# Source code for cornac.models.context_cf.recom_c2pf

    # -*- coding: utf-8 -*-
    """
    @author: Aghiles Salah <salahaghiles@gmail.com>
    """

    import numpy as np
    import scipy.sparse as sp
    from scipy.io import loadmat, savemat
    from ..recommender import Recommender
    import c2pf

    #Recommender class for Collaborative Context Poisson Factorization (C2PF)
    class C2pf(Recommender):
        """Collaborative Context Poisson Factorization.

        Parameters
        ----------
        k: int, optional, default: 100
            The dimension of the latent factors.

        max_iter: int, optional, default: 100
            Maximum number of iterations for variational C2PF.

        aux_info: array, required, shape (n_context_items,3)
            The item-context matrix, noted C in the original paper, \
            in the triplet sparse format: (row_id, col_id, value).

        variant: string, optional, default: 'c2pf'
            C2pf's variant: c2pf: 'c2pf', 'tc2pf' (tied-c2pf) or 'rc2pf' (reduced-c2pf). \
            Please refer to the original paper for details.

        name: string, optional, default: None
            The name of the recommender model. If None, \
            then "variant" is used as the default name of the model.

        trainable: boolean, optional, default: True
            When False, the model is not trained and Cornac assumes that the model already \
            pre-trained (Theta, Beta and Xi are not None).

        init_params: dictionary, optional, default: None
            List of initial parameters, e.g., init_params = {'G_s':G_s, 'G_r':G_r, 'L_s':L_s, 'L_r':L_r, \
            'L2_s':L2_s, 'L2_r':L2_r, 'L3_s':L3_s, 'L3_r':L3_r}, \
            where G_s and G_r are of type csc_matrix or np.array with the same shape as Theta, see below). \
            They represent respectively the "shape" and "rate" parameters of Gamma distribution over \
            Theta. It is the same for L_s, L_r and Beta, L2_s, L2_r and Xi, L3_s, L3_r and Kappa.

        Theta: csc_matrix, shape (n_users,k)
            The expected user latent factors.

        Beta: csc_matrix, shape (n_items,k)
            The expected item latent factors.

        Xi: csc_matrix, shape (n_items,k)
            The expected context item latent factors multiplied by context effects Kappa, \
            please refer to the paper below for details.

        References
        ----------
        * Salah, Aghiles, and Hady W. Lauw. A Bayesian Latent Variable Model of User Preferences with Item Context. \
        In IJCAI, pp. 2667-2674. 2018.
        """

        def __init__(self, k=100, max_iter=100, aux_info = None, variant = 'c2pf', name = None, trainable = True, init_params = None):
            if name is None:
                Recommender.__init__(self, name=variant.upper(), trainable = trainable)
            else:
                Recommender.__init__(self, name=name, trainable = trainable)

            self.k = k
            self.init_params = init_params
            self.max_iter = max_iter

            self.ll = np.full(max_iter, 0)
            self.eps = 0.000000001
            self.Theta = None #user factors
            self.Beta = None  #item factors
            self.Xi = None #context factors Xi multiplied by context effects Kappa
            self.aux_info = aux_info #item-context matrix in the triplet sparse format: (row_id, col_id, value)
            self.variant = variant

        #fit the recommender model to the traning data
        def fit(self,X):
            """Fit the model to observations.

            Parameters
            ----------
            X: scipy sparse matrix, required
                the user-item preference matrix (traning data), in a scipy sparse format\
                (e.g., csc_matrix).
            """
            #recover the striplet sparse format from csc sparse matrix X (needed to feed c++)
            (rid,cid,val)=sp.find(X)
            val = np.array(val,dtype='float32')
            rid = np.array(rid,dtype='int32')
            cid = np.array(cid,dtype='int32')
            tX = np.concatenate((np.concatenate(([rid], [cid]), axis=0).T,val.reshape((len(val),1))),axis = 1)
            del rid, cid, val

            if self.variant == 'c2pf':
                res = c2pf.c2pf(tX, X.shape[0], X.shape[1], self.aux_info, X.shape[1], X.shape[1], self.k, self.max_iter, self.init_params)
            elif self.variant == 'tc2pf':
                res = c2pf.t_c2pf(tX, X.shape[0], X.shape[1], self.aux_info, X.shape[1], X.shape[1], self.k, self.max_iter, self.init_params)
            elif self.variant == 'rc2pf':
                res = c2pf.r_c2pf(tX, X.shape[0], X.shape[1], self.aux_info, X.shape[1], X.shape[1], self.k, self.max_iter, self.init_params)
            else:
                res = c2pf.c2pf(tX, X.shape[0], X.shape[1], self.aux_info, X.shape[1], X.shape[1], self.k, self.max_iter, self.init_params)

            self.Theta = sp.csc_matrix(res['Z']).todense()
            self.Beta = sp.csc_matrix(res['W']).todense()
            self.Xi = sp.csc_matrix(res['Q']).todense()

        #get prefiction for a single user (predictions for one user at a time for efficiency purposes)
        #predictions are not stored for the same efficiency reasons
        def predict(self,index_user):
            """Predic the scores (ratings) of a user for all items.

            Parameters
            ----------
            index_user: int, required
                The index of the user for whom to perform predictions.

            Returns
            -------
            Numpy 1d array
                Array containing the predicted values for all items
            """

            if self.variant == 'c2pf' or self.variant == 'tc2pf':
                user_pred = self.Beta*self.Theta[index_user,:].T + self.Xi*self.Theta[index_user,:].T
            elif self.variant == 'rc2pf':
                user_pred = self.Xi*self.Theta[index_user,:].T
            else:
                user_pred = self.Beta*self.Theta[index_user,:].T + self.Xi*self.Theta[index_user,:].T
            #transform user_pred to a flatten array, but keep thinking about another possible format
            user_pred = np.array(user_pred,dtype='float64').flatten()

            return user_pred