class BPRRecommender(BaseRecommender):
def __init__(self):
super().__init__()
self.URM = None
self.item_factors = None
self.user_factors = None
self.model = BayesianPersonalizedRanking(factors=1000, num_threads=8, verify_negative_samples=True)
def fit(self, URM):
self.URM = URM
URM_transpose = self.URM.T
self.model.fit(URM_transpose)
self.user_factors = self.model.user_factors
self.item_factors = self.model.item_factors
def get_expected_ratings(self, user_id):
scores = np.dot(self.user_factors[user_id], self.item_factors.T)
return np.squeeze(scores)
def recommend(self, user_id, at=10):
expected_ratings = self.get_expected_ratings(user_id)
recommended_items = np.flip(np.argsort(expected_ratings), 0)
unseen_items_mask = np.in1d(recommended_items, self.URM[user_id].indices, assume_unique=True, invert=True)
recommended_items = recommended_items[unseen_items_mask]
return recommended_items[:at]
def train(self):
b_time = time.time()
self.item_idx, self.item_idx_reverse = {}, {}
if self.reload:
with open(self.config['item_vec_file'], 'r') as in_f:
num_items, dim = in_f.readline().strip().split()
print(f'Num of items : {num_items}, dim : {dim}')
self.t = AnnoyIndex(int(dim), 'angular')
for idx, line in tqdm(enumerate(in_f)):
tmp = line.split()
item = tmp[0]
self.item_idx[item] = idx
self.item_idx_reverse[idx] = item
self.t = AnnoyIndex(int(dim), 'angular')
self.t.load(f'{file_name}.ann')
else:
Y = []
with open(self.config['train_file'], 'r') as in_f:
for idx, line in tqdm(enumerate(in_f)):
items_list = line.strip().split()
Y.append([self.__get_id(item) for item in items_list])
# construct the sparse matrix
indptr = np.fromiter(chain((0,), map(len, Y)), int, len(Y) + 1).cumsum()
indices = np.fromiter(chain.from_iterable(Y), int, indptr[-1])
data = np.ones_like(indices)
user_item_table_csr = csr_matrix((data, indices, indptr))
item_user_table_csr = user_item_table_csr.T.tocsr()
print('Matrix size : ', item_user_table_csr.shape)
print("Train finished ... : ", time.time() - b_time)
# Train MF
model_name = "bpr"
self.model = BayesianPersonalizedRanking(num_threads=20)
print("training model %s", model_name)
start = time.time()
self.model.fit(item_user_table_csr)
print("trained model '%s' in %s", model_name, time.time() - start)
print("calculating top movies")
items_count, dim = self.model.item_factors.shape
# Build Ann
self.t = AnnoyIndex(int(dim), 'angular')
with open(config['item_vec_file']) as out_f:
print(f"{items_count} {dim}", file=out_f)
for idx, vec in tqdm(enumerate(self.model.item_factors)):
self.t.add_item(idx, vec)
print(f"{self.item_idx_reverse[idx]} {' '.join(vec.astype(str))}", file=out_f)
print("Read file finished ...")
file_name = self.config['index_file_file']
self.t.build(30) # 10 trees
self.t.save(f'{file_name}.ann')
print(f"Train finished ...{time.time() - b_time}")
def _train_bpr(hyperparameters, train):
h = hyperparameters
model = BayesianPersonalizedRanking(factors=h['factors'],
iterations=h['n_iter'],
num_threads=nproc)
model.fit(train)
# test_eval = {'p@k': precision_at_k(model, train.T.tocsr(), factorization.T.tocsr(), K=10)}
# val_eval = {'p@k': precision_at_k(model, train.T.tocsr(), validation.T.tocsr(), K=10)}
return model
def fit(user_item_matrix,
n_factors=20,
regularization=0.001,
iterations=15,
num_threads=4):
"""Обучает BPR"""
model = BayesianPersonalizedRanking(factors=n_factors,
regularization=regularization,
iterations=iterations,
num_threads=num_threads)
model.fit(csr_matrix(user_item_matrix).T.tocsr())
return model
def get_aucs_vs_factors():
factors = [8, 16, 32, 64, 128]
params_list = [{"factors": factor} for factor in factors]
aucs = []
for params in params_list:
model = BayesianPersonalizedRanking(**params)
model.fit(comments)
aucs.append(
auc(test_set[:20000], model.user_factors, model.item_factors,
subreddits, users))
return aucs
def calculate_similar_movies(output_filename, model_name="als", min_rating=4.0, variant="20m"):
# read in the input data file
start = time.time()
titles, ratings = get_movielens(variant)
# remove things < min_rating, and convert to implicit dataset
# by considering ratings as a binary preference only
ratings.data[ratings.data < min_rating] = 0
ratings.eliminate_zeros()
ratings.data = np.ones(len(ratings.data))
log.info("read data file in %s", time.time() - start)
# generate a recommender model based off the input params
if model_name == "als":
model = AlternatingLeastSquares()
