PCA
Trains a model to project vectors to a low-dimensional space using PCA.
This operation is ported from Spark ML.
For a comprehensive introduction, see
Spark documentation.
For scala docs details, see
org.apache.spark.ml.feature.PCA documentation.
Since: Seahorse 1.0.0
| Port |
Type Qualifier |
Description |
0 | DataFrame | The input DataFrame. |
Output
| Port |
Type Qualifier |
Description |
0 | DataFrame | The output DataFrame. |
1 | Transformer | A Transformer that allows to apply the operation on other DataFrames using a Transform. |
Parameters
| Name |
Type |
Description |
input column |
SingleColumnSelector |
The input column name. |
output |
SingleChoice |
Output generation mode. Possible values: ["replace input column", "append new column"] |
k |
Numeric |
The number of principal components. |
Example
Parameters
| Name |
Value |
input column |
"features" |
output |
append new column |
output column |
"pca_features" |
k |
3.0 |
| features |
| [0.0,1.0,0.0,7.0,0.0] |
| [2.0,0.0,3.0,4.0,5.0] |
| [4.0,0.0,0.0,6.0,7.0] |
Output
| features |
pca_features |
| [0.0,1.0,0.0,7.0,0.0] |
[1.6485728230883807,-4.013282700516296,-5.524543751369388] |
| [2.0,0.0,3.0,4.0,5.0] |
[-4.645104331781534,-1.1167972663619026,-5.524543751369387] |
| [4.0,0.0,0.0,6.0,7.0] |
[-6.428880535676489,-5.337951427775355,-5.524543751369389] |