class FeatureWeightedLinearStacking(base_recommender):
def __init__(self):
self.cb = ContentBasedRecs()
self.cf = NeighborhoodBasedRecs()
self.wcb1 = Decimal(0.65221204)
self.wcb2 = Decimal(-0.14638855)
self.wcf1 = Decimal(-0.0062952)
self.wcf2 = Decimal(0.09139193)
def fun1(self):
return Decimal(1.0)
def fun2(self, user_id):
count = Rating.objects.filter(user_id=user_id).count()
if count > 3.0:
return Decimal(1.0)
return Decimal(0.0)
def recommend_items(self, user_id, num=6):
cb_recs = self.cb.recommend_items(user_id, num * 5)
cf_recs = self.cf.recommend_items(user_id, num * 5)
combined_recs = dict()
for rec in cb_recs:
movie_id = rec[0]
pred = rec[1]['prediction']
combined_recs[movie_id] = {'cb': pred}
for rec in cf_recs:
movie_id = rec[0]
pred = rec[1]['prediction']
if movie_id in combined_recs.keys():
combined_recs[movie_id]['cf'] = pred
else:
combined_recs[movie_id] = {'cf': pred}
fwls_preds = dict()
for key, recs in combined_recs.items():
if 'cb' not in recs.keys():
recs['cb'] = self.cb.predict_score(user_id, key)
if 'cf' not in recs.keys():
recs['cf'] = self.cf.predict_score(user_id, key)
pred = self.prediction(recs['cb'], recs['cf'], user_id)
fwls_preds[key] = {'prediction': pred}
sorted_items = sorted(fwls_preds.items(), key=lambda item: -float(item[1]['prediction']))[:num]
return sorted_items
def predict_score(self, user_id, item_id):
p_cb = self.cb.predict_score(user_id, item_id)
p_cf = self.cf.predict_score(user_id, item_id)
self.prediction(p_cb, p_cf, user_id)
def prediction(self, p_cb, p_cf, user_id):
p = (self.wcb1 * self.fun1() * p_cb +
self.wcb2 * self.fun2(user_id) * p_cb +
self.wcf1 * self.fun1() * p_cf +
self.wcf2 * self.fun2(user_id) * p_cf)
return p
def __init__(self):
self.cb = ContentBasedRecs()
self.cf = NeighborhoodBasedRecs()
self.wcb1 = Decimal(0.65221204)
self.wcb2 = Decimal(-0.14638855)
self.wcf1 = Decimal(-0.0062952)
self.wcf2 = Decimal(0.09139193)
def __init__(self, data_size=1000):
self.logger = logging.getLogger('FWLS')
self.train_data = None
self.test_data = None
self.rating_count = None
self.cb = ContentBasedRecs()
self.cf = NeighborhoodBasedRecs()
self.fwls = FeatureWeightedLinearStacking()
self.data_size = data_size
def recs_cb(request, user_id, num=6):
sorted_items = ContentBasedRecs().recommend_items(user_id, num)
data = {'user_id': user_id, 'data': sorted_items}
return JsonResponse(data, safe=False)
def similar_content(request, content_id, num=6):
# lda = models.ldamodel.LdaModel.load('./lda/model.lda')
#
# dictionary = corpora.Dictionary.load('./lda/dict.lda')
#
# corpus = corpora.MmCorpus('./lda/corpus.mm')
# content_sims = dict()
#
# md = MovieDescriptions.objects.filter(imdb_id=content_id).first()
#
#
# if md is not None:
# index = similarities.MatrixSimilarity.load('./lda/index.lda')
#
# lda_vector = lda[corpus[int(md.lda_vector)]]
# sims = index[lda_vector]
#
# sorted_sims = sorted(enumerate(sims), key=lambda item: -item[1])[:num]
#
# movies = get_movie_ids(sorted_sims, corpus, dictionary)
#
# for movie in movies:
# target = movie['target']
# if target in content_sims.keys():
# if movie['sim'] > content_sims[target]['sim']:
# content_sims[target] = movie
# else:
# content_sims[target] = movie
sorted_items = ContentBasedRecs().recommend_items_from_items([content_id],
num)
data = {'source_id': content_id, 'data': sorted_items}
return JsonResponse(data, safe=False)
def evaluate_cb_recommender():
min_sim = 0
min_num_of_ratings = 0
min_rank = 0
timestr = time.strftime("%Y%m%d-%H%M%S")
file_name = '{}-cb-k.csv'.format(timestr)
with open(file_name, 'a', 1) as logfile:
logfile.write(
"ar, map, mae, k, min_sim, min_num_of_ratings, min_rank\n")
for k in np.arange(5, 20, 3):
recommender = ContentBasedRecs()
er = EvaluationRunner(0, None, recommender, k)
result = er.calculate(10, 5, number_test_users=-1)
