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name: integrated_gradient_computation description: Compute Integrated Gradients with Riemann-summed path gradients and completeness diagnostics for attribution recovery.

Integrated Gradient Computation

Use this skill to compute feature attributions from a differentiable scalar function or from an injected gradient callable. Do not use plain input gradients as a substitute for Integrated Gradients when the function may be saturated at the input.

Inputs

• gradient_fn(point): returns one gradient value per feature.
• input_vector and baseline_vector: equal-length numeric vectors.
• steps: Riemann approximation count, typically 20 to 300 for real models.
• Optional output_fn for completeness diagnostics.

Outputs

• attributions: one attribution per input feature.
• attribution_sum: sum of feature attributions.
• Optional output_difference and completeness_error.

Workflow

1. Generate the straight-line path from baseline to input.
2. Evaluate gradients at each interpolation point.
3. Average gradients per feature.
4. Multiply averaged gradients by the input-baseline feature deltas.
5. If an output function is provided, compare the attribution sum to F(x)-F(baseline).

Validation

Run python tests/test_integrated_gradients.py or the Distiller skill-tree validator with tests enabled.

Limitations

The script expects deterministic Python callables. Framework-specific autograd wrappers should adapt their tensors to this callable interface.