v0.4.2 (#78)

* v0.4.2 * update message * Update .travis.yml * Update .travis.yml
ModelOriented · Dec 9, 2019 · 88c89cc · 88c89cc
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diff --git a/.travis.yml b/.travis.yml
@@ -3,21 +3,39 @@ sudo: false
 cache: packages
 dist: trusty
 
+r:
+  - release
+  - devel
+
+os:
+  - linux
+  - osx
+
+matrix:
+  exclude:
+    - os: osx
+      r: devel
+
 env:
   global:
   - R_CHECK_ARGS="--no-build-vignettes --no-manual --timings --run-donttest"
 
 notifications:
   email: false
 
+#before_install:
+#    - sudo apt-get install --yes udunits-bin libproj-dev libgeos-dev libgdal-dev libgdal1-dev libudunits2-dev
 before_install:
-    - sudo apt-get install --yes udunits-bin libproj-dev libgeos-dev libgdal-dev libgdal1-dev libudunits2-dev
-
+  - if [[ "$TRAVIS_OS_NAME" == "osx" ]]; then brew install llvm &&
+    export PATH="/usr/local/opt/llvm/bin:$PATH" &&
+    export LDFLAGS="-L/usr/local/opt/llvm/lib" &&
+    export CFLAGS="-I/usr/local/opt/llvm/include"; fi
+
 r_packages:
   - ggplot2
   - covr
 
-r_github_packages: modelOriented/DALEX
+#r_github_packages: modelOriented/DALEX
 
 after_success:
   - Rscript -e 'library(covr); codecov()'

diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,13 +1,12 @@
 Package: ingredients
 Title: Effects and Importances of Model Ingredients
-Version: 0.4.1
+Version: 0.4.2
 Authors@R: c(person("Przemyslaw", "Biecek", email = "przemyslaw.biecek@gmail.com",
                   role = c("aut", "cre"),
                   comment = c(ORCID = "0000-0001-8423-1823")),
               person("Hubert", "Baniecki", role = "aut",
                   comment = c(ORCID = "0000-0001-6661-5364")),
-              person("Adam", "Izdebski", role = "aut"),
-              person("Katarzyna", "Pekala", role = "aut"))
+              person("Adam", "Izdebski", role = "aut"))
 Description: Collection of tools for assessment of feature importance and feature effects.
     Key functions are:
     feature_importance() for assessment of global level feature importance,

diff --git a/NEWS.md b/NEWS.md
@@ -1,3 +1,13 @@
+ingredients 0.4.2
+---------------------------------------------------------------
+* change `variable_type` and `variables` arguments usage   in the 
+`aggregate_profiles`, `plot.ceteris_paribus` and `plotD3.ceteris_paribus`
+* remove `variable_type` argument from `plotD3.aggregated_profiles`
+(now the same as in `plot.aggregated_profiles`)
+* Kasia Pekala is removed from the contributors as `aspect_importance` is moved to `DALEXtra`
+([See v0.3.12 changelog](https://modeloriented.github.io/ingredients/news/index.html#ingredients-0-3-12))
+* added Travis-CI for OSX
+
 ingredients 0.4.1
 ---------------------------------------------------------------
 * fixed rounding problem in the describe function ([#76](https://github.com/ModelOriented/ingredients/issues/76))
@@ -31,7 +41,7 @@ cluster_profiles(), plot() and others, as requested in #15
 
 ingredients 0.3.8
 ----------------------------------------------------------------
-* Natural language description generated with `describe()` function for `ceteris_paribus()`, `feature_importance()` and `aggregated_profiles()` explanations. 
+* Natural language description generated with `describe()` function for `ceteris_paribus()`, `feature_importance()` and `aggregate_profiles()` explanations. 
 
 
 ingredients 0.3.7

diff --git a/R/aggregate_profiles.R b/R/aggregate_profiles.R
@@ -104,16 +104,38 @@ aggregate_profiles <- function(x, ...,
     if (length(all_variables_intersect) == 0) stop(paste0("parameter variables do not overlap with ", paste(all_variables, collapse = ", ")))
     all_variables <- all_variables_intersect
   }
+
   # only numerical or only factors?
   is_numeric <- sapply(all_profiles[, all_variables, drop = FALSE], is.numeric)
+
   if (variable_type == "numerical") {
     vnames <- names(which(is_numeric))
-    if (length(vnames) == 0) stop("There are no numerical variables")
     all_profiles$`_x_` <- 0
+
+    # there are no numerical variables
+    if (length(vnames) == 0) {
+      # change to categorical
+      variable_type <- "categorical"
+      all_profiles$`_x_` <- ""
+      # send message
+      message("'variable_type' changed to 'categorical' due to lack of numerical variables.")
+      # take all
+      vnames <- all_variables
+    } else if (!is.null(variables) && length(vnames) != length(variables)) {
+      message("Non-numerical variables (from the 'variables' argument) are rejected.")
+    }
   } else {
     vnames <- names(which(!is_numeric))
-    if (length(vnames) == 0) stop("There are no non-numerical variables")
     all_profiles$`_x_` <- ""
+
+    # there are variables selected
+    if (!is.null(variables)) {
+      # take all
+      vnames <- all_variables
+    } else if (length(vnames) == 0) {
+      # there were no variables selected and there are no categorical variables
+      stop("There are no non-numerical variables.")
+    }
   }
 
