def fit(self,
alpha=0.80849266253816,
beta=0.7286503831547066,
gamma=0.02895704968752022,
sigma=0.453342,
tau=0.542421,
chi=1.8070865821028037,
psi=4.256005405227253,
omega=5.096018341419944,
coeff=39.966898886531645,
normalize=False,
save_model=False,
submission=False,
best_parameters=False,
offline=False,
location="submission"):
if offline:
m = OfflineDataLoader()
folder_path, file_name = m.get_model(self.RECOMMENDER_NAME)
self.loadModel(folder_path=folder_path, file_name=file_name)
else:
if best_parameters:
m = OfflineDataLoader()
folder_path, file_name = m.get_parameter(self.RECOMMENDER_NAME)
self.loadModel(folder_path=folder_path, file_name=file_name)
else:
self.alpha = alpha
self.beta = beta
self.gamma = gamma
self.sigma = sigma
self.tau = tau
self.chi = chi
self.psi = psi
self.omega = omega
self.coeff = coeff
self.normalize = normalize
self.submission = not submission
m = OfflineDataLoader()
self.m_user_knn_cf = UserKNNCFRecommender(self.URM_train)
folder_path_ucf, file_name_ucf = m.get_model(
UserKNNCFRecommender.RECOMMENDER_NAME,
training=self.submission)
self.m_user_knn_cf.loadModel(folder_path=folder_path_ucf,
file_name=file_name_ucf)
self.m_item_knn_cf = ItemKNNCFRecommender(self.URM_train)
folder_path_icf, file_name_icf = m.get_model(
ItemKNNCFRecommender.RECOMMENDER_NAME,
training=self.submission)
self.m_item_knn_cf.loadModel(folder_path=folder_path_icf,
file_name=file_name_icf)
self.m_item_knn_cbf = ItemKNNCBFRecommender(
self.URM_train, self.ICM)
folder_path_icf, file_name_icf = m.get_model(
ItemKNNCBFRecommender.RECOMMENDER_NAME,
training=self.submission)
self.m_item_knn_cbf.loadModel(folder_path=folder_path_icf,
file_name=file_name_icf)
self.m_slim_mark1 = Slim_mark1(self.URM_train)
folder_path_slim, file_name_slim = m.get_model(
Slim_mark1.RECOMMENDER_NAME, training=self.submission)
self.m_slim_mark1.loadModel(folder_path=folder_path_slim,
file_name=file_name_slim)
self.m_slim_mark2 = Slim_mark2(self.URM_train)
folder_path_slim, file_name_slim = m.get_model(
Slim_mark2.RECOMMENDER_NAME, training=self.submission)
self.m_slim_mark2.loadModel(folder_path=folder_path_slim,
file_name=file_name_slim)
self.m_alpha = P3alphaRecommender(self.URM_train)
folder_path_alpha, file_name_alpha = m.get_model(
P3alphaRecommender.RECOMMENDER_NAME, training=self.submission)
self.m_alpha.loadModel(folder_path=folder_path_alpha,
file_name=file_name_alpha)
self.m_beta = RP3betaRecommender(self.URM_train)
folder_path_beta, file_name_beta = m.get_model(
RP3betaRecommender.RECOMMENDER_NAME, training=self.submission)
self.m_beta.loadModel(folder_path=folder_path_beta,
file_name=file_name_beta)
self.m_slim_elastic = SLIMElasticNetRecommender(self.URM_train)
folder_path_elastic, file_name_elastic = m.get_model(
SLIMElasticNetRecommender.RECOMMENDER_NAME,
training=self.submission)
self.m_slim_elastic.loadModel(folder_path=folder_path_elastic,
file_name=file_name_elastic)
self.W_sparse_URM = check_matrix(self.m_user_knn_cf.W_sparse,
"csr",
dtype=np.float32)
#print(self.W_sparse_URM.getrow(0).data)
self.W_sparse_URM_T = check_matrix(self.m_item_knn_cf.W_sparse,
"csr",
dtype=np.float32)
#print(self.W_sparse_URM_T.getrow(0).data)
self.W_sparse_ICM = check_matrix(self.m_item_knn_cbf.W_sparse,
"csr",
dtype=np.float32)
#print(self.W_sparse_ICM.getrow(0).data)