# lets weight these models by bm25weight.
log.debug("weighting matrix by bm25_weight")
ratings = (bm25_weight(ratings, B=0.9) * 5).tocsr()
elif model_name == "bpr":
model = BayesianPersonalizedRanking()
elif model_name == "lmf":
model = LogisticMatrixFactorization()
elif model_name == "tfidf":
model = TFIDFRecommender()
elif model_name == "cosine":
model = CosineRecommender()
elif model_name == "bm25":
model = BM25Recommender(B=0.2)
else:
raise NotImplementedError("TODO: model %s" % model_name)
# train the model
log.debug("training model %s", model_name)
start = time.time()
model.fit(ratings)
log.debug("trained model '%s' in %s", model_name, time.time() - start)
log.debug("calculating top movies")
user_count = np.ediff1d(ratings.indptr)
to_generate = sorted(np.arange(len(titles)), key=lambda x: -user_count[x])
log.debug("calculating similar movies")
with tqdm.tqdm(total=len(to_generate)) as progress:
with codecs.open(output_filename, "w", "utf8") as o:
for movieid in to_generate:
# if this movie has no ratings, skip over (for instance 'Graffiti Bridge' has
# no ratings > 4 meaning we've filtered out all data for it.
if ratings.indptr[movieid] != ratings.indptr[movieid + 1]:
title = titles[movieid]
for other, score in model.similar_items(movieid, 11):
o.write("%s\t%s\t%s\n" % (title, titles[other], score))
progress.update(1)
def __init__(self, *args, **kwargs):
"""
Construct an ALS recommender. The arguments are passed as-is to
:py:class:`implicit.als.BayesianPersonalizedRanking`.
"""
from implicit.bpr import BayesianPersonalizedRanking
super().__init__(BayesianPersonalizedRanking(*args, **kwargs))
def train_implicit_bpr(
train_df: pd.DataFrame, params: Dict[str, Union[str, float, int]],
shape: [int,
int]) -> Tuple[BayesianPersonalizedRanking, sp.csr.csr_matrix]:
train_matrix = create_implicit_train_matrix(train_df, shape)
model_params = dict()
args = inspect.getfullargspec(BayesianPersonalizedRanking.__init__)[0]
for param, param_val in params.items():
if param in args:
model_params[param] = param_val
model = BayesianPersonalizedRanking(**model_params)
model.fit(train_matrix, show_progress=False)
return model, train_matrix
def evaluate_bpr_model(hyperparameters, train, test, validation):
h = hyperparameters
model = BayesianPersonalizedRanking(factors=h['factors'],
iterations=h['n_iter'],
num_threads=nproc)
model.fit(train)
test_eval = {
'p@k': precision_at_k(model, train.T.tocsr(), test.T.tocsr(), K=10)
}
val_eval = {
'p@k': precision_at_k(model,
train.T.tocsr(),
validation.T.tocsr(),
K=10)
}
return test_eval, val_eval
def calculate_similar_movies(input_path,
output_filename,
model_name="als",
min_rating=4.0):
# read in the input data file
logging.debug("reading data from %s", input_path)
start = time.time()
ratings, movies, m = read_data(input_path, min_rating=min_rating)
logging.debug("read data file in %s", time.time() - start)
# generate a recommender model based off the input params
if model_name == "als":
model = AlternatingLeastSquares()
# lets weight these models by bm25weight.
logging.debug("weighting matrix by bm25_weight")
m = bm25_weight(m, B=0.9) * 5
elif model_name == "bpr":
model = BayesianPersonalizedRanking()
elif model_name == "tfidf":
model = TFIDFRecommender()
elif model_name == "cosine":
model = CosineRecommender()
elif model_name == "bm25":
model = BM25Recommender(B=0.2)
else:
raise NotImplementedError("TODO: model %s" % model_name)
# train the model
m = m.tocsr()
logging.debug("training model %s", model_name)
start = time.time()
model.fit(m)
logging.debug("trained model '%s' in %s", model_name, time.time() - start)
logging.debug("calculating top movies")
user_count = ratings.groupby('movieId').size()
movie_lookup = dict(
(i, m) for i, m in zip(movies['movieId'], movies['title']))
to_generate = sorted(list(movies['movieId']),
key=lambda x: -user_count.get(x, 0))
with codecs.open(output_filename, "w", "utf8") as o:
for movieid in to_generate:
