`svr_regression`

Support vector regression (SVR) using an epsilon-insensitive dual margin model with linear, polynomial, and radial basis function kernels. Training uses a per-example dual subgradient descent loop (Pegasos-style), the same optimization pattern used by kernel_svm_classifier, with the one-sided hinge-loss condition replaced by the two-sided epsilon-insensitive tube condition that defines SVR.

The library implements the regressor_protocol defined in the regression_protocols library. It provides predicates for learning a regressor from a dataset object, using it to make predictions, and exporting the learned model as a list of predicate clauses or to a file.

Datasets are represented as objects implementing the regression_dataset_protocol protocol.

API documentation

Open the ../../apis/library_index.html#svr-regression link in a web browser.

Loading

To load this library, load the loader.lgt file:

| ?- logtalk_load(svr_regression(loader)).

Testing

To test this library predicates, load the tester.lgt file:

| ?- logtalk_load(svr_regression(tester)).

To run the performance benchmark suite, load the tester_performance.lgt file:

| ?- logtalk_load(svr_regression(tester_performance)).

Features

Multiple Kernels: Supports linear, polynomial(Degree, Gamma, Coef0), and rbf(Gamma) kernels, selected via the kernel/1 option, matching kernel_svm_classifier’s kernel specifications.
Epsilon-Insensitive Loss: Predictions within epsilon of the target incur no penalty; only residuals outside that tube drive coefficient updates, following Vapnik’s epsilon-SVR formulation.
Mixed Features: Reuses regressor_common’s shared tabular encoders for continuous and categorical attributes, including missing-value indicators.
Regularized Training: Supports configurable learning-rate scheduling, tolerance, and L2 regularization, applied as per-example weight decay in the same style as kernel_svm_classifier.
Diagnostics Metadata: Learned regressors record model name, target, training example count, kernel, optimization stop reason, completed iterations, final parameter delta, encoded feature count, and effective options, accessible using the shared regression diagnostics predicates.
Model Export: Learned regressors can be exported as predicate clauses or written to a file.

A note on convergence and the default options

Unlike lasso_regression’s coordinate descent, which converges to a precise KKT-optimality fixed point, this library’s dual subgradient descent loop converges, like any fixed- or decaying-step-size stochastic method, to a neighborhood of the optimum rather than to it exactly; final_delta in the diagnostics will generally not reach very small values even after many epochs, and convergence will typically read maximum_iterations_exhausted rather than tolerance unless maximum_iterations is set generously.

This matters more for SVR than it does for kernel_svm_classifier: a classifier’s hinge loss only needs the decision boundary on the right side of each example, so a somewhat noisy fit is often good enough, but a regressor is scored on how numerically close its predictions are, which is directly sensitive to how tightly training has converged.

Empirically, training with the constant learning-rate schedule (learning_schedule(constant)) plateaus quickly and stays noisy even with many more epochs. Using learning_schedule(inverse_scaling(Power)) instead — which decays the step size over epochs, satisfying the standard Robbins-Monro conditions for stochastic-approximation convergence — reduced prediction error by roughly an order of magnitude in testing against the same dataset and epoch budget. For this reason inverse_scaling(0.5) is the library default (rather than kernel_svm_classifier’s learning_schedule(constant) default), paired with a larger default maximum_iterations (200 rather than kernel_svm_classifier’s 25). When tighter fits are needed, raising maximum_iterations well into the hundreds or low thousands, optionally combined with a smaller epsilon and a larger initial learning_rate to compensate for the decaying schedule, converges substantially further; how far depends on the dataset.

Because epsilon is compared directly against residuals in the target variable’s own units, it is scale-dependent: an epsilon of 0.1 is a tight tolerance for a target ranging in the thousands and a loose one for a target ranging between 0 and 1. Choose it relative to the acceptable prediction error for the problem at hand, not by leaving it at the default without considering the target’s scale. Continuous features are standardized via feature_scaling/1 as usual, but the target itself is never rescaled, so predictions and epsilon stay in the target’s original units.

Options

The learn/3 predicate supports these options:

kernel/1 - kernel function to use (default: linear)
epsilon/1 - half-width of the epsilon-insensitive tube, in the target variable’s units; residuals within this distance of zero incur no loss (default: 0.1)
learning_rate/1 - base learning rate for the dual optimization loop (default: 0.5)
learning_schedule/1 - learning-rate schedule, either constant or inverse_scaling(Power) (default: inverse_scaling(0.5))
maximum_iterations/1 - maximum number of optimization epochs (default: 200)
tolerance/1 - convergence threshold for the maximum parameter update in an epoch (default: 1.0e-5)
l2_regularization/1 - L2 penalty factor applied as per-example weight decay during optimization (default: 0.001)
feature_scaling/1 - whether to standardize continuous attributes before encoding (default: true)

Usage

Learning a regressor

| ?- svr_regression::learn(dataset, Regressor).

| ?- svr_regression::learn(dataset, Regressor, [kernel(rbf(0.5)), maximum_iterations(500)]).

Making predictions

| ?- svr_regression::learn(dataset, Regressor),
     svr_regression::predict(Regressor, [x1-2.0, x2-3.0], Prediction).

Exporting the regressor

| ?- svr_regression::learn(dataset, Regressor),
     svr_regression::export_to_clauses(dataset, Regressor, predict_target, Clauses).

| ?- svr_regression::learn(dataset, Regressor),
     svr_regression::export_to_file(dataset, Regressor, predict_target, 'regressor.pl').

Regressor representation

The learned regressor is represented as a compound term with the form:

svr_regressor(Encoders, Kernel, TrainingRows, Bias, Coefficients, Diagnostics)

Where:

Encoders: list of continuous scaling descriptors and categorical value encoders
Kernel: selected kernel specification
TrainingRows: encoded feature vectors for the training examples
Bias: the model’s scalar bias (intercept) term
Coefficients: list of one dual coefficient per training row
Diagnostics: training metadata including the effective options

When exported using export_to_clauses/4 or export_to_file/4, this regressor term is serialized directly as the single argument of the generated predicate clause so that the exported model can be loaded and reused as-is.

Diagnostics syntax

The diagnostics/2 predicate returns a list of metadata terms with the form:

[
    model(svr_regression),
    target(Target),
    training_example_count(TrainingExampleCount),
    options(Options),
    convergence(Status),
    iterations(Iterations),
    final_delta(FinalDelta),
    kernel(Kernel),
    encoded_feature_count(FeatureCount)
]

Where convergence(Status) is tolerance when the maximum parameter update in a full epoch fell within the configured tolerance, and maximum_iterations_exhausted when training stopped because the epoch cap was reached — see the convergence note above for why the latter is the common case with the default options. final_delta(FinalDelta) stores the maximum parameter change observed during the final epoch. The remaining fields follow the same conventions as lasso_regression’s diagnostics, with kernel(Kernel) added to record the kernel used for training.

Use the regression_protocols diagnostic/2 and regressor_options/2 helper predicates when only a single metadata term or the effective options are needed.

References

Vapnik, V. (1995). “The Nature of Statistical Learning Theory”.
Smola, A.J. and Scholkopf, B. (2004). “A Tutorial on Support Vector Regression”.
Shalev-Shwartz, S., Singer, Y., Srebro, N. and Cotter, A. (2011). “Pegasos: Primal Estimated Sub-Gradient Solver for SVM”.
Cortes, C. and Vapnik, V. (1995). “Support-Vector Networks”.

svr_regression