import collections
import copy
import statistics

from sklearn import utils

from .. import base


__all__ = ['BaggingClassifier', 'BaggingRegressor']


class BaggingClassifier(base.Classifier):
    """Bagging for classification.

    For each incoming observation, each model's ``fit_one`` method is called ``k`` times where
    ``k`` is sampled from a Poisson distribution of parameter 1. ``k`` thus has a 36% chance of
    being equal to 0, a 36% chance of being equal to 1, an 18% chance of being equal to 2, a 6%
    chance of being equal to 3, a 1% chance of being equal to 4, etc. You can do
    ``scipy.stats.poisson(1).pmf(k)`` to obtain more detailed values.

    Parameters:
        classifier (BinaryClassifier or MultiClassifier): The classifier to bag.

    Example:

        In the following example three logistic regressions are bagged together. The performance is
        slightly better than when using a single logistic regression.

        ::

            >>> from creme import compose
            >>> from creme import ensemble
            >>> from creme import linear_model
            >>> from creme import metrics
            >>> from creme import model_selection
            >>> from creme import optim
            >>> from creme import preprocessing
            >>> from creme import stream
            >>> from sklearn import datasets

            >>> X_y = stream.iter_sklearn_dataset(
            ...     load_dataset=datasets.load_breast_cancer,
            ...     shuffle=True,
            ...     random_state=42
            ... )
            >>> optimiser = optim.VanillaSGD()
            >>> model = compose.Pipeline([
            ...     ('scale', preprocessing.StandardScaler()),
            ...     ('learn', linear_model.LogisticRegression(optimiser))
            ... ])
            >>> model = ensemble.BaggingClassifier(
            ...     classifier=model,
            ...     n_classifiers=3,
            ...     random_state=42
            ... )
            >>> metric = metrics.F1Score()

            >>> model_selection.online_score(X_y, model, metric)
            F1Score: 0.970464

    References:

        1. `Online Bagging and Boosting <https://ti.arc.nasa.gov/m/profile/oza/files/ozru01a.pdf>`_

    """

    def __init__(self, classifier=None, n_classifiers=10, random_state=None):
        self.classifier = classifier
        self.classifiers = [copy.deepcopy(classifier) for _ in range(n_classifiers)]
        self.rng = utils.check_random_state(random_state)

    @property
    def __class__(self):
        return self.classifier.__class__

    def __str__(self):
        return f'BaggingClassifier({str(self.classifier)})'

    def fit_one(self, x, y):

        for classifier in self.classifiers:
            for _ in range(self.rng.poisson(1)):
                classifier.fit_one(x, y)

        return self

    def predict_proba_one(self, x):
        """Averages the predictions of each classifier."""

        y_pred = collections.defaultdict(float)

        # Sum the predictions
        for classifier in self.classifiers:
            for label, proba in classifier.predict_proba_one(x).items():
                y_pred[label] += proba

        # Divide by the number of predictions
        for label in y_pred:
            y_pred[label] /= len(self.classifiers)

        return dict(y_pred)


class BaggingRegressor(base.Regressor):
    """Bagging for regression.

    For each incoming observation, each model's `fit_one` method is called ``k`` times where ``k``
    is sampled from a Poisson distribution of parameter 1. ``k`` thus has a 36% chance of being
    equal to 0, a 36% chance of being equal to 1, an 18% chance of being equal to 2, a 6% chance of
    being equal to 3, a 1% chance of being equal to 4, etc. You can do
    ``scipy.stats.poisson(1).pmf(k)`` for more detailed values.

    Parameters:
        base_regressor (Regressor): The regressor to bag.

    Example:

        In the following example three logistic regressions are bagged together. The performance is
        slightly better than when using a single logistic regression.

        ::

            >>> from creme import compose
            >>> from creme import ensemble
            >>> from creme import linear_model
            >>> from creme import metrics
            >>> from creme import model_selection
            >>> from creme import optim
            >>> from creme import preprocessing
            >>> from creme import stream
            >>> from sklearn import datasets

            >>> X_y = stream.iter_sklearn_dataset(
            ...     load_dataset=datasets.load_boston,
            ...     shuffle=True,
            ...     random_state=42
            ... )
            >>> model = compose.Pipeline([
            ...     ('scale', preprocessing.StandardScaler()),
            ...     ('learn', linear_model.LinearRegression())
            ... ])
            >>> metric = metrics.MSE()

    References:

        1. `Online Bagging and Boosting <https://ti.arc.nasa.gov/m/profile/oza/files/ozru01a.pdf>`_

