scoreHVT: Score which cell each point in the test dataset belongs to.

In HVT: Constructing Hierarchical Voronoi Tessellations and Overlay Heatmaps for Data Analysis

Score which cell each point in the test dataset belongs to.

Description

This function scores each data point in the test dataset based on a trained hierarchical Voronoi tessellations model.

Usage

scoreHVT(
  dataset,
  hvt.results.model,
  child.level = 1,
  mad.threshold = 0.2,
  line.width = 0.6,
  color.vec = c("navyblue", "slateblue", "lavender"),
  normalize = TRUE,
  distance_metric = "L1_Norm",
  error_metric = "max",
  yVar = NULL,
  analysis.plots = FALSE,
  names.column = NULL
)

Arguments

dataset	Data frame. A data frame which to be scored. Can have categorical columns if 'analysis.plots' are required.
hvt.results.model	List. A list obtained from the trainHVT function
child.level	Numeric. A number indicating the depth for which the heat map is to be plotted.
mad.threshold	Numeric. A numeric value indicating the permissible Mean Absolute Deviation.
line.width	Vector. A vector indicating the line widths of the tessellation boundaries for each layer.
color.vec	Vector. A vector indicating the colors of the tessellation boundaries at each layer.
normalize	Logical. A logical value indicating if the dataset should be normalized. When set to TRUE, the data (testing dataset) is standardized by ‘mean’ and ‘sd’ of the training dataset referred from the trainHVT(). When set to FALSE, the data is used as such without any changes.
distance_metric	Character. The distance metric can be L1_Norm(Manhattan) or L2_Norm(Eucledian). L1_Norm is selected by default. The distance metric is used to calculate the distance between an n dimensional point and centroid. The distance metric can be different from the one used during training.
error_metric	Character. The error metric can be mean or max. max is selected by default. max will return the max of m values and mean will take mean of m values where each value is a distance between a point and centroid of the cell.
yVar	Character. A character or a vector representing the name of the dependent variable(s)
analysis.plots	Logical. A logical value indicating that the scored plot should be plotted or not. If TRUE, the identifier column(character column) name should be supplied in 'names.column' argument. The output will be a 2D heatmap plotly which gives info on the cell id and the observations of a cell.
names.column	Character. A character or a vector representing the name of the identifier column/character column.

Value

Dataframe containing scored data, plots and summary

Author(s)

Shubhra Prakash <[e-mail]>, Sangeet Moy Das <[e-mail]> , Vishwavani <[e-mail]>

See Also

trainHVT
plotHVT

Examples

data("EuStockMarkets")
dataset <- data.frame(date = as.numeric(time(EuStockMarkets)),
                     DAX = EuStockMarkets[, "DAX"],
                     SMI = EuStockMarkets[, "SMI"],
                     CAC = EuStockMarkets[, "CAC"],
                     FTSE = EuStockMarkets[, "FTSE"])
rownames(EuStockMarkets) <- dataset$date
# Split in train and test
train <- EuStockMarkets[1:1302, ]
test <- EuStockMarkets[1303:1860, ]
#model training
hvt.results<- trainHVT(train,n_cells = 60, depth = 1, quant.err = 0.1,
                      distance_metric = "L1_Norm", error_metric = "max",
                      normalize = TRUE,quant_method = "kmeans")
scoring <- scoreHVT(test, hvt.results)
data_scored <- scoring$scoredPredictedData

HVT documentation built on April 3, 2025, 8:45 p.m.