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# This file is auto-generated by h2o-3/h2o-bindings/bin/gen_R.py
# Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
#'
# -------------------------- Infogram -------------------------- #
#'
#' H2O Infogram
#'
#' The infogram is a graphical information-theoretic interpretability tool which allows the user to quickly spot the core, decision-making variables
#' that uniquely and safely drive the response, in supervised classification problems. The infogram can significantly cut down the number of predictors needed to build
#' a model by identifying only the most valuable, admissible features. When protected variables such as race or gender are present in the data, the admissibility
#' of a variable is determined by a safety and relevancy index, and thus serves as a diagnostic tool for fairness. The safety of each feature can be quantified and
#' variables that are unsafe will be considered inadmissible. Models built using only admissible features will naturally be more interpretable, given the reduced
#' feature set. Admissible models are also less susceptible to overfitting and train faster, while providing similar accuracy as models built using all available features.
#'
#' The infogram allows the user to quickly spot the admissible decision-making variables that are driving the response.
#' There are two types of infogram plots: Core and Fair Infogram.
#'
#' The Core Infogram plots all the variables as points on two-dimensional grid of total vs net information. The x-axis is total information,
#' a measure of how much the variable drives the response (the more predictive, the higher the total information).
#' The y-axis is net information, a measure of how unique the variable is. The top right quadrant of the infogram plot is the admissible section; the variables
#' located in this quadrant are the admissible features. In the Core Infogram, the admissible features are the strongest, unique drivers of
#' the response.
#'
#' If sensitive or protected variables are present in data, the user can specify which attributes should be protected while training using the \code{protected_columns}
#' argument. All non-protected predictor variables will be checked to make sure that there's no information pathway to the response through a protected feature, and
#' deemed inadmissible if they possess little or no informational value beyond their use as a dummy for protected attributes. The Fair Infogram plots all the features
#' as points on two-dimensional grid of relevance vs safety. The x-axis is relevance index, a measure of how much the variable drives the response (the more predictive,
#' the higher the relevance). The y-axis is safety index, a measure of how much extra information the variable has that is not acquired through the protected variables.
#' In the Fair Infogram, the admissible features are the strongest, safest drivers of the response.
#'
#'
#' @param x (Optional) A vector containing the names or indices of the predictor variables to use in building the model.
#' If x is missing, then all columns except y are used.
#' @param y The name or column index of the response variable in the data.
#' The response must be either a numeric or a categorical/factor variable.
#' If the response is numeric, then a regression model will be trained, otherwise it will train a classification model.
#' @param training_frame Id of the training data frame.
#' @param model_id Destination id for this model; auto-generated if not specified.
#' @param validation_frame Id of the validation data frame.
#' @param seed Seed for random numbers (affects certain parts of the algo that are stochastic and those might or might not be enabled by default).
#' Defaults to -1 (time-based random number).
#' @param keep_cross_validation_models \code{Logical}. Whether to keep the cross-validation models. Defaults to TRUE.
#' @param keep_cross_validation_predictions \code{Logical}. Whether to keep the predictions of the cross-validation models. Defaults to FALSE.
#' @param keep_cross_validation_fold_assignment \code{Logical}. Whether to keep the cross-validation fold assignment. Defaults to FALSE.
#' @param nfolds Number of folds for K-fold cross-validation (0 to disable or >= 2). Defaults to 0.
#' @param fold_assignment Cross-validation fold assignment scheme, if fold_column is not specified. The 'Stratified' option will
#' stratify the folds based on the response variable, for classification problems. Must be one of: "AUTO",
#' "Random", "Modulo", "Stratified". Defaults to AUTO.
#' @param fold_column Column with cross-validation fold index assignment per observation.
#' @param ignore_const_cols \code{Logical}. Ignore constant columns. Defaults to TRUE.
#' @param score_each_iteration \code{Logical}. Whether to score during each iteration of model training. Defaults to FALSE.
#' @param offset_column Offset column. This will be added to the combination of columns before applying the link function.
#' @param weights_column Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from
#' the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative
#' weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the
#' data frame. This is typically the number of times a row is repeated, but non-integer values are supported as
#' well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. If
#' you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get
#' an accurate prediction, remove all rows with weight == 0.
#' @param standardize \code{Logical}. Standardize numeric columns to have zero mean and unit variance. Defaults to FALSE.