    """

    def __init__(self, regressor=None, n_regressors=10, random_state=42):
        self.regressor = regressor
        self.regressors = [copy.deepcopy(regressor) for _ in range(n_regressors)]
        self.rng = utils.check_random_state(random_state)

    def __str__(self):
        return f'BaggingRegressor({str(self.regressor)})'

    def fit_one(self, x, y):

        for regressor in self.regressors:
            for _ in range(self.rng.poisson(1)):
                regressor.fit_one(x, y)

        return self

    def predict_one(self, x):
        """Averages the predictions of each regressor."""
        return statistics.mean((regressor.predict_one(x) for regressor in self.regressors))

import collections
import copy
import statistics

from sklearn import utils

from .. import base


__all__ = ['BaggingClassifier', 'BaggingRegressor']


class BaggingClassifier(base.Classifier):
    """Bagging for classification.

    For each incoming observation, each model's ``fit_one`` method is called ``k`` times where
    ``k`` is sampled from a Poisson distribution of parameter 1. ``k`` thus has a 36% chance of
    being equal to 0, a 36% chance of being equal to 1, an 18% chance of being equal to 2, a 6%
    chance of being equal to 3, a 1% chance of being equal to 4, etc. You can do
    ``scipy.stats.poisson(1).pmf(k)`` to obtain more detailed values.

    Parameters:
        classifier (BinaryClassifier or MultiClassifier): The classifier to bag.

    Example:

        In the following example three logistic regressions are bagged together. The performance is
        slightly better than when using a single logistic regression.

        ::

            >>> from creme import compose
            >>> from creme import ensemble
            >>> from creme import linear_model
            >>> from creme import metrics
            >>> from creme import model_selection
            >>> from creme import optim
            >>> from creme import preprocessing
            >>> from creme import stream
            >>> from sklearn import datasets

            >>> X_y = stream.iter_sklearn_dataset(
            ...     load_dataset=datasets.load_breast_cancer,
            ...     shuffle=True,
            ...     random_state=42
            ... )
            >>> optimiser = optim.VanillaSGD()
            >>> model = compose.Pipeline([
            ...     ('scale', preprocessing.StandardScaler()),
            ...     ('learn', linear_model.LogisticRegression(optimiser))
            ... ])
            >>> model = ensemble.BaggingClassifier(
            ...     classifier=model,
            ...     n_classifiers=3,
            ...     random_state=42
            ... )
            >>> metric = metrics.F1Score()

            >>> model_selection.online_score(X_y, model, metric)
            F1Score: 0.970464

    References:

        1. `Online Bagging and Boosting <https://ti.arc.nasa.gov/m/profile/oza/files/ozru01a.pdf>`_

    """

    def __init__(self, classifier=None, n_classifiers=10, random_state=None):
        self.classifier = classifier
        self.classifiers = [copy.deepcopy(classifier) for _ in range(n_classifiers)]
        self.rng = utils.check_random_state(random_state)

    @property
    def __class__(self):
        return self.classifier.__class__

    def __str__(self):
        return f'BaggingClassifier({str(self.classifier)})'

    def fit_one(self, x, y):

        for classifier in self.classifiers:
            for _ in range(self.rng.poisson(1)):
                classifier.fit_one(x, y)

        return self

    def predict_proba_one(self, x):
        """Averages the predictions of each classifier."""

        y_pred = collections.defaultdict(float)

        # Sum the predictions
        for classifier in self.classifiers:
            for label, proba in classifier.predict_proba_one(x).items():
                y_pred[label] += proba

        # Divide by the number of predictions
        for label in y_pred:
            y_pred[label] /= len(self.classifiers)

        return dict(y_pred)


class BaggingRegressor(base.Regressor):
    """Bagging for regression.

    For each incoming observation, each model's `fit_one` method is called ``k`` times where ``k``
    is sampled from a Poisson distribution of parameter 1. ``k`` thus has a 36% chance of being
    equal to 0, a 36% chance of being equal to 1, an 18% chance of being equal to 2, a 6% chance of
    being equal to 3, a 1% chance of being equal to 4, etc. You can do
    ``scipy.stats.poisson(1).pmf(k)`` for more detailed values.

    Parameters:
        base_regressor (Regressor): The regressor to bag.

    Example:

        In the following example three logistic regressions are bagged together. The performance is
        slightly better than when using a single logistic regression.

