RemixAutoML (version 0.5.0)

H2OIsolationForestScoring: H2OIsolationForestScoring

Description

H2OIsolationForestScoring for dimensionality reduction and / or anomaly detection scoring on new data

Usage

H2OIsolationForestScoring(
  data,
  Features = NULL,
  IDcols = NULL,
  H2OStart = TRUE,
  H2OShutdown = TRUE,
  ModelID = "TestModel",
  SavePath = NULL,
  Threshold = 0.975,
  MaxMem = "28G",
  NThreads = -1,
  Debug = FALSE
)

Arguments

data

The data.table with the columns you wish to have analyzed

Features

A character vector with the column names to utilize in the isolation forest

IDcols

A character vector with the column names to not utilize in the isolation forest but have returned with the data output. Otherwise those columns will be removed

H2OStart

TRUE to have H2O started inside function

H2OShutdown

TRUE to shutdown H2O inside function

ModelID

Name for model that gets saved to file if SavePath is supplied and valid

SavePath

Path directory to store saved model

Threshold

Quantile value to find the cutoff value for classifying outliers

MaxMem

Specify the amount of memory to allocate to H2O. E.g. "28G"

NThreads

Specify the number of threads (E.g. cores * 2)

Debug

Debugging

Value

Source data.table with predictions. Note that any columns not listed in Features nor IDcols will not be returned with data. If you want columns returned but not modeled, supply them as IDcols

See Also

Other Unsupervised Learning: AutoClusteringScoring(), AutoClustering(), GenTSAnomVars(), H2OIsolationForest(), ResidualOutliers()

Examples

# NOT RUN {
# Create simulated data
data <- RemixAutoML::FakeDataGenerator(
  Correlation = 0.70,
  N = 50000,
  ID = 2L,
  FactorCount = 2L,
  AddDate = TRUE,
  ZIP = 0L,
  TimeSeries = FALSE,
  ChainLadderData = FALSE,
  Classification = FALSE,
  MultiClass = FALSE)

# Run algo
data <- RemixAutoML::H2OIsolationForest(
  data,
  Features = names(data)[2L:ncol(data)],
  IDcols = c("Adrian", "IDcol_1", "IDcol_2"),
  ModelID = "Adrian",
  SavePath = getwd(),
  Threshold = 0.95,
  MaxMem = "28G",
  NThreads = -1,
  NTrees = 100,
  SampleRate = (sqrt(5)-1)/2,
  MaxDepth = 8,
  MinRows = 1,
  ColSampleRate = 1,
  ColSampleRatePerLevel = 1,
  ColSampleRatePerTree = 1,
  CategoricalEncoding = c("AUTO"),
  Debug = TRUE)

# Remove output from data and then score
data[, eval(names(data)[17:ncol(data)]) := NULL]

# Run algo
Outliers <- RemixAutoML::H2OIsolationForestScoring(
  data,
  Features = names(data)[2:ncol(data)],
  IDcols = c("Adrian", "IDcol_1", "IDcol_2"),
  H2OStart = TRUE,
  H2OShutdown = TRUE,
  ModelID = "TestModel",
  SavePath = getwd(),
  Threshold = 0.95,
  MaxMem = "28G",
  NThreads = -1,
  Debug = FALSE)
# }