#' @param distribution Distribution function Must be one of: "AUTO", "bernoulli", "multinomial", "gaussian", "poisson", "gamma",
#' "tweedie", "laplace", "quantile", "huber". Defaults to AUTO.
#' @param plug_values Plug Values (a single row frame containing values that will be used to impute missing values of the
#' training/validation frame, use with conjunction missing_values_handling = PlugValues).
#' @param max_iterations Maximum number of iterations. Defaults to 0.
#' @param stopping_rounds Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the
#' stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable) Defaults to 0.
#' @param stopping_metric Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anomaly_score
#' for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python
#' client. Must be one of: "AUTO", "deviance", "logloss", "MSE", "RMSE", "MAE", "RMSLE", "AUC", "AUCPR",
#' "lift_top_group", "misclassification", "mean_per_class_error", "custom", "custom_increasing". Defaults to
#' AUTO.
#' @param stopping_tolerance Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this
#' much) Defaults to 0.001.
#' @param balance_classes \code{Logical}. Balance training data class counts via over/under-sampling (for imbalanced data). Defaults to
#' FALSE.
#' @param class_sampling_factors Desired over/under-sampling ratios per class (in lexicographic order). If not specified, sampling factors will
#' be automatically computed to obtain class balance during training. Requires balance_classes.
#' @param max_after_balance_size Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires
#' balance_classes. Defaults to 5.0.
#' @param max_runtime_secs Maximum allowed runtime in seconds for model training. Use 0 to disable. Defaults to 0.
#' @param custom_metric_func Reference to custom evaluation function, format: `language:keyName=funcName`
#' @param auc_type Set default multinomial AUC type. Must be one of: "AUTO", "NONE", "MACRO_OVR", "WEIGHTED_OVR", "MACRO_OVO",
#' "WEIGHTED_OVO". Defaults to AUTO.
#' @param algorithm Type of machine learning algorithm used to build the infogram. Options include 'AUTO' (gbm), 'deeplearning'
#' (Deep Learning with default parameters), 'drf' (Random Forest with default parameters), 'gbm' (GBM with
#' default parameters), 'glm' (GLM with default parameters), or 'xgboost' (if available, XGBoost with default
#' parameters). Must be one of: "AUTO", "deeplearning", "drf", "gbm", "glm", "xgboost". Defaults to AUTO.
#' @param algorithm_params Customized parameters for the machine learning algorithm specified in the algorithm parameter.
#' @param protected_columns Columns that contain features that are sensitive and need to be protected (legally, or otherwise), if
#' applicable. These features (e.g. race, gender, etc) should not drive the prediction of the response.
#' @param total_information_threshold A number between 0 and 1 representing a threshold for total information, defaulting to 0.1. For a specific
#' feature, if the total information is higher than this threshold, and the corresponding net information is also
#' higher than the threshold ``net_information_threshold``, that feature will be considered admissible. The total
#' information is the x-axis of the Core Infogram. Default is -1 which gets set to 0.1. Defaults to -1.
#' @param net_information_threshold A number between 0 and 1 representing a threshold for net information, defaulting to 0.1. For a specific
#' feature, if the net information is higher than this threshold, and the corresponding total information is also
#' higher than the total_information_threshold, that feature will be considered admissible. The net information
#' is the y-axis of the Core Infogram. Default is -1 which gets set to 0.1. Defaults to -1.
#' @param relevance_index_threshold A number between 0 and 1 representing a threshold for the relevance index, defaulting to 0.1. This is only
#' used when ``protected_columns`` is set by the user. For a specific feature, if the relevance index value is
#' higher than this threshold, and the corresponding safety index is also higher than the
#' safety_index_threshold``, that feature will be considered admissible. The relevance index is the x-axis of
#' the Fair Infogram. Default is -1 which gets set to 0.1. Defaults to -1.
#' @param safety_index_threshold A number between 0 and 1 representing a threshold for the safety index, defaulting to 0.1. This is only used
#' when protected_columns is set by the user. For a specific feature, if the safety index value is higher than
#' this threshold, and the corresponding relevance index is also higher than the relevance_index_threshold, that
#' feature will be considered admissible. The safety index is the y-axis of the Fair Infogram. Default is -1
#' which gets set to 0.1. Defaults to -1.
#' @param data_fraction The fraction of training frame to use to build the infogram model. Defaults to 1.0, and any value greater than
#' 0 and less than or equal to 1.0 is acceptable. Defaults to 1.