self.W_sparse_Slim1 = check_matrix(self.m_slim_mark1.W,
"csr",
dtype=np.float32)
#print(self.W_sparse_Slim1.getrow(0).data)
self.W_sparse_Slim2 = check_matrix(self.m_slim_mark2.W_sparse,
"csr",
dtype=np.float32)
#print(self.W_sparse_Slim2.getrow(0).data)
self.W_sparse_alpha = check_matrix(self.m_alpha.W_sparse,
"csr",
dtype=np.float32)
#print(self.W_sparse_alpha.getrow(0).data)
self.W_sparse_beta = check_matrix(self.m_beta.W_sparse,
"csr",
dtype=np.float32)
#print(self.W_sparse_beta.getrow(0).data)
self.W_sparse_elastic = check_matrix(self.m_slim_elastic.W_sparse,
"csr",
dtype=np.float32)
#print(self.W_sparse_elastic.getrow(0).data)
# Precomputations
#TODO
self.matrix_alpha_beta = self.alpha * self.W_sparse_alpha + (
1 - self.alpha) * self.W_sparse_beta
self.matrix_slim = self.beta * self.W_sparse_Slim2 + (
(1 - self.beta) * self.W_sparse_elastic *
self.coeff) + self.sigma * self.W_sparse_Slim1
self.parameters = "alpha={}, beta={}, gamma={},sigma={}, tau={}, chi={}, psi={}, omega={}, coeff={}".format(
self.alpha, self.beta, self.gamma, self.sigma, self.tau,
self.chi, self.psi, self.omega, self.coeff)
if save_model:
self.saveModel("saved_models/" + location + "/",
file_name=self.RECOMMENDER_NAME)
def printOutMapValues(modelList, URM, ICM, modelsSoFar):
map_dict = {i: dict() for i in modelsSoFar}
m = OfflineDataLoader()
for model in modelList:
folder = str("/".join(model[1].split("/")[:-1]) + "/")
file = model[1].split("/")[-1]
if model[0] == "UserKNNCFRecommender":
mod = UserKNNCFRecommender(URM)
mod.loadModel(folder_path=folder, file_name=file, verbose=False)
map_dict[model[0]][model[2]] = mod.MAP
# print(model[0], model[2], mod.MAP)
elif model[0] == "ItemKNNCFRecommender":
mod = ItemKNNCFRecommender(URM)
mod.loadModel(folder_path=folder, file_name=file, verbose=False)
map_dict[model[0]][model[2]] = mod.MAP
# print(model[0], model[2], mod.MAP)
elif model[0] == "ItemKNNCBFRecommender":
mod = ItemKNNCBFRecommender(URM, ICM)
mod.loadModel(folder_path=folder, file_name=file, verbose=False)
map_dict[model[0]][model[2]] = mod.MAP
# print(model[0], model[2], mod.MAP)
elif model[0] == "SLIM_BPR_Recommender_mark1":
mod = Slim_mark1(URM)
mod.loadModel(folder_path=folder, file_name=file, verbose=False)
map_dict[model[0]][model[2]] = mod.MAP
# print(model[0], model[2], mod.MAP)
elif model[0] == "RP3_Beta_Recommender":
mod = RP3betaRecommender(URM)
mod.loadModel(folder_path=folder, file_name=file, verbose=False)
map_dict[model[0]][model[2]] = mod.MAP
# print(model[0], model[2], mod.MAP)
elif model[0] == "P3_Alpha_Recommender":
mod = P3alphaRecommender(URM)
mod.loadModel(folder_path=folder, file_name=file, verbose=False)
map_dict[model[0]][model[2]] = mod.MAP
# print(model[0], model[2], mod.MAP)
elif model[0] == "PureSVD":
mod = PureSVDRecommender(URM)
mod.loadModel(folder_path=folder, file_name=file, verbose=False)
map_dict[model[0]][model[2]] = mod.MAP
# print(model[0], model[2], mod.MAP)
elif model[0] == "Slim_Elastic_Net_Recommender":
mod = SLIMElasticNetRecommender(URM)
mod.loadModel(folder_path=folder, file_name=file, verbose=False)
map_dict[model[0]][model[2]] = mod.MAP
#print(model[0], model[2], mod.MAP)
elif model[0] == "SLIM_BPR_Recommender_mark2":
mod = Slim_mark2(URM)
mod.loadModel(folder_path=folder, file_name=file, verbose=False)
