# -*- coding: utf-8 -*-
"""Classes (Python) to compute the Bandit Epsilon-First arm allocation and choosing the arm to pull next.

See :class:`moe.bandit.epsilon.epsilon_interface.EpsilonInterface` for further details on bandit.

"""
from moe.bandit.constant import DEFAULT_EPSILON, DEFAULT_TOTAL_SAMPLES, EPSILON_SUBTYPE_FIRST
from moe.bandit.epsilon.epsilon_interface import EpsilonInterface

from moe.bandit.utils import get_equal_arm_allocations


class EpsilonFirst(EpsilonInterface):

    r"""Implementation of EpsilonFirst.

    A class to encapsulate the computation of bandit epsilon first.

    total_samples is the total number of samples (number to sample + number sampled)
    number sampled is calculated by summing up total from each arm sampled.
    total_samples is T from :doc:`bandit`.

    See superclass :class:`moe.bandit.epsilon.epsilon_interface.EpsilonInterface` for further details.

    """

    def __init__(
            self,
            historical_info,
            epsilon=DEFAULT_EPSILON,
            total_samples=DEFAULT_TOTAL_SAMPLES,
    ):
        """Construct an EpsilonFirst object. See superclass :class:`moe.bandit.epsilon.epsilon_interface.EpsilonInterface` for details.

        total_samples is the total number of samples (number to sample + number sampled)
        number sampled is calculated by summing up total from each arm sampled.
        total_samples is T from :doc:`bandit`.

        """
        super(EpsilonFirst, self).__init__(
            historical_info=historical_info,
            subtype=EPSILON_SUBTYPE_FIRST,
            epsilon=epsilon,
            )
        self._total_samples = total_samples

    def allocate_arms(self):
        r"""Compute the allocation to each arm given ``historical_info``, running bandit ``subtype`` endpoint with hyperparameters in ``hyperparameter_info``.

        Computes the allocation to each arm based on the given subtype, historical info, and hyperparameter info.

        Works with k-armed bandits (k >= 1).

        The Algorithm: http://en.wikipedia.org/wiki/Multi-armed_bandit#Approximate_solutions

        This method starts with a pure exploration phase, followed by a pure exploitation phase.
        If we have a total of T trials, the first :math:`\epsilon` T trials, we only explore.
        After that, we only exploit (t = :math:`\epsilon` T, :math:`\epsilon` T + 1, ..., T).

        This method will pull a random arm in the exploration phase.
        Then this method will pull the optimal arm (best expected return) in the exploitation phase.

        In case of a tie in the exploitation phase, the method will split the allocation among the optimal arms.

        For example, if we have three arms, two arms (arm1 and arm2) with an average payoff of 0.5
        (``{win:10, lose:10, total:20}``)
        and a new arm (arm3, average payoff is 0 and total is 0).

        Let the epsilon :math:`\epsilon` be 0.1.

        The allocation depends on which phase we are in:

        *Case 1: T = 50*

        Recall that T = number to sample + number sampled. number sampled :math:`= 20 + 20 + 0 = 40`.
        So we are on trial #41. We explore the first :math:`\epsilon T = 0.1 * 50 = 5` trials
        and thus we are in the exploitation phase. We split the allocation between the optimal arms arm1 and arm2.

        ``{arm1: 0.5, arm2: 0.5, arm3: 0.0}``

        *Case 2: T = 500*

        We explore the first :math:`\epsilon T = 0.1 * 500 = 50` trials.
        Since we are on trail #41, we are in the exploration phase. We choose arms randomly:

        ``{arm1: 0.33, arm2: 0.33, arm3: 0.33}``

        :return: the dictionary of (arm, allocation) key-value pairs
        :rtype: a dictionary of (str, float64) pairs
        :raise: ValueError when ``sample_arms`` are empty.

