# Source code for cornac.models.cf.recom_bpr

    # -*- coding: utf-8 -*-
    """
    @author: Guo Jingyao
    """

    import numpy as np
    from .bpr import *
    from ..recommender import Recommender

    class Bpr(Recommender):
        """Bayesian Personalized Ranking.

        Parameters
        ----------
        k: int, optional, default: 5
            The dimension of the latent factors.

        max_iter: int, optional, default: 100
            Maximum number of iterations or the number of epochs for SGD.

        learning_rate: float, optional, default: 0.001
            The learning rate for SGD.

        lamda: float, optional, default: 0.01
            The regularization parameter.

        batch_size: int, optional, default: 100
            The batch size for SGD.

        name: string, optional, default: 'BRP'
            The name of the recommender model.

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

        init_params: dictionary, optional, default: None
            List of initial parameters, e.g., init_params = {'U':U, 'V':V} \
            please see below the definition of U and V.

        U: csc_matrix, shape (n_users,k)
            The user latent factors, optional initialization via init_params.

        V: csc_matrix, shape (n_items,k)
            The item latent factors, optional initialization via init_params.

        References
        ----------
        * Rendle, Steffen, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. \
        BPR: Bayesian personalized ranking from implicit feedback. In UAI, pp. 452-461. 2009.
        """

        def __init__(self, k=5, max_iter=100, learning_rate = 0.001, lamda = 0.01,batch_size = 100, name="bpr",trainable = True,init_params = None):
            Recommender.__init__(self, name=name, trainable = trainable)
            self.k = k
            self.init_params = init_params
            self.max_iter = max_iter
            self.name = name
            self.learning_rate = learning_rate
            self.lamda = lamda
            self.batch_size = batch_size

            self.U = init_params['U']  # matrix of user factors
            self.V = init_params['V']  # matrix of item factors

        # 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).
            """
            if self.trainable:
                #change the data to original user Id item Id and rating format
                X = X.tocoo()
                data = np.ndarray(shape=(len(X.data), 3), dtype=float)
                data[:, 0] = X.row
                data[:, 1] = X.col
                data[:, 2] = X.data

                print('Learning...')
                res = bpr(X, data, k=self.k, n_epochs=self.max_iter,lamda = self.lamda, learning_rate= self.learning_rate, batch_size = self.batch_size, init_params=self.init_params)
                self.U = res['U']
                self.V = res['V']
                print('Learning completed')
            else:
                print('%s is trained already (trainable = False)' % (self.name))

        #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
            """

            user_pred = self.U[index_user, :].dot(self.V.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