map_dict[model[0]][model[2]] = mod.MAP
#print(model[0], model[2], mod.MAP)
# elif model[0] == "ItemTreeRecommender_offline":
# mod = ItemTreeRecommender_offline(URM,ICM)
# mod.loadModel(folder_path=folder, file_name=file, verbose=False)
# map_dict[model[0]][model[2]] = mod.MAP
#print(model[0], model[2], mod.MAP)
# elif model[0] == "PartyRecommender_offline":
# mod = PartyRecommender_offline(URM)
# mod.loadModel(folder_path=folder, file_name=file, verbose=False)
# map_dict[model[0]][model[2]] = mod.MAP
# #print(model[0], model[2], mod.MAP)
elif model[0] == "SingleNeuronRecommender_offline":
mod = SingleNeuronRecommender_offline(URM, ICM)
mod.loadModel(folder_path=folder, file_name=file, verbose=False)
map_dict[model[0]][model[2]] = mod.MAP
#print(model[0], model[2], mod.MAP)
return map_dict
def fit(
self,
alpha=1.3167219260598073,
beta=15.939928536132701,
gamma=0.6048873602128846,
delta=1.0527588765188267,
epsilon=2.08444591782293,
zeta=1.2588273098979674,
eta=18.41012777389885,
theta=18.000293943452448,
# psi = 0.00130805010990942,
normalize=False,
save_model=False,
submission=False,
best_parameters=False,
offline=False,
location="submission"):
if offline:
m = OfflineDataLoader()
folder_path, file_name = m.get_model(self.RECOMMENDER_NAME)
self.loadModel(folder_path=folder_path, file_name=file_name)
else:
if best_parameters:
m = OfflineDataLoader()
folder_path, file_name = m.get_parameter(self.RECOMMENDER_NAME)
self.loadModel(folder_path=folder_path, file_name=file_name)
else:
self.alpha = alpha
self.beta = beta
self.gamma = gamma
self.delta = delta
self.epsilon = epsilon
self.zeta = zeta
self.eta = eta
self.theta = theta
# self.psi = psi
self.normalize = normalize
self.submission = not submission
m = OfflineDataLoader()
self.m_user_knn_cf = UserKNNCFRecommender(self.URM_train)
folder_path_ucf, file_name_ucf = m.get_model(
UserKNNCFRecommender.RECOMMENDER_NAME,
training=self.submission)
self.m_user_knn_cf.loadModel(folder_path=folder_path_ucf,
file_name=file_name_ucf)
self.m_item_knn_cf = ItemKNNCFRecommender(self.URM_train)
folder_path_icf, file_name_icf = m.get_model(
ItemKNNCFRecommender.RECOMMENDER_NAME,
training=self.submission)
self.m_item_knn_cf.loadModel(folder_path=folder_path_icf,
file_name=file_name_icf)
self.m_item_knn_cbf = ItemKNNCBFRecommender(
self.URM_train, self.ICM)
folder_path_icf, file_name_icf = m.get_model(
ItemKNNCBFRecommender.RECOMMENDER_NAME,
training=self.submission)
self.m_item_knn_cbf.loadModel(folder_path=folder_path_icf,
file_name=file_name_icf)
self.m_slim_mark1 = Slim_mark1(self.URM_train)
folder_path_slim, file_name_slim = m.get_model(
Slim_mark1.RECOMMENDER_NAME, training=self.submission)
self.m_slim_mark1.loadModel(folder_path=folder_path_slim,
file_name=file_name_slim)
self.m_slim_mark2 = Slim_mark2(self.URM_train)
folder_path_slim, file_name_slim = m.get_model(
Slim_mark2.RECOMMENDER_NAME, training=self.submission)
self.m_slim_mark2.loadModel(folder_path=folder_path_slim,
file_name=file_name_slim)
self.m_alpha = P3alphaRecommender(self.URM_train)
folder_path_alpha, file_name_alpha = m.get_model(
P3alphaRecommender.RECOMMENDER_NAME, training=self.submission)
self.m_alpha.loadModel(folder_path=folder_path_alpha,
file_name=file_name_alpha)
self.m_beta = RP3betaRecommender(self.URM_train)
