DATASET PCA (INVERSE)¶
Reconstructs the original curve columns from principal-component scores using the parameters from dataset_pca_fit: x = (scores . components) * std + mean. The input must contain the <pc_prefix>_PC1..n columns (e.g. predicted PCs from ML Predict). If the curve columns were normalized before PCA, follow this with dataset_normalize_inverse to recover fully raw values.
When to use¶
Tagged: pca, inverse, reconstruction, dimensionality_reduction, postprocessing, prediction, curves.
Inputs¶
| Label | ID | Type | Default | Required | Description |
|---|---|---|---|---|---|
| Dataset | dataset | dataset | — | ✓ | Dataset containing the <pc_prefix>_PC1..n score columns to invert; array-of-row-objects. |
| PCA Parameters | pca_parameters | json | — | ✓ | PCA parameters object produced by dataset_pca_fit (components, mean, std, columns, pc_prefix). |
Outputs¶
| Label | ID | Type | Description |
|---|---|---|---|
| Reconstructed Dataset | dataset | dataset | Input rows with the reconstructed curve columns (from params[‘columns’]). |
| Status | status | string | Summary of reconstructed columns. |
Disciplines¶
- data.dataset.transform
- data.statistics
Auto-generated from platform schema. Worker id: dataset_pca_inverse. Schema hash: 2103cf42920f. Hand-curated docs in workerexamples/ override this page when present.