# if this movie has no ratings, skip over (for instance 'Graffiti Bridge' has
# no ratings > 4 meaning we've filtered out all data for it.
if m.indptr[movieid] == m.indptr[movieid + 1]:
continue
movie = movie_lookup[movieid]
for other, score in model.similar_items(movieid, 11):
o.write("%s\t%s\t%s\n" % (movie, movie_lookup[other], score))
def initialize_components(self):
self.bpr_mf = BPR_matrix_factorization(factors=200,
regularization=0.00000,
learning_rate=0.01,
iterations=65)
self.ials_cg_mf = IALS_CG(iterations=15,
calculate_training_loss=True,
factors=500,
use_cg=True,
regularization=1e-3)
def fit(user_item_matrix,
n_factors=20,
regularization=0.001,
iterations=15,
num_threads=0):
"""Обучает ALS"""
# model = AlternatingLeastSquares(factors=n_factors,
model = BayesianPersonalizedRanking(
factors=n_factors,
regularization=regularization,
iterations=iterations,
# calculate_training_loss=True,
num_threads=num_threads,
)
model.fit(csr_matrix(user_item_matrix).T.tocsr(), show_progress=False)
print('LOG = 005')
return model
def BPR_train(self, inputs, rating_df):
model = BayesianPersonalizedRanking(factors=60)
model.fit(inputs)
# user_embeddings = model.user_factors
movie_embeddings = model.item_factors
# id를 영화 이름으로 변경
id_title_dict = {k: v for k, v in self.movie_df['title'].items()}
title = [
id_title_dict[movie_id]
for movie_id in rating_df['movie_id'].cat.categories
]
# movie embedding
movie_embedding_df = pd.DataFrame(movie_embeddings, index=title)
# user_names = [user_id for user_id in rating_df['user_id'].cat.categories]
# user_embedding_df = pd.DataFrame(user_embeddings, index=user_names)
return movie_embedding_df # , user_embedding_df
def bpr(self, database, **kwargs):
from implicit.bpr import BayesianPersonalizedRanking
opts = self.get_option('implicit', 'bpr', **kwargs)
model = BayesianPersonalizedRanking(
**opts
)
ratings = self.get_database(database, **kwargs)
if kwargs.get('return_instance_before_train'):
return (model, ratings)
elapsed, mem_info = self.run(model.fit, ratings)
model = None
return elapsed, mem_info
def initialize_components(self):
self.train = self.rescale_wrt_insertion_order(self.train)
self.item_cosineCF_recommender = Cosine_Similarity(self.train, topK=200, shrink=15, normalize=True, mode='cosine')
self.user_cosineCF_recommender = Cosine_Similarity(self.train.T, topK=200, shrink=15, normalize=True, mode='cosine')
self.svd_recommender = PureSVDRecommender(self.train)
self.cbf_bpr_recommender = SLIM_BPR_Cython(self.icm.T, positive_threshold=0)
self.cbf_recommender = Cosine_Similarity(self.icm.T, topK=50, shrink=10, normalize=True, mode='cosine')
self.item_rp3b_recommender = RP3betaRecommender(self.train)
self.user_rp3b_recommender = RP3betaRecommender(self.train.T)
self.bpr_mf = BPR_matrix_factorization(factors=800, regularization=0.01, learning_rate=0.01, iterations=300)
self.ials_cg_mf = IALS_CG(iterations=15, calculate_training_loss=True, factors=500, use_cg=True, regularization=1e-3)
self.lightfm = LightFM_Recommender(self.train, self.icm, no_components=200)
def BPR(A: sp.coo_matrix, factors: int, lr: float, regularization: float,
iterations: float):
'''
Run BayesianPersonalizedRanking - BPR: Bayesian Personalized Ranking from ImplicitFeedback
:param A: userxitem matrix
:param factors: embedding size
:param lr: learning rate
:param regularization: regularizazion parameter
:param iterations: how many training updates
'''
bpr = BayesianPersonalizedRanking(factors=factors,
learning_rate=lr,
regularization=regularization,
use_gpu=True,
iterations=iterations,
verify_negative_samples=True,
num_threads=10)
bpr.fit(A.T)
