factornode properties

PCA/Factor node iconThe PCA/Factor node provides powerful data-reduction techniques to reduce the complexity of your data. Principal components analysis (PCA) finds linear combinations of the input fields that do the best job of capturing the variance in the entire set of fields, where the components are orthogonal (perpendicular) to each other. Factor analysis attempts to identify underlying factors that explain the pattern of correlations within a set of observed fields. For both approaches, the goal is to find a small number of derived fields that effectively summarizes the information in the original set of fields.


node = stream.create("factor", "My node")
# "Fields" tab
node.setPropertyValue("custom_fields", True)
node.setPropertyValue("inputs", ["BP", "Na", "K"])
node.setPropertyValue("partition", "Test")
# "Model" tab
node.setPropertyValue("use_model_name", True)
node.setPropertyValue("model_name", "Factor_Age")
node.setPropertyValue("use_partitioned_data", False)
node.setPropertyValue("method", "GLS")
# Expert options
node.setPropertyValue("mode", "Expert")
node.setPropertyValue("complete_records", True)
node.setPropertyValue("matrix", "Covariance")
node.setPropertyValue("max_iterations", 30)
node.setPropertyValue("extract_factors", "ByFactors")
node.setPropertyValue("min_eigenvalue", 3.0)
node.setPropertyValue("max_factor", 7)
node.setPropertyValue("sort_values", True)
node.setPropertyValue("hide_values", True) 
node.setPropertyValue("hide_below", 0.7)
# "Rotation" section
node.setPropertyValue("rotation", "DirectOblimin")
node.setPropertyValue("delta", 0.3)
node.setPropertyValue("kappa", 7.0)
Table 1. factornode properties
factornode Properties Values Property description
inputs [field1 ... fieldN] PCA/Factor models use a list of input fields, but no target. Weight and frequency fields are not used. See Common modeling node properties for more information.
method PC ULS GLS ML PAF Alpha Image  
mode Simple Expert  
max_iterations number  
complete_records flag  
matrix Correlation Covariance  
extract_factors ByEigenvalues ByFactors  
min_eigenvalue number  
max_factor number  
rotation None Varimax DirectOblimin Equamax Quartimax Promax  
delta number If you select DirectOblimin as your rotation data type, you can specify a value for delta. If you don't specify a value, the default value for delta is used.
kappa number If you select Promax as your rotation data type, you can specify a value for kappa. If you don't specify a value, the default value for kappa is used.
sort_values flag  
hide_values flag  
hide_below number