Source code for cornac.models.c2pf.recom_c2pf

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import numpy as np
import scipy.sparse as sp

from cornac.models.c2pf import c2pf
from ..recommender import Recommender


# Recommender class for Collaborative Context Poisson Factorization (C2PF)
[docs]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. 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). Item_context: See "cornac/examples/c2pf_example.py" in the GitHub repo for an example of how to use \ cornac's graph modality to load and provide "item context" for C2PF. init_params: dict, 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} Theta: ndarray, shape (n_users, k) The expected user latent factors. Beta: ndarray, shape (n_items, k) The expected item latent factors. Xi: ndarray, shape (n_items, k) The expected context item latent factors multiplied by context effects Kappa. G_s: ndarray, shape (n_users, k) Represent the "shape" parameters of Gamma distribution over Theta. G_r: ndarray, shape (n_users, k) Represent the "rate" parameters of Gamma distribution over Theta. L_s: ndarray, shape (n_items, k) Represent the "shape" parameters of Gamma distribution over Beta. L_r: ndarray, shape (n_items, k) Represent the "rate" parameters of Gamma distribution over Beta. L2_s: ndarray, shape (n_items, k) Represent the "shape" parameters of Gamma distribution over Xi. L2_r: ndarray, shape (n_items, k) Represent the "rate" parameters of Gamma distribution over Xi. L3_s: ndarray Represent the "shape" parameters of Gamma distribution over Kappa. L3_r: ndarray Represent the "rate" parameters of Gamma distribution over Kappa. 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, variant="c2pf", name=None, trainable=True, verbose=False, init_params=None, ): if name is None: Recommender.__init__( self, name=variant.upper(), trainable=trainable, verbose=verbose ) else: Recommender.__init__(self, name=name, trainable=trainable, verbose=verbose) self.k = k self.max_iter = max_iter self.ll = np.full(max_iter, 0) self.eps = 0.000000001 # self.aux_info = aux_info # item-context matrix in the triplet sparse format: (row_id, col_id, value) self.variant = variant # Init params if provided self.init_params = {} if init_params is None else init_params self.Theta = self.init_params.get("Theta", None) self.Beta = self.init_params.get("Beta", None) self.Xi = self.init_params.get("Xi", None) self.Gs = self.init_params.get("G_s", None) self.Gr = self.init_params.get("G_r", None) self.Ls = self.init_params.get("L_s", None) self.Lr = self.init_params.get("L_r", None) self.L2s = self.init_params.get("L2_s", None) self.L2r = self.init_params.get("L2_r", None) self.L3s = self.init_params.get("L3_s", None) self.L3r = self.init_params.get("L3_r", None)
[docs] def fit(self, train_set, val_set=None): """Fit the model to observations. Parameters ---------- train_set: :obj:`cornac.data.Dataset`, required User-Item preference data as well as additional modalities. val_set: :obj:`cornac.data.Dataset`, optional, default: None User-Item preference data for model selection purposes (e.g., early stopping). Returns ------- self : object """ Recommender.fit(self, train_set, val_set) X = sp.csc_matrix(self.train_set.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.trainable: # use pre-trained params if exists, otherwise from constructor init_params = { "G_s": self.Gs, "G_r": self.Gr, "L_s": self.Ls, "L_r": self.Lr, "L2_s": self.L2s, "L2_r": self.L2r, "L3_s": self.L3s, "L3_r": self.L3r, } map_iid = train_set.item_indices (rid, cid, val) = train_set.item_graph.get_train_triplet(map_iid, map_iid) context_info = np.hstack( (rid.reshape(-1, 1), cid.reshape(-1, 1), val.reshape(-1, 1)) ) if self.variant == "c2pf": res = c2pf.c2pf( tX, X.shape[0], X.shape[1], context_info, X.shape[1], X.shape[1], self.k, self.max_iter, init_params, ) elif self.variant == "tc2pf": res = c2pf.t_c2pf( tX, X.shape[0], X.shape[1], context_info, X.shape[1], X.shape[1], self.k, self.max_iter, init_params, ) elif self.variant == "rc2pf": res = c2pf.r_c2pf( tX, X.shape[0], X.shape[1], context_info, X.shape[1], X.shape[1], self.k, self.max_iter, init_params, ) else: res = c2pf.c2pf( tX, X.shape[0], X.shape[1], context_info, X.shape[1], X.shape[1], self.k, self.max_iter, 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() # overwrite init_params for future fine-tuning self.Gs = np.asarray(res["G_s"]) self.Gr = np.asarray(res["G_r"]) self.Ls = np.asarray(res["L_s"]) self.Lr = np.asarray(res["L_r"]) self.L2s = np.asarray(res["L2_s"]) self.L2r = np.asarray(res["L2_r"]) self.L3s = np.asarray(res["L3_s"]) self.L3r = np.asarray(res["L3_r"]) elif self.verbose: print("%s is trained already (trainable = False)" % (self.name)) return self
[docs] def score(self, user_idx, item_idx=None): """Predict the scores/ratings of a user for an item. Parameters ---------- user_idx: int, required The index of the user for whom to perform score prediction. item_idx: int, optional, default: None The index of the item for that to perform score prediction. If None, scores for all known items will be returned. Returns ------- res : A scalar or a Numpy array Relative scores that the user gives to the item or to all known items """ if self.variant == "c2pf" or self.variant == "tc2pf": if item_idx is None: user_pred = ( self.Beta * self.Theta[user_idx, :].T + self.Xi * self.Theta[user_idx, :].T ) else: user_pred = ( self.Beta[item_idx, :] * self.Theta[user_idx, :].T + self.Xi * self.Theta[user_idx, :].T ) elif self.variant == "rc2pf": if item_idx is None: user_pred = self.Xi * self.Theta[user_idx, :].T else: user_pred = self.Xi[item_idx,] * self.Theta[user_idx, :].T else: if item_idx is None: user_pred = ( self.Beta * self.Theta[user_idx, :].T + self.Xi * self.Theta[user_idx, :].T ) else: user_pred = ( self.Beta[item_idx, :] * self.Theta[user_idx, :].T + self.Xi * self.Theta[user_idx, :].T ) # transform user_pred to a flatten array, user_pred = np.array(user_pred, dtype="float64").flatten() return user_pred