def get_recommenders_set():
# nc = ContextNeighbourhoodCalculator()
# ncc = NeighbourContributionCalculator()
# ubc = UserBaselineCalculator()
# usc = PBCSimilarityCalculator()
# cosine_usc = CBCSimilarityCalculator()
# Similarity calculators
cosine_sc = CosineSimilarityCalculator()
pearson_sc = PearsonSimilarityCalculator()
pbc_sc = PBCSimilarityCalculator()
cbu_sc = CBCSimilarityCalculator()
similarity_calculators = [cosine_sc, pearson_sc, pbc_sc, cbu_sc]
# Neighbourhood calculators
simple_nc = SimpleNeighbourhoodCalculator(copy.deepcopy(pearson_sc))
context_nc = ContextNeighbourhoodCalculator()
# hybrid_nc0 = ContextHybridNeighbourhoodCalculator(copy.deepcopy(pearson_sc))
# hybrid_nc0.weight = 0.0
hybrid_nc02 = ContextHybridNeighbourhoodCalculator(
copy.deepcopy(pearson_sc))
hybrid_nc02.weight = 0.2
hybrid_nc05 = ContextHybridNeighbourhoodCalculator(
copy.deepcopy(pearson_sc))
hybrid_nc05.weight = 0.5
hybrid_nc08 = ContextHybridNeighbourhoodCalculator(
copy.deepcopy(pearson_sc))
hybrid_nc08.weight = 0.8
# hybrid_nc1 = ContextHybridNeighbourhoodCalculator(copy.deepcopy(pearson_sc))
# hybrid_nc1.weight = 1.0
neighbourhood_calculators = [
simple_nc,
context_nc,
# hybrid_nc0,
# hybrid_nc02,
hybrid_nc05,
# hybrid_nc08,
# hybrid_nc1
]
# Baseline calculators
simple_ubc = SimpleUserBaselineCalculator()
ubc = UserBaselineCalculator()
baseline_calculators = [ubc, simple_ubc]
# Neighbour contribution calculators
ncc = NeighbourContributionCalculator()
context_ncc = ContextNCCalculator()
neighbour_contribution_calculators = [
ncc,
# context_ncc
]
num_topics = 150
# num_neighbours = None
numpy.random.seed(0)
basic_cosine_knn = ContextualKNN(num_topics,
simple_nc,
ncc,
simple_ubc,
cosine_sc,
has_context=False)
basic_pearson_knn = ContextualKNN(num_topics,
simple_nc,
ncc,
simple_ubc,
pearson_sc,
has_context=False)
contextual_knn = ContextualKNN(num_topics,
context_nc,
ncc,
ubc,
pbc_sc,
has_context=True)
# get_knn_recommender_info(contextual_knn1)
# ocelma_recommender = OcelmaRecommender()
recommenders = [
# basic_cosine_knn,
# basic_pearson_knn,
contextual_knn
# ocelma_recommender
]
num_neighbours_list = [None]
# num_neighbours_list = [None, 3, 6, 10, 15, 20]
threshold_list = [0.0, 0.5, 0.9]
# threshold_list = [0.0]
# num_topics_list = [10, 50, 150, 300, 500]
num_topics_list = [150]
# combined_recommenders = []
# for recommender, num_neighbours in itertools.product(recommenders, num_neighbours_list):
# new_recommender = copy.deepcopy(recommender)
# new_recommender.num_neighbours = num_neighbours
# combined_recommenders.append(new_recommender)
# threshold_list = [None]
#
# combined_recommenders = []
# for recommender, threshold in itertools.product(recommenders, threshold_list):
# new_recommender = copy.deepcopy(recommender)
# new_recommender.threshold1 = threshold
# new_recommender.threshold2 = threshold
# new_recommender.threshold3 = threshold
# new_recommender.threshold4 = threshold
# combined_recommenders.append(new_recommender)
# num_threshold_list = [0.2, 0.5, 0.7]
combined_recommenders = combine_recommenders(
neighbourhood_calculators, neighbour_contribution_calculators,
baseline_calculators, similarity_calculators, num_neighbours_list,
threshold_list, num_topics_list)
baseline_recommender = ContextualKNN(num_topics,
simple_nc,
ncc,