map = result['map']
mae = result['mae']
ar = result['ar']
logfile.write("{}, {}, {}, {}, {}, {}\n".format(
ar, map, mae, k, min_sim, min_num_of_ratings, min_rank))
logfile.flush()
def evaluate_cb_recommender():
K = 20
timestr = time.strftime("%Y%m%d-%H%M%S")
file_name = '{}-cb-k.csv'.format(timestr)
lda_path = './lda/'
corpus = corpora.MmCorpus(lda_path + 'corpus.mm'),
index = similarities.MatrixSimilarity.load(lda_path + 'index.lda')
with open(file_name, 'a', 1) as logfile:
logfile.write(
"rak, pak, mae, min_overlap, min_sim, K, min_num_of_ratings, min_rank, user_coverage, "
"movie_coverage\n")
for K in np.arange(2, 20, 2):
recommender = ContentBasedRecs()
er = EvaluationRunner(0, None, recommender, K)
result = er.calculate(1, 5, number_test_users=-1)
user_coverage, movie_coverage = RecommenderCoverage(
recommender).calculate_coverage()
pak = result['pak']
mae = result['mae']
rak = result['rak']
logfile.write("{}, {}, {}, {}, {}, {}, {}, {}, {}, {}\n".format(
rak, pak, mae, min_overlap, min_sim, K, min_number_of_ratings,
min_rank, user_coverage, movie_coverage))
logfile.flush()
def evaluate_cb_recommender(coverage=False):
timestr = time.strftime("%Y%m%d-%H%M%S")
file_name = '{}-cb-k.csv'.format(timestr)
with open(file_name, 'a', 1) as logfile:
logfile.write(
"ar, map, mae, min_overlap, min_sim, K, min_num_of_ratings, min_rank, user_coverage, "
"movie_coverage\n")
for k in np.arange(5, 20, 3):
recommender = ContentBasedRecs()
er = EvaluationRunner(0, None, recommender, k)
result = er.calculate(10, 5, number_test_users=-1)
user_coverage, movie_coverage = 0, 0
if coverage:
user_coverage, movie_coverage = RecommenderCoverage(
recommender).calculate_coverage(k)
map = result['map']
mae = result['mae']
ar = result['ar']
logfile.write("{}, {}, {}, {}, {}, {}\n".format(
ar, map, mae, k, user_coverage, movie_coverage))
logfile.flush()
class FWLSCalculator(object):
def __init__(self, data_size=1000):
self.logger = logging.getLogger('FWLS')
self.train_data = None
self.test_data = None
self.rating_count = None
self.cb = ContentBasedRecs()
self.cf = NeighborhoodBasedRecs()
self.fwls = FeatureWeightedLinearStacking()
self.data_size = data_size
def get_real_training_data(self):
columns = ['user_id', 'movie_id', 'rating', 'type']
ratings_data = Rating.objects.all().values(*columns)[:self.data_size]
df = pd.DataFrame.from_records(ratings_data, columns=columns)
self.train_data, self.test_data = train_test_split(df, test_size=0.2)
self.logger.debug("training data loaded {}".format(len(ratings_data)))
def calculate_predictions_for_training_data(self):
self.logger.debug("[BEGIN] getting predictions")
self.train_data['cb'] = self.train_data.apply(
lambda data: self.cb.predict_score(data['user_id'], data['movie_id'
]),
axis=1)
self.train_data['cf'] = self.train_data.apply(
lambda data: self.cf.predict_score(data['user_id'], data['movie_id'
]),
axis=1)
self.logger.debug("[END] getting predictions")
return None
def calculate_feature_functions_for_training_data(self):
self.logger.debug("[BEGIN] calculating functions")
self.train_data['cb1'] = self.train_data.apply(
lambda data: data['cb'] * self.fwls.fun1(), axis=1)
self.train_data['cb2'] = self.train_data.apply(
lambda data: data['cb'] * self.fwls.fun2(data['user_id']), axis=1)
self.train_data['cf1'] = self.train_data.apply(
lambda data: data['cf'] * self.fwls.fun1(), axis=1)
self.train_data['cf2'] = self.train_data.apply(
lambda data: data['cf'] * self.fwls.fun2(data['user_id']), axis=1)
self.logger.debug("[END] calculating functions")
return None
def train(self):
#model = sm.ols(formula="rating ~ cb1+cb2+cf1+cf2", data=self.train_data[['rating', 'cb1','cb2','cf1','cf2']])
#results = model.fit()
#self.logger.info(results.summary())
#self.logger.info(results.params)
regr = linear_model.LinearRegression()
