import logging

import numpy as np

from csrank.learner import Learner
import csrank.theano_util as ttu
from csrank.util import print_dictionary
from .discrete_choice import DiscreteObjectChooser
from .likelihoods import create_weight_dictionary
from .likelihoods import fit_pymc3_model
from .likelihoods import likelihood_dict
from .likelihoods import LogLikelihood

try:
    import pymc3 as pm
    from pymc3.variational.callbacks import CheckParametersConvergence
except ImportError:
    from csrank.util import MissingExtraError

    raise MissingExtraError("pymc3", "probabilistic")

try:
    import theano
    from theano import tensor as tt
except ImportError:
    from csrank.util import MissingExtraError

    raise MissingExtraError("theano", "probabilistic")

logger = logging.getLogger(__name__)


class MultinomialLogitModel(DiscreteObjectChooser, Learner):
    def __init__(self, loss_function="", regularization="l2", **kwargs):
        """
        Create an instance of the Multinomial Logit model for learning the discrete choice function. The utility
        score for each object in query set :math:`Q` is defined as :math:`U(x) = w \\cdot x`, where :math:`w` is
        the weight vector. The probability of choosing an object :math:`x_i` is defined by taking softmax over the
        utility scores of the objects:

        .. math::

            P(x_i \\lvert Q) = \\frac{exp(U(x_i))}{\\sum_{x_j \\in Q} exp(U(x_j))}

        The discrete choice for the given query set :math:`Q` is defined as:

        .. math::

            dc(Q) := \\operatorname{argmax}_{x_i \\in Q }  \\; P(x_i \\lvert Q)

        Parameters
        ----------
        loss_function : string , {‘categorical_crossentropy’, ‘binary_crossentropy’, ’categorical_hinge’}
            Loss function to be used for the discrete choice decision from the query set
        regularization : string, {‘l1’, ‘l2’}, string
           Regularizer function (L1 or L2) applied to the `kernel` weights matrix
        random_state : int or object
            Numpy random state
        **kwargs
            Keyword arguments for the algorithms

        References
        ----------
            [1] <NAME>. „Discrete choice methods with simulation“. In: Cambridge university press, 2009. Chap Logit, pp. 41–86.

            [2] <NAME>. Qualitative choice analysis. Cambridge, MA: MIT Press, 1986
        """
        self.loss_function = loss_function
        known_regularization_functions = {"l1", "l2"}
        if regularization not in known_regularization_functions:
            raise ValueError(
                f"Regularization function {regularization} is unknown. Must be one of {known_regularization_functions}"
            )
        self.regularization = regularization

    @property
    def model_configuration(self):
        """
            Constructs the dictionary containing the priors for the weight vectors for the model according to the
            regularization function. The parameters are:
                * **weights** : Weights to evaluates the utility of the objects

            For ``l1`` regularization the priors are:

            .. math::

                \\text{mu}_w \\sim \\text{Normal}(\\text{mu}=0, \\text{sd}=5.0) \\\\
                \\text{b}_w \\sim \\text{HalfCauchy}(\\beta=1.0) \\\\
                \\text{weights} \\sim \\text{Laplace}(\\text{mu}=\\text{mu}_w, \\text{b}=\\text{b}_w)

            For ``l2`` regularization the priors are:

            .. math::

                \\text{mu}_w \\sim \\text{Normal}(\\text{mu}=0, \\text{sd}=5.0) \\\\
                \\text{sd}_w \\sim \\text{HalfCauchy}(\\beta=1.0) \\\\
                \\text{weights} \\sim \\text{Normal}(\\text{mu}=\\text{mu}_w, \\text{sd}=\\text{sd}_w)
        """
        if not hasattr(self, "config_"):
            if self.regularization == "l2":
                weight = pm.Normal
                prior = "sd"
            elif self.regularization == "l1":
                weight = pm.Laplace
                prior = "b"
            self.config_ = {
                "weights": [
                    weight,
                    {
                        "mu": (pm.Normal, {"mu": 0, "sd": 5}),
                        prior: (pm.HalfCauchy, {"beta": 1}),
                    },
                ]
            }
            logger.info(
                "Creating model with config {}".format(print_dictionary(self.config_))
            )
        return self.config_

    def construct_model(self, X, Y):
        """
        Constructs the multinomial logit model which evaluated the utility score as :math:`U(x) = w \\cdot x`, where
        :math:`w` is the weight vector. The probability of choosing the object :math:`x_i` from the query set
        :math:`Q = \\{x_1, \\ldots ,x_n\\}` is:

