DATASET COMPUTE DERIVATIVES¶
Computes numerical derivatives (dy/dx) of one or more target columns with respect to one or more input columns in a dataset. Optionally builds a Full Factorial Design (FFD) dataset from the source data before differentiation, and supports multiple neighbor-based derivative estimation strategies. Use this worker to enrich a design-exploration dataset with gradient information for sensitivity or surrogate workflows.
When to use¶
Classification: process.
Tagged: dataset, derivatives, dydx, ffd, full_factorial, gradient, numerical_differentiation, sensitivity.
Inputs¶
| Label | ID | Type | Default | Required | Description |
|---|---|---|---|---|---|
| Choose Dataset | dataset_1 | dataset | — | Source dataset containing the input and target columns for which derivatives will be computed; accepts any tabular d3VIEW dataset. | |
| Inputs | inputs | scalar | — | One or more column names from dataset_1 to treat as the independent variables (x-axes) when computing dy/dx. | |
| Targets | targets | scalar | — | One or more column names from dataset_1 to treat as the dependent variables (y-axes) whose derivatives are computed with respect to each selected input. | |
| Build FFD If Not One | build_ffd | string | no | Whether to learn from the dataset and construct a Full Factorial Design (FFD) dataset before differentiating; set to ‘yes’ when the source dataset is not already a structured FFD, default ‘no’. | |
| Value Type | value_type | string | avg | Strategy for aggregating derivative estimates from neighboring points: ‘avg’ (average of all neighbors, default), ‘min’, ‘max’, ‘left’ (left-side neighbor), ‘right’ (right-side neighbor), or ‘twoside’ (central difference). | |
| Normalize The Data | normalize | string | no | Whether to normalize the computed derivative values; set to ‘yes’ to produce dimensionless relative sensitivities, default ‘no’. |
Outputs¶
| Label | ID | Type | Description |
|---|---|---|---|
| dataset_compute_derivatives_output_1 | dataset_compute_derivatives_output_1 | dataset | Augmented tabular dataset containing the original columns plus new derivative columns named as dy_dx for each target–input pair computed. |
Disciplines¶
- ai_ml.preprocessing
- data.dataset.transform
- design_exploration.sensitivity
Runnable example¶
A runnable example is registered for this worker. Open the example workflow on the d3VIEW canvas: /api/workflow/example?id=dataset_compute_derivatives
Auto-generated from transformation schema. Worker id: dataset_compute_derivatives. Schema hash: 438f5a91d717. Hand-curated docs in workerexamples/ override this page when present.