syne_tune.optimizer.schedulers.searchers.bayesopt.sklearn package

class syne_tune.optimizer.schedulers.searchers.bayesopt.sklearn.SKLearnPredictor[source]

Bases: object

Base class for predictors generated by scikit-learn based estimators of SKLearnEstimator.

This is only for predictors who return means and stddevs in predict().

predict(X)[source]

Returns signals which are statistics of the predictive distribution at input points inputs.

Parameters:

inputs – Input points, shape (n, d)

Return type:

Tuple[ndarray, ndarray]

Returns:

(means, stds), where predictive means means and predictive stddevs stds have shape (n,)

backward_gradient(input, head_gradients)[source]

Needs to be implemented only if gradient-based local optimization of an acquisition function is supported.

Computes the gradient \(\nabla f(x)\) for an acquisition function \(f(x)\), where \(x\) is a single input point. This is using reverse mode differentiation, the head gradients are passed by the acquisition function. The head gradients are \(\partial_k f\), where \(k\) runs over the statistics returned by predict() for the single input point \(x\). The shape of head gradients is the same as the shape of the statistics.

Parameters:

  • input (ndarray) – Single input point \(x\), shape (d,)

  • head_gradients (Dict[str, ndarray]) – See above

Return type:

ndarray

Returns:

Gradient \(\nabla f(x)\)

class syne_tune.optimizer.schedulers.searchers.bayesopt.sklearn.SKLearnEstimator[source]

Bases: object

Base class scikit-learn based estimators, giving rise to surrogate models for Bayesian optimization.

fit(X, y, update_params)[source]

Implements fit_from_state(), given transformed data. Here, y is normalized (zero mean, unit variance) iff normalize_targets == True.

Parameters:

  • X (ndarray) – Feature matrix, shape (n_samples, n_features)

  • y (ndarray) – Target values, shape (n_samples,)

  • update_params (bool) – Should model (hyper)parameters be updated? Ignored if estimator has no hyperparameters

Return type:

SKLearnPredictor

Returns:

Predictor, wrapping the posterior state

get_params()[source]
Return type:

Dict[str, Any]

Returns:

Current model hyperparameters

set_params(param_dict)[source]
Parameters:

param_dict (Dict[str, Any]) – New model hyperparameters

property normalize_targets: bool
Returns:

Should targets in state be normalized before calling fit()?

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