diff --git a/Dockerfile b/Dockerfile index bc18aabf1f8d6e61ca275bc5ebac4ecb99421cf6..e6a6577cc420b557c46d2fcb3118c0d02ec3ff94 100644 --- a/Dockerfile +++ b/Dockerfile @@ -11,6 +11,7 @@ RUN echo 'options(radiant.shinyFiles = FALSE)' >> /usr/local/lib/R/etc/Rprofile. COPY . /srv/shiny-server/ COPY set_path.R /usr/local/lib/R/etc/Rprofile.site.d/00-radiant-path.R RUN echo 'source("/srv/shiny-server/radiant.model/R/coxp.R")' >> /usr/local/lib/R/etc/Rprofile.site +Run echo 'load("/srv/shiny-server/data/test.state.rda")' >> /usr/local/lib/R/etc/Rprofile.site # 安装所有模块 RUN R -e "remotes::install_local('/srv/shiny-server/radiant.data',dependencies=TRUE, type='source', upgrade='never',force=TRUE)" @@ -22,7 +23,6 @@ RUN R -e "remotes::install_local('/srv/shiny-server/radiant.quickgen',dependenci RUN R -e "remotes::install_local('/srv/shiny-server/radiant-master',dependencies=TRUE, type='source', upgrade='never',force=TRUE)" WORKDIR /data -RUN R -e "load('/data/test.state.rda')" CMD ["R", "-e", "radiant::radiant(host='0.0.0.0', port=3838)"] #docker images | grep radiant 查看镜像 diff --git a/data/Hypertension-risk-model-main.csv b/data/Hypertension-risk-model-main.csv new file mode 100644 index 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Mystique,bad,female,Marvel Batman,good,male,DC Joker,bad,male,DC Catwoman,bad,female,DC Hellboy,good,male,Dark Horse Comics \ No newline at end of file diff --git a/data/test.state.rda b/data/test.state.rda index 113ad167111a0778482e46e07016fa1b455abdcf..f6e2ce70bb6efae649df2a23cbedcd5be66f1a28 100644 Binary files a/data/test.state.rda and b/data/test.state.rda differ diff --git a/radiant-master/inst/translations/translation_zh.csv b/radiant-master/inst/translations/translation_zh.csv index 993fcb67a4a53ddc8036b5a03698d9710ed066a4..e4a94ef35fb0d1ac699f5bb1fdbf69fd8d144f49 100644 --- a/radiant-master/inst/translations/translation_zh.csv +++ b/radiant-master/inst/translations/translation_zh.csv @@ -1192,3 +1192,8 @@ Survival curves,生存曲线,coxp_ui.R Cumulative hazard,累积风险,coxp_ui.R Schoenfeld residuals,Schoenfeld残差图,coxp_ui.R Martingale residuals,鞅残差图,coxp_ui.R +Decimal places:,小数位数：,svm_ui.R +AI chat guidance,大模型对话引导助手,init.R +One-click generation > AI chat guidance,一键生成 > 大模型对话引导助手,init.R +Decision Boundary (2 vars),决策边界（两变量）,svm_ui.R +Support Vectors & Margin(2 vars),支持向量与间隔（两变量）,svm_ui.R diff --git a/radiant.model/DESCRIPTION b/radiant.model/DESCRIPTION index 5c0210bef65b8db9fa3b018093b700a01fec2544..ac402239640687b9fe39e049f03c5d1993308b1f 100644 --- a/radiant.model/DESCRIPTION +++ b/radiant.model/DESCRIPTION @@ -1,57 +1,58 @@ -Package: radiant.model -Type: Package -Title: Model Menu for Radiant: Business Analytics using R and Shiny -Version: 1.6.7 -Date: 2024-10-7 -Authors@R: person("Vincent", "Nijs", , "radiant@rady.ucsd.edu", c("aut", "cre")) -Description: The Radiant Model menu includes interfaces for linear and logistic - regression, naive Bayes, neural networks, classification and regression trees, - model evaluation, collaborative filtering, decision analysis, and simulation. - The application extends the functionality in 'radiant.data'. -Depends: - R (>= 4.3.0), - radiant.data (>= 1.6.6) -Imports: - radiant.basics (>= 1.6.6), - shiny (>= 1.8.1), - nnet (>= 7.3.12), - NeuralNetTools (>= 1.5.1), - sandwich (>= 2.3.4), - car (>= 2.1.3), - ggplot2 (>= 3.4.2), - scales (>= 1.2.1), - data.tree (>= 0.7.4), - stringr (>= 1.1.0), - lubridate (>= 1.7.2), - tidyr (>= 0.8.2), - dplyr (>= 1.1.2), - tidyselect (>= 1.2.0), - rlang (>= 0.4.10), - magrittr (>= 1.5), - DiagrammeR (>= 1.0.9), - import (>= 1.1.0), - psych (>= 1.8.4), - e1071 (>= 1.6.8), - rpart (>= 4.1.11), - ggrepel (>= 0.8), - broom (>= 0.7.0), - patchwork (>= 1.0.0), - ranger (>= 0.11.2), - xgboost (>= 1.6.0.1), - pdp (>= 0.8.1), - vip (>= 0.3.2), - stringi, - yaml, - shiny.i18n -Suggests: - testthat (>= 2.0.0), - pkgdown (>= 1.1.0) -URL: https://github.com/radiant-rstats/radiant.model/, - https://radiant-rstats.github.io/radiant.model/, - https://radiant-rstats.github.io/docs/ -BugReports: https://github.com/radiant-rstats/radiant.model/issues/ -License: AGPL-3 | file LICENSE -LazyData: true -Encoding: UTF-8 -Language: en-US -RoxygenNote: 7.3.2 +Package: radiant.model +Type: Package +Title: Model Menu for Radiant: Business Analytics using R and Shiny +Version: 1.6.7 +Date: 2024-10-7 +Authors@R: person("Vincent", "Nijs", , "radiant@rady.ucsd.edu", c("aut", "cre")) +Description: The Radiant Model menu includes interfaces for linear and logistic + regression, naive Bayes, neural networks, classification and regression trees, + model evaluation, collaborative filtering, decision analysis, and simulation. + The application extends the functionality in 'radiant.data'. +Depends: + R (>= 4.3.0), + radiant.data (>= 1.6.6) +Imports: + radiant.basics (>= 1.6.6), + shiny (>= 1.8.1), + nnet (>= 7.3.12), + NeuralNetTools (>= 1.5.1), + sandwich (>= 2.3.4), + car (>= 2.1.3), + ggplot2 (>= 3.4.2), + scales (>= 1.2.1), + data.tree (>= 0.7.4), + stringr (>= 1.1.0), + lubridate (>= 1.7.2), + tidyr (>= 0.8.2), + dplyr (>= 1.1.2), + tidyselect (>= 1.2.0), + rlang (>= 0.4.10), + magrittr (>= 1.5), + DiagrammeR (>= 1.0.9), + import (>= 1.1.0), + psych (>= 1.8.4), + e1071 (>= 1.6.8), + rpart (>= 4.1.11), + ggrepel (>= 0.8), + broom (>= 0.7.0), + patchwork (>= 1.0.0), + ranger (>= 0.11.2), + xgboost (>= 1.6.0.1), + pdp (>= 0.8.1), + vip (>= 0.3.2), + stringi, + yaml, + survminer, + shiny.i18n +Suggests: + testthat (>= 2.0.0), + pkgdown (>= 1.1.0) +URL: https://github.com/radiant-rstats/radiant.model/, + https://radiant-rstats.github.io/radiant.model/, + https://radiant-rstats.github.io/docs/ +BugReports: https://github.com/radiant-rstats/radiant.model/issues/ +License: AGPL-3 | file LICENSE +LazyData: true +Encoding: UTF-8 +Language: en-US +RoxygenNote: 7.3.2 diff --git a/radiant.model/NAMESPACE b/radiant.model/NAMESPACE index 2dbca831e8bdb7828098ca68288b33d4aae09dca..d1cd23715c42f15c42c1720eb7ec3905d998962b 100644 --- a/radiant.model/NAMESPACE +++ b/radiant.model/NAMESPACE @@ -20,6 +20,7 @@ S3method(plot,repeater) S3method(plot,rforest) S3method(plot,rforest.predict) S3method(plot,simulater) +S3method(plot,svm) S3method(plot,uplift) S3method(predict,coxp) S3method(predict,crtree) @@ -84,6 +85,7 @@ export(cv.crtree) export(cv.gbt) export(cv.nn) export(cv.rforest) +export(cv.svm) export(dtree) export(dtree_parser) export(evalbin) diff --git a/radiant.model/R/coxp.R b/radiant.model/R/coxp.R index cc51f4a32a9ee3472a1ac9dd35814bac1d93b73d..4b010a9a062632a1b91f9c533e35a8449f803235 100644 --- a/radiant.model/R/coxp.R +++ b/radiant.model/R/coxp.R @@ -15,8 +15,6 @@ coxp <- function(dataset, if (!requireNamespace("survival", quietly = TRUE)) stop("survival package is required but not installed.") - attachNamespace("survival") - on.exit(detach("package:survival"), add = TRUE) ## ---- 公式入口 ---------------------------------------------------------- if (!missing(form)) { diff --git a/radiant.model/R/svm.R b/radiant.model/R/svm.R index b4b3d851b309e9e29fa587c3b9beaa177794fd64..cadd0dc2128a426d69b5cb1d86d866a3b3e19a1d 100644 --- a/radiant.model/R/svm.R +++ b/radiant.model/R/svm.R @@ -1,122 +1,692 @@ -#' Support Vector Machine using e1071 -#' +#' Support Vector Machine #' @export svm <- function(dataset, rvar, evar, type = "classification", lev = "", - kernel = "radial", cost = 1, gamma = "auto", - degree = 3, coef0 = 0, nu = 0.5, epsilon = 0.1, - probability = FALSE, wts = "None", seed = 1234, - check = NULL, form, data_filter = "", arr = "", rows = NULL, - envir = parent.frame()) { + kernel = "radial", cost = 1, gamma = 1, wts = "None", + seed = NA, check = "standardize", + form, data_filter = "", arr = "", + rows = NULL, envir = parent.frame()) { - ## ---- 公式入口 ---------------------------------------------------------- - if (!missing(form)) { - form <- as.formula(format(form)) - vars <- all.vars(form) - rvar <- vars[1] - evar <- vars[-1] + ## ---- 参数合法性检查（SVM特有） ---- + valid_kernels <- c("linear", "radial", "poly", "sigmoid") + if (!kernel %in% valid_kernels) { + return(paste0("Kernel must be one of: ", paste(valid_kernels, collapse = ", ")) %>% + add_class("svm")) + } + if (is.empty(cost) || cost <= 0) { + return("Cost should be greater than 0." %>% add_class("svm")) + } + if (is.empty(gamma) || gamma <= 0) { + return("Gamma should be greater than 0." %>% add_class("svm")) + } + if (rvar %in% evar) { + return("Response variable contained in the set of explanatory variables.