp{fqv;wzjpUqiK76>jMwEgrK~3mQ&7!DtbtS{miqV3)R+cX=tcz z+S$<5-cj38|8!8^L6+A)R~PJpya>R_?-!`s0O!rq=i%;u zl|_h$R9m~TqrRi1;jyhPuF#;oLw}%|+gV zfcEyw>tT5@F4nwX%Paa(f$KZnORP-T*Yx!BTWVX|8eAgeJ-MdpmkdKc%lqxewCCF) zk3PkacU(7&&xInd{^_T;H~l)rqhH=f&T2XDaYuOXe6U5`@YYK3zsPIZy19*rL3xQE z>+;`@Xk*Ssv^fLw3)L@D|JC8>0`#BA+g`tQOA`VyDDS(MxnaMgi~RX4%1p=yTME^` zp~NzSJOrWpV}`sZb@_5f+xO?S8h4rXe*yp1x3{-8)o!hSj?BxjujDD+vWL2L;UCpW zOB;z<<+VNijN8CL`~K!{b@`=S{7?QNH+imo?ez^oc|T%#W!$X)B}aK3FSKW^?3s!m|5g9GQ2l33 z3iz+7xpwn*w1)mMm|w;}uH{wSu74kTP|IuolR|YLd;m4g`WGqB{jFuID>3N5;x$^{ zzpByv=PEQlcBoJ~hR?oFf~^=|?QR+*^g!`(Kt=dYpxh8_qe8^j`F|Ix-ve`d8lB_Q zu6?Q!bo%u2_p7+x0iIn%yvomoU-|dLmtS;-FRxX-=c8&rTB|4KlwT~USG@&$)l|%J zdM`xp*x&WLc~cPGC%-#)tKKx|L`z2#e7$4;_qRC1Q?-%d_nwTx_B{Aw7X0D#PVbq0{D;x}{qIF*@BdMB*8Ztz-u{cx=>GSk zk^SeR&i)I=U#d6u)|+KRA5stA1^>MmEx*`|@6hY3hQ(j>`(pgI5A;}(daw&u|jyT83 z_mfe;Z!`RQq<0`1(e$-|moWUc(>t)1;XQyG7+wN+55q@P@4)LCKHs2vC-EIPqVac* zsNQ$M`_6w+!$WJM$KQ>rivzDmkDodc?fqUuwD)b;a2mS*7CQg#ZKwC|_c#=qeM8ZW zQ$u?aQvrbJ_IUj6n>t2uOE7?Q% zzuoiHl-K9Yv(~Wx^U0kEUObf_VRTE%GF)W zUQX(2^=dtD2FiC%U0wMRl&Ab4Heu9FcyX$WDw4XAb#GllE=c9eT6)C@1F-JW(ge%3;w*s-W06gEpZ_P3F z;25sy==}Y}M=`Z~f!g+#Qq@vteeJ9&L0xy>uC|pIs_NbI)V8}F&BOlftN&{GD)B9q z!L-Kp+wMZOtv;fv?^c~vB2&tc-3sx3igPsmE_HFJ9{?}sayyWV9;Num({cu)cewpS zIisJ`WsnQ@yt0~bj7XnTr$$$+bm!`loobb|7s(^tLpx5C#V$=hIDo!SwEr&m zP=k5F&K}bad#wfrW5`;l}~Z5=K$w1mKk33nPw-LTQO%qf4KbOI_O-BochQxZ0M!=0`wr4 z>UOL9N%r;@*}?qw_o&xLxGlt%3jtfC|Bfl$f4zC0+rj8p)7kY0 z_S}Bz^>+}y<$pcS-tzxH&G>)s$^R310PBwzBg2>X=rzbn^m{52bAM0rct-v2@{5l^?i%EmBQj6Z3WK34S+9x<9g|J6nM;BQpRm}jp5_fJ{bD) z=&E7YHzfaGxbj`SK3>c{Jh_|3eaVgQk&Qmy1wP(|dX8Z1`5yRbE^{in(B<>ns(<@l zlx0BfchM%DXftV(1)xo${F{*lK04Vz?dRo9fJS3Wou@qMzWm}^`1uj|dkyB{M@_rJ z7Bh}gy|7Q6pzDYDisf`-61EYo0J2M*0-tQM;mtq{iCtbBh_*Yxm*zTqZ6Nsai*F7K z?Mg8;y(jG#{~)`}(tMUT*8pN$_={E;J4gFIh9} zU3(7wG1i`YJnK%Zvt>*P2H>Yt6SA#v0A6JKx5da!JHpi%stFtUH%@)}81hvhFl% z&DV7qT5Aq1i5`FFSJd#p9;`8;|9eraF|qzT4LyJRw$uA}d$7h_qSuE*d$7jDTG?G= z9?>!emgu#htQ*phNo|qlGFQ9CBs-}5^xemU17+>F-67_c1lw5DB`?ahoYh1Z{P5Gn;{%O{l zB!|YeEAF4J&A+Ps(=qIe=CXfUdYgCrdSv)d?