acquisition-functions

name	acquisition-functions
description	Use when designing custom acquisition functions for gpCAM that encode experimental priorities — exploration vs exploitation balance, multi-objective targets, constrained search regions, cost-aware moves, UCB/LCB, or probability-of-improvement criteria.

Skill: gpCAM Acquisition Functions

Design custom acquisition functions that control where gpCAM measures next.

When to Use

When a user needs acquisition behavior beyond the built-in options:

• Balancing exploration and exploitation
• Multi-objective optimization
• Constrained search (avoid forbidden regions)
• Cost-aware acquisition (expensive moves)
• Upper/lower confidence bounds
• Probability of improvement

Acquisition Function Contract

def my_acquisition(x, gp_optimizer):
    """
    Parameters
    ----------
    x : np.ndarray, shape (V, D)
        Candidate points to evaluate.
    gp_optimizer : GPOptimizer
        The GP model. Use its posterior_mean() and posterior_covariance() methods.
    
    Returns
    -------
    scores : np.ndarray, shape (V,)
        Score for each candidate. HIGHER = more desirable (function is MAXIMIZED).
    """

Key rule: The acquisition function is maximized. Return higher values for points you want to measure.

Built-in Options

Pass these as strings to gpo.ask(acquisition_function=...):

Name	String key	Best for
Variance	"variance"	Pure exploration / mapping
Expected Improvement	"expected improvement"	Optimization (find max)
Probability of Improvement	"probability of improvement"	Optimization, risk-averse
Upper Confidence Bound	"ucb"	Maximization with tunable exploration/exploitation
Lower Confidence Bound	"lcb"	Minimization with tunable exploration/exploitation
Predicted Maximum	"maximum"	Pure exploitation — mean only, no uncertainty
Predicted Minimum	"minimum"	Pure exploitation for minimization
Gradient	"gradient"	Seek steepest regions of the posterior mean
Target Probability	"target probability"	Find points with output near a target value
Relative Information Entropy	"relative information entropy"	Information-theoretic exploration
RIE Set	"relative information entropy set"	Batch acquisition
Total Correlation	"total correlation"	Batch acquisition

To sanity-check any built-in or custom acquisition on a grid of candidates without calling ask():

scores = gpo.evaluate_acquisition_function(x_grid, acquisition_function="ucb")

Custom Acquisition Recipes

Upper Confidence Bound (UCB)

Available as the built-in string "ucb" — pass directly to gpo.ask(acquisition_function="ucb"). Write the callable form below only when you need to tune beta or otherwise customize the score:

def ucb(x, gpo):
    """
    beta controls exploration/exploitation tradeoff:
      beta=0: pure exploitation (just go to predicted max)
      beta=1: mild exploration
      beta=3: strong exploration (~95% confidence)
    """
    beta = 2.0  # TUNE THIS
    mean = gpo.posterior_mean(x)["m(x)"]
    var = gpo.posterior_covariance(x, variance_only=True)["v(x)"]
    return mean + beta * np.sqrt(var)

Lower Confidence Bound (for minimization)

gpCAM maximizes acquisition, so flip the sign for minimization:

def lcb(x, gpo):
    """Find the minimum of the function."""
    beta = 2.0
    mean = gpo.posterior_mean(x)["m(x)"]
    var = gpo.posterior_covariance(x, variance_only=True)["v(x)"]
    return -(mean - beta * np.sqrt(var))  # note the negation

Expected Improvement (custom version with minimization)

from scipy.stats import norm

def expected_improvement_minimize(x, gpo):
    """Expected improvement for finding the MINIMUM."""
    mean = gpo.posterior_mean(x)["m(x)"]
    var = gpo.posterior_covariance(x, variance_only=True)["v(x)"]
    std = np.sqrt(np.maximum(var, 1e-10))
    
    y_best = np.min(gpo.y_data)  # current best (minimum)
    z = (y_best - mean) / std
    ei = std * (z * norm.cdf(z) + norm.pdf(z))
    return ei