simple_ubc,
pearson_sc,
has_context=True)
best_recommender = ContextualKNN(num_topics,
hybrid_nc05,
ncc,
simple_ubc,
pbc_sc,
has_context=True)
# best_recommender = ContextualKNN(num_topics, simple_nc, ncc, ubc, cosine_sc, has_context=True)
best_recommender.threshold1 = 0.9
best_recommender.threshold2 = 0.9
best_recommender.threshold3 = 0.9
best_recommender.threshold4 = 0.9
my_recommenders = [
# baseline_recommender,
best_recommender
]
return my_recommenders
def get_recommenders_set():
# nc = ContextNeighbourhoodCalculator()
# ncc = NeighbourContributionCalculator()
# ubc = UserBaselineCalculator()
# usc = PBCSimilarityCalculator()
# cosine_usc = CBCSimilarityCalculator()
# Similarity calculators
cosine_sc = CosineSimilarityCalculator()
pearson_sc = PearsonSimilarityCalculator()
pbc_sc = PBCSimilarityCalculator()
cbu_sc = CBCSimilarityCalculator()
similarity_calculators = [
cosine_sc,
pearson_sc,
pbc_sc,
cbu_sc
]
# Neighbourhood calculators
simple_nc = SimpleNeighbourhoodCalculator(copy.deepcopy(pearson_sc))
context_nc = ContextNeighbourhoodCalculator()
# hybrid_nc0 = ContextHybridNeighbourhoodCalculator(copy.deepcopy(pearson_sc))
# hybrid_nc0.weight = 0.0
hybrid_nc02 = ContextHybridNeighbourhoodCalculator(copy.deepcopy(pearson_sc))
hybrid_nc02.weight = 0.2
hybrid_nc05 = ContextHybridNeighbourhoodCalculator(copy.deepcopy(pearson_sc))
hybrid_nc05.weight = 0.5
hybrid_nc08 = ContextHybridNeighbourhoodCalculator(copy.deepcopy(pearson_sc))
hybrid_nc08.weight = 0.8
# hybrid_nc1 = ContextHybridNeighbourhoodCalculator(copy.deepcopy(pearson_sc))
# hybrid_nc1.weight = 1.0
neighbourhood_calculators = [
simple_nc,
context_nc,
# hybrid_nc0,
# hybrid_nc02,
hybrid_nc05,
# hybrid_nc08,
# hybrid_nc1
]
# Baseline calculators
simple_ubc = SimpleUserBaselineCalculator()
ubc = UserBaselineCalculator()
baseline_calculators = [
ubc,
simple_ubc
]
# Neighbour contribution calculators
ncc = NeighbourContributionCalculator()
context_ncc = ContextNCCalculator()
neighbour_contribution_calculators = [
ncc,
# context_ncc
]
num_topics = 150
# num_neighbours = None
numpy.random.seed(0)
basic_cosine_knn = ContextualKNN(num_topics, simple_nc, ncc, simple_ubc, cosine_sc, has_context=False)
basic_pearson_knn = ContextualKNN(num_topics, simple_nc, ncc, simple_ubc, pearson_sc, has_context=False)
contextual_knn = ContextualKNN(num_topics, context_nc, ncc, ubc, pbc_sc, has_context=True)
# get_knn_recommender_info(contextual_knn1)
# ocelma_recommender = OcelmaRecommender()
recommenders = [
# basic_cosine_knn,
# basic_pearson_knn,
contextual_knn
# ocelma_recommender
]
num_neighbours_list = [None]
# num_neighbours_list = [None, 3, 6, 10, 15, 20]
threshold_list = [0.0, 0.5, 0.9]
# threshold_list = [0.0]
# num_topics_list = [10, 50, 150, 300, 500]
num_topics_list = [150]
# combined_recommenders = []
# for recommender, num_neighbours in itertools.product(recommenders, num_neighbours_list):
# new_recommender = copy.deepcopy(recommender)
# new_recommender.num_neighbours = num_neighbours
# combined_recommenders.append(new_recommender)
# threshold_list = [None]
#
# combined_recommenders = []
# for recommender, threshold in itertools.product(recommenders, threshold_list):
# new_recommender = copy.deepcopy(recommender)
# new_recommender.threshold1 = threshold
# new_recommender.threshold2 = threshold
# new_recommender.threshold3 = threshold
# new_recommender.threshold4 = threshold
# combined_recommenders.append(new_recommender)
# num_threshold_list = [0.2, 0.5, 0.7]
combined_recommenders = combine_recommenders(