(>gf`~P>0VSl`Gc<6|C9Lw{JV|jM} z^mWhv=~}?ih#tG%_UxbT0lb#!O8_@8e8jVV+5q@6ZgAKYJ?{)cg zURdUVOFi(%Jn%1g;CnnU?nPq!)pC}2;E#IXyFGBy1JmB8l-2t;nR0&C@%&F^JidqG z+lEz3x_=wR7spxM`?$1+30jq$Pr}%ppz+vSK8F4J*h^oYx;1JKhsB?<5RU!+ylzPy&#-BeQ`GK zgF85jtM@^4POhDb%__gR;9~jj%l*cZa`nxx-ma?Yo^Qz}_5DA>z0w~=aqqcSt$N&1 z-@>Wl#Ly$ExA-4+p-izGv-lnNeAoQh4_{Zq=_vJ+g}9%)VE^~%j2hy3`k7CcNuToG zo9M#1R=U?rXF^e~H;MJhWbNoG+M7<(eMFS)!tb#H+c(KBw~xTiM?R#g={~mDiTkj< z7hq>K+t~#>7rJ)pd&_j6b~3W&&n_4{7r@T>uv6dr{_Zm?%do-M^CRfof%9B<@O^*M zF$o&oSI7CF>Q8|#_q%7o9~bc343wvXx{@;c$)9kbPK?s-1cSMDup zTeVKhqwj;0ujrft`S)$UPwcgE)X3EJ`XTJY{3Ss)dTm>Px_$@0HO~s3Db%@$+M>dl zdT8>^(N$fzQk^fq_)CbVM-f-+aBp$#FyhR;rzq`3ykkF^elYzZ-G1kh>&RYeGki_H zE1%&WVlY?IxmGEkRLK)w8FlYx&$pw2{_!6+d zXYh1gOnbQdZW8knlXZw6vB$&B(b)sH4Nq~Ky7l9}q07lxngq^8B=$>xb!DsA znI!s;#5!d44Jd`U(s|-NTrarot>aw!N$?y8^|$QjIPBTKsoFK(^M4e3eRQ^%WTH;4 z-@Wq5b~#f*wq~Bk&^D4B`p)Ecp$Err@{@{mR=vbIh34ck{}}T5Psc$Q&f$Db({T=` z&pn5u&&t>{q_6{Rq1#e^yPM@_`)$n?eyiu-$d=5yXPx)Bv7Mb?$3QNA5`P7++0H** zz%vp*#4{4_VGN@&rw;xUyUCwdIs-Kcf4*b<`3G73Nik3Ulk-M|lh0%vqxKVhZX98Z zvS+PKd<5ELzB%)o*(UQ{edMQXzmabGErxccIjBBob%z;OGwIszG1&EH;Ox_U&~``W zpQ-c2Io0n+qH#JE+pE;EmE}wi%@+p2K zPnX2S{`qeI2-+|0Krvf_oc|3s=d*82*+kZ|CF+hd1K>}_L@Zd>dmgwKdIMPjgJlIGn97UViacV8buNmVbsh+xlVH#&b5U zBj(V2Hs;PhsJ#wK5QoJ@G=xrb9fnYR4|{h}WD0P?QG z`(-r8qO3jR6jjpn(a-KAbQzsEhdf-*X<5223dUytIvsB@g2M?4jc^nUMc z{oCYsF&~a$O|pA*)ev33`_`&S7k_J2X~EP(L+2;TnsJ>>{pG45!l8HQd~sQG_t&BK z-)tY+vv8z&9%x4v?v(SSq-Qc(z;j!zhkTvpy1O#z8Js;&y7#63ZQ$7~^p~DK__zzt z1a{%-&YiKA9e`YFH)*rWFK$FER3Rod;5=p}`r$0MAKroXHaQc|w!}Qo7Rs2Pt!z@2 zd?$T}9xq&f{0QqVJTpT&$uG@>e@cFbe+d7W#;Hz|@8NkWu9NF8;OQ^w-~UYKM|ZAI z^2%p;F0eDO)*20z8=aw?)K@vmx3}?^C{O(XI*o4EpRgfF{|v?@qLuNLVq=W<4m>{T z!n%v{3;Cy%)?gn0Ow!tR_gkxmi0<)Ab415aX>{r#eI5ntbt?apX*b@QX?^SQ>kxFF zW4m2>m#5u0oV*>A;vK`P#dG z8;kn+>Tr)_2cF-|@{E+p<>fivBS{CxkxRMl*_{lGA6I&wJX_urS~mavJl#Ku0MH|SXG?5SK9+ntgqXB_Nbmn* zJ@JM&=gPgBN!+U*1HWqn)))z;-k|cC>;9~Jwk8j!XUM~&f%!b$Yb4tdPo}JQy`Ek7 z)vwosz6|=0?pwwx%ZKNqKZ`HO4>X?Dx%*<;ZxsLf-k8QU&mAt0?C)n8pWMCdY`n6! 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