Manual Point Injection#

This guide shows how to inject custom parameter combinations based on domain knowledge or external sources, alongside optimizer-driven suggestions.

Basic Usage#

To evaluate manually-specified points, use the sample_suggestions method with parameter combinations (without "_id" keys). The optimizer will automatically register these trials and incorporate the results into the Bayesian model.

from blop.ax import Agent, RangeDOF, Objective

# Configure agent
agent = Agent(
    sensors=[sensor],
    dofs=[
        RangeDOF(actuator=motor_x, bounds=(-10, 10), parameter_type="float"),
        RangeDOF(actuator=motor_y, bounds=(-10, 10), parameter_type="float"),
    ],
    objectives=[Objective(name="signal", minimize=False)],
    evaluation_function=evaluation_function,
)

# Define points of interest
manual_points = [
    {'motor_x': 0.5, 'motor_y': 1.0},  # Center region
    {'motor_x': 0.0, 'motor_y': 0.0},  # Origin
]

# Evaluate them
RE(agent.sample_suggestions(manual_points))

The manual points will be treated just like optimizer suggestions - they’ll be tracked, evaluated, and used to improve the model.

Mixed Workflows#

You can combine optimizer suggestions with manual points throughout your optimization:

from blop.ax import Agent, RangeDOF, Objective

agent = Agent(
    sensors=[sensor],
    dofs=[
        RangeDOF(actuator=motor_x, bounds=(-10, 10), parameter_type="float"),
        RangeDOF(actuator=motor_y, bounds=(-10, 10), parameter_type="float"),
    ],
    objectives=[Objective(name="signal", minimize=False)],
    evaluation_function=evaluation_function,
)

# Run optimizer for initial exploration
RE(agent.optimize(iterations=3))

# Try a manual point based on domain insight
manual_point = [{'motor_x': 0.75, 'motor_y': 0.25}]
RE(agent.sample_suggestions(manual_point))

# Continue optimization
RE(agent.optimize(iterations=3))

The optimizer will incorporate your manual point into its model and use it to inform future suggestions.

Manual Approval Workflow#

You can review optimizer suggestions before running them by using suggest() to get suggestions without acquiring data:

from blop.ax import Agent, RangeDOF, Objective

agent = Agent(
    sensors=[sensor],
    dofs=[
        RangeDOF(actuator=motor_x, bounds=(-10, 10), parameter_type="float"),
        RangeDOF(actuator=motor_y, bounds=(-10, 10), parameter_type="float"),
    ],
    objectives=[Objective(name="signal", minimize=False)],
    evaluation_function=evaluation_function,
)

# Get suggestions without running
suggestions = agent.suggest(num_points=5)

# Review and filter
print("Reviewing suggestions:")
for s in suggestions:
    trial_id = s['_id']
    x = s['motor_x']
    y = s['motor_y']
    print(f"  Trial {trial_id}: x={x:.2f}, y={y:.2f}")

# Only run approved suggestions
approved = [s for s in suggestions if s['motor_x'] > -5.0]

if approved:
    RE(agent.sample_suggestions(approved))
else:
    print("No suggestions approved")

Reviewing suggestions:
...

This workflow allows you to apply safety checks, domain constraints, or other validation before running trials.

Iterative Refinement#

A common pattern is to alternate between automated optimization and targeted manual exploration:

from blop.ax import Agent, RangeDOF, Objective

agent = Agent(
    sensors=[sensor],
    dofs=[
        RangeDOF(actuator=motor_x, bounds=(-10, 10), parameter_type="float"),
        RangeDOF(actuator=motor_y, bounds=(-10, 10), parameter_type="float"),
    ],
    objectives=[Objective(name="signal", minimize=False)],
    evaluation_function=evaluation_function,
)

for cycle in range(3):
    # Automated exploration
    RE(agent.optimize(iterations=2, n_points=2))

    # Review results and manually probe interesting regions
    # (Look at plots, current best, etc.)

    # Try edge cases or special points
    if cycle == 1:
        # After first cycle, check boundaries
        boundary_points = [
            {'motor_x': -10.0, 'motor_y': 0.0},
            {'motor_x': 10.0, 'motor_y': 0.0},
        ]
        RE(agent.sample_suggestions(boundary_points))

See Also#