skgrad

Fast analytic input gradients for fitted scikit-learn models.

skgrad differentiates a fitted model's prediction with respect to its input features. It provides one NumPy-based interface for supported linear models, classifiers, and multilayer perceptrons without finite differences, model conversion, or an automatic-differentiation framework. Unsupported estimators raise TypeError; there is no numerical fallback.

gradient = skgrad.input_gradient(model, X)

For scalar-output models, gradient[i, j] is the derivative of prediction i with respect to feature j. Multi-output models expose one gradient per target or the complete input Jacobian.

Read the skgrad documentation for worked examples, the API contract, model coverage, and numerical conventions.

Installation

pip install skgrad

skgrad requires Python 3.9 or later and installs NumPy, scikit-learn, and its small runtime dependencies automatically.

Quick start

import numpy as np
from sklearn.neural_network import MLPRegressor

import skgrad

rng = np.random.default_rng(0)
X_train = rng.normal(size=(200, 4))
y_train = np.sin(X_train[:, 0]) + X_train[:, 1] * X_train[:, 2]

model = MLPRegressor(
    hidden_layer_sizes=(32, 32),
    activation="tanh",
    max_iter=1000,
    random_state=0,
).fit(X_train, y_train)

X_eval = X_train[:5]

values = skgrad.model_output(model, X_eval)       # (5, 1)
gradient = skgrad.input_gradient(model, X_eval)   # (5, 4)

values, jacobian = skgrad.value_and_jacobian(model, X_eval)
# values:   (samples, outputs)
# jacobian: (samples, outputs, features)

For a multi-output model, select one scalar output explicitly:

gradient = skgrad.input_gradient(model, X_eval, target=1)

Use skgrad.supports(model) to check model coverage before calculation.

For a ReLU MLP, a forward pass identifies the active hidden units at each input. Their known slopes and fitted weights then combine in a batched reverse pass. With multiple features, the scalar slopes pictured above become input gradient vectors computed by the same matrix operations.

Supported models

MLP hidden activations may be identity, logistic, tanh, or ReLU. At ReLU's nondifferentiable origin, skgrad uses a zero derivative, matching scikit-learn's backpropagation convention. Scalar and multi-output regression, binary classification, and multiclass classification are supported.

Kernel SVMs support scikit-learn's linear, poly, rbf, and sigmoid kernels. Multiclass kernel classifiers, callable kernels, and precomputed kernels are not currently supported.

Tree models are intentionally excluded. Their predictions are piecewise constant, so ordinary gradients are zero almost everywhere and undefined at split boundaries. Use TreeIG, which computes exact Integrated Gradients from the prediction jumps at tree split crossings.

skgrad expects finite dense numeric inputs. Sequential and nested sklearn pipelines may combine StandardScaler, RobustScaler, MaxAbsScaler, MinMaxScaler, PolynomialFeatures, PCA, and supported fitted feature selectors before a supported estimator. Gradients refer to the inputs of the supplied pipeline, including every supported preprocessing chain rule. Unknown transformers reject the entire analytic route; preprocessing is never silently removed. See pipeline conventions for clipping, whitening, feature selection, and attribution coordinates.

Output semantics

skgrad differentiates prediction functions with respect to input features, not training losses with respect to fitted parameters.

• Regressors return their prediction output.
• Binary classifiers expose one score or logit for the positive class.
• Multiclass classifiers expose one score or logit per class in model.classes_ order.
• Classification probabilities are deliberately not differentiated. Scores and logits compose cleanly with downstream attribution methods and avoid the redundant common direction of multiclass probabilities.
• Affine models and MLPs follow NumPy/scikit-learn dtype promotion, preserving float32 when the input and fitted parameters are both float32. Scikit-learn's LibSVM estimators use float64 fitted parameters and outputs.

See the shape and output semantics for the complete contract.

Performance

Fast input gradients are the reason skgrad exists. Central finite differences require two model evaluations per feature. skgrad instead reuses fitted coefficients for affine models and computes MLP gradients with a forward and reverse pass, obtaining all feature derivatives together. This matters especially for Integrated Gradients, which evaluates gradients repeatedly along paths.

In the documented CPU benchmarks, logistic regression and MLP gradients were roughly 14× faster at 10 features, 100–170× at 100 features, and 950–1,300× at 1,000 features than central differences. The tested MLP also achieved speeds comparable to PyTorch CPU autodiff, directly from the fitted scikit-learn model. Results depend on the model, batch size, and runtime environment.

Use input_gradient when you need one output: it avoids constructing the full multi-output Jacobian. See the performance guide for accuracy checks, full timing tables, benchmark methodology, and reproducible scripts.

API

skgrad.supports(model)
skgrad.gradient_properties(model)
skgrad.model_output(model, X)
skgrad.input_gradient(model, X, target=None)
skgrad.input_jacobian(model, X)
skgrad.value_and_jacobian(model, X)

value_and_jacobian is the general composition primitive. Its result contains values with shape (samples, outputs) and jacobian with shape (samples, outputs, features). input_gradient is the faster convenience API when one scalar output is required.

gradient_properties(model) reports useful computational metadata. In particular, downstream consumers can detect constant affine Jacobians and avoid redundant evaluations. exact_quadrature_steps also reports when a supported polynomial pipeline with an affine downstream estimator has a known finite Gauss–Legendre order for exact straight-path gradient integration.

Scope

skgrad deliberately provides input derivatives, not an explanation method. It does not choose baselines, perform numerical differentiation, integrate gradients, calculate parameter gradients, or produce attribution plots. This narrow scope keeps it useful as a small computational backend that other packages can compose.

The project is licensed under the BSD 3-Clause License.

Unified IG

Unified IG is an important consumer of skgrad. It uses skgrad for analytic gradients of supported smooth scikit-learn models, TreeIG for tree paths, and automatic or numerical backends for other model families, presenting them through one Integrated Gradients interface. Use Unified IG when the goal is feature attribution rather than direct access to model input gradients.

CBaseline constructs reference baseline distributions; skgrad supplies analytic input derivatives; TreeIG handles tree paths; UnifiedIG composes these components into attributions.