README.md

In pridiltal/outfit: Outlier Detection in Multivariate Functional Data

outfit

The goal of outfit is to detect outliers in multivariate functional data

Installation

You can install the released version of outfit from CRAN with:

install.packages("outfit")

And the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("pridiltal/outfit")

Explore utilization_curves dataset

library(outfit)
library(tidyverse)
## basic example code
head(utilization_curves)
#>   basisid id         cpu   y             subspaceid
#> 1       1  1 0.001007227 129 1 constant_scale_shape
#> 2       1  1 0.002784417 131 1 constant_scale_shape
#> 3       1  1 0.003465359 105 1 constant_scale_shape
#> 4       1  1 0.005992975  96 1 constant_scale_shape
#> 5       1  1 0.010085913  97 1 constant_scale_shape
#> 6       1  1 0.013900140 113 1 constant_scale_shape

p <- utilization_curves %>%
  ggplot(aes(x = cpu, y = y, group = id, color= subspaceid)) +
  geom_line() +
  facet_wrap(~subspaceid, scales = "free_y", nrow = 2) +
  theme(text = element_text(size=10), legend.position = "bottom" )+
  scale_color_viridis_d()

print(p)

frq <- utilization_curves %>%
  dplyr::select(id, subspaceid) %>%
  unique() %>% 
  group_by(subspaceid) %>% 
  tally()

print(frq)
#> # A tibble: 8 x 2
#>   subspaceid                   n
#>   <chr>                    <int>
#> 1 0 anomaly_location_shift     1
#> 2 0 anomaly_rw                 1
#> 3 0 anomaly_scale_shift        2
#> 4 0 anomaly_shape_shift        1
#> 5 1 constant_scale_shape      75
#> 6 2 constant_scale_shape      75
#> 7 3 constant_scale_shape      75
#> 8 4 constant_scale_shape      75

Outlying Series

types <- unique(utilization_curves$subspaceid)

out_data <- utilization_curves %>%
  filter(subspaceid %in% c("0 anomaly_location_shift", "0 anomaly_scale_shift", "0 anomaly_shape_shift", "0 anomaly_rw" ))

p <- out_data %>%
  ggplot(aes(x = cpu, y = y, group = subspaceid, color= subspaceid)) +
  geom_line() +
  facet_wrap(vars(subspaceid, id), scales = "free_y", nrow = 2) +
  theme(text = element_text(size=10), legend.position = "bottom" )+
  scale_color_viridis_d()
p

Extract features

library(GGally)
data <- utilization_curves %>%
  dplyr::select(id, y)
features <- outfit::get_features(data = data, family = "NO")

d <- utilization_curves %>%
  dplyr::select(id, subspaceid) %>%
  unique()

features <- dplyr::full_join(features, d, by = "id" )



p <- GGally::ggparcoord(features,
  columns = 2: (ncol(features)-1), groupColumn = ncol(features), order = "Outlying",
  showPoints = TRUE,
  alphaLines = 0.4, scale = "uniminmax"
) +
  #scale_color_brewer(palette = "RdYlGn") +
  scale_color_viridis_d() +
  theme(legend.position = "bottom") +
  xlab("Features") +
  ylab("Values")

# plotly::ggplotly(p)
print(p)

out <- stray::find_HDoutliers(features[, 2:(ncol(features)-1)], alpha = 0.01
)

utilization_curves %>%
  filter(id %in%(out$outliers)) %>%
  select(id, subspaceid) %>%
  unique()
#>    id   subspaceid
#> 1 305 0 anomaly_rw

score <- data.frame(id = 1:nrow(features), score = out$out_scores)

d <- utilization_curves %>%
  dplyr::select(id, subspaceid) %>%
  unique()

data_score <- full_join(d,score, by = "id")
data_score %>% arrange(desc(score)) %>% head(10)
#>     id               subspaceid     score
#> 1  305             0 anomaly_rw 1.5584558
#> 2  281   4 constant_scale_shape 0.8457432
#> 3  301 0 anomaly_location_shift 0.6165525
#> 4  202   1 constant_scale_shape 0.4346108
#> 5  204   1 constant_scale_shape 0.4205720
#> 6  212   1 constant_scale_shape 0.4158072
#> 7  214   1 constant_scale_shape 0.3613625
#> 8  279   4 constant_scale_shape 0.3202449
#> 9  296   4 constant_scale_shape 0.2727066
#> 10 234   2 constant_scale_shape 0.2691825

pridiltal/outfit documentation built on Aug. 13, 2020, 12:32 a.m.