\nPlease update model specification." %>% + add_class("svm")) } - ## ---- 基础检查 ---------------------------------------------------------- - if (rvar %in% evar) - return("Response variable contained in explanatory variables" %>% add_class("svm")) - + ## ---- 1. 权重变量处理 ---- vars <- c(rvar, evar) - if (is.empty(wts, "None")) { + wtsname <- NULL + if (wts == "None" || is.empty(wts)) { wts <- NULL - } else { - vars <- c(vars, wts) + } else if (is_string(wts)) { + wtsname <- wts + vars <- c(rvar, evar, wtsname) } - ## ---- 数据提取 ---------------------------------------------------------- + ## ---- 2. 数据集筛选与标准化 ---- df_name <- if (is_string(dataset)) dataset else deparse(substitute(dataset)) dataset <- get_data(dataset, vars, filt = data_filter, arr = arr, rows = rows, envir = envir) - if (!is.empty(wts)) { - wts_vec <- dataset[[wts]] - dataset <- select_at(dataset, setdiff(colnames(dataset), wts)) - } else { - wts_vec <- NULL + if ("standardize" %in% check) { + dataset <- scale_df(dataset) } - rv <- dataset[[rvar]] + ## ---- 3. 分类任务的响应变量（转为因子） ---- if (type == "classification") { - if (lev == "") lev <- levels(as.factor(rv))[1] - dataset[[rvar]] <- factor(dataset[[rvar]] == lev, levels = c(TRUE, FALSE)) - } - - ## ---- 标准化（占位） ---------------------------------------------------- - if ("standardize" %in% check) dataset <- scale_df(dataset, wts = wts_vec) - - ## ---- 构造公式 ---------------------------------------------------------- - if (missing(form)) form <- as.formula(paste(rvar, "~ .")) - - ## ---- 设定种子 ---------------------------------------------------------- - seed <- gsub("[^0-9]", "", seed) - if (!is.empty(seed)) set.seed(as.integer(seed)) - - ## ---- 调 e1071::svm ----------------------------------------------------- - svm_call <- list( - formula = form, - data = dataset, - type = ifelse(type == "classification", "C-classification", "eps-regression"), - kernel = kernel, - cost = cost, - gamma = if (gamma == "auto") 1 / ncol(select(dataset, -rvar)) else as.numeric(gamma), - degree = degree, - coef0 = coef0, - nu = nu, - epsilon = epsilon, - probability = probability, - weights = wts_vec, - fitted = TRUE + dataset[[rvar]] <- as.factor(dataset[[rvar]]) + if (lev == "") { + lev <- levels(dataset[[rvar]])[1] + } + } + + ## ---- 4. 构建SVM训练参数 ---- + if (missing(form)) { + form <- as.formula(paste(rvar, "~ .")) + } + weights_vec <- if (!is.null(wtsname)) dataset[[wtsname]] else NULL + + svm_input <- list( + formula = form, + data = dataset, + kernel = kernel, + cost = cost, + gamma = gamma, + weights = weights_vec, + type = ifelse(type == "classification", "C-classification", "eps-regression"), + na.action = na.omit, + scale = FALSE, + probability = (type == "classification") ) - model <- do.call(e1071::svm, svm_call) - - ## ---- 打包返回 ---------------------------------------------------------- - out <- as.list(environment()) - out$model <- model - out$df_name <- df_name - out$type <- type - out$lev <- if (type == "classification") lev else NULL - out$check <- check - add_class(out, c("svm", "model")) + + if (!is.na(seed)) set.seed(seed) + + ## ---- 6. 训练模型 ---- + model <- do.call(e1071::svm, svm_input) + + ## ---- 7. 附加关键信息 ---- + model$df_name <- df_name + model$rvar <- rvar + model$evar <- evar + model$type <- type + model$lev <- lev + model$wtsname <- wtsname + model$seed <- seed + model$cost <- cost + model$gamma <- gamma + model$kernel <- kernel + + as.list(environment()) %>% add_class(c("svm", "model")) } -#' Summary method +#' Center or standardize variables in a data frame #' @export -summary.svm <- function(object, ...) { +scale_df <- function(dataset, center = TRUE, scale = TRUE, + sf = 2, wts = NULL, calc = TRUE) { + isNum <- sapply(dataset, function(x) is.numeric(x)) + if (length(isNum) == 0 || sum(isNum) == 0) { + return(dataset) + } + cn <- names(isNum)[isNum] # 数值变量列名 + descr <- attr(dataset, "description") # 保留原描述属性 + + if (calc) { + # 计算均值（忽略NA） + ms <- sapply(dataset[, cn, drop = FALSE], function(x) mean(x, na.rm = TRUE)) + # 计算标准差（忽略NA，样本标准差ddof=1，避免除以零） + sds <- sapply(dataset[, cn, drop = FALSE], function(x) { + sd_val <- sd(x, na.rm = TRUE) + ifelse(sd_val == 0, 1, sd_val) + }) + attr(dataset, "radiant_ms") <- ms + attr(dataset, "radiant_sds") <- sds + attr(dataset, "radiant_sf") <- sf + } else { + ms <- attr(dataset, "radiant_ms") + sds <- attr(dataset, "radiant_sds") + if (is.null(ms) || is.null(sds)) { + warning("Training data mean/std not found; skipping standardization.") + return(dataset) + } + } + + dataset[, cn] <- lapply(seq_along(cn), function(i) { + (dataset[[cn[i]]] - ms[i]) / sds[i] + }) + + attr(dataset, "description") <- descr + return(dataset) +} + +#' Summary method for the svm function +#' @export +summary.svm <- function(object, prn = TRUE, ...) { if (is.character(object)) return(object) + + svm_model <- object$model + n_obs <- nrow(object$dataset) + wtsname <- object$wtsname # 可能是 NULL 或长度 0 字符 + cat("Support Vector Machine\n") - cat("Data :", object$df_name, "\n") - if (!is.empty(object$data_filter)) cat("Filter :", object$data_filter, "\n") - cat("Response :", object$rvar, "\n") - if (object$type == "classification") cat("Level :", object$lev, "\n") - cat("Variables :", paste(object$evar, collapse = ", "), "\n") - cat("Kernel :", object$model$kernel, "\n") - cat("Cost (C) :", object$model$cost, "\n") - if (object$model$kernel != "linear") cat("Gamma :", object$model$gamma, "\n") - cat("Support vectors :", length(object$model$SV), "\n") - invisible(object) + cat(sprintf("Kernel type : %s (%s)\n", object$kernel, object$type)) + cat(sprintf("Data : %s\n", object$df_name)) + cat(sprintf("Response variable : %s\n", object$rvar)) + if (object$type == "classification") { + cat(sprintf("Level : %s in %s\n", object$lev, object$rvar)) + } + cat(sprintf("Explanatory variables: %s\n", paste(object$evar, collapse = ", "))) + + if (!is.null(wtsname) && length(wtsname) && wtsname != "") { + cat(sprintf("Weights used : %s\n", wtsname)) + } + + cat(sprintf("Cost (C) : %.2f\n", object$cost)) + if (object$kernel %in% c("radial", "poly", "sigmoid")) { + cat(sprintf("Gamma : %.2f\n", object$gamma)) + } + if (!is.na(object$seed)) cat(sprintf("Seed : %s\n", object$seed)) + + if (object$type == "classification") { + n_sv_per_class <- svm_model$nSV + total_sv <- sum(n_sv_per_class) + sv_info <- paste(sprintf("class %s: %d", seq_along(n_sv_per_class), n_sv_per_class), collapse = ", ") + cat(sprintf("Support vectors : %d (%s)\n", total_sv, sv_info)) + } else { + total_sv <- sum(svm_model$nSV) + cat(sprintf("Support vectors : %d\n", total_sv)) + } + + ## ---- 权重样本数计算同样保护 ---- + if (!is.null(wtsname) && length(wtsname) && wtsname != "") { + weights_values <- as.numeric(object$dataset[[wtsname]]) + nr_obs <- if (all(!is.na(weights_values))) sum(weights_values, na.rm = TRUE) else n_obs + } else { + nr_obs <- n_obs + } + cat(sprintf("Nr_obs : %s\n", format_nr(nr_obs, dec = 0))) + + ## ---- 系数输出（仅线性核） ---- + if (prn) { + cat("Coefficients/Support Vectors:\n") + if (object$kernel == "linear") { + if (is.null(svm_model$w) || length(svm_model$w) == 0) { + cat(" Linear kernel coefficients not available (possible reasons:\n") + cat(" - Insufficient support vectors (data may be linearly inseparable)\n") + cat(" - Model training did not converge)\n") + if (object$type == "classification") { + cat(sprintf(" Support vectors count: %d\n", sum(svm_model$nSV))) + } + } else { + feat_coefs_raw <- as.numeric(svm_model$w) + bias <- as.numeric(-svm_model$rho) + n_evar <- length(object$evar) + feat_coefs <- matrix(data = feat_coefs_raw, nrow = 1, ncol = n_evar, byrow = TRUE, + dimnames = list(NULL, object$evar)) + coef_data <- data.frame( + Variable = c(object$evar, "bias"), + Value = as.numeric(c(as.vector(feat_coefs), bias)), + stringsAsFactors = FALSE, + check.names = FALSE + ) + for (i in seq(1, nrow(coef_data), 2)) { + if (i + 1 > nrow(coef_data)) { + cat(sprintf(" %-12s: %.2f\n", coef_data$Variable[i], coef_data$Value[i])) + } else { + cat(sprintf(" %-12s: %.2f %-12s: %.2f\n", + coef_data$Variable[i], coef_data$Value[i], + coef_data$Variable[i+1], coef_data$Value[i+1])) + } + } + } + } else { + cat(" Non-linear kernel: Coefficients not available\n") + } + } +} + +#' Plot method for the svm function +#' @export +plot.svm <- function(x, + plots = "none", + size = 12, + shiny = FALSE, + custom = FALSE, + ...) { + if (is.character(x) || !inherits(x, "svm")) return(x) + + plot_list <- list() + + if ("decision_boundary" %in% plots) { + if (length(x$evar) != 2) { + warning("Decision boundary plot requires exactly 2 explanatory variables") + } else if (x$type != "classification") { + warning("Decision boundary plot only available for classification") + } else if (nlevels(x$dataset[[x$rvar]]) != 2) { + warning("Decision boundary plot only available for binary classification") + } else { + plot_list[["decision_boundary"]] <- + svm_boundary_plot(x, size = size, custom = custom) + } + } + + if ("margin" %in% plots) { + plot_list[["margin"]] <- + svm_margin_plot(x, size = size, custom = custom) + } + if ("vip" %in% plots) { + if (length(x$evar) < 2) { + warning("Variable importance needs at least 2 explanatory variables") + } else { + plot_list[["vip"]] <- + svm_vip_plot(x, size = size, custom = custom) + } + } + + if (length(plot_list) == 0) { + return("No valid plots selected for SVM") + } + + ## 返回 patchwork 对象（Shiny 自动打印） + patchwork::wrap_plots(plot_list, ncol = min(2, length(plot_list))) %>% + { if (isTRUE(shiny)) . else print(.) } } -#' Predict method +#' Predict method for the svm function #' @export predict.svm <- function(object, pred_data = NULL, pred_cmd = "", dec = 3, envir = parent.frame(), ...) { + ## 1. 基础校验 if (is.character(object)) return(object) + if (!inherits(object, "svm")) stop("Object must be of class 'svm'") + + ## 2.1 确定预测数据源 + if (is.null(pred_data) || is.character(pred_data)) { + # 当pred_data为NULL或字符(数据集名)时，使用训练数据 + pred_data_raw <- object$dataset[, object$evar, drop = FALSE] + } else { + # 当pred_data是数据框时，直接使用 + pred_data_raw <- as.data.frame(pred_data) + } + + pred_names <- colnames(pred_data_raw) + missing_vars <- setdiff(object$evar, pred_names) + + if (length(missing_vars) > 0) { + msg <- paste0( + "NA\n" + ) + return(msg %>% add_class("svm.predict")) + } + + pred_data <- pred_data_raw[, object$evar, drop = FALSE] + + if (!is.empty(pred_cmd)) { + pred_cmd <- gsub("\\s{2,}", " ", pred_cmd) %>% + gsub(";\\s+", ";", .) %>% + strsplit(";")[[1]] + for (cmd in pred_cmd) { + if (grepl("=", cmd)) { + var_val <- strsplit(trimws(cmd), "=")[[1]] + var <- trimws(var_val[1]) + val <- try(eval(parse(text = trimws(var_val[2])), envir = envir), silent = TRUE) + if (!inherits(val, "try-error")) pred_data[[var]] <- val + else warning(sprintf("Invalid command '%s': using original values", cmd)) + } + } + pred_data <- unique(pred_data) + } + + ## 3. 变量类型与因子水平校验 + train_types <- sapply(object$dataset[, object$evar], class) + pred_types <- sapply(pred_data, class) + type_mismatch <- names(which(train_types != pred_types)) + if (length(type_mismatch) > 0) { + return(paste0("Variable type mismatch (train vs pred):\n", + paste(sprintf(" %s: %s vs %s", type_mismatch, + train_types[type_mismatch], pred_types[type_mismatch]), + collapse = "\n")) %>% add_class("svm.predict")) + } + + for (var in object$evar) { + if (is.factor(object$dataset[[var]])) { + train_levs <- levels(object$dataset[[var]]) + pred_levs <- levels(pred_data[[var]]) + if (!identical(train_levs, pred_levs)) { + pred_data[[var]] <- factor(pred_data[[var]], levels = train_levs) + warning(sprintf("Aligned factor levels for '%s' to match training data", var)) + } + } + } + + ## 4. 标准化对齐 + train_ms <- attr(object$dataset, "radiant_ms") + train_sds <- attr(object$dataset, "radiant_sds") + train_sf <- attr(object$dataset, "radiant_sf") %||% 2 + if (!is.null(train_ms) && !is.null(train_sds)) { + pred_data <- scale_df( + dataset = pred_data, + center = TRUE, scale = TRUE, + sf = train_sf, wts = NULL, + calc = FALSE + ) + attr(pred_data, "radiant_ms") <- train_ms + attr(pred_data, "radiant_sds") <- train_sds + } + + ## 5. 生成预测值 + predict_args <- list( + object = object$model, + newdata = pred_data, + na.action = na.omit + ) - pfun <- function(model, newdata, ...) { - predict(model, newdata, probability = object$model$probability)[, 1] + pred_result <- if (object$type == "classification") { + predict_args$type <- "class" + pred_class <- do.call(predict, predict_args) + if (isTRUE(object$model$param$probability)) { + predict_args$type <- "probabilities" + pred_prob <- do.call(predict, predict_args) %>% + as.data.frame() %>% + set_colnames(paste0("Prob_", colnames(.))) + data.frame( + Predicted_Class = as.character(pred_class), + pred_prob, + stringsAsFactors = FALSE + ) + } else { + data.frame(Predicted_Class = as.character(pred_class), stringsAsFactors = FALSE) + } + } else { + predict_args$type <- "response" + pred_val <- do.call(predict, predict_args) + pred_val <- as.numeric(pred_val) + data.frame(Predicted_Value = round(pred_val, dec), stringsAsFactors = FALSE) } - predict_model(object, pfun, "svm.predict", - pred_data, pred_cmd, - dec = dec, envir = envir) + pred_result <- cbind(pred_data, pred_result) + attr(pred_result, "svm_meta") <- list( + kernel = object$kernel, + cost = object$cost, + gamma = if (object$kernel != "linear") object$gamma else NA, + seed = object$seed, + train_data = object$df_name, + model_type = object$type + ) + attr(pred_result, "class") <- c("svm.predict", "data.frame") + return(pred_result) } -#' Print predictions +#' Print method for predict.svm #' @export print.svm.predict <- function(x, ..., n = 10) { - print_predict_model(x, ..., n = n, header = "SVM") + if (is.character(x)) { + cat(x, "\n") + return(invisible(x)) + } + if (!inherits(x, "svm.predict")) stop("Object must be of class 'svm.predict'") + + meta <- attr(x, "svm_meta") + n_pred <- nrow(x) + show_n <- if (n < 0 || n >= n_pred) n_pred else n + + cat("SVM Predictions\n") + cat(sprintf("Model Type : %s\n", tools::toTitleCase(meta$model_type))) + cat(sprintf("Kernel : %s\n", meta$kernel)) + cat(sprintf("Cost (C) : %.2f\n", meta$cost)) + if (!is.na(meta$gamma)) cat(sprintf("Gamma : %.2f\n", meta$gamma)) + if (!is.na(meta$seed)) cat(sprintf("Seed : %s\n", meta$seed)) + cat(sprintf("Training Dataset : %s\n", meta$train_data)) + cat(sprintf("Total Predictions : %d\n", n_pred)) + + if (n_pred == 0) { + cat("No predictions generated.\n") + return(invisible(x)) + } + + x_show <- x[1:show_n, , drop = FALSE] # 确保保持数据框结构 + + col_widths <- sapply(colnames(x_show), function(cn) { + max(nchar(cn), max(nchar(as.character(x_show[[cn]])), na.rm = TRUE)) + }) + + fmt_parts <- paste0("%-", col_widths, "s") + fmt <- paste(fmt_parts, collapse = " ") + + header_vals <- as.character(colnames(x_show)) + cat(do.call(sprintf, c(list(fmt), header_vals)), "\n") + + divider <- paste0(rep("-", sum(col_widths) + 2*(length(col_widths)-1)), collapse = "") + cat(divider, "\n") + + for (i in 1:show_n) { + row_vals <- as.character(x_show[i, ]) + row_vals[is.na(row_vals)] <- "NA" + cat(do.call(sprintf, c(list(fmt), row_vals)), "\n") + } + + if (show_n < n_pred) { + cat(sprintf("\n... (showing first %d of %d; use 'n=-1' to view all)\n", + show_n, n_pred)) + } + return(invisible(x)) +} + + +#' Cross-validation for SVM +#' @export +cv.svm <- function(object, K = 5, repeats = 1, + kernel = c("linear", "radial"), + cost = 2^(-2:2), + gamma = 2^(-2:2), + seed = 1234, trace = TRUE, fun, ...) { + if (!inherits(object, "svm")) stop("Object must be of class 'svm'") + + tune_grid <- expand.grid( + kernel = kernel, + cost = cost, + gamma = if (any(kernel %in% c("radial", "poly", "sigmoid"))) gamma else NA + ) + + cv_results <- data.frame( + mean_perf = rep(NA, nrow(tune_grid)), + std_perf = rep(NA, nrow(tune_grid)), + kernel = tune_grid$kernel, + cost = tune_grid$cost, + gamma = tune_grid$gamma + ) + cv_results +} + +# ---- 决策边界 ---- +#' @export +svm_boundary_plot <- function(object, size, custom) { + df <- object$dataset + rvar <- object$rvar + f1 <- object$evar[1] + f2 <- object$evar[2] + + ## 1. 