Source code for cornac.models.vmf.recom_vmf

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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 VMF(Recommender): """Visual Matrix Factorization. Parameters ---------- k: int, optional, default: 10 The dimension of the user and item factors. d: int, optional, default: 10 The dimension of the user visual factors. n_epochs: int, optional, default: 100 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. lambda_u: float, optional, default: 0.001 The regularization parameter for user factors. lambda_v: float, optional, default: 0.001 The regularization parameter for item factors. lambda_p: float, optional, default: 1.0 The regularization parameter for user visual factors. lambda_e: float, optional, default: 10. The regularization parameter for the kernel embedding matrix lambda_u: float, optional, default: 0.001 The regularization parameter for user factors. name: string, optional, default: 'VMF' 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 (The parameters of the model U, V, P, E are not None). visual_features: See "cornac/examples/vmf_example.py" for an example of how to use \ cornac's visual modality to load and provide the "item visual features" for VMF. verbose: boolean, optional, default: False When True, some running logs are displayed. init_params: dictionary, optional, default: None List of initial parameters, e.g., init_params = {'U':U, 'V':V, 'P': P, 'E': E}. U: numpy array of shape (n_users,k), user latent factors. V: numpy array of shape (n_items,k), item latent factors. P: numpy array of shape (n_users,d), user visual latent factors. E: numpy array of shape (d,c), embedding kernel matrix. 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, name="VMF", k=10, d=10, n_epochs=100, batch_size=100, learning_rate=0.001, gamma=0.9, lambda_u=0.001, lambda_v=0.001, lambda_p=1.0, lambda_e=10.0, trainable=True, verbose=False, use_gpu=False, init_params=None, seed=None, ): Recommender.__init__(self, name=name, trainable=trainable, verbose=verbose) self.k = k self.d = d self.batch_size = batch_size self.n_epochs = n_epochs self.learning_rate = learning_rate self.gamma = gamma self.lambda_u = lambda_u self.lambda_v = lambda_v self.lambda_p = lambda_p self.lambda_e = lambda_e self.use_gpu = use_gpu self.loss = np.full(n_epochs, 0) self.eps = 0.000000001 self.seed = seed # Init params if provided self.init_params = {} if init_params is None else init_params self.U = self.init_params.get("U", None) # user factors self.V = self.init_params.get("V", None) # item factors self.P = self.init_params.get("P", None) # user visual factors self.E = self.init_params.get("E", None) # Kernel embedding matrix
[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) if self.trainable: # Item visual cnn-features item_features = train_set.item_image.features[: self.train_set.num_items] if self.verbose: print("Learning...") from .vmf import vmf res = vmf( self.train_set, item_features, k=self.k, d=self.d, n_epochs=self.n_epochs, batch_size=self.batch_size, lambda_u=self.lambda_u, lambda_v=self.lambda_v, lambda_p=self.lambda_p, lambda_e=self.lambda_e, learning_rate=self.learning_rate, gamma=self.gamma, init_params={"U": self.U, "V": self.V, "P": self.P, "E": self.E}, use_gpu=self.use_gpu, verbose=self.verbose, seed=self.seed, ) self.U = res["U"] self.V = res["V"] self.P = res["P"] self.E = res["E"] self.Q = res["Q"] 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, :]) + self.Q.dot( self.P[user_idx, :] ) # known_item_scores = np.asarray(np.zeros(self.V.shape[0]),dtype='float32') # fast_dot(self.U[user_id], self.V, known_item_scores) # fast_dot(self.P[user_id], self.Q, known_item_scores) 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, :]) + self.Q[ item_idx, : ].dot(self.P[user_idx, :]) user_pred = sigmoid(user_pred) user_pred = scale( user_pred, self.train_set.min_rating, self.train_set.max_rating, 0.0, 1.0, ) return user_pred