   # select only suitable variables
@@ -132,7 +154,7 @@ aggregate_profiles <- function(x, ...,
   if (variable_type == "categorical") {
     all_profiles$`_x_` <- as.character(apply(all_profiles, 1, function(x) x[x["_vname_"]]))
   }
-  
+
   if (!is.null(groups) && ! groups %in% colnames(all_profiles)) {
     stop("groups parameter is not a name of any column")
   }

diff --git a/R/plotD3_aggregated_profiles.R b/R/plotD3_aggregated_profiles.R
@@ -12,8 +12,6 @@
 #' @param color a character.  Set line/bar color
 #' @param size a numeric. Set width of lines
 #' @param alpha a numeric between \code{0} and \code{1}. Opacity of lines
-#' @param variable_type a character. If "numerical" then only numerical variables will be plotted.
-#' If "categorical" then only categorical variables will be plotted.
 #' @param facet_ncol number of columns for the \code{\link[ggplot2]{facet_wrap}}
 #' @param scale_plot a logical. If \code{TRUE}, the height of plot scales with window size. By default it's \code{FALSE}
 #' @param variables if not \code{NULL} then only \code{variables} will be presented
@@ -49,25 +47,22 @@
 #' pdp_rf_a <- aggregate_profiles(cp_rf, type = "accumulated", variable_type = "numerical")
 #' pdp_rf_a$`_label_` <- "RF_accumulated"
 #'
-#' plotD3(pdp_rf_p, pdp_rf_c, pdp_rf_a, variable_type = "numerical", scale_plot = TRUE)
+#' plotD3(pdp_rf_p, pdp_rf_c, pdp_rf_a, scale_plot = TRUE)
 #'
 #' pdp <- aggregate_profiles(cp_rf, type = "partial", variable_type = "categorical")
 #' pdp$`_label_` <- "RF_partial"
 #'
-#' plotD3(pdp, variables = c("gender","class"), variable_type = "categorical", label_margin = 70)
+#' plotD3(pdp, variables = c("gender","class"), label_margin = 70)
 #'
 #' @export
 #' @rdname plotD3_aggregated_profiles
 plotD3.aggregated_profiles_explainer <- function(x, ..., size = 2, alpha = 1,
                                                  color = "#46bac2",
-                                                 variable_type = "numerical",
                                                  facet_ncol = 2, scale_plot = FALSE,
                                                  variables = NULL,
                                                  chart_title = "Aggregated Profiles",
                                                  label_margin = 60) {
 
-  check_variable_type(variable_type)
-
   # if there is more explainers, they should be merged into a single data frame
   dfl <- c(list(x), list(...))
   aggregated_profiles <- do.call(rbind, dfl)
@@ -77,35 +72,13 @@ plotD3.aggregated_profiles_explainer <- function(x, ..., size = 2, alpha = 1,
   if (!is.null(variables)) {
     all_variables <- intersect(all_variables, variables)
     if (length(all_variables) == 0) stop(paste0("variables do not overlap with ", paste(all_variables, collapse = ", ")))
-  }
 
-  hl <- split(aggregated_profiles, f = as.character(aggregated_profiles$`_vname_`), drop = FALSE)[all_variables]
-
-  # only numerical or only factor?
-  is_numeric <- unlist(lapply(hl, function(x){
-    is.numeric(x$`_x_`)
-  }))
-
-  if (variable_type == "numerical") {
-    vnames <- names(which(is_numeric))
-
-    if (length(vnames) == 0) {
-      # but `variables` are selected, then change to factor
-      if (length(variables) > 0) {
-        variable_type <- "categorical"
-        vnames <- variables
-      } else {
-        stop("There are no numerical variables")
-      }
-    }
-  } else {
-    vnames <- names(which(!is_numeric))
-    # there are no numerical features
-    if (length(vnames) == 0) stop("There are no non-numerical variables")
+    aggregated_profiles <- aggregated_profiles[aggregated_profiles$`_vname_` %in% all_variables, ]
   }
 
+  is_x_numeric <- is.numeric(aggregated_profiles$`_x_`)
+
   # prepare profiles data
-  aggregated_profiles <- aggregated_profiles[aggregated_profiles$`_vname_` %in% vnames, ]
   aggregated_profiles$`_vname_` <- droplevels(aggregated_profiles$`_vname_`)
   rownames(aggregated_profiles) <- NULL
 
