Source code for cornac.models.mcf.recom_mcf

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#     http://www.apache.org/licenses/LICENSE-2.0
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import numpy as np

from ..recommender import Recommender
from ...utils.common import sigmoid
from ...utils.common import scale
from ...exception import ScoreException


[docs]class MCF(Recommender): """Matrix Co-Factorization. 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_RMSProp. gamma: float, optional, default: 0.9 The weight for previous/current gradient in RMSProp. lamda: float, optional, default: 0.001 The regularization parameter. name: string, optional, default: 'MCF' The name of the recommender model. trainable: boolean, optional, default: True When False, the model is not trained and Cornac assumes that the model is already \ pre-trained (U and V are not None). item-affinity network: See "cornac/examples/mcf_office.py" for an example of how to use \ cornac's graph modality to load and provide the "item-affinity network" for MCF. verbose: boolean, optional, default: False When True, some running logs are displayed. init_params: dictionary, optional, default: {} List of initial parameters, e.g., init_params = {'U':U, 'V':V}. U: a csc_matrix of shape (n_users,k), containing the user latent factors. V: a csc_matrix of shape (n_items,k), containing the item latent factors. Z: a csc_matrix of shape (n_items,k), containing the "Also-Viewed" item latent factors. seed: int, optional, default: None Random seed for parameters initialization. References ---------- * Park, Chanyoung, Donghyun Kim, Jinoh Oh, and Hwanjo Yu. "Do Also-Viewed Products Help User Rating Prediction?."\ In Proceedings of WWW, pp. 1113-1122. 2017. """ def __init__(self, k=5, max_iter=100, learning_rate=0.001, gamma=0.9, lamda=0.001, name="MCF", trainable=True, verbose=False, init_params={}, seed=None): Recommender.__init__(self, name=name, trainable=trainable, verbose=verbose) self.k = k self.init_params = init_params self.max_iter = max_iter self.learning_rate = learning_rate self.gamma = gamma self.lamda = lamda self.ll = np.full(max_iter, 0) self.eps = 0.000000001 self.U = self.init_params.get('U') # matrix of user factors self.V = self.init_params.get('V') # matrix of item factors self.Z = self.init_params.get('Z') # matrix of Also-Viewed item factors self.seed = seed
[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 """ from cornac.models.mcf import mcf Recommender.fit(self, train_set, val_set) if self.trainable: # user-item interactions (rat_uid, rat_iid, rat_val) = train_set.uir_tuple # item-item affinity network map_iid = train_set.item_indices (net_iid, net_jid, net_val) = train_set.item_graph.get_train_triplet(map_iid, map_iid) if [self.train_set.min_rating, self.train_set.max_rating] != [0, 1]: if self.train_set.min_rating == self.train_set.max_rating: rat_val = scale(rat_val, 0., 1., 0., self.train_set.max_rating) else: rat_val = scale(rat_val, 0., 1., self.train_set.min_rating, self.train_set.max_rating) if [min(net_val), max(net_val)] != [0, 1]: if min(net_val) == max(net_val): net_val = scale(net_val, 0., 1., 0., max(net_val)) else: net_val = scale(net_val, 0., 1., min(net_val), max(net_val)) rat_val = np.array(rat_val, dtype='float32') rat_uid = np.array(rat_uid, dtype='int32') rat_iid = np.array(rat_iid, dtype='int32') net_val = np.array(net_val, dtype='float32') net_iid = np.array(net_iid, dtype='int32') net_jid = np.array(net_jid, dtype='int32') if self.verbose: print('Learning...') res = mcf.mcf(rat_uid, rat_iid, rat_val, net_iid, net_jid, net_val, k=self.k, n_users=train_set.num_users, n_items=train_set.num_items, n_ratings=len(rat_val), n_edges=len(net_val), n_epochs=self.max_iter, lamda=self.lamda, learning_rate=self.learning_rate, gamma=self.gamma, init_params=self.init_params, verbose=self.verbose, seed=self.seed) self.U = np.asarray(res['U']) self.V = np.asarray(res['V']) self.Z = np.asarray(res['Z']) if self.verbose: print('Learning completed') 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 item_idx is None: if self.train_set.is_unk_user(user_idx): raise ScoreException("Can't make score prediction for (user_id=%d)" % user_idx) known_item_scores = self.V.dot(self.U[user_idx, :]) return known_item_scores else: if self.train_set.is_unk_user(user_idx) or self.train_set.is_unk_item(item_idx): raise ScoreException("Can't make score prediction for (user_id=%d, item_id=%d)" % (user_idx, item_idx)) user_pred = self.V[item_idx, :].dot(self.U[user_idx, :]) user_pred = sigmoid(user_pred) if self.train_set.min_rating == self.train_set.max_rating: user_pred = scale(user_pred, 0., self.train_set.max_rating, 0., 1.) else: user_pred = scale(user_pred, self.train_set.min_rating, self.train_set.max_rating, 0., 1.) return user_pred