        .. math::

            P_i = P(x_i \\lvert Q) = \\frac{exp(U(x_i))}{\\sum_{x_j \\in Q} exp(U(x_j))}

        Parameters
        ----------
        X : numpy array
            (n_instances, n_objects, n_features)
            Feature vectors of the objects
        Y : numpy array
            (n_instances, n_objects)
            Preferences in the form of discrete choices for given objects

        Returns
        -------
         model : pymc3 Model :class:`pm.Model`
        """
        self.trace_ = None
        self.trace_vi_ = None
        logger.info(
            "Creating model_args config {}".format(
                print_dictionary(self.model_configuration)
            )
        )
        self.loss_function_ = likelihood_dict.get(self.loss_function, None)
        with pm.Model() as self.model:
            self.Xt_ = theano.shared(X)
            self.Yt_ = theano.shared(Y)
            shapes = {"weights": self.n_object_features_fit_}
            # shapes = {'weights': (self.n_object_features_fit_, 3)}
            weights_dict = create_weight_dictionary(self.model_configuration, shapes)
            intercept = pm.Normal("intercept", mu=0, sd=10)
            utility = tt.dot(self.Xt_, weights_dict["weights"]) + intercept
            self.p_ = ttu.softmax(utility, axis=1)

            LogLikelihood(
                "yl", loss_func=self.loss_function_, p=self.p_, observed=self.Yt_
            )
        logger.info("Model construction completed")

    def fit(
        self,
        X,
        Y,
        sampler="variational",
        tune=500,
        draws=500,
        vi_params={
            "n": 20000,
            "method": "advi",
            "callbacks": [CheckParametersConvergence()],
        },
        **kwargs,
    ):
        """
        Fit a multinomial logit model on the provided set of queries X and choices Y of those objects. The
        provided queries and corresponding preferences are of a fixed size (numpy arrays). For learning this network
        the categorical cross entropy loss function for each object :math:`x_i \\in Q` is defined as:

        .. math::

            C_{i} =  -y(i)\\log(P_i) \\enspace,

        where :math:`y` is ground-truth discrete choice vector of the objects in the given query set :math:`Q`.
        The value :math:`y(i) = 1` if object :math:`x_i` is chosen else :math:`y(i) = 0`.

        Parameters
        ----------
        X : numpy array (n_instances, n_objects, n_features)
            Feature vectors of the objects
        Y : numpy array (n_instances, n_objects)
            Choices for given objects in the query
        sampler : {‘variational’, ‘metropolis’, ‘nuts’}, string
            The sampler used to estimate the posterior mean and mass matrix from the trace

                * **variational** : Run inference methods to estimate posterior mean and diagonal mass matrix
                * **metropolis** : Use the MAP as starting point and Metropolis-Hastings sampler
                * **nuts** : Use the No-U-Turn sampler
        vi_params : dict
            The parameters for the **variational** inference method
        draws : int
            The number of samples to draw. Defaults to 500. The number of tuned samples are discarded by default
        tune : int
            Number of iterations to tune, defaults to 500. Ignored when using 'SMC'. Samplers adjust
            the step sizes, scalings or similar during tuning. Tuning samples will be drawn in addition
            to the number specified in the `draws` argument, and will be discarded unless
            `discard_tuned_samples` is set to False.
        **kwargs :
            Keyword arguments for the fit function of :meth:`pymc3.fit`or :meth:`pymc3.sample`
        """
        self._pre_fit()
        _n_instances, self.n_objects_fit_, self.n_object_features_fit_ = X.shape
        self.construct_model(X, Y)
        fit_pymc3_model(self, sampler, draws, tune, vi_params, **kwargs)
        return self

    def _predict_scores_fixed(self, X, **kwargs):
        d = dict(pm.summary(self.trace_)["mean"])
        intercept = 0.0
        weights = np.array(
            [d["weights[{}]".format(i)] for i in range(self.n_object_features_fit_)]
        )
        if "intercept" in d:
            intercept = intercept + d["intercept"]
        return np.dot(X, weights) + intercept

import logging

import numpy as np

from csrank.learner import Learner
import csrank.theano_util as ttu
from csrank.util import print_dictionary
from .discrete_choice import DiscreteObjectChooser
from .likelihoods import create_weight_dictionary
from .likelihoods import fit_pymc3_model
from .likelihoods import likelihood_dict
from .likelihoods import LogLikelihood

