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recommenders.py
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import numpy as np
from Compute_Similarity_Python import Compute_Similarity_Python
from Base.Recommender_utils import check_matrix
from Base.Recommender import Recommender
from Base.SimilarityMatrixRecommender import SimilarityMatrixRecommender
from Base.cosine_similarity import Cosine_Similarity
from Base.Similarity.Compute_Similarity import Compute_Similarity
class TopPopRecommender(object):
def fit(self, URM_train):
self.URM_train = URM_train
itemPopularity = (URM_train > 0).sum(axis=0)
itemPopularity = np.array(itemPopularity).squeeze()
# We are not interested in sorting the popularity value,
# but to order the items according to it
self.popularItems = np.argsort(itemPopularity)
self.popularItems = np.flip(self.popularItems, axis=0)
def recommend(self, user_id, at=5, remove_seen=True):
if remove_seen:
unseen_items_mask = np.in1d(self.popularItems, self.URM_train[user_id].indices,
assume_unique=True, invert=True)
unseen_items = self.popularItems[unseen_items_mask]
recommended_items = unseen_items[0:at]
else:
recommended_items = self.popularItems[0:at]
return recommended_items
class ItemCBFKNNRecommender(object):
def __init__( self, URM, ICM):
self.URM = URM
self.ICM = ICM
def fit( self, k=50, shrink=100, normalize=True, similarity="cosine" ):
similarity_object = Compute_Similarity_Python(self.ICM.T, shrink=shrink,
topK=k, normalize=normalize,
similarity=similarity)
self.W_sparse = similarity_object.compute_similarity()
def recommend( self, user_id, at=None, exclude_seen=True ):
# compute the scores using the dot product
user_profile = self.URM[user_id]
scores = user_profile.dot(self.W_sparse).toarray().ravel()
if exclude_seen:
scores = self.filter_seen(user_id, scores)
# rank items
ranking = scores.argsort()[::-1]
return ranking[:at]
def filter_seen( self, user_id, scores ):
start_pos = self.URM.indptr[user_id]
end_pos = self.URM.indptr[user_id + 1]
user_profile = self.URM.indices[start_pos:end_pos]
scores[user_profile] = -np.inf
return scores
class ItemKNNCFRecommender(Recommender, SimilarityMatrixRecommender):
""" ItemKNN recommender"""
def __init__(self, URM_train, sparse_weights=True):
super(ItemKNNCFRecommender, self).__init__()
# CSR is faster during evaluation
self.URM_train = check_matrix(URM_train, 'csr')
self.dataset = None
self.sparse_weights = sparse_weights
def fit(self, k=50, shrink=100, similarity='cosine', normalize=True):
self.k = k
self.shrink = shrink
self.similarity = Cosine_Similarity(self.URM_train, shrink=shrink, topK=k, normalize=normalize, mode = similarity)
if self.sparse_weights:
self.W_sparse = self.similarity.compute_similarity()
else:
self.W = self.similarity.compute_similarity()
self.W = self.W.toarray()
class UserKNNCFRecommender(Recommender, SimilarityMatrixRecommender):
""" UserKNN recommender"""
def __init__(self, URM_train, sparse_weights=True):
super(UserKNNCFRecommender, self).__init__()
# Not sure if CSR here is faster
self.URM_train = check_matrix(URM_train, 'csr')
self.dataset = None
self.sparse_weights = sparse_weights
def fit(self, k=50, shrink=100, similarity='cosine', normalize=True):
self.k = k
self.shrink = shrink
self.similarity = Cosine_Similarity(self.URM_train.T, shrink=shrink, topK=k, normalize=normalize, mode = similarity)
if self.sparse_weights:
self.W_sparse = self.similarity.compute_similarity()
else:
self.W = self.similarity.compute_similarity()