造网格（因子用水平，数值用序列） + grid <- expand.grid( + f1 = if (is.factor(df[[f1]])) levels(df[[f1]]) else + seq(min(df[[f1]], na.rm = TRUE), max(df[[f1]], na.rm = TRUE), length.out = 200), + f2 = if (is.factor(df[[f2]])) levels(df[[f2]]) else + seq(min(df[[f2]], na.rm = TRUE), max(df[[f2]], na.rm = TRUE), length.out = 200) + ) + names(grid) <- c(f1, f2) + + ## 2. 预测：分类用决策函数值，回归用响应值 + pred <- predict(object$model, newdata = grid, + type = ifelse(object$type == "classification", "decision", "response"), + na.action = na.pass) # 保留 NA + grid$pred <- as.numeric(pred) # 强制数值 + + ## 3. 绘图 + p <- ggplot(df, aes_string(f1, f2)) + + geom_tile(data = grid, aes_string(fill = "pred"), alpha = 0.65) + + geom_point(aes(color = .data[[rvar]]), size = 2) + + scale_fill_gradient(low = "#A4C4FF", high = "#FF9A9A", na.value = "grey90") + + labs(title = "SVM Decision Boundary", + x = f1, y = f2, fill = "Score", color = rvar) + + theme_gray(base_size = size) + + theme(legend.position = "bottom") + + if (custom) p else print(p) +} + +# ---- 支持向量 / 间隔 ---- +#' @export +svm_margin_plot <- function(object, size, custom) { + mod <- object$model + df <- object$dataset + # 把支持向量原始行号转成逻辑标记 + sv_idx <- seq_len(nrow(df)) %in% mod$index + df$sv <- sv_idx # 长度 = nrow(df) + + p <- ggplot(df, aes_string(object$evar[1], object$evar[2])) + + geom_point(aes(color = sv, shape = sv), size = 2, alpha = 0.8) + + scale_color_manual(values = c("FALSE" = "grey60", "TRUE" = "red"), + labels = c("Ordinary", "Support Vector")) + + labs(title = "Support Vectors & Margin", + color = NULL, shape = NULL) + + theme_gray(base_size = size) + + theme(legend.position = "bottom") + + if (custom) p else print(p) +} + +#' Variable importance for SVM using permutation importance +#' @export +varimp <- function(object, rvar = NULL, lev = NULL, data = NULL, seed = 1234, nperm = 10) { + if (!inherits(object, "svm")) { + stop("Object must be of class 'svm'") + } + + # 使用训练数据作为默认 + if (is.null(data)) { + data <- object$dataset + } + + # 确定响应变量 + if (is.null(rvar)) { + rvar <- object$rvar + } + + # 确定分类水平 + if (is.null(lev) && object$type == "classification") { + lev <- object$lev + } + + # 创建仅包含解释变量的数据集 + X <- data[, object$evar, drop = FALSE] + y <- data[[rvar]] + + # 设置随机种子 + if (!is.na(seed)) { + set.seed(seed) + } + + # 基准预测 + base_pred <- predict(object, pred_data = data, envir = environment()) + + # 根据任务类型选择性能指标 + if (object$type == "classification") { + # 处理二分类或多分类 + base_metric <- if (nlevels(as.factor(y)) == 2) { + # 二分类：计算AUC + pred_prob_col <- if (length(grep("Prob_", colnames(base_pred))) > 0) { + grep(paste0("Prob_", lev), colnames(base_pred), value = TRUE) + } else { + NULL + } + + if (!is.null(pred_prob_col)) { + pROC::roc(response = y, predictor = base_pred[[pred_prob_col]])$auc[[1]] + } else { + # 无概率输出，使用准确率 + mean(base_pred$Predicted_Class == y, na.rm = TRUE) + } + } else { + # 多分类：使用准确率 + mean(base_pred$Predicted_Class == y, na.rm = TRUE) + } + } else { + # 回归：计算R² + base_metric <- 1 - sum((base_pred$Predicted_Value - y)^2, na.rm = TRUE) / + sum((y - mean(y, na.rm = TRUE))^2, na.rm = TRUE) + } + + # 为每个变量计算排列重要性 + importance_scores <- sapply(object$evar, function(var) { + metric_diffs <- numeric(nperm) + + for (i in 1:nperm) { + # 创建数据副本 + perm_data <- data + # 随机打乱当前变量 + perm_data[[var]] <- sample(perm_data[[var]], replace = FALSE) + + # 预测 + perm_pred <- predict(object, pred_data = perm_data, envir = environment()) + + # 计算性能变化 + if (object$type == "classification") { + if (nlevels(as.factor(y)) == 2 && length(grep("Prob_", colnames(perm_pred))) > 0) { + pred_prob_col <- grep(paste0("Prob_", lev), colnames(perm_pred), value = TRUE) + if (length(pred_prob_col) > 0) { + perm_metric <- pROC::roc(response = y, predictor = perm_pred[[pred_prob_col]])$auc[[1]] + } else { + perm_metric <- mean(perm_pred$Predicted_Class == y, na.rm = TRUE) + } + } else { + perm_metric <- mean(perm_pred$Predicted_Class == y, na.rm = TRUE) + } + metric_diffs[i] <- base_metric - perm_metric + } else { + perm_metric <- 1 - sum((perm_pred$Predicted_Value - y)^2, na.rm = TRUE) / + sum((y - mean(y, na.rm = TRUE))^2, na.rm = TRUE) + metric_diffs[i] <- base_metric - perm_metric + } + } + + mean(metric_diffs, na.rm = TRUE) + }) + + # 创建结果数据框 + result <- data.frame( + Variable = names(importance_scores), + Importance = as.numeric(importance_scores), + stringsAsFactors = FALSE + ) + + # 按重要性排序 + result <- result[order(-result$Importance), ] + rownames(result) <- NULL + return(result) +} + + +#' @export +svm_vip_plot <- function(object, size, custom) { + tryCatch({ + vi_scores <- varimp( + object, + rvar = object$rvar, + lev = if (object$type == "classification") object$lev else NULL, + data = object$dataset, + seed = 1234 + ) + + # 确保重要性值是数值类型 + vi_scores$Importance <- as.numeric(pmax(vi_scores$Importance, 0)) + + # 检查数据有效性 + if (nrow(vi_scores) == 0 || all(is.na(vi_scores$Importance))) { + p <- ggplot() + + annotate("text", x = 0.5, y = 0.5, + label = "Could not compute variable importance\n(check model/data)", + size = 5, color = "red") + + theme_void() + } else { + # 创建条形图 + p <- ggplot(vi_scores, aes(x = reorder(Variable, Importance), y = Importance)) + + geom_col(fill = "#377eb8") + + coord_flip() + + labs( + title = "SVM Variable Importance (Permutation)", + x = NULL, + y = ifelse(object$type == "regression", + "Importance (R² decrease)", + "Importance (Performance decrease)") + ) + + theme_gray(base_size = size) + + theme( + axis.text.y = element_text(hjust = 0), + panel.grid.major.y = element_line(color = "grey90"), + panel.grid.minor = element_blank() + ) + } + }, error = function(e) { + p <- ggplot() + + annotate("text", x = 0.5, y = 0.5, + label = paste("Error calculating importance:\n", e$message), + size = 4, color = "red") + + theme_void() + }) + + if (custom) { + return(p) + } else { + print(p) + invisible(p) + } } \ No newline at end of file diff --git a/radiant.model/inst/app/tools/analysis/svm_ui.R b/radiant.model/inst/app/tools/analysis/svm_ui.R index 8bb64e71e7991432e52863c361a8d996946d5e9f..565b11f26a8706c2df5be185413ab28fd5471e9f 100644 --- a/radiant.model/inst/app/tools/analysis/svm_ui.R +++ b/radiant.model/inst/app/tools/analysis/svm_ui.R @@ -1,50 +1,42 @@ -## ========== svm_ui.R ========== - -## 1. plot 列表 ---------------------------------------------------------- svm_plots <- c( - "none", "dist", "correlations", "scatter", "vip", "pred_plot", "pdp", "dashboard", "residuals", "coef", "influence" + "none", "decision_boundary", "margin", "vip" ) names(svm_plots) <- c( i18n$t("None"), - i18n$t("Distribution"), - i18n$t("Correlations"), - i18n$t("Scatter"), - i18n$t("Permutation Importance"), - i18n$t("Prediction plots"), - i18n$t("Partial Dependence"), - i18n$t("Dashboard"), - i18n$t("Residuals vs fitted"), - i18n$t("Coefficient plot"), - i18n$t("Influential observations") + i18n$t("Decision Boundary (2 vars)"), + i18n$t("Support Vectors & Margin(2 vars)"), + i18n$t("Variable importance") ) -## 2. 函数缺省参数 ------------------------------------------------------- +## SVM函数参数列表 svm_args <- as.list(formals(svm)) -## 3. 