try:
    import pymc3 as pm
    from pymc3.variational.callbacks import CheckParametersConvergence
except ImportError:
    from csrank.util import MissingExtraError

    raise MissingExtraError("pymc3", "probabilistic")

try:
    import theano
    from theano import tensor as tt
except ImportError:
    from csrank.util import MissingExtraError

    raise MissingExtraError("theano", "probabilistic")

logger = logging.getLogger(__name__)


class MultinomialLogitModel(DiscreteObjectChooser, Learner):
    def __init__(self, loss_function="", regularization="l2", **kwargs):
        """
        Create an instance of the Multinomial Logit model for learning the discrete choice function. The utility
        score for each object in query set :math:`Q` is defined as :math:`U(x) = w \\cdot x`, where :math:`w` is
        the weight vector. The probability of choosing an object :math:`x_i` is defined by taking softmax over the
        utility scores of the objects:

        .. math::

            P(x_i \\lvert Q) = \\frac{exp(U(x_i))}{\\sum_{x_j \\in Q} exp(U(x_j))}

        The discrete choice for the given query set :math:`Q` is defined as:

        .. math::

            dc(Q) := \\operatorname{argmax}_{x_i \\in Q }  \\; P(x_i \\lvert Q)

        Parameters
        ----------
        loss_function : string , {‘categorical_crossentropy’, ‘binary_crossentropy’, ’categorical_hinge’}
            Loss function to be used for the discrete choice decision from the query set
        regularization : string, {‘l1’, ‘l2’}, string
           Regularizer function (L1 or L2) applied to the `kernel` weights matrix
        random_state : int or object
            Numpy random state
        **kwargs
            Keyword arguments for the algorithms

        References
        ----------
            [1] <NAME>. „Discrete choice methods with simulation“. In: Cambridge university press, 2009. Chap Logit, pp. 41–86.

            [2] <NAME>. Qualitative choice analysis. Cambridge, MA: MIT Press, 1986
        """
        self.loss_function = loss_function
        known_regularization_functions = {"l1", "l2"}
        if regularization not in known_regularization_functions:
            raise ValueError(
                f"Regularization function {regularization} is unknown. Must be one of {known_regularization_functions}"
            )
        self.regularization = regularization

    @property
    def model_configuration(self):
        """
            Constructs the dictionary containing the priors for the weight vectors for the model according to the
            regularization function. The parameters are:
                * **weights** : Weights to evaluates the utility of the objects

            For ``l1`` regularization the priors are:

            .. math::

                \\text{mu}_w \\sim \\text{Normal}(\\text{mu}=0, \\text{sd}=5.0) \\\\
                \\text{b}_w \\sim \\text{HalfCauchy}(\\beta=1.0) \\\\
                \\text{weights} \\sim \\text{Laplace}(\\text{mu}=\\text{mu}_w, \\text{b}=\\text{b}_w)

            For ``l2`` regularization the priors are:

            .. math::

                \\text{mu}_w \\sim \\text{Normal}(\\text{mu}=0, \\text{sd}=5.0) \\\\
                \\text{sd}_w \\sim \\text{HalfCauchy}(\\beta=1.0) \\\\
                \\text{weights} \\sim \\text{Normal}(\\text{mu}=\\text{mu}_w, \\text{sd}=\\text{sd}_w)
        """
        if not hasattr(self, "config_"):
            if self.regularization == "l2":
                weight = pm.Normal
                prior = "sd"
            elif self.regularization == "l1":
                weight = pm.Laplace
                prior = "b"
            self.config_ = {
                "weights": [
                    weight,
                    {
                        "mu": (pm.Normal, {"mu": 0, "sd": 5}),
                        prior: (pm.HalfCauchy, {"beta": 1}),
                    },
                ]
            }
            logger.info(
                "Creating model with config {}".format(print_dictionary(self.config_))
            )
        return self.config_

    def construct_model(self, X, Y):
        """
        Constructs the multinomial logit model which evaluated the utility score as :math:`U(x) = w \\cdot x`, where
        :math:`w` is the weight vector. The probability of choosing the object :math:`x_i` from the query set
        :math:`Q = \\{x_1, \\ldots ,x_n\\}` is:

        .. math::

