Source code for cornac.models.most_pop.recom_most_pop

# Copyright 2018 The Cornac Authors. All Rights Reserved.
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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 ...exception import ScoreException


[docs]class MostPop(Recommender): """Most Popular. Item are recommended based on their popularity (not personalized). Parameters ---------- name: string, default: 'MostPop' The name of the recommender model. """ def __init__(self, name='MostPop'): super().__init__(name=name, trainable=False)
[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) self.item_pop = np.ediff1d(train_set.csc_matrix.indptr) 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: return self.item_pop else: if 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)) return self.item_pop[item_idx]