# Last one is the bias term. However user_bias is 1 (not used) so a simple dot product works.
item_factors = bpr.item_factors
user_factors = bpr.user_factors
return user_factors.dot(item_factors.T)
def __init__(self):
super().__init__()
self.URM = None
self.item_factors = None
self.user_factors = None
self.model = BayesianPersonalizedRanking(factors=1000, num_threads=8, verify_negative_samples=True)
class MF_kNN(Model):
def __init__(self, config):
self.requirement = ['test_file', 'lastN', 'topN', 'train_file', 'index_file_file']
self.config = config
miss = set()
for item in self.requirement:
if item not in self.config:
miss.add(item)
if len(miss) > 0:
raise Exception(f"Miss the key : {miss}")
Model.__init__(self,
self.config['test_file'],
self.config['lastN'],
self.config['topN']
)
self.reload = self.config.get('reload', False)
def __get_id(self, item):
if item in self.item_idx:
_id = self.item_idx[item]
else:
_id = len(self.item_idx)
self.item_idx[item] = _id
self.item_idx_reverse[_id] = item
return _id
def train(self):
b_time = time.time()
self.item_idx, self.item_idx_reverse = {}, {}
if self.reload:
with open(self.config['item_vec_file'], 'r') as in_f:
num_items, dim = in_f.readline().strip().split()
print(f'Num of items : {num_items}, dim : {dim}')
self.t = AnnoyIndex(int(dim), 'angular')
for idx, line in tqdm(enumerate(in_f)):
tmp = line.split()
item = tmp[0]
self.item_idx[item] = idx
self.item_idx_reverse[idx] = item
self.t = AnnoyIndex(int(dim), 'angular')
self.t.load(f'{file_name}.ann')
else:
Y = []
with open(self.config['train_file'], 'r') as in_f:
for idx, line in tqdm(enumerate(in_f)):
items_list = line.strip().split()
Y.append([self.__get_id(item) for item in items_list])
# construct the sparse matrix
indptr = np.fromiter(chain((0,), map(len, Y)), int, len(Y) + 1).cumsum()
indices = np.fromiter(chain.from_iterable(Y), int, indptr[-1])
data = np.ones_like(indices)
user_item_table_csr = csr_matrix((data, indices, indptr))
item_user_table_csr = user_item_table_csr.T.tocsr()
print('Matrix size : ', item_user_table_csr.shape)
print("Train finished ... : ", time.time() - b_time)
# Train MF
model_name = "bpr"
self.model = BayesianPersonalizedRanking(num_threads=20)
print("training model %s", model_name)
start = time.time()
self.model.fit(item_user_table_csr)
print("trained model '%s' in %s", model_name, time.time() - start)
print("calculating top movies")
items_count, dim = self.model.item_factors.shape
# Build Ann
self.t = AnnoyIndex(int(dim), 'angular')
with open(config['item_vec_file']) as out_f:
print(f"{items_count} {dim}", file=out_f)
for idx, vec in tqdm(enumerate(self.model.item_factors)):
self.t.add_item(idx, vec)
print(f"{self.item_idx_reverse[idx]} {' '.join(vec.astype(str))}", file=out_f)
print("Read file finished ...")