用户输入收集 ------------------------------------------------------- + +## 用户选择的输入参数 svm_inputs <- reactive({ svm_args$data_filter <- if (input$show_filter) input$data_filter else "" - svm_args$arr <- if (input$show_filter) input$data_arrange else "" - svm_args$rows <- if (input$show_filter) input$data_rows else "" - svm_args$dataset <- input$dataset + svm_args$arr <- if (input$show_filter) input$data_arrange else "" + svm_args$rows <- if (input$show_filter) input$data_rows else "" + svm_args$dataset <- input$dataset for (i in r_drop(names(svm_args))) { svm_args[[i]] <- input[[paste0("svm_", i)]] } svm_args }) -## 4. predict 参数 ------------------------------------------------------- +## 预测参数（保留命令模式，未改动） svm_pred_args <- as.list(if (exists("predict.svm")) { formals(predict.svm) } else { - formals(e1071:::predict.svm) + formals(radiant.model:::predict.svm) }) svm_pred_inputs <- reactive({ for (i in names(svm_pred_args)) { svm_pred_args[[i]] <- input[[paste0("svm_", i)]] } + svm_pred_args$dec <- input$svm_dec %||% 3 - svm_pred_args$pred_cmd <- svm_pred_args$pred_data <- "" + svm_pred_args$pred_cmd <- svm_pred_args$pred_data <- "" if (input$svm_predict == "cmd") { svm_pred_args$pred_cmd <- gsub("\\s{2,}", " ", input$svm_pred_cmd) %>% gsub(";\\s+", ";", .) %>% @@ -57,14 +49,14 @@ svm_pred_inputs <- reactive({ gsub("\"", "\'", .) svm_pred_args$pred_data <- input$svm_pred_data } - svm_pred_args + return(svm_pred_args) }) -## 5. plot 参数 --------------------------------------------------------- +## 绘图参数（砍掉vip、pdp、svm_margin） svm_plot_args <- as.list(if (exists("plot.svm")) { formals(plot.svm) } else { - formals(e1071:::plot.svm) + formals(radiant.model:::plot.svm) }) svm_plot_inputs <- reactive({ @@ -74,52 +66,36 @@ svm_plot_inputs <- reactive({ svm_plot_args }) -## 6. pred-plot 参数 ---------------------------------------------------- -svm_pred_plot_args <- as.list(if (exists("plot.model.predict")) { - formals(plot.model.predict) -} else { - formals(radiant.model:::plot.model.predict) -}) - -svm_pred_plot_inputs <- reactive({ - for (i in names(svm_pred_plot_args)) { - svm_pred_plot_args[[i]] <- input[[paste0("svm_", i)]] - } - svm_pred_plot_args -}) - -## 7. 响应变量 ---------------------------------------------------------- +## 响应变量UI output$ui_svm_rvar <- renderUI({ + req(input$svm_type) withProgress(message = i18n$t("Acquiring variable information"), value = 1, { if (input$svm_type == "classification") { - vars <- two_level_vars() + vars <- two_level_vars() # 仅二分类 } else { isNum <- .get_class() %in% c("integer", "numeric", "ts") vars <- varnames()[isNum] } }) - init <- if (input$svm_type == "classification") { if (is.empty(input$logit_rvar)) isolate(input$svm_rvar) else input$logit_rvar } else { if (is.empty(input$reg_rvar)) isolate(input$svm_rvar) else input$reg_rvar } - selectInput( inputId = "svm_rvar", - label = i18n$t("Response variable:"), + label = i18n$t("Response variable:"), choices = vars, selected = state_single("svm_rvar", vars, init), multiple = FALSE ) }) -## 8. 分类时选正类 ------------------------------------------------------ +## 分类水平UI output$ui_svm_lev <- renderUI({ req(input$svm_type == "classification") req(available(input$svm_rvar)) levs <- .get_data()[[input$svm_rvar]] %>% as_factor() %>% levels() - init <- if (is.empty(input$logit_lev)) isolate(input$svm_lev) else input$logit_lev selectInput( inputId = "svm_lev", label = i18n$t("Choose level:"), @@ -128,21 +104,19 @@ output$ui_svm_lev <- renderUI({ ) }) -## 9. 解释变量 ---------------------------------------------------------- +## 解释变量UI output$ui_svm_evar <- renderUI({ if (not_available(input$svm_rvar)) return() vars <- varnames() if (length(vars) > 0) vars <- vars[-which(vars == input$svm_rvar)] - init <- if (input$svm_type == "classification") { if (is.empty(input$logit_evar)) isolate(input$svm_evar) else input$logit_evar } else { if (is.empty(input$reg_evar)) isolate(input$svm_evar) else input$reg_evar } - selectInput( inputId = "svm_evar", - label = i18n$t("Explanatory variables:"), + label = i18n$t("Explanatory variables:"), choices = vars, selected = state_multiple("svm_evar", vars, init), multiple = TRUE, @@ -151,16 +125,15 @@ output$ui_svm_evar <- renderUI({ ) }) -## 10. 权重变量 --------------------------------------------------------- +## 权重变量UI output$ui_svm_wts <- renderUI({ isNum <- .get_class() %in% c("integer", "numeric", "ts") - vars <- varnames()[isNum] + vars <- varnames()[isNum] if (length(vars) > 0 && any(vars %in% input$svm_evar)) { vars <- base::setdiff(vars, input$svm_evar) - names(vars) <- varnames() %>% { .[match(vars, .)] } %>% names() + names(vars) <- varnames() %>% {.[match(vars, .)]} %>% names() } vars <- c("None", vars) - selectInput( inputId = "svm_wts", label = i18n$t("Weights:"), choices = vars, selected = state_single("svm_wts", vars), @@ -168,11 +141,11 @@ output$ui_svm_wts <- renderUI({ ) }) -## 11. 存储预测/残差名 -------------------------------------------------- +## 存储预测值UI（残差存储已删除） output$ui_svm_store_pred_name <- renderUI({ init <- state_init("svm_store_pred_name", "pred_svm") %>% sub("\\d{1,}$", "", .) %>% - paste0(., ifelse(is.empty(input$svm_cost), "", input$svm_cost)) + paste0(., ifelse(is.empty(input$svm_kernel), "", input$svm_kernel)) textInput( "svm_store_pred_name", i18n$t("Store predictions:"), @@ -180,43 +153,36 @@ output$ui_svm_store_pred_name <- renderUI({ ) }) -output$ui_svm_store_res_name <- renderUI({ - req(input$dataset) - textInput("svm_store_res_name", i18n$t("Store residuals:"), "", placeholder = i18n$t("Provide variable name")) -}) - -## 12. 预测与绘图重置 --------------------------------------------------- +## 数据集/模型类型切换时重置预测与绘图 observeEvent(input$dataset, { - updateSelectInput(session, "svm_predict", selected = "none") - updateSelectInput(session, "svm_plots", selected = "none") -}) -observeEvent(input$svm_type, { - updateSelectInput(session, "svm_predict", selected = "none") - updateSelectInput(session, "svm_plots", selected = "none") + updateSelectInput(session = session, inputId = "svm_predict", selected = "none") + updateSelectInput(session = session, inputId = "svm_plots", selected = "none") }) -## 13. 预测控制 ---------------------------------------------------------- -output$ui_svm_predict_plot <- renderUI({ - predict_plot_controls("svm") +observeEvent(input$svm_type, { + updateSelectInput(session = session, inputId = "svm_predict", selected = "none") + updateSelectInput(session = session, inputId = "svm_plots", selected = "none") }) -## 14. 绘图数量 ---------------------------------------------------------- +## 绘图选项UI（已删vip、pdp、svm_margin） output$ui_svm_plots <- renderUI({ req(input$svm_type) - if (input$svm_type != "regression") { - svm_plots <- head(svm_plots, -1) # 去掉 regression 专用图 + avail_plots <- svm_plots + if (input$svm_type != "classification") { + avail_plots <- avail_plots[!names(avail_plots) %in% i18n$t("Decision Boundary (2 vars)")] } selectInput( "svm_plots", i18n$t("Plots:"), - choices = svm_plots, - selected = state_single("svm_plots", svm_plots) + choices = avail_plots, + selected = state_single("svm_plots", avail_plots) ) }) -## 15. 绘图点数 ---------------------------------------------------------- +## 数据点数量UI（仅dashboard用，保留） output$ui_svm_nrobs <- renderUI({ nrobs <- nrow(.get_data()) - choices <- c("1,000" = 1000, "5,000" = 5000, "10,000" = 10000, "All" = -1) %>% .[. < nrobs] + choices <- c("1,000" = 1000, "5,000" = 5000, "10,000" = 10000, "All" = -1) %>% + .[. < nrobs] selectInput( "svm_nrobs", i18n$t("Number of data points plotted:"), choices = choices, @@ -224,20 +190,14 @@ output$ui_svm_nrobs <- renderUI({ ) }) -## 16. 刷新按钮 ---------------------------------------------------------- -run_refresh(svm_args, "svm", tabs = "tabs_svm", - label = i18n$t("Estimate model"), - relabel = i18n$t("Re-estimate model")) - -## 17. 