folder_path_beta, file_name_beta = m.get_model(
RP3betaRecommender.RECOMMENDER_NAME, training=self.submission)
self.m_beta.loadModel(folder_path=folder_path_beta,
file_name=file_name_beta)
self.m_slim_elastic = SLIMElasticNetRecommender(self.URM_train)
folder_path_elastic, file_name_elastic = m.get_model(
SLIMElasticNetRecommender.RECOMMENDER_NAME,
training=self.submission)
self.m_slim_elastic.loadModel(folder_path=folder_path_elastic,
file_name=file_name_elastic)
# self.m_cfw = CFWBoostingRecommender(self.URM_train,self.ICM,Slim_mark2,training=self.submission)
# fold, file = m.get_model(CFWBoostingRecommender.RECOMMENDER_NAME,training= self.submission)
# self.m_cfw.loadModel(folder_path=fold,file_name=file)
self.W_sparse_URM = check_matrix(self.m_user_knn_cf.W_sparse,
"csr",
dtype=np.float32)
#print(self.W_sparse_URM.getrow(0).data)
self.W_sparse_URM_T = check_matrix(self.m_item_knn_cf.W_sparse,
"csr",
dtype=np.float32)
#print(self.W_sparse_URM_T.getrow(0).data)
self.W_sparse_ICM = check_matrix(self.m_item_knn_cbf.W_sparse,
"csr",
dtype=np.float32)
#print(self.W_sparse_ICM.getrow(0).data)
self.W_sparse_Slim1 = check_matrix(self.m_slim_mark1.W,
"csr",
dtype=np.float32)
#print(self.W_sparse_Slim1.getrow(0).data)
self.W_sparse_Slim2 = check_matrix(self.m_slim_mark2.W_sparse,
"csr",
dtype=np.float32)
#print(self.W_sparse_Slim2.getrow(0).data)
self.W_sparse_alpha = check_matrix(self.m_alpha.W_sparse,
"csr",
dtype=np.float32)
#print(self.W_sparse_alpha.getrow(0).data)
self.W_sparse_beta = check_matrix(self.m_beta.W_sparse,
"csr",
dtype=np.float32)
#print(self.W_sparse_beta.getrow(0).data)
self.W_sparse_elastic = check_matrix(self.m_slim_elastic.W_sparse,
"csr",
dtype=np.float32)
#print(self.W_sparse_elastic.getrow(0).data)
#self.W_sparse_cfw = check_matrix(self.m_cfw.W_sparse,"csr",dtype=np.float32)
# Precomputations
self.matrix_wo_user = self.alpha * self.W_sparse_URM_T +\
self.beta * self.W_sparse_ICM +\
self.gamma * self.W_sparse_Slim1 +\
self.delta * self.W_sparse_Slim2 +\
self.epsilon * self.W_sparse_alpha +\
self.zeta * self.W_sparse_beta + \
self.eta * self.W_sparse_elastic #+ \
#self.psi * self.W_sparse_cfw
self.parameters = "alpha={}, beta={}, gamma={},delta={}, epsilon={}, zeta={}, eta={}, theta={}".format(
self.alpha, self.beta, self.gamma, self.delta, self.epsilon,
self.zeta, self.eta, self.theta)
if save_model:
self.saveModel("saved_models/" + location + "/",
file_name=self.RECOMMENDER_NAME)
def fit(self,
alpha=0.0500226666668111,
beta=0.9996482062853596,
gamma=0.36595766622100967,
theta=0.22879224932897924,
omega=0.5940982982110466,
normalize=False,
save_model=False,
submission=False,
best_parameters=False):
if best_parameters:
m = OfflineDataLoader()
folder_path, file_name = m.get_parameter(self.RECOMMENDER_NAME)
self.loadModel(folder_path=folder_path, file_name=file_name)
else:
self.alpha = alpha
self.beta = beta
self.gamma = gamma
self.theta = theta
self.omega = omega
self.normalize = normalize
self.submission = not submission
m = OfflineDataLoader()
self.m_user_knn_cf = UserKNNCFRecommender(self.URM_train)
folder_path_ucf, file_name_ucf = m.get_model(
UserKNNCFRecommender.RECOMMENDER_NAME, training=self.submission)
self.m_user_knn_cf.loadModel(folder_path=folder_path_ucf,
file_name=file_name_ucf)
