| Label | Explanation | Data Type |
Input Rasters
| The single-band, multidimensional, or multiband raster datasets, or mosaic datasets containing explanatory variables. | Raster Dataset; Raster Layer; Mosaic Dataset; Mosaic Layer; Image Service; String |
Input Regression Definition File | A JSON format file that contains attribute information, statistics, or other information from the regression model. The file has an .ecd extension. The file is the output of the Train Random Trees Regression Model tool. | File |
Output predicted raster | A raster of the predicted values. | Raster Dataset |
Available with Image Analyst license.
Summary
Predicts data values using the output from the Train Random Trees Regression Model tool.
Usage
If the Input Raster value is a multiband raster, each band represents an explanatory variable. The sequence of the bands in the multiband raster must be consistent with the inputs used for training the model with the Train Random Trees Regression Model tool.
If the Input Rasters value is a multidimensional raster (a multidimensional raster layer, multidimensional CRF, or multidimensional mosaic dataset), all multidimensional variables must be single band and have a StdTime or StdZ dimension value. Each multidimensional variable is treated as a predictor variable. All multidimensional variables will be used.
The regression model is defined in an Esri Regression Definition file (.ecd). It contains all the information for a specific dataset, or a set of datasets, and a regression model. It is generated by the regression model training tools, such as the Train Random Trees Regression tool.
The input rasters must be in the same form as when the regression model was trained. For example, the input must contain the same number of items in the list, in the same order, and each item must match (including the variables of a multidimensional raster).
If any of the explanatory variables are NoData at a location, the corresponding pixel in the output will be NoData.
The output cell size is determined by the first input raster or the environment settings.
If the output is a multidimensional raster, use CRF or NetCDF format. Other raster formats such as TIFF can store a single raster dataset. A dimensionless raster cannot store multidimensional raster output information.
Parameters
PredictUsingRegressionModel(in_rasters, in_regression_definition, out_raster_dataset)
| Name | Explanation | Data Type |
in_rasters [in_rasters,...] | The single-band, multidimensional, or multiband raster datasets, or mosaic datasets containing explanatory variables. | Raster Dataset; Raster Layer; Mosaic Dataset; Mosaic Layer; Image Service; String |
in_regression_definition | A JSON format file that contains attribute information, statistics, or other information from the regression model. The file has an .ecd extension. The file is the output of the Train Random Trees Regression Model tool. | File |
out_raster_dataset | A raster of the predicted values. | Raster Dataset |
Code sample
This Python window script predicts data values using the output from the PredictUsingRegressionModel function.
# Import system modules
import arcpy
from arcpy.ia import *
# Check out the ArcGIS Image Analyst extension license
arcpy.CheckOutExtension("ImageAnalyst")
predicted_raster = arcpy.ia.PredictUsingRegressionModel("weather_variables.crf";"dem.tif", r"c:\data\pm_trained.ecd")
predicted_raster.save("C:/data/pm2.5_prediction.crf")This Python stand-alone script predicts data values using the output from the PredictUsingRegressionModel function.
# Import system modules
import arcpy
from arcpy.ia import *
# Check out the ArcGIS Image Analyst extension license
arcpy.CheckOutExtension("ImageAnalyst")
# Define input parameters
in_weather_variables = "C:/Data/ClimateVariables.crf"
in_dem_varaible = "C:/Data/dem.tif"
in_target = "C:/Data/pm2.5_observations.shp"
target_value_field = "mean_pm2.5"
Target_date_field = "date_collected"
Raster_dimension = “StdTime”
out_model_definition = "C:/Data/pm2.5_trained_model.ecd"
Out_importance_table = "C:/Data/pm2.5_importance_table.csv"
max_num_trees = 50
max_tree_depth = 30
max_num_samples = 10000
# Execute - train with random tree regression model
arcpy.ia.TrainRandomTreesRegressionModel(in_weather_variables;in_dem_varaible, in_target, out_model_definition, target_value_field, Target_date_field, Raster_dimension, max_num_trees, max_tree_depth, max_num_samples)