        """
        arms_sampled = self._historical_info.arms_sampled

        if not arms_sampled:
            raise ValueError('sample_arms is empty!')

        num_sampled = sum([sampled_arm.total for sampled_arm in arms_sampled.itervalues()])
        # Exploration phase, trials 1,2,..., epsilon * T
        # Allocate equal probability to all arms
        if num_sampled < self._total_samples * self._epsilon:
            return get_equal_arm_allocations(arms_sampled)

        # Exploitation phase, trials epsilon * T+1, ..., T
        return get_equal_arm_allocations(arms_sampled, self.get_winning_arm_names(arms_sampled))

# -*- coding: utf-8 -*-
"""Classes (Python) to compute the Bandit Epsilon-First arm allocation and choosing the arm to pull next.

See :class:`moe.bandit.epsilon.epsilon_interface.EpsilonInterface` for further details on bandit.

"""
from moe.bandit.constant import DEFAULT_EPSILON, DEFAULT_TOTAL_SAMPLES, EPSILON_SUBTYPE_FIRST
from moe.bandit.epsilon.epsilon_interface import EpsilonInterface

from moe.bandit.utils import get_equal_arm_allocations


class EpsilonFirst(EpsilonInterface):

    r"""Implementation of EpsilonFirst.

    A class to encapsulate the computation of bandit epsilon first.

    total_samples is the total number of samples (number to sample + number sampled)
    number sampled is calculated by summing up total from each arm sampled.
    total_samples is T from :doc:`bandit`.

    See superclass :class:`moe.bandit.epsilon.epsilon_interface.EpsilonInterface` for further details.

    """

    def __init__(
            self,
            historical_info,
            epsilon=DEFAULT_EPSILON,
            total_samples=DEFAULT_TOTAL_SAMPLES,
    ):
        """Construct an EpsilonFirst object. See superclass :class:`moe.bandit.epsilon.epsilon_interface.EpsilonInterface` for details.

        total_samples is the total number of samples (number to sample + number sampled)
        number sampled is calculated by summing up total from each arm sampled.
        total_samples is T from :doc:`bandit`.

        """
        super(EpsilonFirst, self).__init__(
            historical_info=historical_info,
            subtype=EPSILON_SUBTYPE_FIRST,
            epsilon=epsilon,
            )
        self._total_samples = total_samples

    def allocate_arms(self):
        r"""Compute the allocation to each arm given ``historical_info``, running bandit ``subtype`` endpoint with hyperparameters in ``hyperparameter_info``.

        Computes the allocation to each arm based on the given subtype, historical info, and hyperparameter info.

        Works with k-armed bandits (k >= 1).

        The Algorithm: http://en.wikipedia.org/wiki/Multi-armed_bandit#Approximate_solutions

        This method starts with a pure exploration phase, followed by a pure exploitation phase.
        If we have a total of T trials, the first :math:`\epsilon` T trials, we only explore.
        After that, we only exploit (t = :math:`\epsilon` T, :math:`\epsilon` T + 1, ..., T).

        This method will pull a random arm in the exploration phase.
        Then this method will pull the optimal arm (best expected return) in the exploitation phase.

        In case of a tie in the exploitation phase, the method will split the allocation among the optimal arms.

        For example, if we have three arms, two arms (arm1 and arm2) with an average payoff of 0.5
        (``{win:10, lose:10, total:20}``)
        and a new arm (arm3, average payoff is 0 and total is 0).

        Let the epsilon :math:`\epsilon` be 0.1.

        The allocation depends on which phase we are in:

        *Case 1: T = 50*

        Recall that T = number to sample + number sampled. number sampled :math:`= 20 + 20 + 0 = 40`.
        So we are on trial #41. We explore the first :math:`\epsilon T = 0.1 * 50 = 5` trials
        and thus we are in the exploitation phase. We split the allocation between the optimal arms arm1 and arm2.