self.m_item_knn_cf = ItemKNNCFRecommender(self.URM_train)
folder_path_icf, file_name_icf = m.get_model(
ItemKNNCFRecommender.RECOMMENDER_NAME, training=self.submission)
self.m_item_knn_cf.loadModel(folder_path=folder_path_icf,
file_name=file_name_icf)
self.m_item_knn_cbf = ItemKNNCBFRecommender(self.URM_train, self.ICM)
folder_path_icbf, file_name_icbf = m.get_model(
ItemKNNCBFRecommender.RECOMMENDER_NAME, training=self.submission)
self.m_item_knn_cbf.loadModel(folder_path=folder_path_icbf,
file_name=file_name_icbf)
self.m_slim_mark1 = Slim_mark1(self.URM_train)
folder_path_slim, file_name_slim = m.get_model(
Slim_mark1.RECOMMENDER_NAME, training=self.submission)
self.m_slim_mark1.loadModel(folder_path=folder_path_slim,
file_name=file_name_slim)
self.m_alpha = P3alphaRecommender(self.URM_train)
folder_path_alpha, file_name_alpha = m.get_model(
P3alphaRecommender.RECOMMENDER_NAME, training=self.submission)
self.m_alpha.loadModel(folder_path=folder_path_alpha,
file_name=file_name_alpha)
self.m_beta = RP3betaRecommender(self.URM_train)
folder_path_beta, file_name_beta = m.get_model(
RP3betaRecommender.RECOMMENDER_NAME, training=self.submission)
self.m_beta.loadModel(folder_path=folder_path_beta,
file_name=file_name_beta)
self.W_sparse_URM = check_matrix(self.m_user_knn_cf.W_sparse,
"csr",
dtype=np.float32)
self.W_sparse_ICM = check_matrix(self.m_item_knn_cbf.W_sparse,
"csr",
dtype=np.float32)
self.W_sparse_URM_T = check_matrix(self.m_item_knn_cf.W_sparse,
"csr",
dtype=np.float32)
self.W_sparse_Slim = check_matrix(self.m_slim_mark1.W,
"csr",
dtype=np.float32)
self.W_sparse_alpha = check_matrix(self.m_alpha.W_sparse,
"csr",
dtype=np.float32)
self.W_sparse_beta = check_matrix(self.m_beta.W_sparse,
"csr",
dtype=np.float32)
# Precomputations
self.matrix_first_branch = self.alpha * self.W_sparse_ICM + (
1 - self.alpha) * self.W_sparse_Slim
self.matrix_right = self.beta * self.matrix_first_branch + (
1 - self.beta) * self.W_sparse_URM_T
self.matrix_alpha_beta = self.gamma * self.W_sparse_alpha + (
1 - self.gamma) * self.W_sparse_beta
self.parameters = "alpha={}, beta={}, gamma={}, omega={}, theta={}".format(
self.alpha, self.beta, self.gamma, self.omega, self.theta)
if save_model:
self.saveModel("saved_models/submission/",
file_name="ItemTreeRecommender_offline")
def extract_models(self, dataReader, submission=False):
print(
"Configurator: The models are being extracted from the config file"
)
recsys = list()
models = list(self.configs.models)
data = dataReader.get_URM_train()
if submission:
data = dataReader.get_URM_all()
for model in models:
# User Collaborative Filtering with KNN
if model["model_name"] == "user_knn_cf":
recsys.append(
UserKNNCFRecommender(
data, sparse_weights=model["sparse_weights"]))
# Item Collaborative Filtering with KNN
elif model["model_name"] == "item_knn_cf":
recsys.append(
ItemKNNCFRecommender(
data, sparse_weights=model["sparse_weights"]))
# Item Content Based Filtering with KNN
elif model["model_name"] == "item_knn_cbf":
recsys.append(
ItemKNNCBFRecommender(
data,
dataReader.get_ICM(),
sparse_weights=model["sparse_weights"]))
# Slim BPR with Python
elif model["model_name"] == "slim_bpr_python":
recsys.append(
Slim_BPR_Recommender_Python(
data,
positive_threshold=model["positive_threshold"],
sparse_weights=model["sparse_weights"]))
