RemixAutoML (version 0.5.0)

AutoClusteringScoring: AutoClusteringScoring

Description

AutoClusteringScoring adds a column to your original data with a cluster number identifier. You can run request an autoencoder to be built to reduce the dimensionality of your data before running the clusering algo.

Usage

AutoClusteringScoring(
  data,
  FeatureColumns = NULL,
  ModelID = "TestModel",
  SavePath = NULL,
  NThreads = 8,
  MaxMemory = "28G",
  DimReduction = TRUE
)

Arguments

data

is the source time series data.table

FeatureColumns

Independent variables

ModelID

This is returned from the training run in the output list with element named 'model_name'. It's not identical to the ModelID used in training due to the grid tuning.

SavePath

Directory path for saving models

NThreads

set based on number of threads your machine has available

MaxMemory

set based on the amount of memory your machine has available

DimReduction

Set to TRUE if you set RunDimReduction in the training version of this function

Value

Original data.table with added column with cluster number identifier

See Also

Other Unsupervised Learning: AutoClustering(), GenTSAnomVars(), H2OIsolationForestScoring(), H2OIsolationForest(), ResidualOutliers()

Examples

# NOT RUN {
#########################
# Training Setup
#########################

# Create fake data
data <- RemixAutoML::FakeDataGenerator(
  Correlation = 0.85,
  N = 1000,
  ID = 2,
  ZIP = 0,
  AddDate = TRUE,
  Classification = FALSE,
  MultiClass = FALSE)

# Run function
data <- RemixAutoML::AutoClustering(
  data,
  FeatureColumns = names(data)[2:(ncol(data)-1)],
  ModelID = "TestModel",
  SavePath = getwd(),
  NThreads = 8,
  MaxMemory = "28G",
  MaxClusters = 50,
  ClusterMetric = "totss",
  RunDimReduction = TRUE,
  ShrinkRate = (sqrt(5) - 1) / 2,
  Epochs = 5L,
  L2_Reg = 0.10,
  ElasticAveraging = TRUE,
  ElasticAveragingMovingRate = 0.90,
  ElasticAveragingRegularization = 0.001)

#########################
# Scoring Setup
#########################

Sys.sleep(10)

# Create fake data
data <- RemixAutoML::FakeDataGenerator(
  Correlation = 0.85,
  N = 1000,
  ID = 2,
  ZIP = 0,
  AddDate = TRUE,
  Classification = FALSE,
  MultiClass = FALSE)

# Run function
data <- RemixAutoML::AutoClusteringScoring(
  data,
  FeatureColumns = names(data)[2:(ncol(data)-1)],
  ModelID = "TestModel",
  SavePath = getwd(),
  NThreads = 8,
  MaxMemory = "28G",
  DimReduction = TRUE)
# }
# NOT RUN {
# }