主 UI 组装 -------------------------------------------------------- +## 主UI面板（已删残差存储入口） output$ui_svm <- renderUI({ req(input$dataset) tagList( conditionalPanel( condition = "input.tabs_svm == 'Summary'", wellPanel( - actionButton("svm_run", i18n$t("Estimate model"), width = "100%", - icon = icon("play", verify_fa = FALSE), class = "btn-success") + actionButton("svm_run", i18n$t("Estimate model"), width = "100%", icon = icon("play", verify_fa = FALSE), class = "btn-success") ) ), wellPanel( @@ -246,88 +206,59 @@ output$ui_svm <- renderUI({ radioButtons( "svm_type", label = NULL, - choices = c("classification", "regression") %>% { - names(.) <- c(i18n$t("Classification"), i18n$t("Regression")); . - }, + choices = c("classification", "regression") %>% + { names(.) <- c(i18n$t("Classification"), i18n$t("Regression")); . }, inline = TRUE ), uiOutput("ui_svm_rvar"), uiOutput("ui_svm_lev"), uiOutput("ui_svm_evar"), uiOutput("ui_svm_wts"), - selectInput( - "svm_kernel", - label = i18n$t("Kernel:"), - choices = c("linear", "polynomial", "radial", "sigmoid") %>% { - names(.) <- c(i18n$t("Linear"), i18n$t("Polynomial"), - i18n$t("Radial"), i18n$t("Sigmoid")); . - }, - selected = state_init("svm_kernel", "radial") - ), + # SVM特有参数 fluidRow( - column(6, + column(width = 6, + selectInput( + "svm_kernel", + label = i18n$t("Kernel:"), + choices = c("linear", "radial", "poly", "sigmoid"), + selected = state_init("svm_kernel", "radial"), + width = "100%" + ) + ), + column(width = 6, numericInput( "svm_cost", label = i18n$t("Cost (C):"), - min = 0.01, max = 100, - value = state_init("svm_cost", 1), - step = 0.01, width = "100%" - ) - ), - column(6, - conditionalPanel( - "input.svm_kernel != 'linear'", - numericInput( - "svm_gamma", - label = i18n$t("Gamma:"), - min = 0.001, max = 10, - value = state_init("svm_gamma", "auto"), - step = 0.001, width = "100%" - ) + min = 0.1, max = 100, + step = 0.1, value = state_init("svm_cost", 1), + width = "100%" ) ) ), fluidRow( - column(6, + column(width = 6, conditionalPanel( - "input.svm_kernel %in% c('polynomial', 'sigmoid')", + condition = "input.svm_kernel != 'linear'", numericInput( - "svm_coef0", - label = i18n$t("Coef0:"), - min = 0, max = 100, - value = state_init("svm_coef0", 0), + "svm_gamma", + label = i18n$t("Gamma:"), + min = 0.1, max = 20, + step = 0.1, value = state_init("svm_gamma", 1), width = "100%" ) ) ), - column(6, - conditionalPanel( - "input.svm_type == 'regression'", - numericInput( - "svm_epsilon", - label = i18n$t("Epsilon (tube):"), - min = 0.001, max = 1, - value = state_init("svm_epsilon", 0.1), - step = 0.001, width = "100%" - ) + column(width = 6, + numericInput( + "svm_seed", + label = i18n$t("Seed:"), + value = state_init("svm_seed", 1234), + width = "100%" ) ) - ), - numericInput( - "svm_seed", - label = i18n$t("Seed:"), - value = state_init("svm_seed", 12345), - width = "90px" - ), - conditionalPanel( - "input.svm_type == 'classification'", - checkboxInput( - "svm_probability", - label = i18n$t("Estimate class probabilities"), - value = state_init("svm_probability", FALSE) - ) - ), + ) ), + # 预测面板（残差存储已删除） conditionalPanel( condition = "input.tabs_svm == 'Predict'", selectInput( @@ -336,6 +267,16 @@ output$ui_svm <- renderUI({ choices = reg_predict, selected = state_single("svm_predict", reg_predict, "none") ), + conditionalPanel( + "input.svm_predict != 'none'", + numericInput( + "svm_dec", + label = i18n$t("Decimal places:"), + min = 1, max = 10, + value = state_init("svm_dec", 3), + width = "200px" + ) + ), conditionalPanel( "input.svm_predict == 'data' | input.svm_predict == 'datacmd'", selectizeInput( @@ -354,14 +295,6 @@ output$ui_svm <- renderUI({ placeholder = i18n$t("Type a formula to set values for model variables (e.g., carat = 1; cut = 'Ideal') and press return") ) ), - conditionalPanel( - condition = "input.svm_predict != 'none'", - checkboxInput("svm_pred_plot", i18n$t("Plot predictions"), state_init("svm_pred_plot", FALSE)), - conditionalPanel( - "input.svm_pred_plot == true", - uiOutput("ui_svm_predict_plot") - ) - ), conditionalPanel( "input.svm_predict == 'data' | input.svm_predict == 'datacmd'", tags$table( @@ -370,11 +303,12 @@ output$ui_svm <- renderUI({ ) ) ), + # 绘图面板（已删vip、pdp、svm_margin） conditionalPanel( condition = "input.tabs_svm == 'Plot'", uiOutput("ui_svm_plots"), conditionalPanel( - condition = "input.svm_plots == 'pdp' | input.svm_plots == 'pred_plot'", + condition = "input.svm_plots == 'pred_plot'", uiOutput("ui_svm_incl"), uiOutput("ui_svm_incl_int") ), @@ -382,15 +316,9 @@ output$ui_svm <- renderUI({ condition = "input.svm_plots == 'dashboard'", uiOutput("ui_svm_nrobs") ) - ), - conditionalPanel( - condition = "input.tabs_svm == 'Summary'", - tags$table( - tags$td(uiOutput("ui_svm_store_res_name")), - tags$td(actionButton("svm_store_res", i18n$t("Store"), icon = icon("plus", verify_fa = FALSE)), class = "top") - ) ) ), + # 帮助和报告面板 help_and_report( modal_title = i18n$t("Support Vector Machine (SVM)"), fun_name = "svm", @@ -399,56 +327,43 @@ output$ui_svm <- renderUI({ ) }) -## 18. 绘图尺寸动态计算 ------------------------------------------------- +## 绘图尺寸计算（已删vip、pdp、svm_margin） svm_plot <- reactive({ if (svm_available() != "available") return() if (is.empty(input$svm_plots, "none")) return() + res <- .svm() + if (is.character(res)) return() - plot_width <- 650 - if (input$svm_plots == "dashboard") { - plot_height <- 750 - } else if (input$svm_plots %in% c("pdp", "pred_plot")) { + plot_width <- 650 + if ("decision_boundary" %in% input$svm_plots) { + plot_height <- 500 + } else if (input$svm_plots == "pred_plot") { nr_vars <- length(input$svm_incl) + length(input$svm_incl_int) plot_height <- max(250, ceiling(nr_vars / 2) * 250) - if (length(input$svm_incl_int) > 0) { - plot_width <- plot_width + min(2, length(input$svm_incl_int)) * 90 - } } else { - plot_height <- 500 + plot_height <- max(500, length(res$evar) * 30) } list(plot_width = plot_width, plot_height = plot_height) }) -svm_plot_width <- function() svm_plot() %>% (function(x) if (is.list(x)) x$plot_width else 650) -svm_plot_height <- function() svm_plot() %>% (function(x) if (is.list(x)) x$plot_height else 500) -svm_pred_plot_height <- function() if (input$svm_pred_plot) 500 else 1 +svm_plot_width <- function() svm_plot()$plot_width %||% 650 +svm_plot_height <- function() svm_plot()$plot_height %||% 500 -## 19. 输出注册 ---------------------------------------------------------- +## 主输出面板（已删残差存储） output$svm <- renderUI({ register_print_output("summary_svm", ".summary_svm") register_print_output("predict_svm", ".predict_print_svm") - register_plot_output("predict_plot_svm", ".predict_plot_svm", - height_fun = "svm_pred_plot_height") - register_plot_output("plot_svm", ".plot_svm", - height_fun = "svm_plot_height", - width_fun = "svm_plot_width") + register_plot_output( + "plot_svm", ".plot_svm", + height_fun = "svm_plot_height", + width_fun = "svm_plot_width" + ) svm_output_panels <- tabsetPanel( id = "tabs_svm", - tabPanel(i18n$t("Summary"), value = "Summary", - download_link("dl_svm_coef"), br(), - verbatimTextOutput("summary_svm")), - tabPanel(i18n$t("Predict"), value = "Predict", - conditionalPanel( - "input.svm_pred_plot == true", - download_link("dlp_svm_pred"), - plotOutput("predict_plot_svm", width = "100%", height = "100%") - ), - download_link("dl_svm_pred"), br(), - verbatimTextOutput("predict_svm")), - tabPanel(i18n$t("Plot"), value = "Plot", - download_link("dlp_svm"), - plotOutput("plot_svm", width = "100%", height = "100%")) + tabPanel(i18n$t("Summary"), value = "Summary", verbatimTextOutput("summary_svm")), + tabPanel(i18n$t("Predict"), value = "Predict", download_link("dl_svm_pred"), br(), verbatimTextOutput("predict_svm")), + tabPanel(i18n$t("Plot"), value = "Plot", download_link("dlp_svm"), plotOutput("plot_svm", width = "100%", height = "100%")) ) stat_tab_panel( @@ -459,7 +374,7 @@ output$svm <- renderUI({ ) }) -## 20. 可用性检查 ------------------------------------------------------- +## 模型可用性检查 svm_available <- reactive({ req(input$svm_type) if (not_available(input$svm_rvar)) { @@ -471,46 +386,48 @@ svm_available <- reactive({ suggest_data("diamonds") } } else if (not_available(input$svm_evar)) { - if (input$svm_type == "classification") { - i18n$t("Please select one or more explanatory variables.") %>% suggest_data("titanic") - } else { - i18n$t("Please select one or more explanatory variables.") %>% suggest_data("diamonds") - } + i18n$t("Please select one or more explanatory variables.") %>% + suggest_data(ifelse(input$svm_type == "classification", "titanic", "diamonds")) } else { "available" } }) -## 21. 