# Slim BPR with Cython Extension
elif model["model_name"] == "slim_bpr_mark1":
recsys.append(
Slim_mark1(data,
positive_threshold=model["positive_threshold"],
recompile_cython=model["recompile_cython"],
symmetric=model["symmetric"]))
elif model["model_name"] == "slim_bpr_mark2":
recsys.append(
Slim_mark2(data,
positive_threshold=model["positive_threshold"],
recompile_cython=model["recompile_cython"],
symmetric=model["symmetric"]))
# Funk SVD Recommender
elif model["model_name"] == "funksvd":
recsys.append(FunkSVD(data))
elif model["model_name"] == "asysvd":
recsys.append(AsySVD(data))
elif model["model_name"] == "puresvd":
recsys.append(PureSVDRecommender(data))
elif model["model_name"] == "mf_bpr_cython":
recsys.append(
MF_BPR_Cython(data,
recompile_cython=model["recompile_cython"]))
elif model["model_name"] == "mf_cython":
recsys.append(
MatrixFactorization_Cython(
data,
positive_threshold=model["positive_threshold"],
URM_validation=dataReader.get_URM_test(),
recompile_cython=model["recompile_cython"],
algorithm=model["algorithm"]))
elif model["model_name"] == "ials_numpy":
recsys.append(IALS_numpy())
elif model["model_name"] == "bprmf":
recsys.append(BPRMF())
elif model["model_name"] == "user_item_avg":
recsys.append(
UserItemAvgRecommender(
data,
dataReader.get_UCM(),
dataReader.get_ICM(),
sparse_weights=model["sparse_weights"],
verbose=model["verbose"],
similarity_mode=model["similarity_mode"],
normalize=model["normalize"],
alpha=model["alpha"]))
elif model["model_name"] == "2levelhybrid":
recsys.append(
TwoLevelHybridRecommender(
data,
dataReader.get_UCM(),
dataReader.get_ICM(),
sparse_weights=model["sparse_weights"],
verbose=model["verbose"],
similarity_mode=model["similarity_mode"],
normalize=model["normalize"],
alpha=model["alpha"],
avg=model["avg"]))
elif model["model_name"] == "seqrand":
recsys.append(
SeqRandRecommender(
data,
dataReader.get_URM_train_tfidf(),
dataReader.get_UCM(),
dataReader.get_ICM(),
dataReader.get_target_playlists_seq(),
sparse_weights=model["sparse_weights"],
verbose=model["verbose"],
similarity_mode=model["similarity_mode"],
normalize=model["normalize"],
alpha=model["alpha"],
beta=model["beta"],
gamma=model["gamma"]))
elif model["model_name"] == "itemtree":
recsys.append(
ItemTreeRecommender(
data,
dataReader.get_URM_train_okapi(),
dataReader.get_ICM(),
sparse_weights=model["sparse_weights"]))
elif model["model_name"] == "itemtree_offline":
recsys.append(
ItemTreeRecommender_offline(data, dataReader.get_ICM()))
elif model["model_name"] == "slim":
recsys.append(
Slim(data,
sparse_weights=model["sparse_weights"],
normalize=model["normalize"]))
elif model["model_name"] == "p3alpha":
recsys.append(P3alphaRecommender(data))
elif model["model_name"] == "rp3beta":
recsys.append(RP3betaRecommender(data))
elif model["model_name"] == "slim_elastic":
recsys.append(SLIMElasticNetRecommender(data))
elif model["model_name"] == "party":
recsys.append(PartyRecommender_offline(data))
elif model["model_name"] == "pyramid":
recsys.append(PyramidRecommender_offline(data))
elif model["model_name"] == "pyramid_item_tree":
recsys.append(
PyramidItemTreeRecommender_offline(data,
dataReader.get_ICM()))
elif model["model_name"] == "hybrid_eight":
recsys.append(
HybridEightRecommender_offline(data, dataReader.get_ICM()))