file_name = self.config['index_file_file']
self.t.build(30) # 10 trees
self.t.save(f'{file_name}.ann')
print(f"Train finished ...{time.time() - b_time}")
def predict(self, last_n_events, topN):
b_time = time.time()
item_similar = list()
candidate_items = set()
last_n_items = [self.item_idx[e.split(':', 1)[1]] for e in last_n_events[::-1] if e.split(':', 1)[1] in self.item_idx]
if len(last_n_items) == 0:
return []
for item_idx in last_n_items:
similar_res = self.__item_topK_similar(item_idx, topN)
item_similar.append(similar_res)
candidate_items.update(set(similar_res.keys()))
candidate_list = list(candidate_items)
score_matric = np.zeros((len(last_n_items), len(candidate_list)))
for i, item_id in enumerate(last_n_items):
score_matric[i] = self.__item_item_arr_norm_score(item_id, candidate_list, item_similar[i])
rank_weight = np.array([1 / np.log2(rank + 2) for rank in range(len(last_n_items))])
final_score = rank_weight.dot(score_matric).tolist()
# print(last_n_items, list(zip(candidate_list, final_score)))
final_items = sorted(zip(candidate_list, final_score), key=lambda x:x[1], reverse=True)
return [item for item, score in final_items[:topN]]
def __item_topK_similar(self, given_idx, topK):
item_idx_arr, score_arr = self.t.get_nns_by_item(given_idx, topK, include_distances=True)
res = {}
for idx, score in zip(item_idx_arr, score_arr):
try:
item_raw = self.item_idx_reverse[idx]
if item_raw not in res:
# return to cosine score
res[item_raw] = 1 - score**2/2
except:
pass
return res
def __item_item_arr_norm_score(self, item, candidate_item_arr, similar_items):
res = np.zeros(len(candidate_item_arr))
for _item in similar_items:
_score = similar_items[_item]
if _item in candidate_item_arr:
res[candidate_item_arr.index(_item)] = float(_score)
return res / np.linalg.norm(res)
def calculate_similar_movies(input_filename,
output_filename,
model_name="als", min_rating=4.0,
variant='20m'):
# read in the input data file
start = time.time()
# titles, ratings = get_movielens(variant)
user_item_df = read_user_item_data(input_filename)
print(user_item_df)
unique_user, unique_item, user_item_df = get_user_item_sparse_data_presto(
user_item_df)
#user_item_df = user_item_df.sort_values(by=['user_index','item_index'])
user_item_ratings = scipy.sparse.csr_matrix(
(user_item_df['score'], (user_item_df['item_index'], user_item_df['user_index'])))
print(user_item_ratings)
'''
# remove things < min_rating, and convert to implicit dataset
# by considering ratings as a binary preference only
ratings.data[ratings.data < min_rating] = 0
ratings.eliminate_zeros()
ratings.data = np.ones(len(ratings.data))
'''
log.info("read data file in %s", time.time() - start)
# generate a recommender model based off the input params
if model_name == "als":
model = AlternatingLeastSquares(
factors=128, regularization=0.01, use_native=True, iterations=20, calculate_training_loss=True)
# lets weight these models by bm25weight.
log.debug("weighting matrix by bm25_weight")
# ratings = (bm25_weight(ratings, B=0.9) * 5).tocsr()
elif model_name == "bpr":
model = BayesianPersonalizedRanking()
elif model_name == "lmf":
model = LogisticMatrixFactorization()
elif model_name == "tfidf":
model = TFIDFRecommender()
elif model_name == "cosine":
model = CosineRecommender()
elif model_name == "bm25":
model = BM25Recommender(B=0.2)
else:
raise NotImplementedError("TODO: model %s" % model_name)
# train the model
log.debug("training model %s", model_name)
start = time.time()
model.fit(user_item_ratings)
log.debug("trained model '%s' in %s", model_name, time.time() - start)
log.debug("calculating top movies")
k=10
iterations = 10000
similar_df_gen = similar_to_csv(model, k, unique_item, iterations)
with tqdm.tqdm(total=len(unique_item) // iterations + 1) as progress:
for similar_df_slice in similar_df_gen:
similar_df_slice.to_csv(args.outputfile, mode='a', header=False, index=False)
print("finsih a batch")
progress.update(1)
'''
def _get_model(self):
return BayesianPersonalizedRanking(factors=3,
regularization=0,
use_gpu=False)
class HHimmlerEnsemble:
def __init__(self, urm_train, urm_test, icm, parameters=None):
if parameters is None:
parameters = {