模型估计 ---------------------------------------------------------- +## 核心函数壳子 .svm <- eventReactive(input$svm_run, { - svmi <- svm_inputs() - svmi$envir <- r_data - withProgress(message = i18n$t("Estimating SVM"), value = 1, - do.call(svm, svmi)) + svi <- svm_inputs() + svi$envir <- r_data + withProgress(message = i18n$t("Estimating SVM model"), value = 1, do.call(svm, svi)) }) -## 22. summary ------------------------------------------------------------ +## 辅助输出函数壳子 .summary_svm <- reactive({ - if (not_pressed(input$svm_run)) return(i18n$t("** Press the Estimate button to estimate the model **")) + if (not_pressed(input$svm_run)) return(i18n$t("** Press the Estimate button to estimate the SVM model **")) if (svm_available() != "available") return(svm_available()) summary(.svm()) }) -## 23. predict ------------------------------------------------------------ .predict_svm <- reactive({ - if (not_pressed(input$svm_run)) return(i18n$t("** Press the Estimate button to estimate the model **")) + if (not_pressed(input$svm_run)) return(i18n$t("** Press 'Estimate model' first to train the SVM **")) if (svm_available() != "available") return(svm_available()) - if (is.empty(input$svm_predict, "none")) return(i18n$t("** Select prediction input **")) - if ((input$svm_predict == "data" || input$svm_predict == "datacmd") && is.empty(input$svm_pred_data)) - return(i18n$t("** Select data for prediction **")) - if (input$svm_predict == "cmd" && is.empty(input$svm_pred_cmd)) - return(i18n$t("** Enter prediction commands **")) + if (is.empty(input$svm_predict, "none")) return(i18n$t("** Select prediction type **")) + + if (input$svm_predict %in% c("data", "datacmd") &&(is.null(input$svm_pred_data) || is.empty(input$svm_pred_data))) { + return(i18n$t("** Select a dataset for prediction (under 'Prediction data') **")) + } + if (input$svm_predict %in% c("cmd", "datacmd") && is.empty(input$svm_pred_cmd)) + return(i18n$t("** Enter commands (e.g., 'carat = 1; cut = 'Ideal'') **")) - withProgress(message = i18n$t("Generating predictions"), value = 1, { - spi <- svm_pred_inputs() - spi$object <- .svm() - spi$envir <- r_data - do.call(predict, spi) + withProgress(message = i18n$t("Generating SVM predictions (scaling applied)"), value = 1, { + pred_args <- svm_pred_inputs() + pred_args$object <- .svm() + pred_args$envir <- r_data + if (input$svm_predict %in% c("data", "datacmd")) { + pred_args$pred_data <- r_data[[input$svm_pred_data]] + if (is.null(pred_args$pred_data)) + return(sprintf(i18n$t("** Dataset '%s' not found **"), input$svm_pred_data)) + } + do.call(predict, pred_args) }) }) @@ -518,125 +435,59 @@ svm_available <- reactive({ .predict_svm() %>% { if (is.character(.)) cat(., "\n") else print(.) } }) -## 24. pred-plot ---------------------------------------------------------- -.predict_plot_svm <- reactive({ - req(pressed(input$svm_run), input$svm_pred_plot, available(input$svm_xvar), - !is.empty(input$svm_predict, "none")) - withProgress(message = i18n$t("Generating prediction plot"), value = 1, - do.call(plot, c(list(x = .predict_svm()), svm_pred_plot_inputs()))) -}) - -## 25. plot -------------------------------------------------------------- .plot_svm <- reactive({ - if (not_pressed(input$svm_run)) return(i18n$t("** Press the Estimate button to estimate the model **")) + if (not_pressed(input$svm_run)) return(i18n$t("** Press the Estimate button to estimate the SVM model **")) if (svm_available() != "available") return(svm_available()) if (is.empty(input$svm_plots, "none")) return(i18n$t("Please select an SVM plot from the drop-down menu")) pinp <- svm_plot_inputs() pinp$shiny <- TRUE - if (input$svm_plots == "dashboard") req(input$svm_nrobs) - - withProgress(message = i18n$t("Generating plots"), value = 1, - do.call(plot, c(list(x = .svm()), pinp))) -}) - -## 26. 存储 -------------------------------------------------------------- -observeEvent(input$svm_store_res, { - req(pressed(input$svm_run)) - robj <- .svm() - if (!is.list(robj)) return() - fixed <- fix_names(input$svm_store_res_name) - updateTextInput(session, "svm_store_res_name", value = fixed) - withProgress(message = i18n$t("Storing residuals"), value = 1, - r_data[[input$dataset]] <- store(r_data[[input$dataset]], robj, name = fixed)) + withProgress(message = i18n$t("Generating SVM plots"), value = 1, do.call(plot, c(list(x = .svm()), pinp))) }) +## 存储预测值（残差存储已删除） observeEvent(input$svm_store_pred, { - req(!is.empty(input$svm_pred_data), pressed(input$svm_run)) - pred <- .predict_svm() - if (is.null(pred)) return() - fixed <- fix_names(input$svm_store_pred_name) - updateTextInput(session, "svm_store_pred_name", value = fixed) - withProgress(message = i18n$t("Storing predictions"), value = 1, - r_data[[input$svm_pred_data]] <- store(r_data[[input$svm_pred_data]], pred, name = fixed)) -}) - -## 27. report ------------------------------------------------------------ -svm_report <- function() { - if (is.empty(input$svm_evar)) return(invisible()) - - outputs <- c("summary") - inp_out <- list(list(prn = TRUE), "") - figs <- FALSE - - if (!is.empty(input$svm_plots, "none")) { - inp <- check_plot_inputs(svm_plot_inputs()) - inp_out[[2]] <- clean_args(inp, svm_plot_args[-1]) - inp_out[[2]]$custom <- FALSE - outputs <- c(outputs, "plot") - figs <- TRUE - } + req( + pressed(input$svm_run), + !is.empty(input$svm_pred_data), + !is.empty(input$svm_store_pred_name), + inherits(.predict_svm(), "svm.predict") + ) + pred_result <- .predict_svm() + target_data <- r_data[[input$svm_pred_data]] + base_col_name <- fix_names(input$svm_store_pred_name) + meta <- attr(pred_result, "svm_meta") - if (!is.empty(input$svm_store_res_name)) { - fixed <- fix_names(input$svm_store_res_name) - updateTextInput(session, "svm_store_res_name", value = fixed) - xcmd <- paste0(input$dataset, " <- store(", input$dataset, ", result, name = \"", fixed, "\")\n") + pred_cols <- if (meta$model_type == "classification") { + colnames(pred_result)[grepl("^Predicted_Class|^Prob_", colnames(pred_result))] } else { - xcmd <- "" + "Predicted_Value" } - - if (!is.empty(input$svm_predict, "none") && - (!is.empty(input$svm_pred_data) || !is.empty(input$svm_pred_cmd))) { - pred_args <- clean_args(svm_pred_inputs(), svm_pred_args[-1]) - - if (!is.empty(pred_args$pred_cmd)) - pred_args$pred_cmd <- strsplit(pred_args$pred_cmd, ";\\s*")[[1]] - else - pred_args$pred_cmd <- NULL - - if (!is.empty(pred_args$pred_data)) - pred_args$pred_data <- as.symbol(pred_args$pred_data) - else - pred_args$pred_data <- NULL - - inp_out[[2 + figs]] <- pred_args - outputs <- c(outputs, "pred <- predict") - xcmd <- paste0(xcmd, "print(pred, n = 10)") - if (input$svm_predict %in% c("data", "datacmd")) { - fixed <- fix_names(input$svm_store_pred_name) - updateTextInput(session, "svm_store_pred_name", value = fixed) - xcmd <- paste0(xcmd, "\n", input$svm_pred_data, " <- store(", - input$svm_pred_data, ", pred, name = \"", fixed, "\")") - } - - if (input$svm_pred_plot && !is.empty(input$svm_xvar)) { - inp_out[[3 + figs]] <- clean_args(svm_pred_plot_inputs(), svm_pred_plot_args[-1]) - inp_out[[3 + figs]]$result <- "pred" - outputs <- c(outputs, "plot") - figs <- TRUE - } + new_col_names <- if (length(pred_cols) == 1) base_col_name else { + suffix <- gsub("^Predicted_|^Prob_", "", pred_cols) + paste0(base_col_name, "_", suffix) } + colnames(pred_result)[match(pred_cols, colnames(pred_result))] <- new_col_names - svm_inp <- svm_inputs() - if (input$svm_type == "regression") svm_inp$lev <- NULL - - update_report( - inp_main = clean_args(svm_inp, svm_args), - fun_name = "svm", - inp_out = inp_out, - outputs = outputs, - figs = figs, - fig.width = svm_plot_width(), - fig.height= svm_plot_height(), - xcmd = xcmd + merged_data <- merge( + target_data, + pred_result[, c(meta$evar, new_col_names), drop = FALSE], + by = meta$evar, all.x = TRUE ) -} + r_data[[input$svm_pred_data]] <- merged_data + showNotification( + sprintf(i18n$t("SVM predictions stored as: %s (in '%s')"), + paste(new_col_names, collapse = ", "), input$svm_pred_data), + type = "message" + ) + updateTextInput(session, "svm_store_pred_name", value = base_col_name) +}) -## 28. 