#' @param top_n_features An integer specifying the number of columns to evaluate in the infogram. The columns are ranked by variable
#' importance, and the top N are evaluated. Defaults to 50. Defaults to 50.
#' @examples
#' \dontrun{
#' h2o.init()
#'
#' # Convert iris dataset to an H2OFrame
#' df <- as.h2o(iris)
#'
#' # Infogram
#' ig <- h2o.infogram(y = "Species", training_frame = df)
#' plot(ig)
#'
#' }
#' @export
h2o.infogram <- function(x,
y,
training_frame,
model_id = NULL,
validation_frame = NULL,
seed = -1,
keep_cross_validation_models = TRUE,
keep_cross_validation_predictions = FALSE,
keep_cross_validation_fold_assignment = FALSE,
nfolds = 0,
fold_assignment = c("AUTO", "Random", "Modulo", "Stratified"),
fold_column = NULL,
ignore_const_cols = TRUE,
score_each_iteration = FALSE,
offset_column = NULL,
weights_column = NULL,
standardize = FALSE,
distribution = c("AUTO", "bernoulli", "multinomial", "gaussian", "poisson", "gamma", "tweedie", "laplace", "quantile", "huber"),
plug_values = NULL,
max_iterations = 0,
stopping_rounds = 0,
stopping_metric = c("AUTO", "deviance", "logloss", "MSE", "RMSE", "MAE", "RMSLE", "AUC", "AUCPR", "lift_top_group", "misclassification", "mean_per_class_error", "custom", "custom_increasing"),
stopping_tolerance = 0.001,
balance_classes = FALSE,
class_sampling_factors = NULL,
max_after_balance_size = 5.0,
max_runtime_secs = 0,
custom_metric_func = NULL,
auc_type = c("AUTO", "NONE", "MACRO_OVR", "WEIGHTED_OVR", "MACRO_OVO", "WEIGHTED_OVO"),
algorithm = c("AUTO", "deeplearning", "drf", "gbm", "glm", "xgboost"),
algorithm_params = NULL,
protected_columns = NULL,
total_information_threshold = -1,
net_information_threshold = -1,
relevance_index_threshold = -1,
safety_index_threshold = -1,
data_fraction = 1,
top_n_features = 50)
{
# Validate required training_frame first and other frame args: should be a valid key or an H2OFrame object
training_frame <- .validate.H2OFrame(training_frame, required=TRUE)
validation_frame <- .validate.H2OFrame(validation_frame, required=FALSE)
# Validate other required args
# If x is missing, then assume user wants to use all columns as features.
if (missing(x)) {
if (is.numeric(y)) {
x <- setdiff(col(training_frame), y)
} else {
x <- setdiff(colnames(training_frame), y)
}
}
# Build parameter list to send to model builder
parms <- list()
parms$training_frame <- training_frame
args <- .verify_dataxy(training_frame, x, y)
if (missing(protected_columns)) {
# core infogram
if (!missing(safety_index_threshold)) {
warning("Should not set safety_index_threshold for Core Infogram runs. Set net_information_threshold instead.")
}
if (!missing(relevance_index_threshold)) {
warning("Should not set relevance_index_threshold for Core Infogram runs. Set total_information_threshold instead.")
}
} else {
# fair infogram
if (!missing(net_information_threshold)) {
warning("Should not set net_information_threshold for Fair Infogram runs, set safety_index_threshold instead.")
}
if (!missing(total_information_threshold)) {
warning("Should not set total_information_threshold for Fair Infogram runs, set relevance_index_threshold instead.")