"USER_CF": 0.8,
"USER_BPR": 0.7,
"ITEM_CF": 1,
"ITEM_BPR": 0.8,
"CBF": 0.3,
"IALS": 1.0,
"CBF_BPR": 1
}
self.ensemble_weights = parameters
self.train = urm_train.tocsr()
self.test = urm_test.tocsr()
self.icm = icm.tocsr()
self.initialize_components()
def initialize_components(self):
self.bpr_mf = BPR_matrix_factorization(factors=200,
regularization=0.00000,
learning_rate=0.01,
iterations=65)
self.ials_cg_mf = IALS_CG(iterations=15,
calculate_training_loss=True,
factors=500,
use_cg=True,
regularization=1e-3)
def fit(self):
self.bpr_mf.fit(self.train.T.tocoo())
self.ials_cg_mf.fit(40 * self.train.T)
self.bpr_mf_latent_x = self.bpr_mf.user_factors.copy()
self.bpr_mf_latent_y = self.bpr_mf.item_factors.copy()
self.ials_cg_mf_latent_x = self.ials_cg_mf.user_factors.copy()
self.ials_cg_mf_latent_y = self.ials_cg_mf.item_factors.copy()
def recommend(self, user_id, combiner, at=10):
bpr_mf_r = np.dot(self.bpr_mf_latent_x[user_id],
self.bpr_mf_latent_y.T).ravel()
ials_cg_mf_r = np.dot(self.ials_cg_mf_latent_x[user_id],
self.ials_cg_mf_latent_y.T).ravel()
scores = [
# [bpr_mf_r, self.ensemble_weights["BPR_MF"], "BPR_MF"],
[ials_cg_mf_r, 1, "IALS_CG"]
]
for r in scores:
self.filter_seen(user_id, r[0])
return combiner.combine(scores, at)
def filter_seen(self, user_id, scores):
start_pos = int(self.train.indptr[user_id])
end_pos = int(self.train.indptr[user_id + 1])
user_profile = self.train.indices[start_pos:end_pos]
scores[user_profile] = -1000000 #-np.inf
return scores
def recommend_batch(self, user_list, combiner, at=10):
res = np.array([])
n = 0
for i in user_list:
bpr = self.bpr_mf.recommend(user_items=self.train,
userid=i,
N=at,
recalculate_user=False)
ials = self.ials_cg_mf.recommend(userid=i,
user_items=self.train,
N=10)
list = [x[0] for x in ials]
recList = np.array(list)
tuple = np.concatenate(([i], recList))
if (res.size == 0):
res = tuple
else:
res = np.vstack([res, tuple])
return res
def get_component_data(self):
print('cyka')
class BMussoliniEnsemble:
def __init__(self, urm_train, urm_test, icm, parameters=None):
if parameters is None:
parameters = {
"USER_CF" : 7,
"SVD" : 26,
"ITEM_CF" : 0,
"ITEM_BPR" : 16,
"CBF" : 7,
"IALS" : 26,
"CBF_BPR" : 64,
"BPR_MF": 6,
"ITEM_RP3B": 16,
"USER_RP3B": 0,
"FM": 10
}
self.ensemble_weights = parameters
self.train = urm_train.tocsr()
self.test = urm_test.tocsr()
self.icm = icm.tocsr()
self.sequential_playlists = None
self.sequential_playlists = load_sequential.load_train_sequential()
self.initialize_components()
def initialize_components(self):
self.train = self.rescale_wrt_insertion_order(self.train)
self.item_cosineCF_recommender = Cosine_Similarity(self.train, topK=200, shrink=15, normalize=True, mode='cosine')
self.user_cosineCF_recommender = Cosine_Similarity(self.train.T, topK=200, shrink=15, normalize=True, mode='cosine')
self.svd_recommender = PureSVDRecommender(self.train)
self.cbf_bpr_recommender = SLIM_BPR_Cython(self.icm.T, positive_threshold=0)
self.cbf_recommender = Cosine_Similarity(self.icm.T, topK=50, shrink=10, normalize=True, mode='cosine')
self.item_rp3b_recommender = RP3betaRecommender(self.train)
self.user_rp3b_recommender = RP3betaRecommender(self.train.T)
self.bpr_mf = BPR_matrix_factorization(factors=800, regularization=0.01, learning_rate=0.01, iterations=300)
self.ials_cg_mf = IALS_CG(iterations=15, calculate_training_loss=True, factors=500, use_cg=True, regularization=1e-3)
self.lightfm = LightFM_Recommender(self.train, self.icm, no_components=200)
def fit(self):
self.svd_latent_x, self.svd_latent_y = self.svd_recommender.fit(num_factors=500)
self.min_svd = np.dot(self.svd_latent_x, self.svd_latent_y).min()
self.cbf_bpr_w = self.cbf_bpr_recommender.fit(epochs=10, topK=200, batch_size=20, sgd_mode='adagrad', learning_rate=1e-2)
self.item_cosineCF_w = self.item_cosineCF_recommender.compute_similarity()
self.user_cosineCF_w = self.user_cosineCF_recommender.compute_similarity()
self.cbf_w = self.cbf_recommender.compute_similarity()
self.item_rp3b_w = self.item_rp3b_recommender.fit()
self.user_rp3b_w = self.user_rp3b_recommender.fit()
self.ials_cg_mf.fit(40*self.train.T)
self.ials_latent_x = self.ials_cg_mf.user_factors.copy()