            P_i = P(x_i \\lvert Q) = \\frac{exp(U(x_i))}{\\sum_{x_j \\in Q} exp(U(x_j))}

        Parameters
        ----------
        X : numpy array
            (n_instances, n_objects, n_features)
            Feature vectors of the objects
        Y : numpy array
            (n_instances, n_objects)
            Preferences in the form of discrete choices for given objects

        Returns
        -------
         model : pymc3 Model :class:`pm.Model`
        """
        self.trace_ = None
        self.trace_vi_ = None
        logger.info(
            "Creating model_args config {}".format(
                print_dictionary(self.model_configuration)
            )
        )
        self.loss_function_ = likelihood_dict.get(self.loss_function, None)
        with pm.Model() as self.model:
            self.Xt_ = theano.shared(X)
            self.Yt_ = theano.shared(Y)
            shapes = {"weights": self.n_object_features_fit_}
            # shapes = {'weights': (self.n_object_features_fit_, 3)}
            weights_dict = create_weight_dictionary(self.model_configuration, shapes)
            intercept = pm.Normal("intercept", mu=0, sd=10)
            utility = tt.dot(self.Xt_, weights_dict["weights"]) + intercept
            self.p_ = ttu.softmax(utility, axis=1)

            LogLikelihood(
                "yl", loss_func=self.loss_function_, p=self.p_, observed=self.Yt_
            )
        logger.info("Model construction completed")

    def fit(
        self,
        X,
        Y,
        sampler="variational",
        tune=500,
        draws=500,
        vi_params={
            "n": 20000,
            "method": "advi",
            "callbacks": [CheckParametersConvergence()],
        },
        **kwargs,
    ):
        """
        Fit a multinomial logit model on the provided set of queries X and choices Y of those objects. The
        provided queries and corresponding preferences are of a fixed size (numpy arrays). For learning this network
        the categorical cross entropy loss function for each object :math:`x_i \\in Q` is defined as:

        .. math::

            C_{i} =  -y(i)\\log(P_i) \\enspace,

        where :math:`y` is ground-truth discrete choice vector of the objects in the given query set :math:`Q`.
        The value :math:`y(i) = 1` if object :math:`x_i` is chosen else :math:`y(i) = 0`.

        Parameters
        ----------
        X : numpy array (n_instances, n_objects, n_features)
            Feature vectors of the objects
        Y : numpy array (n_instances, n_objects)
            Choices for given objects in the query
        sampler : {‘variational’, ‘metropolis’, ‘nuts’}, string
            The sampler used to estimate the posterior mean and mass matrix from the trace

                * **variational** : Run inference methods to estimate posterior mean and diagonal mass matrix
                * **metropolis** : Use the MAP as starting point and Metropolis-Hastings sampler
                * **nuts** : Use the No-U-Turn sampler
        vi_params : dict
            The parameters for the **variational** inference method
        draws : int
            The number of samples to draw. Defaults to 500. The number of tuned samples are discarded by default
        tune : int
            Number of iterations to tune, defaults to 500. Ignored when using 'SMC'. Samplers adjust
            the step sizes, scalings or similar during tuning. Tuning samples will be drawn in addition
            to the number specified in the `draws` argument, and will be discarded unless
            `discard_tuned_samples` is set to False.
        **kwargs :
            Keyword arguments for the fit function of :meth:`pymc3.fit`or :meth:`pymc3.sample`
        """
        self._pre_fit()
        _n_instances, self.n_objects_fit_, self.n_object_features_fit_ = X.shape
        self.construct_model(X, Y)
        fit_pymc3_model(self, sampler, draws, tune, vi_params, **kwargs)
        return self

    def _predict_scores_fixed(self, X, **kwargs):
        d = dict(pm.summary(self.trace_)["mean"])
        intercept = 0.0
        weights = np.array(
            [d["weights[{}]".format(i)] for i in range(self.n_object_features_fit_)]
        )
        if "intercept" in d:
            intercept = intercept + d["intercept"]
        return np.dot(X, weights) + intercept