下载 -------------------------------------------------------------- +## 下载处理 dl_svm_pred <- function(path) { if (pressed(input$svm_run)) { write.csv(.predict_svm(), file = path, row.names = FALSE) } else { - cat(i18n$t("No output available. Press the Estimate button to generate results"), file = path) + cat(i18n$t("No output available. Press the Estimate button to generate SVM results"), file = path) } } @@ -648,17 +499,6 @@ download_handler( caption = i18n$t("Save SVM predictions") ) -download_handler( - id = "dlp_svm_pred", - fun = download_handler_plot, - fn = function() paste0(input$dataset, "_svm_pred"), - type = "png", - caption = i18n$t("Save SVM prediction plot"), - plot = .predict_plot_svm, - width = plot_width, - height = svm_pred_plot_height -) - download_handler( id = "dlp_svm", fun = download_handler_plot, @@ -670,7 +510,9 @@ download_handler( height = svm_plot_height ) -## 29. report / screenshot 监听 ----------------------------------------- +## 报告生成（空壳，保留接口） +svm_report <- function() invisible() + observeEvent(input$svm_report, { r_info[["latest_screenshot"]] <- NULL svm_report() diff --git a/radiant.model/man/cv.svm.Rd b/radiant.model/man/cv.svm.Rd new file mode 100644 index 0000000000000000000000000000000000000000..11260bf3a2aaf50a508bd65fd9f3f42c50059ab1 --- /dev/null +++ b/radiant.model/man/cv.svm.Rd @@ -0,0 +1,22 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/svm.R +\name{cv.svm} +\alias{cv.svm} +\title{Cross-validation for SVM} +\usage{ +cv.svm( + object, + K = 5, + repeats = 1, + kernel = c("linear", "radial"), + cost = seq(0.1, 10, by = 0.5), + gamma = seq(0.1, 5, by = 0.5), + seed = 1234, + trace = TRUE, + fun, + ... +) +} +\description{ +Cross-validation for SVM +} diff --git a/radiant.model/man/plot.svm.Rd b/radiant.model/man/plot.svm.Rd new file mode 100644 index 0000000000000000000000000000000000000000..2a62b49b05300fda13f9ea8511347d30fb2000a1 --- /dev/null +++ b/radiant.model/man/plot.svm.Rd @@ -0,0 +1,21 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/svm.R +\name{plot.svm} +\alias{plot.svm} +\title{Plot method for the svm function} +\usage{ +\method{plot}{svm}( + x, + plots = "vip", + size = 12, + nrobs = -1, + incl = NULL, + incl_int = NULL, + shiny = FALSE, + custom = FALSE, + ... +) +} +\description{ +Plot method for the svm function +} diff --git a/radiant.model/man/predict.svm.Rd b/radiant.model/man/predict.svm.Rd index 96103d5e55fbb5d2cd8f41a281c78de33f30972f..e759df66988ebcb241a1a814381eaad0ef2c27ee 100644 --- a/radiant.model/man/predict.svm.Rd +++ b/radiant.model/man/predict.svm.Rd @@ -2,7 +2,7 @@ % Please edit documentation in R/svm.R \name{predict.svm} \alias{predict.svm} -\title{Predict method} +\title{Predict method for the svm function} \usage{ \method{predict}{svm}( object, @@ -14,5 +14,5 @@ ) } \description{ -Predict method +Predict method for the svm function } diff --git a/radiant.model/man/print.svm.predict.Rd b/radiant.model/man/print.svm.predict.Rd index d65cec104dbb6a92eb0c36d11811ed9eac0939e4..996b46dc44b21badce123dda598c74f901e09064 100644 --- a/radiant.model/man/print.svm.predict.Rd +++ b/radiant.model/man/print.svm.predict.Rd @@ -2,10 +2,10 @@ % Please edit documentation in R/svm.R \name{print.svm.predict} \alias{print.svm.predict} -\title{Print predictions} +\title{Print method for predict.svm} \usage{ \method{print}{svm.predict}(x, ..., n = 10) } \description{ -Print predictions +Print method for predict.svm } diff --git a/radiant.model/man/scale_df.Rd b/radiant.model/man/scale_df.Rd index d5c196e174f004226d1a8d130e33035769367c7e..03535533ec95ddc17eabaf37332d701bf86960a8 100644 --- a/radiant.model/man/scale_df.Rd +++ b/radiant.model/man/scale_df.Rd @@ -1,9 +1,11 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/nn.R +% Please edit documentation in R/nn.R, R/svm.R \name{scale_df} \alias{scale_df} \title{Center or standardize variables in a data frame} \usage{ +scale_df(dataset, center = TRUE, scale = TRUE, sf = 2, wts = NULL, calc = TRUE) + scale_df(dataset, center = TRUE, scale = TRUE, sf = 2, wts = NULL, calc = TRUE) } \arguments{ @@ -23,5 +25,7 @@ scale_df(dataset, center = TRUE, scale = TRUE, sf = 2, wts = NULL, calc = TRUE) Scaled data frame } \description{ +Center or standardize variables in a data frame + Center or standardize variables in a data frame } diff --git a/radiant.model/man/summary.svm.Rd b/radiant.model/man/summary.svm.Rd index 3e97ec3afc804ca26e56e4c7bb2204e003cc190d..ebdd11b1488d613240bfdc7b3077c9e0366c7301 100644 --- a/radiant.model/man/summary.svm.Rd +++ b/radiant.model/man/summary.svm.Rd @@ -2,10 +2,10 @@ % Please edit documentation in R/svm.R \name{summary.svm} \alias{summary.svm} -\title{Summary method} +\title{Summary method for the svm function} \usage{ -\method{summary}{svm}(object, ...) +\method{summary}{svm}(object, prn = TRUE, ...) } \description{ -Summary method +Summary method for the svm function } diff --git a/radiant.model/man/svm.Rd b/radiant.model/man/svm.Rd index d7c5600c507f40594f205b6febf5ffc7e22df59a..5b0d0e196317e7b7e07bc6b0b94ac856e6e083fe 100644 --- a/radiant.model/man/svm.Rd +++ b/radiant.model/man/svm.Rd @@ -2,7 +2,7 @@ % Please edit documentation in R/svm.R \name{svm} \alias{svm} -\title{Support Vector Machine using e1071} +\title{Support Vector Machine} \usage{ svm( dataset, @@ -12,15 +12,10 @@ svm( lev = "", kernel = "radial", cost = 1, - gamma = "auto", - degree = 3, - coef0 = 0, - nu = 0.5, - epsilon = 0.1, - probability = FALSE, + gamma = 1, wts = "None", - seed = 1234, - check = NULL, + seed = NA, + check = "standardize", form, data_filter = "", arr = "", @@ -29,5 +24,5 @@ svm( ) } \description{ -Support Vector Machine using e1071 +Support Vector Machine } diff --git a/radiant.model/man/varimp.Rd b/radiant.model/man/varimp.Rd index a7931a650fb5fada0774e6d9bd99737b0271fc06..0eb6704c1988a73cd3fbcaed0564a49b2343d310 100644 --- a/radiant.model/man/varimp.Rd +++ b/radiant.model/man/varimp.Rd @@ -1,9 +1,11 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/nn.R +% Please edit documentation in R/nn.R, R/svm.R \name{varimp} \alias{varimp} \title{Variable importance using the vip package and permutation importance} \usage{ +varimp(object, rvar, lev, data = NULL, seed = 1234) + varimp(object, rvar, lev, data = NULL, seed = 1234) } \arguments{ @@ -18,5 +20,7 @@ varimp(object, rvar, lev, data = NULL, seed = 1234) \item{seed}{Random seed for reproducibility} } \description{ +Variable importance using the vip package and permutation importance + Variable importance using the vip package and permutation importance } diff --git a/radiant.model/man/varimp_plot.Rd b/radiant.model/man/varimp_plot.Rd index dda339c5875feb7f4a7723650aef24ef78ce8f6a..1480b815d0e7437501dc02ca0e23349b44b74bff 100644 --- a/radiant.model/man/varimp_plot.Rd +++ b/radiant.model/man/varimp_plot.Rd @@ -1,9 +1,11 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/nn.R +% Please edit documentation in R/nn.R, R/svm.R \name{varimp_plot} \alias{varimp_plot} \title{Plot permutation importance} \usage{ +varimp_plot(object, rvar, lev, data = NULL, seed = 1234) + varimp_plot(object, rvar, lev, data = NULL, seed = 1234) } \arguments{ @@ -18,5 +20,7 @@ varimp_plot(object, rvar, lev, data = NULL, seed = 1234) \item{seed}{Random seed for reproducibility} } \description{ +Plot permutation importance + Plot permutation importance } diff --git a/radiant.quickgen/R/quickgen_ai.R b/radiant.quickgen/R/quickgen_ai.R index b5c2f751e7c74d87acbe3589a4f432661fb9c68c..21d68e0b43675bd4193432fb9717707a91c7890c 100644 --- a/radiant.quickgen/R/quickgen_ai.R +++ b/radiant.quickgen/R/quickgen_ai.R @@ -1,6 +1,6 @@ # === 配置 === MODELSCOPE_OPENAI_URL <- "https://api-inference.modelscope.cn/v1" -MODELSCOPE_API_KEY <- Sys.getenv("MODELSCOPE_API_KEY", "ms-6638b00e-57e4-4623-996d-214e375d220f") +MODELSCOPE_API_KEY <- Sys.getenv("MODELSCOPE_API_KEY", "ms-5b9f3668-ea8e-4a2c-8cd3-a1a9ba04810b") MODEL_ID <- "deepseek-ai/DeepSeek-V3.1" # === 低层封装：单次对话 ===