}
}
if (!missing(offset_column) && !is.null(offset_column)) args$x_ignore <- args$x_ignore[!( offset_column == args$x_ignore )]
if (!missing(weights_column) && !is.null(weights_column)) args$x_ignore <- args$x_ignore[!( weights_column == args$x_ignore )]
if (!missing(fold_column) && !is.null(fold_column)) args$x_ignore <- args$x_ignore[!( fold_column == args$x_ignore )]
parms$ignored_columns <- args$x_ignore
parms$response_column <- args$y
if (!missing(model_id))
parms$model_id <- model_id
if (!missing(validation_frame))
parms$validation_frame <- validation_frame
if (!missing(seed))
parms$seed <- seed
if (!missing(keep_cross_validation_models))
parms$keep_cross_validation_models <- keep_cross_validation_models
if (!missing(keep_cross_validation_predictions))
parms$keep_cross_validation_predictions <- keep_cross_validation_predictions
if (!missing(keep_cross_validation_fold_assignment))
parms$keep_cross_validation_fold_assignment <- keep_cross_validation_fold_assignment
if (!missing(nfolds))
parms$nfolds <- nfolds
if (!missing(fold_assignment))
parms$fold_assignment <- fold_assignment
if (!missing(fold_column))
parms$fold_column <- fold_column
if (!missing(ignore_const_cols))
parms$ignore_const_cols <- ignore_const_cols
if (!missing(score_each_iteration))
parms$score_each_iteration <- score_each_iteration
if (!missing(offset_column))
parms$offset_column <- offset_column
if (!missing(weights_column))
parms$weights_column <- weights_column
if (!missing(standardize))
parms$standardize <- standardize
if (!missing(distribution))
parms$distribution <- distribution
if (!missing(plug_values))
parms$plug_values <- plug_values
if (!missing(max_iterations))
parms$max_iterations <- max_iterations
if (!missing(stopping_rounds))
parms$stopping_rounds <- stopping_rounds
if (!missing(stopping_metric))
parms$stopping_metric <- stopping_metric
if (!missing(stopping_tolerance))
parms$stopping_tolerance <- stopping_tolerance
if (!missing(balance_classes))
parms$balance_classes <- balance_classes
if (!missing(class_sampling_factors))
parms$class_sampling_factors <- class_sampling_factors
if (!missing(max_after_balance_size))
parms$max_after_balance_size <- max_after_balance_size
if (!missing(max_runtime_secs))
parms$max_runtime_secs <- max_runtime_secs
if (!missing(custom_metric_func))
parms$custom_metric_func <- custom_metric_func
if (!missing(auc_type))
parms$auc_type <- auc_type
if (!missing(algorithm))
parms$algorithm <- algorithm
if (!missing(protected_columns))
parms$protected_columns <- protected_columns
if (!missing(total_information_threshold))
parms$total_information_threshold <- total_information_threshold
if (!missing(net_information_threshold))
parms$net_information_threshold <- net_information_threshold
if (!missing(relevance_index_threshold))
parms$relevance_index_threshold <- relevance_index_threshold
if (!missing(safety_index_threshold))
parms$safety_index_threshold <- safety_index_threshold
if (!missing(data_fraction))
parms$data_fraction <- data_fraction
if (!missing(top_n_features))
parms$top_n_features <- top_n_features
if (!missing(algorithm_params))
parms$algorithm_params <- as.character(toJSON(algorithm_params, pretty = TRUE))
# Error check and build model
model <- .h2o.modelJob('infogram', parms, h2oRestApiVersion=3, verbose=FALSE)
# Convert algorithm_params back to list if not NULL, added after obtaining model
if (!missing(algorithm_params)) {
model@parameters$algorithm_params <- list(fromJSON(model@parameters$algorithm_params))[[1]] #Need the `[[ ]]` to avoid a nested list
}
infogram_model <- new("H2OInfogram", model_id=model@model_id)
model <- infogram_model
return(model)
}
.h2o.train_segments_infogram <- function(x,
y,
training_frame,
validation_frame = NULL,
seed = -1,
keep_cross_validation_models = TRUE,
keep_cross_validation_predictions = FALSE,
keep_cross_validation_fold_assignment = FALSE,
nfolds = 0,
fold_assignment = c("AUTO", "Random", "Modulo", "Stratified"),
fold_column = NULL,
ignore_const_cols = TRUE,
score_each_iteration = FALSE,
offset_column = NULL,
weights_column = NULL,
standardize = FALSE,
distribution = c("AUTO", "bernoulli", "multinomial", "gaussian", "poisson", "gamma", "tweedie", "laplace", "quantile", "huber"),
plug_values = NULL,
max_iterations = 0,
stopping_rounds = 0,
stopping_metric = c("AUTO", "deviance", "logloss", "MSE", "RMSE", "MAE", "RMSLE", "AUC", "AUCPR", "lift_top_group", "misclassification", "mean_per_class_error", "custom", "custom_increasing"),
stopping_tolerance = 0.001,
balance_classes = FALSE,
class_sampling_factors = NULL,
max_after_balance_size = 5.0,
max_runtime_secs = 0,
custom_metric_func = NULL,
auc_type = c("AUTO", "NONE", "MACRO_OVR", "WEIGHTED_OVR", "MACRO_OVO", "WEIGHTED_OVO"),
algorithm = c("AUTO", "deeplearning", "drf", "gbm", "glm", "xgboost"),
algorithm_params = NULL,
protected_columns = NULL,
total_information_threshold = -1,
net_information_threshold = -1,
relevance_index_threshold = -1,
safety_index_threshold = -1,
data_fraction = 1,
top_n_features = 50,
segment_columns = NULL,
segment_models_id = NULL,
parallelism = 1)
{
# formally define variables that were excluded from function parameters
model_id <- NULL
verbose <- NULL
destination_key <- NULL
# Validate required training_frame first and other frame args: should be a valid key or an H2OFrame object
training_frame <- .validate.H2OFrame(training_frame, required=TRUE)
validation_frame <- .validate.H2OFrame(validation_frame, required=FALSE)