regr.fit(self.train_data[['cb1', 'cb2', 'cf1', 'cf2']],
self.train_data['rating'])
self.logger.info(regr.coef_)
return regr.coef_
def recs_cb(request, user_id, num=6):
start_time = datetime.now()
print(f"lda loaded in {datetime.now()-start_time}")
sorted_items = ContentBasedRecs().recommend_items(user_id, num)
data = {'user_id': user_id, 'data': sorted_items}
return JsonResponse(data, safe=False)
def similar_content(request, content_id, num=6):
sorted_items = ContentBasedRecs().seeded_rec([content_id], num)
data = {
'source_id': content_id,
'data': sorted_items
}
return JsonResponse(data, safe=False)
def __init__(self, save_path, data_size=1000):
self.save_path = save_path
self.logger = logging.getLogger('FWLS')
self.train_data = None
self.test_data = None
self.rating_count = None
self.cb = ContentBasedRecs()
self.cf = NeighborhoodBasedRecs()
self.fwls = FeatureWeightedLinearStacking()
self.data_size = data_size
class FWLSCalculator(object):
def __init__(self):
self.train = None
self.test = None
self.rating_count = None
self.cb = ContentBasedRecs()
self.cf = NeighborhoodBasedRecs()
def get_real_training_data(self):
columns = ['user_id', 'movie_id', 'rating', 'type']
ratings_data = Rating.objects.all().values(*columns)
df = pd.DataFrame.from_records(ratings_data, columns=columns)
self.train, self.test = train_test_split(df, test_size=0.2)
def get_training_data(self):
print('load data')
data = np.array([['1', '2', 3.6], ['1', '3', 5.0], ['1', '4', 5.0],
['2', '2', 3.0]])
self.train = pd.DataFrame(data,
columns=['user_id', 'movie_id', 'rating'])
self.rating_count = self.train.groupby('user_id').count().reset_index()
return self.train
def calculate_predictions_for_training_data(self):
self.train['cb'] = self.train.apply(lambda data: self.cb.predict_score(
data['user_id'], data['movie_id']),
axis=1)
self.train['cf'] = self.train.apply(lambda data: self.cf.predict_score(
data['user_id'], data['movie_id']),
axis=1)
return None
def calculate_feature_functions_for_training_data(self):
self.train['cb1'] = self.train.apply(
lambda data: data.cb * self.func1())
self.train['cb2'] = self.train.apply(
lambda data: data.cb * self.func2(data['user_id']), axis=1)
self.train['cf1'] = self.train.apply(
lambda data: data.cf * self.func1())
self.train['cf2'] = self.train.apply(
lambda data: data.cf * self.func2(data['user_id']), axis=1)
return None
def train(self):
result = sm.ols(formula="rating ~ cb1+cb2+cf1+cf2",
data=fwls.train).fit()
print(result)
class FWLSCalculator(object):
def __init__(self, save_path, data_size = 1000):
self.save_path = save_path
self.logger = logging.getLogger('FWLS')
self.train_data = None
self.test_data = None
self.rating_count = None
self.cb = ContentBasedRecs()
self.cf = NeighborhoodBasedRecs()
self.fwls = FeatureWeightedLinearStacking()
self.data_size = data_size
def get_real_training_data(self):
columns = ['user_id', 'movie_id', 'rating', 'type']
ratings_data = Rating.objects.all().values(*columns)[:self.data_size]
df = pd.DataFrame.from_records(ratings_data, columns=columns)
self.train_data, self.test_data = train_test_split(df, test_size=0.2)
self.logger.debug("training data loaded {}".format(len(ratings_data)))
def calculate_predictions_for_training_data(self):
self.logger.debug("[BEGIN] getting predictions")
self.train_data['cb'] = self.train_data.apply(lambda data:
self.cb.predict_score(data['user_id'], data['movie_id']), axis=1)
self.train_data['cf'] = self.train_data.apply(lambda data:
self.cf.predict_score(data['user_id'], data['movie_id']), axis=1)
self.logger.debug("[END] getting predictions")
return None
def calculate_feature_functions_for_training_data(self):
self.logger.debug("[BEGIN] calculating functions")
self.train_data['cb1'] = self.train_data.apply(lambda data:
data['cb'] * self.fwls.fun1(), axis=1)
