# Base definition of algorithms¶

## `orion.algo.base` – What is a search algorithm, optimizer of a process¶

class `orion.algo.base.``BaseAlgorithm`(space, **kwargs)[source]

Base class describing what an algorithm can do.

Parameters: space : `orion.algo.space.Space` Definition of a problem’s parameter space. kwargs : dict Tunable elements of a particular algorithm, a dictionary from hyperparameter names to values.

Notes

We are using the No Free Lunch theorem’s [R367dceedad15-1]_[R367dceedad15-3]_ formulation of an `BaseAlgorithm`. We treat it as a part of a procedure which in each iteration suggests a sample of the parameter space of the problem as a candidate solution and observes the results of its evaluation.

Developer Note: Each algorithm’s complete specification, i.e. implementation of its methods and parameters of its own, lies in a separate concrete algorithm class, which must be an immediate subclass of `BaseAlgorithm`. [The reason for this is current implementation of `Factory` metaclass which uses BaseAlgorithm.__subclasses__().] Second, one must declare an algorithm’s own parameters (tunable elements which could be set by configuration). This is done by passing them to `BaseAlgorithm.__init__` by calling Python’s super with a Space object as a positional argument plus algorithm’s own parameters as keyword arguments. The keys of the keyword arguments passed to `BaseAlgorithm.__init__` are interpreted as the algorithm’s parameter names. So for example, a subclass could be as simple as this (regarding the logistics, not an actual algorithm’s implementation):

References

 [1] D. H. Wolpert and W. G. Macready, “No Free Lunch Theorems for Optimization,” IEEE Transactions on Evolutionary Computation, vol. 1, no. 1, pp. 67–82, Apr. 1997.
 [2] W. G. Macready and D. H. Wolpert, “What Makes An Optimization Problem Hard?,” Complexity, vol. 1, no. 5, pp. 40–46, 1996.
 [3] D. H. Wolpert and W. G. Macready, “No Free Lunch Theorems for Search,” Technical Report SFI-TR-95-02-010, Santa Fe Institute, 1995.

Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22``` ```from orion.algo.base import BaseAlgorithm from orion.algo.space import (Integer, Space) class MySimpleAlgo(BaseAlgorithm): def __init__(self, space, multiplier=1, another_param="a string param"): super().__init__(space, multiplier=multiplier, another_param=another_param) def suggest(self, num=1): print(self.another_param) return list(map(lambda x: tuple(map(lambda y: self.multiplier * y, x)), self.space.sample(num))) def observe(self, points, results): pass dim = Integer('named_param', 'norm', 3, 2, shape=(2, 3)) s = Space() s.register(dim) algo = MySimpleAlgo(s, 2, "I am just sampling!") algo.suggest() ```
Attributes: `configuration` Return tunable elements of this algorithm in a dictionary form appropriate for saving. `is_done` Return True, if an algorithm holds that there can be no further improvement. `should_suspend` Allow algorithm to decide whether a particular running trial is still worth to complete its evaluation, based on information provided by the `judge` method. `space` Domain of problem associated with this algorithm’s instance. `state_dict` Return a state dict that can be used to reset the state of the algorithm.

Methods

 `judge`(point, measurements) Inform an algorithm about online measurements of a running trial. `observe`(points, results) Observe the results of the evaluation of the points in the process defined in user’s script. `score`(point) Allow algorithm to evaluate point based on a prediction about this parameter set’s performance. `seed_rng`(seed) Seed the state of the random number generator. `set_state`(state_dict) Reset the state of the algorithm based on the given state_dict `suggest`([num]) Suggest a num of new sets of parameters.
`configuration`

Return tunable elements of this algorithm in a dictionary form appropriate for saving.

`is_done`

Return True, if an algorithm holds that there can be no further improvement. By default, the cardinality of the specified search space will be used to check if all possible sets of parameters has been tried.

`judge`(point, measurements)[source]

Inform an algorithm about online measurements of a running trial.

Parameters: point – A tuple which specifies the values of the (hyper)parameters used to execute user’s script with.

This method is to be used as a callback in a client-server communication between user’s script and a orion’s worker using a `BaseAlgorithm`. Data returned from this method must be serializable and will be used as a response to the running environment. Default response is None.

Note

Calling algorithm to `judge` a point based on its online measurements will effectively change a state in the algorithm (like a reinforcement learning agent’s hidden state or an automatic early stopping mechanism’s regression), which it may change the value of the property `should_suspend`.

Returns: None or a serializable dictionary containing named data
`observe`(points, results)[source]

Observe the results of the evaluation of the points in the process defined in user’s script.

Parameters: points : list of tuples of array-likes Points from a `orion.algo.space.Space`. Evaluated problem parameters by a consumer. results : list of dicts Contains the result of an evaluation; partial information about the black-box function at each point in params.
`score`(point)[source]

By default, return the same score any parameter (no preference).

Returns: A subjective measure of expected perfomance. float
`seed_rng`(seed)[source]

Seed the state of the random number generator.

Parameters: seed – Integer seed for the random number generator.

Note

This methods does nothing if the algorithm is deterministic.

`set_state`(state_dict)[source]

Reset the state of the algorithm based on the given state_dict

Parameters: state_dict – Dictionary representing state of an algorithm
`should_suspend`

Allow algorithm to decide whether a particular running trial is still worth to complete its evaluation, based on information provided by the `judge` method.

`space`

Domain of problem associated with this algorithm’s instance.

`state_dict`

Return a state dict that can be used to reset the state of the algorithm.

`suggest`(num=1)[source]

Suggest a num of new sets of parameters.

Parameters: num: int, optional Number of points to suggest. Defaults to 1. list of points or None A list of lists representing points suggested by the algorithm. The algorithm may opt out if it cannot make a good suggestion at the moment (it may be waiting for other trials to complete), in which case it will return None.

Notes

New parameters must be compliant with the problem’s domain `orion.algo.space.Space`.

class `orion.algo.base.``OptimizationAlgorithm`(space, **kwargs)[source]

Class used to inject dependency on an algorithm implementation.

Attributes: `configuration` Return tunable elements of this algorithm in a dictionary form appropriate for saving. `is_done` Return True, if an algorithm holds that there can be no further improvement. `should_suspend` Allow algorithm to decide whether a particular running trial is still worth to complete its evaluation, based on information provided by the `judge` method. `space` Domain of problem associated with this algorithm’s instance. `state_dict` Return a state dict that can be used to reset the state of the algorithm.

Methods

 `judge`(point, measurements) Inform an algorithm about online measurements of a running trial. `observe`(points, results) Observe the results of the evaluation of the points in the process defined in user’s script. `score`(point) Allow algorithm to evaluate point based on a prediction about this parameter set’s performance. `seed_rng`(seed) Seed the state of the random number generator. `set_state`(state_dict) Reset the state of the algorithm based on the given state_dict `suggest`([num]) Suggest a num of new sets of parameters.
`orion.algo.base.``infer_trial_id`(point)[source]

Compute a hashing of a point