self.ials_latent_y = self.ials_cg_mf.item_factors.copy()
self.min_ials = np.dot(self.ials_latent_x, self.ials_latent_y.T).min()
self.bpr_mf.fit(self.train.T.tocoo())
self.bpr_mf_latent_x = self.bpr_mf.user_factors.copy()
self.bpr_mf_latent_y = self.bpr_mf.item_factors.copy()
self.lightfm.fit(100)
def recommend(self, user_id, combiner, at=10):
user_profile = self.train[user_id, :]
svd_r = self.svd_latent_x[user_id, :].dot(self.svd_latent_y)
item_cosineCF_r = user_profile.dot(self.item_cosineCF_w).toarray().ravel()
user_cosineCF_r = self.user_cosineCF_w[user_id].dot(self.train).toarray().ravel()
cbf_r = user_profile.dot(self.cbf_w).toarray().ravel()
cbf_bpr_r = user_profile.dot(self.cbf_bpr_w).toarray().ravel()
ials_r = np.dot(self.ials_latent_x[user_id], self.ials_latent_y.T + self.min_ials).ravel()
bpr_mf_r = np.dot(self.bpr_mf_latent_x[user_id], self.bpr_mf_latent_y.T).ravel()
item_rp3b_r = user_profile.dot(self.item_rp3b_w).toarray().ravel()
user_rp3b_r = self.user_rp3b_w[user_id].dot(self.train).toarray().ravel()
lightfm_r = self.lightfm.scores(user_id)
scores = [
# [item_bpr_r, self.ensemble_weights["ITEM_BPR"], "ITEM_BPR" ],
# [user_bpr_r, self.ensemble_weights["USER_BPR"], "USER_BPR" ],
[svd_r, self.ensemble_weights["SVD"], "SVD"],
[item_cosineCF_r, self.ensemble_weights["ITEM_CF"], "ITEM_CF" ],
[user_cosineCF_r, self.ensemble_weights["USER_CF"], "USER_CF" ],
[ials_r, self.ensemble_weights["IALS"], "IALS" ],
[cbf_r, self.ensemble_weights["CBF"], "CBF" ],
[cbf_bpr_r, self.ensemble_weights["CBF_BPR"], "CBF_BPR"],
[bpr_mf_r, self.ensemble_weights["BPR_MF"], "BPR_MF"],
[item_rp3b_r, self.ensemble_weights["ITEM_RP3B"], "ITEM_RP3B"],
[user_rp3b_r, self.ensemble_weights["USER_RP3B"], "USER_RP3B"],
[lightfm_r, self.ensemble_weights["FM"], "FM"]
]
for r in scores:
self.filter_seen(user_id, r[0])
R = combiner.combine(scores, at)
return R
def rescale_wrt_insertion_order(self, R):
R = R.copy()
R = R.tolil()
R = R*0.8
for i in self.sequential_playlists:
pl = i["id"]
k = 1
for j in i["songs"]:
factor = 1/(k**POPULARITY_SCALING_EXP)
R[pl, j] = factor*(R[pl,j] + 0.2)
k += 1
R = R.tocsr()
return R
def filter_seen(self, user_id, scores):
start_pos = int(self.train.indptr[user_id])
end_pos = int(self.train.indptr[user_id + 1])
user_profile = self.train.indices[start_pos:end_pos]
scores[user_profile] = -1000000 #-np.inf
return scores
def recommend_batch(self, user_list, combiner, at=10):
res = np.array([])
n=0
for i in user_list:
recList = self.recommend(i, combiner, at).T
tuple = np.concatenate(([i], recList))
if (res.size == 0):
res = tuple
else:
res = np.vstack([res, tuple])
return res
def get_component_data(self):
item_cf_rating = self.ensemble_weights["ITEM_CF"]*self.train.dot(self.item_cosineCF_w)
item_cf = {
"min" : item_cf_rating.min(),
"max" : item_cf_rating.max(),
"mean" : item_cf_rating.mean(),
}
del item_cf_rating
user_cf_rating = self.ensemble_weights["USER_CF"]*self.user_cosineCF_w.dot(self.train)
user_cf = {
"min": user_cf_rating.min(),
"max": user_cf_rating.max(),
"mean": user_cf_rating.mean(),
}
del user_cf_rating
ials_rating = self.ensemble_weights["IALS"]*(np.dot(self.ials_latent_x, self.ials_latent_y.T)+self.min_ials)
ials = {
"min": ials_rating.min(),
"max": ials_rating.max(),
"mean": np.mean(ials_rating),
}
del ials_rating
cbf_rating = self.ensemble_weights["CBF"]*self.train.dot(self.cbf_w)
cbf = {
"min": cbf_rating.min(),
"max": cbf_rating.max(),
"mean": cbf_rating.mean(),
}
del cbf_rating
cbf_bpr_rating = self.ensemble_weights["CBF_BPR"]*self.train.dot(self.cbf_bpr_w)
cbf_bpr = {
"min": cbf_bpr_rating.min(),
"max": cbf_bpr_rating.max(),
"mean": cbf_bpr_rating.mean(),
}
del cbf_bpr_rating
svd_ratings = self.ensemble_weights["SVD"] * (np.dot(self.svd_latent_x, self.svd_latent_y) + self.min_svd)
svd = {
"min": svd_ratings.min(),
"max": svd_ratings.max(),
"mean": svd_ratings.mean(),
}
del svd_ratings
return {
"ITEM_CF" : item_cf,
"USER_CF": user_cf ,
"SVD" : svd ,
"IALS" : ials,
"CBF" : cbf,
"CBF_BPR" : cbf_bpr
}
while (time.time() - start) / 60 / 60 < RUN_LIMIT_HOURS:
print(str(timedelta(seconds=time.time() - start)), ' -- config #',
len(performance_list) + 1, ' >> training starting...')
