TrainIsolationForest Task

This task executes the Isolation Forest anomaly detection algorithm against the provided input training rasters. The IsolationForest task isolates detections by randomly selecting a feature, then randomly selecting a split value between the maximum and minimum values of the selected feature.

For background on the algorithm used, see Isolation Forest Classification.

Example

; Start the application

e = ENVI()

; Open an input raster file

RasterFile = Filepath('qb_boulder_msi', Subdir=['data'], $

  Root_Dir=e.Root_Dir)

Raster = e.OpenRaster(RasterFile)

; Get the Spectral Index task from the catalog of ENVITasks

SpectralTask=ENVITask('SpectralIndex')

; Define inputs

SpectralTask.INDEX = 'Normalized Difference Vegetation Index'

SpectralTask.INPUT_RASTER = Raster

; Run the task

SpectralTask.Execute

; Get the Image Threshold ROI task from the catalog of ENVITasks

ThresholdROITask=ENVITask('ImageThresholdToROI')

; Define inputs

ThresholdROITask.INPUT_RASTER = SpectralTask.OUTPUT_RASTER

ThresholdROITask.ROI_NAME = 'Water'

ThresholdROITask.ROI_COLOR = [0, 0, 255]

ThresholdROITask.THRESHOLD = [-1, -0.10000000149012, 0]

; Run the task

ThresholdROITask.Execute

; Get the statistics task from the catalog of ENVITasks

StatsTask = ENVITask('NormalizationStatistics')

; Define inputs

StatsTask.INPUT_RASTERS = Raster

; Run the task

StatsTask.Execute

; Get the data prep task from the catalog of ENVITasks

DataPrepTask = ENVITask('MLTrainingDataFromROIs')

; Define inputs

DataPrepTask.INPUT_RASTER = Raster

DataPrepTask.INPUT_ROI = ThresholdROITask.OUTPUT_ROI

DataPrepTask.BACKGROUND_LABELS = []

DataPrepTask.NORMALIZE_MIN_MAX = StatsTask.Normalization

DataPrepTask.Execute

; Get the training task from the catalog of ENVITasks

TrainTask = ENVITask('TrainIsolationForest')

; Define inputs

TrainTask.INPUT_RASTER = DataPrepTask.OUTPUT_RASTER

TrainTask.NUM_ESTIMATORS = 100

; Run the task

TrainTask.Execute

; Output model metadata

outputModelUri = TrainTask.OUTPUT_MODEL_URI

print, 'Model URI: ' + outputModelUri

outputModel = TrainTask.OUTPUT_MODEL

print, outputModel.Attributes

Syntax

Result = ENVITask('TrainIsolationForest')

Input properties (Set, Get): BALANCE_CLASSES, INPUT_RASTERS, MODEL_NAME, MODEL_DESCRIPTION, NUM_ESTIMATORS, OUTPUT_MODEL_URI

Output properties (Get only): OUTPUT_MODEL

Properties marked as "Set" are those that you can set to specific values. You can also retrieve their current values any time. Properties marked as "Get" are those whose values you can retrieve but not set.

Methods

This task inherits the following methods from ENVITask. See the ENVITask topic in ENVI Help.

AddParameter
Execute
Parameter
ParameterNames
RemoveParameters

Properties

This task inherits the following properties from ENVITask:

COMMUTE_ON_DOWNSAMPLE

COMMUTE_ON_SUBSET

DESCRIPTION

DISPLAY_NAME

NAME

REVISION

See the ENVITask topic in ENVI Help for details.

This task also contains the following properties:

BALANCE_CLASSES (optional)

Specify whether all classes should be considered equal during training. This helps to account for classes with few samples compared to classes with many samples.

INPUT_RASTERS (required)

Specify one or more preprocessed training rasters to be used for training.

MODEL_NAME (optional)

Specify the name of the model. The default is Isolation Forest Anomaly Detector.

MODEL_DESCRIPTION (optional)

Specify the purpose of the model.

NUM_ESTIMATORS (optional)

Specify the number of decision trees to use. The estimators are the predictors of the algorithm. The default is 100.

OUTPUT_MODEL (required)

This is a reference to the output model file.

OUTPUT_MODEL_URI (optional)

Specify a string with the fully qualified filename and path of the associated OUTPUT_MODEL. If you do not specify this property, or set it to an exclamation symbol (!), a temporary file will be created.

Version History

Deep Learning 2.0

Introduced