Create an instance of the Multinomial Logit model for learning the discrete choice function. The utility         score for each object in query set :math:`Q` is defined as :math:`U(x) = w \\cdot x`, where :math:`w` is         the weight vector. The probability of choosing an object :math:`x_i` is defined by taking softmax over the         utility scores of the objects:          .. math::              P(x_i \\lvert Q) = \\frac{exp(U(x_i))}{\\sum_{x_j \\in Q} exp(U(x_j))}          The discrete choice for the given query set :math:`Q` is defined as:          .. math::              dc(Q) := \\operatorname{argmax}_{x_i \\in Q }  \\; P(x_i \\lvert Q)          Parameters         ----------         loss_function : string , {‘categorical_crossentropy’, ‘binary_crossentropy’, ’categorical_hinge’}             Loss function to be used for the discrete choice decision from the query set         regularization : string, {‘l1’, ‘l2’}, string            Regularizer function (L1 or L2) applied to the `kernel` weights matrix         random_state : int or object             Numpy random state         **kwargs             Keyword arguments for the algorithms          References         ----------             [1] <NAME>. „Discrete choice methods with simulation“. In: Cambridge university press, 2009. Chap Logit, pp. 41–86.              [2] <NAME>. Qualitative choice analysis. Cambridge, MA: MIT Press, 1986 Constructs the dictionary containing the priors for the weight vectors for the model according to the             regularization function. The parameters are:                 * **weights** : Weights to evaluates the utility of the objects              For ``l1`` regularization the priors are:              .. math::                  \\text{mu}_w \\sim \\text{Normal}(\\text{mu}=0, \\text{sd}=5.0) \\\\                 \\text{b}_w \\sim \\text{HalfCauchy}(\\beta=1.0) \\\\                 \\text{weights} \\sim \\text{Laplace}(\\text{mu}=\\text{mu}_w, \\text{b}=\\text{b}_w)              For ``l2`` regularization the priors are:              .. math::                  \\text{mu}_w \\sim \\text{Normal}(\\text{mu}=0, \\text{sd}=5.0) \\\\                 \\text{sd}_w \\sim \\text{HalfCauchy}(\\beta=1.0) \\\\                 \\text{weights} \\sim \\text{Normal}(\\text{mu}=\\text{mu}_w, \\text{sd}=\\text{sd}_w) Constructs the multinomial logit model which evaluated the utility score as :math:`U(x) = w \\cdot x`, where         :math:`w` is the weight vector. The probability of choosing the object :math:`x_i` from the query set         :math:`Q = \\{x_1, \\ldots ,x_n\\}` is:          .. math::              P_i = P(x_i \\lvert Q) = \\frac{exp(U(x_i))}{\\sum_{x_j \\in Q} exp(U(x_j))}          Parameters         ----------         X : numpy array             (n_instances, n_objects, n_features)             Feature vectors of the objects         Y : numpy array             (n_instances, n_objects)             Preferences in the form of discrete choices for given objects          Returns         -------          model : pymc3 Model :class:`pm.Model` # shapes = {'weights': (self.n_object_features_fit_, 3)} Fit a multinomial logit model on the provided set of queries X and choices Y of those objects. The         provided queries and corresponding preferences are of a fixed size (numpy arrays). For learning this network         the categorical cross entropy loss function for each object :math:`x_i \\in Q` is defined as:          .. math::              C_{i} =  -y(i)\\log(P_i) \\enspace,          where :math:`y` is ground-truth discrete choice vector of the objects in the given query set :math:`Q`.         The value :math:`y(i) = 1` if object :math:`x_i` is chosen else :math:`y(i) = 0`.          Parameters         ----------         X : numpy array (n_instances, n_objects, n_features)             Feature vectors of the objects         Y : numpy array (n_instances, n_objects)             Choices for given objects in the query         sampler : {‘variational’, ‘metropolis’, ‘nuts’}, string             The sampler used to estimate the posterior mean and mass matrix from the trace                  * **variational** : Run inference methods to estimate posterior mean and diagonal mass matrix                 * **metropolis** : Use the MAP as starting point and Metropolis-Hastings sampler                 * **nuts** : Use the No-U-Turn sampler         vi_params : dict             The parameters for the **variational** inference method         draws : int             The number of samples to draw. Defaults to 500. The number of tuned samples are discarded by default         tune : int             Number of iterations to tune, defaults to 500. Ignored when using 'SMC'. Samplers adjust             the step sizes, scalings or similar during tuning. Tuning samples will be drawn in addition             to the number specified in the `draws` argument, and will be discarded unless             `discard_tuned_samples` is set to False.         **kwargs :             Keyword arguments for the fit function of :meth:`pymc3.fit`or :meth:`pymc3.sample`