# Validate other required args
# If x is missing, then assume user wants to use all columns as features.
if (missing(x)) {
if (is.numeric(y)) {
x <- setdiff(col(training_frame), y)
} else {
x <- setdiff(colnames(training_frame), y)
}
}
# Build parameter list to send to model builder
parms <- list()
parms$training_frame <- training_frame
args <- .verify_dataxy(training_frame, x, y)
if (missing(protected_columns)) {
# core infogram
if (!missing(safety_index_threshold)) {
warning("Should not set safety_index_threshold for Core Infogram runs. Set net_information_threshold instead.")
}
if (!missing(relevance_index_threshold)) {
warning("Should not set relevance_index_threshold for Core Infogram runs. Set total_information_threshold instead.")
}
} else {
# fair infogram
if (!missing(net_information_threshold)) {
warning("Should not set net_information_threshold for Fair Infogram runs, set safety_index_threshold instead.")
}
if (!missing(total_information_threshold)) {
warning("Should not set total_information_threshold for Fair Infogram runs, set relevance_index_threshold instead.")
}
}
if (!missing(offset_column) && !is.null(offset_column)) args$x_ignore <- args$x_ignore[!( offset_column == args$x_ignore )]
if (!missing(weights_column) && !is.null(weights_column)) args$x_ignore <- args$x_ignore[!( weights_column == args$x_ignore )]
if (!missing(fold_column) && !is.null(fold_column)) args$x_ignore <- args$x_ignore[!( fold_column == args$x_ignore )]
parms$ignored_columns <- args$x_ignore
parms$response_column <- args$y
if (!missing(validation_frame))
parms$validation_frame <- validation_frame
if (!missing(seed))
parms$seed <- seed
if (!missing(keep_cross_validation_models))
parms$keep_cross_validation_models <- keep_cross_validation_models
if (!missing(keep_cross_validation_predictions))
parms$keep_cross_validation_predictions <- keep_cross_validation_predictions
if (!missing(keep_cross_validation_fold_assignment))
parms$keep_cross_validation_fold_assignment <- keep_cross_validation_fold_assignment
if (!missing(nfolds))
parms$nfolds <- nfolds
if (!missing(fold_assignment))
parms$fold_assignment <- fold_assignment
if (!missing(fold_column))
parms$fold_column <- fold_column
if (!missing(ignore_const_cols))
parms$ignore_const_cols <- ignore_const_cols
if (!missing(score_each_iteration))
parms$score_each_iteration <- score_each_iteration
if (!missing(offset_column))
parms$offset_column <- offset_column
if (!missing(weights_column))
parms$weights_column <- weights_column
if (!missing(standardize))
parms$standardize <- standardize
if (!missing(distribution))
parms$distribution <- distribution
if (!missing(plug_values))
parms$plug_values <- plug_values
if (!missing(max_iterations))
parms$max_iterations <- max_iterations
if (!missing(stopping_rounds))
parms$stopping_rounds <- stopping_rounds
if (!missing(stopping_metric))
parms$stopping_metric <- stopping_metric
if (!missing(stopping_tolerance))
parms$stopping_tolerance <- stopping_tolerance
if (!missing(balance_classes))
parms$balance_classes <- balance_classes
if (!missing(class_sampling_factors))
parms$class_sampling_factors <- class_sampling_factors
if (!missing(max_after_balance_size))
parms$max_after_balance_size <- max_after_balance_size
if (!missing(max_runtime_secs))
parms$max_runtime_secs <- max_runtime_secs
if (!missing(custom_metric_func))
parms$custom_metric_func <- custom_metric_func
if (!missing(auc_type))
parms$auc_type <- auc_type
if (!missing(algorithm))