@@ -118,7 +91,7 @@ plotD3.aggregated_profiles_explainer <- function(x, ..., size = 2, alpha = 1,
   min_max_list <- ymean <- label_names <- NULL
 
   # line plot or bar plot?
-  if (variable_type == "numerical") {
+  if (is_x_numeric) {
     aggregated_profiles_list <- lapply(aggregated_profiles_list, function(x){
       ret <- x[, c('_x_', "_yhat_", "_vname_", "_label_")]
       colnames(ret) <- c("xhat", "yhat", "vname", "label")
@@ -150,12 +123,12 @@ plotD3.aggregated_profiles_explainer <- function(x, ..., size = 2, alpha = 1,
     ymean <- round(attr(x, "mean_prediction"),3)
   }
 
-  options <- list(variableNames = as.list(vnames),
-                  n = length(vnames), c = length(list(...)) + 1,
+  options <- list(variableNames = as.list(all_variables),
+                  n = length(all_variables), c = length(list(...)) + 1,
                   yMax = ymax + ymargin, yMin = ymin - ymargin,
                   yMean = ymean, labelNames = label_names,
                   size = size, alpha = alpha, color = color,
-                  onlyNumerical = variable_type == "numerical",
+                  onlyNumerical = is_x_numeric,
                   facetNcol = facet_ncol, scalePlot = scale_plot,
                   chartTitle = chart_title, labelMargin = label_margin)
 

diff --git a/R/plotD3_ceteris_paribus.R b/R/plotD3_ceteris_paribus.R
@@ -85,20 +85,32 @@ plotD3.ceteris_paribus_explainer <- function(x, ..., size = 2, alpha = 1,
 
   if (variable_type == "numerical") {
     vnames <- names(which(is_numeric))
+    all_profiles$`_x_` <- 0
 
+    # there are no numerical variables
     if (length(vnames) == 0) {
-      # but `variables` are selected, then change to factor
-      if (length(variables) > 0) {
-        variable_type <- "categorical"
-        vnames <- variables
-      } else {
-        stop("There are no numerical variables")
-      }
+      # change to categorical
+      variable_type <- "categorical"
+      all_profiles$`_x_` <- ""
+      # send message
+      message("'variable_type' changed to 'categorical' due to lack of numerical variables.")
+      # take all
+      vnames <- all_variables
+    } else if (!is.null(variables) && length(vnames) != length(variables)) {
+      message("Non-numerical variables (from the 'variables' argument) are rejected.")
     }
   } else {
     vnames <- names(which(!is_numeric))
-    # there are no numerical features
-    if (length(vnames) == 0) stop("There are no non-numerical variables")
+    all_profiles$`_x_` <- ""
+
+    # there are variables selected
+    if (!is.null(variables)) {
+      # take all
+      vnames <- all_variables
+    } else if (length(vnames) == 0) {
+      # there were no variables selected and there are no categorical variables
+      stop("There are no non-numerical variables.")
+    }
   }
 
   # prepare clean observations data for tooltips

diff --git a/R/plot_ceteris_paribus.R b/R/plot_ceteris_paribus.R
@@ -102,29 +102,38 @@ plot.ceteris_paribus_explainer <- function(x, ...,
   }
   # is color a variable or literal?
   is_color_a_variable <- color %in% c(all_variables, "_label_", "_vname_", "_ids_")
+
   # only numerical or only factors?
   is_numeric <- sapply(all_profiles[, all_variables, drop = FALSE], is.numeric)
+
   if (variable_type == "numerical") {
     vnames <- names(which(is_numeric))
     all_profiles$`_x_` <- 0
 
+    # there are no numerical variables
     if (length(vnames) == 0) {
-      # but `variables` are selected, then change to factor
-      if (length(variables) > 0) {
-        variable_type <- "categorical"
-        vnames <- variables
-        all_profiles$`_x_` <- ""
-      } else {
-        stop("There are no numerical variables")
-      }
+      # change to categorical
+      variable_type <- "categorical"
+      all_profiles$`_x_` <- ""
+      # send message
+      message("'variable_type' changed to 'categorical' due to lack of numerical variables.")
+      # take all
+      vnames <- all_variables
+    } else if (!is.null(variables) && length(vnames) != length(variables)) {
+      message("Non-numerical variables (from the 'variables' argument) are rejected.")
     }
-
   } else {
     vnames <- names(which(!is_numeric))
-    # there are no numerical features
-    if (length(vnames) == 0) stop("There are no non-numerical variables")
-
     all_profiles$`_x_` <- ""
+
+    # there are variables selected
+    if (!is.null(variables)) {
+      # take all
+      vnames <- all_variables
+    } else if (length(vnames) == 0) {
+      # there were no variables selected and there are no categorical variables
+      stop("There are no non-numerical variables.")
+    }
   }
 
   # how to plot profiles

diff --git a/docs/404.html b/docs/404.html
diff --git a/docs/articles/index.html b/docs/articles/index.html