aux_time = time.time()
# hyperparameters
factors = 25 * np.random.randint(1, 31) # 25, 50, 75, ... , 750
learning_rate = (10**(-np.random.randint(2, 5))) * np.random.randint(1, 10)
regularization = (10**(-np.random.randint(2, 5))) * np.random.randint(
1, 10)
iterations = 25 * np.random.randint(1, 31) # 25, 50, 75, ... , 750
alg = BayesianPersonalizedRanking(num_threads=NUM_THREADS,
factors=factors,
learning_rate=learning_rate,
regularization=regularization,
iterations=iterations)
alg.fit(data_to_fit)
perf_ndcg_at_100 = []
rec_list = []
print(' >> took ', str(timedelta(seconds=time.time() - aux_time)))
print(str(timedelta(seconds=time.time() - start)), ' -- config #',
len(performance_list) + 1, ' >> evaluation starting...')
aux_time = time.time()
with Pool(NUM_THREADS) as p:
perf_ndcg_at_100 = p.map(
paralelize_ndcg,
# Read matrix item/playtime
df_matrix = read_user_item_playtime(DATA_FILEPATH)
# Create index for items
index2item = pd.Series(list(df_matrix.columns.values), dtype="category").cat.categories
# Create normalized hours matrix
df_scaled_matrix = normalize_hours_matrix(df_matrix)
# compress matrix
csr_df_matrix = csr_matrix(df_scaled_matrix)
np.random.seed()
# Train
user_item_train, user_item_test = train_test_split(csr_df_matrix, train_percentage=train_percent)
bpr = BayesianPersonalizedRanking(iterations=train_interactions)
bpr.fit(user_item_train.T.tocsr())
print(user_item_train[user_id])
interacted_ids = user_item_train[user_id].nonzero()[1]
index2item = index2item.astype('int32')
interacted_items = [item_mapping[index2item[index]] for index in interacted_ids if
index2item[index] in item_mapping.keys()]
# it returns the recommended index and their corresponding score
reco = bpr.recommend(user_id, user_item_train, N=topn)
print(reco)
# map the index to Item
reco_items = [item_mapping[index2item[index]] for index, _ in reco if index2item[index] in item_mapping.keys()]
def test_fit_almost_empty_matrix(self):
raw = [[0, 0, 0], [0, 1, 0], [0, 0, 0]]
return BayesianPersonalizedRanking(use_gpu=False).fit(
csr_matrix(raw), show_progress=False)
#%% [markdown]
# ### Bayesian Personalized Ranking
#%%
from implicit.bpr import BayesianPersonalizedRanking
params = {"factors": 63}
#%%
import logging
import tqdm
import time
import codecs
#%%
model = BayesianPersonalizedRanking(**params)
#%%
model_name = 'bpr'
output_filename = 'subreddits_recs_bpr'
#%%
model.fit(comments)
#%%
def bpr_related_subreddits(subreddit):
found = np.where(subreddits == subreddit)
if len(found[0]) == 0:
raise ValueError("Subreddit doesn't exist in the dataset.")
_id = found[0][0]
def _get_model(self):
return BayesianPersonalizedRanking(factors=3,
regularization=0,
use_gpu=True,
random_state=42)
def test_implicit_bpr(Rtr, Rts, k=20):
from implicit.bpr import BayesianPersonalizedRanking
bpr = BayesianPersonalizedRanking(k)
bpr.fit(Rtr.T)
return bpr
