scoreHVT {HVT}

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(
  data,
  hvt.results.model,
  child.level = 1,
  mad.threshold = 0.2,
  line.width = c(0.6, 0.4, 0.2),
  color.vec = c("navyblue", "slateblue", "lavender"),
  normalize = TRUE,
  seed = 300,
  distance_metric = "L1_Norm",
  error_metric = "max",
  yVar = NULL
)

Arguments

data

Data frame. A data frame containing the test dataset.

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.

seed

Numeric. Random Seed to preserve the repeatability

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)

Value

Dataframe containing scored data, plots and summary

Author(s)

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