self.train_data['cb2'] = self.train_data.apply(lambda data:
data['cb'] * self.fwls.fun2(data['user_id']), axis = 1)
self.train_data['cf1'] = self.train_data.apply(lambda data:
data['cf'] * self.fwls.fun1(), axis=1)
self.train_data['cf2'] = self.train_data.apply(lambda data:
data['cf'] * self.fwls.fun2(data['user_id']), axis = 1)
self.logger.debug("[END] calculating functions")
return None
def build(self, train_data = None, params = None):
if params:
self.save_path = params['save_path']
if train_data is None:
self.get_real_training_data()
self.train_data = train_data
self.calculate_predictions_for_training_data()
self.calculate_feature_functions_for_training_data()
return self.train()
def train(self, ratings = None, train_feature_recs= False):
if train_feature_recs:
ItemSimilarityMatrixBuilder().build(ratings)
LdaModel.build()
regr = linear_model.LinearRegression()
regr.fit(self.train_data[['cb1','cb2','cf1','cf2']], self.train_data['rating'])
self.logger.info(regr.coef_)
result = {'cb1': regr.coef_[0],
'cb2': regr.coef_[1],
'cf1': regr.coef_[2],
'cf2': regr.coef_[3]
}
ensure_dir(self.save_path)
with open(self.save_path + 'fwls_parameters.data', 'wb') as ub_file:
pickle.dump(result, ub_file)
return result
class FeatureWeightedLinearStacking(base_recommender):
def __init__(self):
self.cb = ContentBasedRecs()
self.cf = NeighborhoodBasedRecs()
self.wcb1 = Decimal(0.65221204)
self.wcb2 = Decimal(-0.14638855)
self.wcf1 = Decimal(-0.0062952)
self.wcf2 = Decimal(0.09139193)
self.intercept = Decimal(0)
def fun1(self):
return Decimal(1.0)
def fun2(self, user_id):
count = Rating.objects.filter(user_id=user_id).count()
if count > 3.0:
return Decimal(1.0)
return Decimal(0.0)
def set_save_path(self, save_path):
with open(save_path + 'fwls_parameters.data', 'rb') as ub_file:
parameters = pickle.load(ub_file)
self.wcb1 = Decimal(parameters['cb1'])
self.wcb2 = Decimal(parameters['cb2'])
self.wcf1 = Decimal(parameters['cb1'])
self.wcf2 = Decimal(parameters['cf2'])
self.intercept = Decimal(parameters['intercept'])
def recommend_items_by_ratings(self, user_id, active_user_items, num=6):
cb_recs = self.cb.recommend_items_by_ratings(user_id,
active_user_items,
num * 5)
cf_recs = self.cf.recommend_items_by_ratings(user_id,
active_user_items,
num * 5)
return self.merge_predictions(user_id, cb_recs, cf_recs, num)
def recommend_items(self, user_id, num=6):
cb_recs = self.cb.recommend_items(user_id, num * 5)
cf_recs = self.cf.recommend_items(user_id, num * 5)
return self.merge_predictions(user_id, cb_recs, cf_recs, num)
def merge_predictions(self, user_id, cb_recs, cf_recs, num):
combined_recs = dict()
for rec in cb_recs:
movie_id = rec[0]
pred = rec[1]['prediction']
combined_recs[movie_id] = {'cb': pred}
for rec in cf_recs:
movie_id = rec[0]
pred = rec[1]['prediction']
if movie_id in combined_recs.keys():
combined_recs[movie_id]['cf'] = pred
else:
combined_recs[movie_id] = {'cf': pred}
fwls_preds = dict()
for key, recs in combined_recs.items():
if 'cb' not in recs.keys():
recs['cb'] = self.cb.predict_score(user_id, key)
if 'cf' not in recs.keys():
recs['cf'] = self.cf.predict_score(user_id, key)
pred = self.prediction(recs['cb'], recs['cf'], user_id)
fwls_preds[key] = {'prediction': pred}
sorted_items = sorted(
fwls_preds.items(),
key=lambda item: -float(item[1]['prediction']))[:num]
return sorted_items
def predict_score(self, user_id, item_id):
p_cb = self.cb.predict_score(user_id, item_id)
p_cf = self.cf.predict_score(user_id, item_id)
self.prediction(p_cb, p_cf, user_id)
def prediction(self, p_cb, p_cf, user_id):
p = (self.wcb1 * self.fun1() * p_cb +
self.wcb2 * self.fun2(user_id) * p_cb +
self.wcf1 * self.fun1() * p_cf +
self.wcf2 * self.fun2(user_id) * p_cf)
return p + self.intercept
def __init__(self):
self.train = None
self.test = None
self.rating_count = None