Probability of Improvement

from scipy.stats import norm

def probability_of_improvement(x, gpo):
    """Probability that measurement improves on current best."""
    mean = gpo.posterior_mean(x)["m(x)"]
    var = gpo.posterior_covariance(x, variance_only=True)["v(x)"]
    std = np.sqrt(np.maximum(var, 1e-10))
    
    y_best = np.max(gpo.y_data)
    z = (mean - y_best) / std
    return norm.cdf(z)

Constrained Acquisition (Avoid Regions)

def constrained_variance(x, gpo):
    """Explore but avoid a circular forbidden zone."""
    var = gpo.posterior_covariance(x, variance_only=True)["v(x)"]
    
    # Forbidden zone: circle at (5, 5) with radius 1
    center = np.array([5.0, 5.0])
    dist = np.linalg.norm(x - center, axis=1)
    penalty = np.where(dist < 1.0, -1e6, 0.0)
    
    return var + penalty

Multi-Objective (Weighted)

def multi_objective(x, gpo):
    """Balance finding the max with reducing uncertainty."""
    mean = gpo.posterior_mean(x)["m(x)"]
    var = gpo.posterior_covariance(x, variance_only=True)["v(x)"]
    
    w_exploit = 0.7  # weight on exploitation
    w_explore = 0.3  # weight on exploration
    
    # Normalize each component to [0, 1]
    mean_norm = (mean - mean.min()) / (mean.max() - mean.min() + 1e-10)
    var_norm = (var - var.min()) / (var.max() - var.min() + 1e-10)
    
    return w_exploit * mean_norm + w_explore * var_norm

Threshold Finder (Find Boundary)

Useful when searching for where a signal crosses a threshold:

def threshold_finder(x, gpo):
    """Find the boundary where f(x) = threshold."""
    threshold = 0.5  # EDIT THIS
    
    mean = gpo.posterior_mean(x)["m(x)"]
    var = gpo.posterior_covariance(x, variance_only=True)["v(x)"]
    std = np.sqrt(np.maximum(var, 1e-10))
    
    # Score is high near the threshold AND where uncertainty is high
    distance_to_threshold = np.abs(mean - threshold)
    return std / (distance_to_threshold + 0.01)

Usage in the Experiment Loop

# Built-in string or a callable are both accepted:
result = gpo.ask(
    input_set=parameter_bounds,
    acquisition_function=ucb,   # or "ucb", "expected improvement", ...
)

Useful ask() options

Argument	Meaning
n=N	Request N points at once (batch). For vectorized single-task, use a batch-aware acquisition like "relative information entropy set" or "total correlation".
vectorized=True (default)	The acquisition function is called once with all candidate points, shape (V, D) — required for custom callables written against the contract above.
vectorized=False	Candidates are evaluated one at a time (list of 1-D arrays). Used for non-vectorizable acquisition or non-Euclidean inputs.
method="global"|"local"|"hgdl"|"hgdlAsync"	Inner optimizer that searches for the argmax of the acquisition over input_set. hgdl requires dask_client=; hgdlAsync starts a background search and returns an opt_obj you can poll or kill_client().
dask_client=client	Distribute the inner optimization across Dask workers.
batch_size=B	When candidates are a list, evaluate them in chunks of B on the cluster.
max_iter, pop_size, info=True	Inner optimizer controls.

input_set can be continuous bounds (np.array([[lo,hi], ...])), a list of candidate points (discrete finite set), or a list of arbitrary objects (non-Euclidean — strings, graphs — provided your kernel handles them).

Hyperparameter Coordination

Acquisition functions don't add hyperparameters — they read the GP state via gpo.posterior_mean() and gpo.posterior_covariance(). However:

• If you access gpo.y_data directly (e.g., for y_best), make sure it's up to date after tell()
• The GP must be trained before acquisition makes sense — always call train() first
• For variance_only=True: faster, returns just diagonal variances (usually what you want)
• For full covariance: use variance_only=False but this is O(V²) memory

Common Pitfalls

1. Returning negative scores for points you want: Remember, acquisition is MAXIMIZED.
2. Division by zero in std: Always use np.maximum(var, 1e-10) before taking sqrt.
3. Not handling edge cases: Early in the loop with few points, the GP posterior can be unreliable.
4. Expensive acquisition functions: They're evaluated many times during optimization. Keep them fast.