DATASET PCA (FIT)

Fits PCA on a set of numeric dataset columns – selected by a column name prefix (e.g. ‘MEAS_ENGSS_YY_’), an explicit list, or BOTH combined (the union) so columns from different patterns can be decomposed together. Appends the principal-component scores as new columns (‘<pc_prefix>_PC1’..’_PCn’) and emits the fitted projection (components/mean/std/columns) as a reusable parameters object. Standardizes over available values only; missing values handled by the chosen strategy (‘zero’ = fill with training mean; ‘iterative’ = iterative SVD imputation). SVD via numpy. Use on the TRAINING dataset, then feed ‘pca_parameters’ into dataset_pca_transform and dataset_pca_inverse.

When to use

Tagged: pca, dimensionality_reduction, svd, fit, decomposition, preprocessing, curves, iterative_svd.

Inputs

Label	ID	Type	Default	Required	Description
Dataset	dataset	dataset	—	✓	Training dataset with the curve columns to decompose; array-of-row-objects.
Column Prefix	column_prefix	text	—		Prefix selecting numeric columns to decompose, e.g. ‘MEAS_ENGSS_YY_’. Columns whose remainder after the prefix is an integer are used, sorted numerically. Can be combined WITH the explicit Columns list (the union is decomposed).
Columns	columns	text	—		Explicit list of additional columns to decompose, e.g. MEAS_ENGSS_xfirst, MEAS_ENGSS_xlast. Combined with Column Prefix (union) – use this to add columns that don’t share the prefix pattern.
PC Column Prefix	pc_prefix	text	PC		Prefix for the emitted score columns, e.g. ‘EngSS’ -> EngSS_PC1, EngSS_PC2, … Must match what dataset_pca_inverse expects.
Number of Components	n_components	number	10		Number of principal components to keep (clamped to the achievable rank).
Missing Value Strategy	missing_strategy	select	zero		How to handle missing values when fitting. ‘zero’: missing standardized values filled with 0 (the training mean), single SVD. ‘iterative’: iterative SVD imputation (re-impute missing cells from a low-rank estimate, repeat) with per-row least-squares scores – better when columns are heavily missing (e.g. curves with shorter x-ranges).

Outputs

Label	ID	Type	Description
Dataset With PCs	dataset	dataset	Input dataset with appended <pc_prefix>_PC1..n score columns.
PCA Parameters	pca_parameters	json	Fitted projection: columns, mean, std, components, pc_prefix, missing_strategy, explained_variance_ratio. Reuse with dataset_pca_transform / dataset_pca_inverse.
Status	status	string	Summary of columns decomposed and components kept.

Disciplines

• data.dataset.transform
• data.statistics

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