Writing Your Own Algorithm

Now we will take a look at how to create a new algorithm which will define the hyperparameters we will use to train the models. It defines the hyperparameters to use in the trials. It does not define the algorithm to train the model used in the trial, e.g. Stochasting Gradient Descent or Adam.

Every new algorithm inherits from the Algorithm Class and the main function we need to define is get_suggestion(). This function will receive information about the parameters it needs to define and returns a dictionary of hyperparameter values needed to train the next trial. The function get_suggestion() receives:

• parameters: List of Parameter objects.
• results: Dataframe storing the results of past trials.
• lower_is_better: Boolean specifying if lower is better in performance metric of trials.

With this information you are free to select the new hyperparameters in any way you want.

import sherpa
class MyAlgorithm(sherpa.algorithms.Algorithm):
    def get_suggestion(self, parameters, results, lower_is_better):
        # your code here
        return params_values_for_next_trial

For example let’s create a genetic-like algorithm which takes the trials from the top 1/3 of the trials and combines them to create the new set of hyperparameters. It will also randomly introduce a mutation 1/3 of the time.

The function get_candidate() will get the hyperparameters of a random trial among the top 1/3 and if there are very few trials, then it will generate them randomly. get_suggestion() is where the values for the hyperparameters of the new trial will be decided.

import sherpa
import numpy as np
class MyAlgorithm(sherpa.algorithms.Algorithm):
    def get_suggestion(self, parameters, results, lower_is_better):
        """
        Create a new parameter value as a random mixture of some of the best
        trials and sampling from original distribution.

        Returns:
            dict: parameter values dictionary
        """
        # Choose 2 of the top trials and get their parameter values
        trial_1_params = self._get_candidate(parameters, results, lower_is_better)
        trial_2_params = self._get_candidate(parameters, results, lower_is_better)
        params_values_for_next_trial = {}
        for param_name in trial_1_params.keys():
            param_origin = np.random.randint(3)  # randomly choose where to get the value from
            if param_origin == 1:
                params_values_for_next_trial[param_name] = trial_1_params[param_name]
            elif param_origin == 2:
                params_values_for_next_trial[param_name] = trial_2_params[param_name]
            else:
                for parameter_object in parameters:
                    if param_name == parameter_object.name:
                        params_values_for_next_trial[param_name] = parameter_object.sample()
        return params_values_for_next_trial

    def _get_candidate(self, parameters, results, lower_is_better, min_candidates=10):
        """
        Samples candidates parameters from the top 33% of population.

        Returns:
            dict: parameter dictionary.
        """
        if results.shape[0] > 0: # In case this is the first trial
            population = results.loc[results['Status'] != 'INTERMEDIATE', :]  # select only completed trials
        else: # In case this is the first trial
            population = None
        if population is None or population.shape[0] < min_candidates: # Generate random values
            for parameter_object in parameters:
                trial_param_values[parameter_object.name] = parameter_object.sample()
                    return trial_param_values
        population = population.sort_values(by='Objective', ascending=lower_is_better)
        idx = numpy.random.randint(low=0, high=population.shape[0]//3)  # pick randomly among top 33%
        trial_all_values = population.iloc[idx].to_dict()  # extract the trial values on results table
        trial_param_values = {param.name: d[param.name] for param in parameters} # Select only parameter values
        return trial_param_values