Last updated: Feb 11, 2025
The 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.
Example
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)
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 |