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# SteppableOptimizer¶

class SteppableOptimizer(maxiter=100)[código fonte]

Bases: `Optimizer`

Base class for a steppable optimizer.

This family of optimizers uses the ask and tell interface. When using this interface the user has to call `ask()` to get information about how to evaluate the fucntion (we are asking the optimizer about how to do the evaluation). This information is typically the next points at which the function is evaluated, but depending on the optimizer it can also determine whether to evaluate the function or its gradient. Once the function has been evaluated, the user calls the method `tell()` to tell the optimizer what the result of the function evaluation(s) is. The optimizer then updates its state accordingly and the user can decide whether to stop the optimization process or to repeat a step.

This interface is more customizable, and allows the user to have full control over the evaluation of the function.

Examples

An example where the evaluation of the function has a chance of failing. The user, with specific knowledge about his function can catch this errors and handle them before passing the result to the optimizer.

```import random
import numpy as np

def objective(x):
if random.choice([True, False]):
return None
else:
return (np.linalg.norm(x) - 1) ** 2

if random.choice([True, False]):
return None
else:
return 2 * (np.linalg.norm(x) - 1) * x / np.linalg.norm(x)

initial_point = np.random.normal(0, 1, size=(100,))

while optimizer.continue_condition():

optimizer.state.njev += 1

optmizer.state.nit += 1

result = optimizer.create_result()
```

Users that aren’t dealing with complicated functions and who are more familiar with step by step optimization algorithms can use the `step()` method which wraps the `ask()` and `tell()` methods. In the same spirit the method `minimize()` will optimize the function and return the result.

To see other libraries that use this interface one can visit: https://optuna.readthedocs.io/en/stable/tutorial/20_recipes/009_ask_and_tell.html

Parâmetros

maxiter (int) – Number of steps in the optimization process before ending the loop.

Methods

 `ask` Ask the optimizer for a set of points to evaluate. `continue_condition` Condition that indicates the optimization process should continue. `create_result` Returns the result of the optimization. `evaluate` Evaluates the function according to the instructions contained in `ask_data`. `get_support_level` Return support level dictionary `gradient_num_diff` We compute the gradient with the numeric differentiation in the parallel way, around the point x_center. `minimize` Minimizes the function. `print_options` Print algorithm-specific options. `set_max_evals_grouped` Set max evals grouped `set_options` Sets or updates values in the options dictionary. `start` Populates the state of the optimizer with the data provided and sets all the counters to 0. `step` Performs one step in the optimization process. `tell` Updates the optimization state using the results of the function evaluation. `wrap_function` Wrap the function to implicitly inject the args at the call of the function.

Attributes

bounds_support_level

Returns bounds support level

initial_point_support_level

Returns initial point support level

is_bounds_ignored

Returns is bounds ignored

is_bounds_required

Returns is bounds required

is_bounds_supported

Returns is bounds supported

is_initial_point_ignored

Returns is initial point ignored

is_initial_point_required

Returns is initial point required

is_initial_point_supported

Returns is initial point supported

setting

Return setting

settings

The optimizer settings in a dictionary format.

The settings can for instance be used for JSON-serialization (if all settings are serializable, which e.g. doesn’t hold per default for callables), such that the optimizer object can be reconstructed as

```settings = optimizer.settings
# JSON serialize and send to another server
optimizer = OptimizerClass(**settings)
```
state

Return the current state of the optimizer.