        ``{arm1: 0.5, arm2: 0.5, arm3: 0.0}``

        *Case 2: T = 500*

        We explore the first :math:`\epsilon T = 0.1 * 500 = 50` trials.
        Since we are on trail #41, we are in the exploration phase. We choose arms randomly:

        ``{arm1: 0.33, arm2: 0.33, arm3: 0.33}``

        :return: the dictionary of (arm, allocation) key-value pairs
        :rtype: a dictionary of (str, float64) pairs
        :raise: ValueError when ``sample_arms`` are empty.

        """
        arms_sampled = self._historical_info.arms_sampled

        if not arms_sampled:
            raise ValueError('sample_arms is empty!')

        num_sampled = sum([sampled_arm.total for sampled_arm in arms_sampled.itervalues()])
        # Exploration phase, trials 1,2,..., epsilon * T
        # Allocate equal probability to all arms
        if num_sampled < self._total_samples * self._epsilon:
            return get_equal_arm_allocations(arms_sampled)

        # Exploitation phase, trials epsilon * T+1, ..., T
        return get_equal_arm_allocations(arms_sampled, self.get_winning_arm_names(arms_sampled))

# -*- coding: utf-8 -*- Classes (Python) to compute the Bandit Epsilon-First arm allocation and choosing the arm to pull next.  See :class:`moe.bandit.epsilon.epsilon_interface.EpsilonInterface` for further details on bandit. Implementation of EpsilonFirst.      A class to encapsulate the computation of bandit epsilon first.      total_samples is the total number of samples (number to sample + number sampled)     number sampled is calculated by summing up total from each arm sampled.     total_samples is T from :doc:`bandit`.      See superclass :class:`moe.bandit.epsilon.epsilon_interface.EpsilonInterface` for further details. Construct an EpsilonFirst object. See superclass :class:`moe.bandit.epsilon.epsilon_interface.EpsilonInterface` for details.          total_samples is the total number of samples (number to sample + number sampled)         number sampled is calculated by summing up total from each arm sampled.         total_samples is T from :doc:`bandit`. Compute the allocation to each arm given ``historical_info``, running bandit ``subtype`` endpoint with hyperparameters in ``hyperparameter_info``.          Computes the allocation to each arm based on the given subtype, historical info, and hyperparameter info.          Works with k-armed bandits (k >= 1).          The Algorithm: http://en.wikipedia.org/wiki/Multi-armed_bandit#Approximate_solutions          This method starts with a pure exploration phase, followed by a pure exploitation phase.         If we have a total of T trials, the first :math:`\epsilon` T trials, we only explore.         After that, we only exploit (t = :math:`\epsilon` T, :math:`\epsilon` T + 1, ..., T).          This method will pull a random arm in the exploration phase.         Then this method will pull the optimal arm (best expected return) in the exploitation phase.          In case of a tie in the exploitation phase, the method will split the allocation among the optimal arms.          For example, if we have three arms, two arms (arm1 and arm2) with an average payoff of 0.5         (``{win:10, lose:10, total:20}``)         and a new arm (arm3, average payoff is 0 and total is 0).          Let the epsilon :math:`\epsilon` be 0.1.          The allocation depends on which phase we are in:          *Case 1: T = 50*          Recall that T = number to sample + number sampled. number sampled :math:`= 20 + 20 + 0 = 40`.         So we are on trial #41. We explore the first :math:`\epsilon T = 0.1 * 50 = 5` trials         and thus we are in the exploitation phase. We split the allocation between the optimal arms arm1 and arm2.          ``{arm1: 0.5, arm2: 0.5, arm3: 0.0}``          *Case 2: T = 500*          We explore the first :math:`\epsilon T = 0.1 * 500 = 50` trials.         Since we are on trail #41, we are in the exploration phase. We choose arms randomly:          ``{arm1: 0.33, arm2: 0.33, arm3: 0.33}``          :return: the dictionary of (arm, allocation) key-value pairs         :rtype: a dictionary of (str, float64) pairs         :raise: ValueError when ``sample_arms`` are empty. # Exploration phase, trials 1,2,..., epsilon * T # Allocate equal probability to all arms # Exploitation phase, trials epsilon * T+1, ..., T