neighbourhood_calculators,
neighbour_contribution_calculators,
baseline_calculators,
similarity_calculators,
num_neighbours_list,
threshold_list,
num_topics_list
)
baseline_recommender = ContextualKNN(num_topics, simple_nc, ncc, simple_ubc, pearson_sc, has_context=True)
best_recommender = ContextualKNN(num_topics, hybrid_nc05, ncc, simple_ubc, pbc_sc, has_context=True)
# best_recommender = ContextualKNN(num_topics, simple_nc, ncc, ubc, cosine_sc, has_context=True)
best_recommender.threshold1 = 0.9
best_recommender.threshold2 = 0.9
best_recommender.threshold3 = 0.9
best_recommender.threshold4 = 0.9
my_recommenders = [
# baseline_recommender,
best_recommender
]
return my_recommenders
def combine_recommenders(neighbourhood_calculators,
neighbour_contribution_calculators,
baseline_calculators, similarity_calculators,
num_neighbours_list, thresholds, num_topics_list):
combined_recommenders = []
for neighbourhood_calculator,\
neighbour_contribution_calculator,\
baseline_calculator,\
similarity_calculator,\
num_neighbours,\
threshold,\
num_topics\
in itertools.product(
neighbourhood_calculators,
neighbour_contribution_calculators,
baseline_calculators,
similarity_calculators,
num_neighbours_list,
thresholds,
num_topics_list):
recommender = ContextualKNN(None,
None,
None,
None,
None,
has_context=True)
recommender.neighbourhood_calculator = neighbourhood_calculator
recommender.neighbour_contribution_calculator =\
neighbour_contribution_calculator
recommender.user_baseline_calculator = baseline_calculator
recommender.user_similarity_calculator = similarity_calculator
recommender.num_neighbours = num_neighbours
recommender.threshold1 = threshold
recommender.threshold2 = threshold
recommender.threshold3 = threshold
recommender.threshold4 = threshold
recommender.num_topics = num_topics
combined_recommenders.append(recommender)
return combined_recommenders
def combine_recommenders(
neighbourhood_calculators,
neighbour_contribution_calculators,
baseline_calculators,
similarity_calculators,
num_neighbours_list,
thresholds,
num_topics_list):
combined_recommenders = []
for neighbourhood_calculator,\
neighbour_contribution_calculator,\
baseline_calculator,\
similarity_calculator,\
num_neighbours,\
threshold,\
num_topics\
in itertools.product(
neighbourhood_calculators,
neighbour_contribution_calculators,
baseline_calculators,
similarity_calculators,
num_neighbours_list,
thresholds,
num_topics_list):
recommender = ContextualKNN(
None, None, None, None, None, has_context=True)
recommender.neighbourhood_calculator = neighbourhood_calculator
recommender.neighbour_contribution_calculator =\
neighbour_contribution_calculator
recommender.user_baseline_calculator = baseline_calculator
recommender.user_similarity_calculator = similarity_calculator
recommender.num_neighbours = num_neighbours
recommender.threshold1 = threshold
recommender.threshold2 = threshold
recommender.threshold3 = threshold
recommender.threshold4 = threshold
recommender.num_topics = num_topics
combined_recommenders.append(recommender)
return combined_recommenders