parms$algorithm <- algorithm
if (!missing(protected_columns))
parms$protected_columns <- protected_columns
if (!missing(total_information_threshold))
parms$total_information_threshold <- total_information_threshold
if (!missing(net_information_threshold))
parms$net_information_threshold <- net_information_threshold
if (!missing(relevance_index_threshold))
parms$relevance_index_threshold <- relevance_index_threshold
if (!missing(safety_index_threshold))
parms$safety_index_threshold <- safety_index_threshold
if (!missing(data_fraction))
parms$data_fraction <- data_fraction
if (!missing(top_n_features))
parms$top_n_features <- top_n_features
if (!missing(algorithm_params))
parms$algorithm_params <- as.character(toJSON(algorithm_params, pretty = TRUE))
# Build segment-models specific parameters
segment_parms <- list()
if (!missing(segment_columns))
segment_parms$segment_columns <- segment_columns
if (!missing(segment_models_id))
segment_parms$segment_models_id <- segment_models_id
segment_parms$parallelism <- parallelism
# Error check and build segment models
segment_models <- .h2o.segmentModelsJob('infogram', segment_parms, parms, h2oRestApiVersion=3)
return(segment_models)
}
#' Plot an H2O Infogram
#'
#' Plots the Infogram for an H2OInfogram object.
#'
#' @param x A fitted \linkS4class{H2OInfogram} object.
#' @param ... additional arguments to pass on.
#' @return A ggplot2 object.
#' @seealso \code{\link{h2o.infogram}}
#' @examples
#' \dontrun{
#' h2o.init()
#'
#' # Convert iris dataset to an H2OFrame
#' train <- as.h2o(iris)
#'
#' # Create and plot infogram
#' ig <- h2o.infogram(y = "Species", training_frame = train)
#' plot(ig)
#'
#' }
#' @method plot H2OInfogram
#' @export
plot.H2OInfogram <- function(x, ...) {
.check_for_ggplot2() # from explain.R
.data <- NULL
varargs <- list(...)
if ("title" %in% names(varargs)) {
title <- varargs$title
} else {
title <- "Infogram"
}
if ("total_information" %in% names(x@admissible_score)) {
# core infogram
xlab <- "Total Information"
ylab <- "Net Information"
xthresh <- x@total_information_threshold
ythresh <- x@net_information_threshold
} else {
# fair infogram
xlab <- "Relevance Index"
ylab <- "Safety Index"
xthresh <- x@relevance_index_threshold
ythresh <- x@safety_index_threshold
}
df <- as.data.frame(x@admissible_score)
# use generic names for x, y for easier ggplot code
names(df) <- c("column",
"admissible",
"admissible_index",
"ig_x",
"ig_y",
"raw")
ggplot2::ggplot(data = df, ggplot2::aes_(~ig_x, ~ig_y)) +
ggplot2::geom_point() +
ggplot2::geom_polygon(ggplot2::aes(.data$x_coordinates, .data$y_coordinates), data = data.frame(
x_coordinates = c(xthresh, xthresh, -Inf, -Inf, Inf, Inf, xthresh),
y_coordinates = c(ythresh, Inf, Inf, -Inf, -Inf, ythresh, ythresh)
), alpha = 0.1, fill = "#CC663E") +
ggplot2::geom_path(ggplot2::aes(.data$x_coordinates, .data$y_coordinates), data = data.frame(
x_coordinates = c(xthresh, xthresh, NA, xthresh, Inf),
y_coordinates = c(ythresh, Inf, NA, ythresh, ythresh)
), color = "red", linetype = "dashed") +
ggplot2::geom_text(ggplot2::aes_(~ig_x, ~ig_y, label = ~column),
data = df[as.logical(df$admissible),], nudge_y = -0.0325,
color = "blue", size = 2.5) +
ggplot2::xlab(xlab) +
ggplot2::ylab(ylab) +
ggplot2::coord_fixed(xlim = c(0, 1.1), ylim = c(0, 1.1), expand = FALSE) +
ggplot2::theme_bw() +
ggplot2::ggtitle(title) +
ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
}
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