<commit_before>import logging
import pickle
from typing import Dict, Optional, Union, List, Sequence

from ray.tune.result import DEFAULT_METRIC
from ray.tune.sample import Categorical, Domain, Float, Integer, LogUniform, \
    Quantized
from ray.tune.suggest.suggestion import UNRESOLVED_SEARCH_SPACE, \
    UNDEFINED_METRIC_MODE, UNDEFINED_SEARCH_SPACE
from ray.tune.suggest.variant_generator import parse_spec_vars
from ray.tune.utils.util import flatten_dict, unflatten_dict

try:
    import nevergrad as ng
    from nevergrad.optimization import Optimizer
    from nevergrad.optimization.base import ConfiguredOptimizer
    Parameter = ng.p.Parameter
except ImportError:
    ng = None
    Optimizer = None
    ConfiguredOptimizer = None
    Parameter = None

from ray.tune.suggest import Searcher

logger = logging.getLogger(__name__)


class NevergradSearch(Searcher):
    """Uses Nevergrad to optimize hyperparameters.

    Nevergrad is an open source tool from Facebook for derivative free
    optimization.  More info can be found at:
    https://github.com/facebookresearch/nevergrad.

    You will need to install Nevergrad via the following command:

    .. code-block:: bash

        $ pip install nevergrad

    Parameters:
        optimizer (nevergrad.optimization.Optimizer|class): Optimizer provided
            from Nevergrad. Alter
        space (list|nevergrad.parameter.Parameter): Nevergrad parametrization
            to be passed to optimizer on instantiation, or list of parameter
            names if you passed an optimizer object.
        metric (str): The training result objective value attribute. If None
            but a mode was passed, the anonymous metric `_metric` will be used
            per default.
        mode (str): One of {min, max}. Determines whether objective is
            minimizing or maximizing the metric attribute.
        points_to_evaluate (list): Initial parameter suggestions to be run
            first. This is for when you already have some good parameters
            you want to run first to help the algorithm make better suggestions
            for future parameters. Needs to be a list of dicts containing the
            configurations.
        use_early_stopped_trials: Deprecated.
        max_concurrent: Deprecated.

    Tune automatically converts search spaces to Nevergrad's format:

    .. code-block:: python

        import nevergrad as ng

        config = {
            "width": tune.uniform(0, 20),
            "height": tune.uniform(-100, 100),
            "activation": tune.choice(["relu", "tanh"])
        }

        current_best_params = [{
            "width": 10,
            "height": 0,
            "activation": relu",
        }]

        ng_search = NevergradSearch(
            optimizer=ng.optimizers.OnePlusOne,
            metric="mean_loss",
            mode="min",
            points_to_evaluate=current_best_params)

        run(my_trainable, config=config, search_alg=ng_search)

    If you would like to pass the search space manually, the code would
    look like this:

    .. code-block:: python

        import nevergrad as ng

        space = ng.p.Dict(
            width=ng.p.Scalar(lower=0, upper=20),
            height=ng.p.Scalar(lower=-100, upper=100),
            activation=ng.p.Choice(choices=["relu", "tanh"])
        )

        ng_search = NevergradSearch(
            optimizer=ng.optimizers.OnePlusOne,
            space=space,
            metric="mean_loss",
            mode="min")

        run(my_trainable, search_alg=ng_search)

    """

    def __init__(self,
                 optimizer: Union[None, Optimizer, ConfiguredOptimizer] = None,
                 space: Optional[Union[Dict, Parameter]] = None,
                 metric: Optional[str] = None,
                 mode: Optional[str] = None,
                 points_to_evaluate: Optional[List[Dict]] = None,
                 max_concurrent: Optional[int] = None,
                 **kwargs):
        assert ng is not None, """Nevergrad must be installed!
            You can install Nevergrad with the command:
            `pip install nevergrad`."""
        if mode:
            assert mode in ["min", "max"], "`mode` must be 'min' or 'max'."

        super(NevergradSearch, self).__init__(
            metric=metric, mode=mode, max_concurrent=max_concurrent, **kwargs)

        self._space = None
        self._opt_factory = None
        self._nevergrad_opt = None

        if points_to_evaluate is None:
            self._points_to_evaluate = None
        elif not isinstance(points_to_evaluate, Sequence):
            raise ValueError(
                f"Invalid object type passed for `points_to_evaluate`: "
                "{type(points_to_evaluate)}. "
                f"Please pass a list of points (dictionaries) instead.")
        else:
            self._points_to_evaluate = list(points_to_evaluate)

        if isinstance(space, dict) and space:
            resolved_vars, domain_vars, grid_vars = parse_spec_vars(space)
            if domain_vars or grid_vars:
                logger.warning(
                    UNRESOLVED_SEARCH_SPACE.format(
                        par="space", cls=type(self)))
                space = self.convert_search_space(space)

        if isinstance(optimizer, Optimizer):
            if space is not None and not isinstance(space, list):
                raise ValueError(
                    "If you pass a configured optimizer to Nevergrad, either "
                    "pass a list of parameter names or None as the `space` "
                    "parameter.")
            self._parameters = space
            self._nevergrad_opt = optimizer
        elif isinstance(optimizer, ConfiguredOptimizer):
            self._opt_factory = optimizer
            self._parameters = None
            self._space = space
        else:<commit_msg>[tune] Fix f-string in error message (#13423)<commit_after>import logging
import pickle
from typing import Dict, Optional, Union, List, Sequence