self.W = self.W.toarray()
def recommend( self, user_id, n=None, exclude_seen=True, filterTopPop=False, filterCustomItems=False ):
if n == None:
n = self.URM_train.shape[1] - 1
# compute the scores using the dot product
if self.sparse_weights:
scores = self.W_sparse[user_id].dot(self.URM_train).toarray().ravel()
else:
# Numpy dot does not recognize sparse matrices, so we must
# invoke the dot function on the sparse one
scores = self.URM_train.T.dot(self.W[user_id])
if self.normalize:
# normalization will keep the scores in the same range
# of value of the ratings in dataset
user_profile = self.URM_train[user_id]
rated = user_profile.copy()
rated.data = np.ones_like(rated.data)
if self.sparse_weights:
den = rated.dot(self.W_sparse).toarray().ravel()
else:
den = rated.dot(self.W).ravel()
den[np.abs(den) < 1e-6] = 1.0 # to avoid NaNs
scores /= den
if exclude_seen:
scores = self._filter_seen_on_scores(user_id, scores)
if filterTopPop:
scores = self._filter_TopPop_on_scores(scores)
if filterCustomItems:
scores = self._filterCustomItems_on_scores(scores)
# rank items and mirror column to obtain a ranking in descending score
# ranking = scores.argsort()
# ranking = np.flip(ranking, axis=0)
# Sorting is done in three steps. Faster then plain np.argsort for higher number of items
# - Partition the data to extract the set of relevant items
# - Sort only the relevant items
# - Get the original item index
relevant_items_partition = (-scores).argpartition(n)[0:n]
relevant_items_partition_sorting = np.argsort(-scores[relevant_items_partition])
ranking = relevant_items_partition[relevant_items_partition_sorting]
return ranking
class HybridRecommender(Recommender, SimilarityMatrixRecommender):
def __init__(self, URM ,URM_train, recommender_1, recommender_2, sparse_weights=True, normalize=True):
super(HybridRecommender, self).__init__()
self.normalize = normalize
self.URM = URM
self.URM_train = check_matrix(URM_train, 'csr')
self.recommender_CB = recommender_1
self.recommender_CF = recommender_2
#self.recommender_UCF = recommender_3
self.sparse_weights = sparse_weights
def fit( self, k_1=5, shrink_1=0.5, k_2=800, shrink_2=10, k_3=300, shrink_3=300, similarity='cosine', normalize=True ):
self.k_CB = k_1
self.k_CF = k_2
self.k_UCF = k_3
self.shrink_CB= shrink_1
self.shrink_CF = shrink_2
self.shrink_UCF = shrink_3
self.recommender_CB.fit(shrink=self.shrink_CB, k=self.k_CB)
self.recommender_CF.fit(shrink=self.shrink_CF, k=self.k_CF)
#self.recommender_UCF.fit(shrink=self.shrink_UCF, k=self.k_UCF)
def recommend( self, user_id, at=None, exclude_seen=True, weight_CB=0.1, weight_CF=0.9):
# compute the scores using the dot product
user_profile = self.URM[user_id]
scores_CB = user_profile.dot(self.recommender_CB.W_sparse).toarray().ravel()
scores_CF = user_profile.dot(self.recommender_CF.W_sparse).toarray().ravel()
#scores_UCF = self.recommender_UCF.W_sparse[user_id].dot(self.URM_train).toarray().ravel()
# use weights
scores_CB = scores_CB * weight_CB
scores_CF = scores_CF * weight_CF
#scores_UCF = scores_UCF * weight_UCF
self.scores = scores_CB + scores_CF # + scores_UCF
if exclude_seen:
scores = self.filter_seen(user_id, self.scores)
# rank items
ranking = scores.argsort()[::-1]
return ranking[:at]
def filter_seen( self, user_id, scores ):
start_pos = self.URM.indptr[user_id]
end_pos = self.URM.indptr[user_id + 1]
user_profile = self.URM.indices[start_pos:end_pos]
scores[user_profile] = -np.inf
return scores