        ::

            >>> from creme import compose
            >>> from creme import ensemble
            >>> from creme import linear_model
            >>> from creme import metrics
            >>> from creme import model_selection
            >>> from creme import optim
            >>> from creme import preprocessing
            >>> from creme import stream
            >>> from sklearn import datasets

            >>> X_y = stream.iter_sklearn_dataset(
            ...     load_dataset=datasets.load_boston,
            ...     shuffle=True,
            ...     random_state=42
            ... )
            >>> model = compose.Pipeline([
            ...     ('scale', preprocessing.StandardScaler()),
            ...     ('learn', linear_model.LinearRegression())
            ... ])
            >>> metric = metrics.MSE()

    References:

        1. `Online Bagging and Boosting <https://ti.arc.nasa.gov/m/profile/oza/files/ozru01a.pdf>`_

    """

    def __init__(self, regressor=None, n_regressors=10, random_state=42):
        self.regressor = regressor
        self.regressors = [copy.deepcopy(regressor) for _ in range(n_regressors)]
        self.rng = utils.check_random_state(random_state)

    def __str__(self):
        return f'BaggingRegressor({str(self.regressor)})'

    def fit_one(self, x, y):

        for regressor in self.regressors:
            for _ in range(self.rng.poisson(1)):
                regressor.fit_one(x, y)

        return self

    def predict_one(self, x):
        """Averages the predictions of each regressor."""
        return statistics.mean((regressor.predict_one(x) for regressor in self.regressors))

Bagging for classification.      For each incoming observation, each model's ``fit_one`` method is called ``k`` times where     ``k`` is sampled from a Poisson distribution of parameter 1. ``k`` thus has a 36% chance of     being equal to 0, a 36% chance of being equal to 1, an 18% chance of being equal to 2, a 6%     chance of being equal to 3, a 1% chance of being equal to 4, etc. You can do     ``scipy.stats.poisson(1).pmf(k)`` to obtain more detailed values.      Parameters:         classifier (BinaryClassifier or MultiClassifier): The classifier to bag.      Example:          In the following example three logistic regressions are bagged together. The performance is         slightly better than when using a single logistic regression.          ::              >>> from creme import compose             >>> from creme import ensemble             >>> from creme import linear_model             >>> from creme import metrics             >>> from creme import model_selection             >>> from creme import optim             >>> from creme import preprocessing             >>> from creme import stream             >>> from sklearn import datasets              >>> X_y = stream.iter_sklearn_dataset(             ...     load_dataset=datasets.load_breast_cancer,             ...     shuffle=True,             ...     random_state=42             ... )             >>> optimiser = optim.VanillaSGD()             >>> model = compose.Pipeline([             ...     ('scale', preprocessing.StandardScaler()),             ...     ('learn', linear_model.LogisticRegression(optimiser))             ... ])             >>> model = ensemble.BaggingClassifier(             ...     classifier=model,             ...     n_classifiers=3,             ...     random_state=42             ... )             >>> metric = metrics.F1Score()              >>> model_selection.online_score(X_y, model, metric)             F1Score: 0.970464      References:          1. `Online Bagging and Boosting <https://ti.arc.nasa.gov/m/profile/oza/files/ozru01a.pdf>`_ Averages the predictions of each classifier. # Sum the predictions # Divide by the number of predictions Bagging for regression.      For each incoming observation, each model's `fit_one` method is called ``k`` times where ``k``     is sampled from a Poisson distribution of parameter 1. ``k`` thus has a 36% chance of being     equal to 0, a 36% chance of being equal to 1, an 18% chance of being equal to 2, a 6% chance of     being equal to 3, a 1% chance of being equal to 4, etc. You can do     ``scipy.stats.poisson(1).pmf(k)`` for more detailed values.      Parameters:         base_regressor (Regressor): The regressor to bag.      Example:          In the following example three logistic regressions are bagged together. The performance is         slightly better than when using a single logistic regression.          ::              >>> from creme import compose             >>> from creme import ensemble             >>> from creme import linear_model             >>> from creme import metrics             >>> from creme import model_selection             >>> from creme import optim             >>> from creme import preprocessing             >>> from creme import stream             >>> from sklearn import datasets              >>> X_y = stream.iter_sklearn_dataset(             ...     load_dataset=datasets.load_boston,             ...     shuffle=True,             ...     random_state=42             ... )             >>> model = compose.Pipeline([             ...     ('scale', preprocessing.StandardScaler()),             ...     ('learn', linear_model.LinearRegression())             ... ])             >>> metric = metrics.MSE()      References:          1. `Online Bagging and Boosting <https://ti.arc.nasa.gov/m/profile/oza/files/ozru01a.pdf>`_ Averages the predictions of each regressor.