self.cb = ContentBasedRecs()
self.cf = NeighborhoodBasedRecs()
args = parser.parse_args()
print(args.fwls)
k = 10
cov = None
if args.fwls:
logger.debug("evaluating coverage of fwls")
cov = RecommenderCoverage(FeatureWeightedLinearStacking)
cov.calculate_coverage(K=k, recName='fwls{}'.format(k))
if args.cf:
logger.debug("evaluating coverage of cf")
cov = RecommenderCoverage(NeighborhoodBasedRecs())
cov.calculate_coverage(K=k, recName='cf{}'.format(k))
if args.cb:
logger.debug("evaluating coverage of cb")
cov = RecommenderCoverage(ContentBasedRecs())
cov.calculate_coverage(K=k, recName='cb{}'.format(k))
if args.ltr:
logger.debug("evaluating coverage of ltr")
cov = RecommenderCoverage(BPRRecs())
cov.calculate_coverage(K=k, recName='bpr{}'.format(k))
if args.funk:
logger.debug("evaluating coverage of funk")
cov = RecommenderCoverage(FunkSVDRecs())
cov.calculate_coverage(K=k, recName='funk{}'.format(k))
class FWLSCalculator(object):
def __init__(self, save_path, data_size=1000):
self.save_path = save_path
self.logger = logging.getLogger('FWLS')
self.train_data = None
self.test_data = None
self.rating_count = None
self.cb = ContentBasedRecs()
self.cf = NeighborhoodBasedRecs()
self.fwls = FeatureWeightedLinearStacking()
self.data_size = data_size
def get_real_training_data(self):
columns = ['user_id', 'movie_id', 'rating', 'type']
ratings_data = Rating.objects.all().values(*columns)[:self.data_size]
df = pd.DataFrame.from_records(ratings_data, columns=columns)
self.train_data, self.test_data = train_test_split(df, test_size=0.2)
self.logger.debug("training data loaded {}".format(len(ratings_data)))
def calculate_predictions_for_training_data(self):
self.logger.debug("[BEGIN] getting predictions")
self.train_data['cb'] = self.train_data.apply(lambda data:
self.cb.predict_score(data['user_id'],
data['movie_id']), axis=1)
self.train_data['cf'] = self.train_data.apply(lambda data:
self.cf.predict_score(data['user_id'],
data['movie_id']), axis=1)
self.logger.debug("[END] getting predictions")
return None
def calculate_feature_functions_for_training_data(self):
self.logger.debug("[BEGIN] calculating functions")
self.train_data['cb1'] = self.train_data.apply(lambda data:
data['cb'] * self.fwls.fun1(), axis=1)
self.train_data['cb2'] = self.train_data.apply(lambda data:
data['cb'] * self.fwls.fun2(data['user_id']), axis=1)
self.train_data['cf1'] = self.train_data.apply(lambda data:
data['cf'] * self.fwls.fun1(), axis=1)
self.train_data['cf2'] = self.train_data.apply(lambda data:
data['cf'] * self.fwls.fun2(data['user_id']), axis=1)
self.logger.debug("[END] calculating functions")
return None
def build(self, train_data=None, params=None):
if params:
self.save_path = params['save_path']
self.data_size = params['data_sample']
if train_data is not None:
self.train_data = train_data
if self.data_size > 0:
self.train_data = self.train_data.sample(self.data_size)
self.logger.debug("training sample of size {}".format(self.train_data.shape[0]))
else:
self.get_real_training_data()
self.calculate_predictions_for_training_data()
self.calculate_feature_functions_for_training_data()
return self.train()
def train(self, ratings=None, train_feature_recs=False):
if train_feature_recs:
ItemSimilarityMatrixBuilder().build(ratings)
LdaModel.build()
regr = linear_model.LinearRegression(fit_intercept=True,
n_jobs=-1,
normalize=True)
regr.fit(self.train_data[['cb1', 'cb2', 'cf1', 'cf2']], self.train_data['rating'])
self.logger.info(regr.coef_)
result = {'cb1': regr.coef_[0],
'cb2': regr.coef_[1],
'cf1': regr.coef_[2],
'cf2': regr.coef_[3],
'intercept': regr.intercept_}
self.logger.debug(result)
self.logger.debug(self.train_data.iloc[100])
ensure_dir(self.save_path)
with open(self.save_path + 'fwls_parameters.data', 'wb') as ub_file:
pickle.dump(result, ub_file)
return result