from ray.tune.result import DEFAULT_METRIC
from ray.tune.sample import Categorical, Domain, Float, Integer, LogUniform, \
    Quantized
from ray.tune.suggest.suggestion import UNRESOLVED_SEARCH_SPACE, \
    UNDEFINED_METRIC_MODE, UNDEFINED_SEARCH_SPACE
from ray.tune.suggest.variant_generator import parse_spec_vars
from ray.tune.utils.util import flatten_dict, unflatten_dict

try:
    import nevergrad as ng
    from nevergrad.optimization import Optimizer
    from nevergrad.optimization.base import ConfiguredOptimizer
    Parameter = ng.p.Parameter
except ImportError:
    ng = None
    Optimizer = None
    ConfiguredOptimizer = None
    Parameter = None

from ray.tune.suggest import Searcher

logger = logging.getLogger(__name__)


class NevergradSearch(Searcher):
    """Uses Nevergrad to optimize hyperparameters.

    Nevergrad is an open source tool from Facebook for derivative free
    optimization.  More info can be found at:
    https://github.com/facebookresearch/nevergrad.

    You will need to install Nevergrad via the following command:

    .. code-block:: bash

        $ pip install nevergrad

    Parameters:
        optimizer (nevergrad.optimization.Optimizer|class): Optimizer provided
            from Nevergrad. Alter
        space (list|nevergrad.parameter.Parameter): Nevergrad parametrization
            to be passed to optimizer on instantiation, or list of parameter
            names if you passed an optimizer object.
        metric (str): The training result objective value attribute. If None
            but a mode was passed, the anonymous metric `_metric` will be used
            per default.
        mode (str): One of {min, max}. Determines whether objective is
            minimizing or maximizing the metric attribute.
        points_to_evaluate (list): Initial parameter suggestions to be run
            first. This is for when you already have some good parameters
            you want to run first to help the algorithm make better suggestions
            for future parameters. Needs to be a list of dicts containing the
            configurations.
        use_early_stopped_trials: Deprecated.
        max_concurrent: Deprecated.

    Tune automatically converts search spaces to Nevergrad's format:

    .. code-block:: python

        import nevergrad as ng

        config = {
            "width": tune.uniform(0, 20),
            "height": tune.uniform(-100, 100),
            "activation": tune.choice(["relu", "tanh"])
        }

        current_best_params = [{
            "width": 10,
            "height": 0,
            "activation": relu",
        }]

        ng_search = NevergradSearch(
            optimizer=ng.optimizers.OnePlusOne,
            metric="mean_loss",
            mode="min",
            points_to_evaluate=current_best_params)

        run(my_trainable, config=config, search_alg=ng_search)

    If you would like to pass the search space manually, the code would
    look like this:

    .. code-block:: python

        import nevergrad as ng

        space = ng.p.Dict(
            width=ng.p.Scalar(lower=0, upper=20),
            height=ng.p.Scalar(lower=-100, upper=100),
            activation=ng.p.Choice(choices=["relu", "tanh"])
        )

        ng_search = NevergradSearch(
            optimizer=ng.optimizers.OnePlusOne,
            space=space,
            metric="mean_loss",
            mode="min")

        run(my_trainable, search_alg=ng_search)

    """

    def __init__(self,
                 optimizer: Union[None, Optimizer, ConfiguredOptimizer] = None,
                 space: Optional[Union[Dict, Parameter]] = None,
                 metric: Optional[str] = None,
                 mode: Optional[str] = None,
                 points_to_evaluate: Optional[List[Dict]] = None,
                 max_concurrent: Optional[int] = None,
                 **kwargs):
        assert ng is not None, """Nevergrad must be installed!
            You can install Nevergrad with the command:
            `pip install nevergrad`."""
        if mode:
            assert mode in ["min", "max"], "`mode` must be 'min' or 'max'."

        super(NevergradSearch, self).__init__(
            metric=metric, mode=mode, max_concurrent=max_concurrent, **kwargs)

        self._space = None
        self._opt_factory = None
        self._nevergrad_opt = None

        if points_to_evaluate is None:
            self._points_to_evaluate = None
        elif not isinstance(points_to_evaluate, Sequence):
            raise ValueError(
                "Invalid object type passed for `points_to_evaluate`: "
                f"{type(points_to_evaluate)}. "
                "Please pass a list of points (dictionaries) instead.")
        else:
            self._points_to_evaluate = list(points_to_evaluate)

        if isinstance(space, dict) and space:
            resolved_vars, domain_vars, grid_vars = parse_spec_vars(space)
            if domain_vars or grid_vars:
                logger.warning(
                    UNRESOLVED_SEARCH_SPACE.format(
                        par="space", cls=type(self)))
                space = self.convert_search_space(space)

        if isinstance(optimizer, Optimizer):
            if space is not None and not isinstance(space, list):
                raise ValueError(
                    "If you pass a configured optimizer to Nevergrad, either "
                    "pass a list of parameter names or None as the `space` "
                    "parameter.")
            self._parameters = space
            self._nevergrad_opt = optimizer
        elif isinstance(optimizer, ConfiguredOptimizer):
            self._opt_factory = optimizer
            self._parameters = None
            self._space = space
        else:
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