CrossValidationResult

Résumé

The CrossValidationResult class is returned by the Cross Validation tool and contains access to the cross-validation results that can be generated for any geostatistical layer.

Discussion

The CrossValidationResult class is similar to the Result class except for the additional read-only properties that it contains. For detailed help, see the Cross Validation tool.

Only the mean and root mean square error results are available for IDW, Global Polynomial Interpolation, Radial Basis Functions, Diffusion Interpolation With Barriers, and Kernel Interpolation With Barriers.

Percent in 90% Interval, Percent in 95% Interval, and Average CRPS are only available for Empirical Bayesian Kriging and EBK Regression Prediction models.

Propriétés

PropriétéExplicationType de données
averageCRPS
(Lecture seule)

The average of the Continuous Ranked Probability Scores (CRPS) for all points.

Double
averageStandard
(Lecture seule)

The average of the prediction standard errors.

Double
count
(Lecture seule)

The number of input samples.

Long
inputCount
(Lecture seule)

Returns the number of inputs.

Integer
maxSeverity
(Lecture seule)

Returns the maximum severity of the messages.

  • 0If the tool produced only informative messages.
  • 1 If the tool produced a warning message, but no error messages.
  • 2 If the tool produced an error message.
Integer
meanError
(Lecture seule)

The averaged difference between the measured and the predicted values.

Double
meanStandardized
(Lecture seule)

Mean standardized error.

Double
messageCount
(Lecture seule)

Returns the number of messages.

Integer
outputCount
(Lecture seule)

Returns the number of outputs.

Integer
percentIn90Interval
(Lecture seule)

The percentage of points that are contained in a 90 percent cross validated confidence interval.

Double
percentIn95Interval
(Lecture seule)

The percentage of points that are contained in a 95 percent cross validated confidence interval.

Double
resultID
(Lecture seule)

Gets the job ID. If the tool is not a geoprocessing service, the resultID will be "".

String
rootMeanSquare
(Lecture seule)

The root mean square error.

Double
rootMeanSquareStandardized
(Lecture seule)

The root-mean-square-standardized error should be close to 1 if the prediction standard errors are valid. If the root-mean-square-standardized error is greater than 1, you are underestimating the variability in your predictions. If the root-mean-square-standardized error is less than 1, you are overestimating the variability in your predictions.

Double
status
(Lecture seule)

Gets the job status.

  • 0New
  • 1Submitted
  • 2Waiting
  • 3Executing
  • 4Succeeded
  • 5Failed
  • 6Timed out
  • 7Cancelling
  • 8Cancelled
  • 9Deleting
  • 10Deleted
Integer

Vue d’ensemble des méthodes

MéthodeExplication
cancel ()

Cancels an associated job

getInput (index)

Returns a given input, either as a recordset or string.

getMapImageURL ({parameter_list}, {height}, {width}, {resolution})

Returns a map service image for a given output, if one exists.

getMessage (index)

Returns a specific message by index position.

getMessages ({severity})

Returns the geoprocessing tool messages.

getOutput (index)

Returns a given output, either as a string or a RecordSet.

If the output of the tool, such as Make Feature Layer, is a layer, getOutput will return a Layer object.

getSeverity (index)

Returns the severity of a specific message.

Méthodes

cancel ()
getInput (index)
ParamètreExplicationType de données
index

The index position of the input.

Integer
Valeur renvoyée
Type de donnéesExplication
Object

The input, either as a recordset or string.

getMapImageURL ({parameter_list}, {height}, {width}, {resolution})
ParamètreExplicationType de données
parameter_list

The parameters on which the map service image will be based.

Integer
height

The height of the image.

Double
width

The width of the image.

Double
resolution

The resolution of the image.

Double
Valeur renvoyée
Type de donnéesExplication
String

The URL of the map image.

getMessage (index)
ParamètreExplicationType de données
index

The index position of the message.

Integer
Valeur renvoyée
Type de donnéesExplication
String

The geoprocessing message.

getMessages ({severity})
ParamètreExplicationType de données
severity

The type of messages to be returned.

  • 0Informative, warning, and error messages are returned.
  • 1Only warning messages are returned.
  • 2Only error messages are returned.

Not specifying a severity level will return all types of messages.

(La valeur par défaut est 0)

Integer
Valeur renvoyée
Type de donnéesExplication
String

The geoprocessing tool messages.

getOutput (index)
ParamètreExplicationType de données
index

The index position of the outputs.

Integer
Valeur renvoyée
Type de donnéesExplication
Object

The output, either as a recordset or a string.

If the output of the tool, such as Make Feature Layer, is a layer, getOutput will return a Layer object.

Result outputs can also be accessed by index, so result.getOutput(0) and result[0] are equivalent.

getSeverity (index)
ParamètreExplicationType de données
index

The message index position.

Integer
Valeur renvoyée
Type de donnéesExplication
Integer

The severity of the specific message.

  • 0Informative, warning, and error messages are returned.
  • 1Only warning messages are returned.
  • 2Only error messages are returned.

Exemple de code

CrossValidation (Python window)

Perform cross-validation on an input geostatistical layer.

import arcpy
arcpy.env.workspace = "C:/gapyexamples/data"
cvResult = arcpy.CrossValidation_ga("C:/gapyexamples/data/kriging.lyr")
print("Root Mean Square error = " + str(cvResult.rootMeanSquare))
CrossValidation (stand-alone script)

Perform cross-validation on an input geostatistical layer.

# Name: CrossValidation_Example_02.py
# Description: Perform cross validation on an input geostatistical layer.
# Requirements: Geostatistical Analyst Extension

# Import system modules
import arcpy

# Set environment settings
arcpy.env.workspace = "C:/gapyexamples/data"

# Set local variables
inLayer = "C:/gapyexamples/data/kriging.lyr"

# Execute CrossValidation
cvResult = arcpy.CrossValidation_ga(inLayer)
print("Root Mean Square error = " + str(cvResult.rootMeanSquare))