bayesmark/builtin_opt/random_optimizer.py [19:67]:
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class RandomOptimizer(AbstractOptimizer):
    # Unclear what is best package to list for primary_import here.
    primary_import = "bayesmark"

    def __init__(self, api_config, random=np_util.random):
        """Build wrapper class to use random search function in benchmark.

        Settings for `suggest_dict` can be passed using kwargs.

        Parameters
        ----------
        api_config : dict-like of dict-like
            Configuration of the optimization variables. See API description.
        """
        AbstractOptimizer.__init__(self, api_config)
        self.random = random

    def suggest(self, n_suggestions=1):
        """Get suggestion.

        Parameters
        ----------
        n_suggestions : int
            Desired number of parallel suggestions in the output

        Returns
        -------
        next_guess : list of dict
            List of `n_suggestions` suggestions to evaluate the objective
            function. Each suggestion is a dictionary where each key
            corresponds to a parameter being optimized.
        """
        x_guess = rs.suggest_dict([], [], self.api_config, n_suggestions=n_suggestions, random=self.random)
        return x_guess

    def observe(self, X, y):
        """Feed an observation back.

        Parameters
        ----------
        X : list of dict-like
            Places where the objective function has already been evaluated.
            Each suggestion is a dictionary where each key corresponds to a
            parameter being optimized.
        y : array-like, shape (n,)
            Corresponding values where objective has been evaluated
        """
        # Random search so don't do anything
        pass
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example_opt_root/random_optimizer.py [7:55]:
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class RandomOptimizer(AbstractOptimizer):
    # Unclear what is best package to list for primary_import here.
    primary_import = "bayesmark"

    def __init__(self, api_config, random=np_util.random):
        """Build wrapper class to use random search function in benchmark.

        Settings for `suggest_dict` can be passed using kwargs.

        Parameters
        ----------
        api_config : dict-like of dict-like
            Configuration of the optimization variables. See API description.
        """
        AbstractOptimizer.__init__(self, api_config)
        self.random = random

    def suggest(self, n_suggestions=1):
        """Get suggestion.

        Parameters
        ----------
        n_suggestions : int
            Desired number of parallel suggestions in the output

        Returns
        -------
        next_guess : list of dict
            List of `n_suggestions` suggestions to evaluate the objective
            function. Each suggestion is a dictionary where each key
            corresponds to a parameter being optimized.
        """
        x_guess = rs.suggest_dict([], [], self.api_config, n_suggestions=n_suggestions, random=self.random)
        return x_guess

    def observe(self, X, y):
        """Feed an observation back.

        Parameters
        ----------
        X : list of dict-like
            Places where the objective function has already been evaluated.
            Each suggestion is a dictionary where each key corresponds to a
            parameter being optimized.
        y : array-like, shape (n,)
            Corresponding values where objective has been evaluated
        """
        # Random search so don't do anything
        pass
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