elif model["model_name"] == "combo":
recsys.append(
ComboRecommender_offline(data, dataReader.get_ICM()))
elif model["model_name"] == "neuron":
recsys.append(
SingleNeuronRecommender_offline(data,
dataReader.get_ICM()))
elif model["model_name"] == "cfw":
m = OfflineDataLoader()
#fold,file = m.get_model(Slim_mark2.RECOMMENDER_NAME,training=True)
m1 = Slim_mark2(data)
#m1.loadModel(folder_path=fold,file_name=file)
recsys.append(
CFWBoostingRecommender(data, dataReader.get_ICM(),
Slim_mark2))
print("Configurator: Models are extracted")
return recsys
def fit(self,
alpha=0.0029711141561171717,
beta=0.9694720669481413,
gamma=0.9635187725527589,
theta=0.09930388487311004,
omega=0.766047309541692,
coeff = 5.4055892529064735,
normalize=False,
save_model=False,
submission=False,
best_parameters=False,
location="submission"):
if best_parameters:
m = OfflineDataLoader()
folder_path, file_name = m.get_parameter(self.RECOMMENDER_NAME)
self.loadModel(folder_path=folder_path, file_name=file_name)
else:
self.alpha = alpha
self.beta = beta
self.gamma = gamma
self.theta = theta
self.omega = omega
self.coeff = coeff
self.normalize = normalize
self.submission = not submission
m = OfflineDataLoader()
self.m_user_knn_cf = UserKNNCFRecommender(self.URM_train)
folder_path_ucf, file_name_ucf = m.get_model(UserKNNCFRecommender.RECOMMENDER_NAME, training=self.submission)
self.m_user_knn_cf.loadModel(folder_path=folder_path_ucf, file_name=file_name_ucf)
self.m_item_knn_cf = ItemKNNCFRecommender(self.URM_train)
folder_path_icf, file_name_icf = m.get_model(ItemKNNCFRecommender.RECOMMENDER_NAME, training=self.submission)
self.m_item_knn_cf.loadModel(folder_path=folder_path_icf, file_name=file_name_icf)
self.m_slim_mark2 = Slim_mark2(self.URM_train)
folder_path_slim, file_name_slim = m.get_model(Slim_mark2.RECOMMENDER_NAME, training=self.submission)
self.m_slim_mark2.loadModel(folder_path=folder_path_slim, file_name=file_name_slim)
self.m_alpha = P3alphaRecommender(self.URM_train)
folder_path_alpha, file_name_alpha = m.get_model(P3alphaRecommender.RECOMMENDER_NAME, training=self.submission)
self.m_alpha.loadModel(folder_path=folder_path_alpha, file_name=file_name_alpha)
self.m_beta = RP3betaRecommender(self.URM_train)
folder_path_beta, file_name_beta = m.get_model(RP3betaRecommender.RECOMMENDER_NAME, training=self.submission)
self.m_beta.loadModel(folder_path=folder_path_beta, file_name=file_name_beta)
self.m_slim_elastic = SLIMElasticNetRecommender(self.URM_train)
folder_path_elastic, file_name_elastic = m.get_model(SLIMElasticNetRecommender.RECOMMENDER_NAME,
training=self.submission)
self.m_slim_elastic.loadModel(folder_path=folder_path_elastic, file_name=file_name_elastic)
self.W_sparse_URM = check_matrix(self.m_user_knn_cf.W_sparse, "csr", dtype=np.float32)
self.W_sparse_URM_T = check_matrix(self.m_item_knn_cf.W_sparse, "csr", dtype=np.float32)
self.W_sparse_Slim = check_matrix(self.m_slim_mark2.W_sparse, "csr", dtype=np.float32)
self.W_sparse_alpha = check_matrix(self.m_alpha.W_sparse, "csr", dtype=np.float32)
self.W_sparse_beta = check_matrix(self.m_beta.W_sparse, "csr", dtype=np.float32)
self.W_sparse_elastic = check_matrix(self.m_slim_elastic.W_sparse, "csr", dtype=np.float32)
# Precomputations
self.matrix_alpha_beta = self.alpha * self.W_sparse_alpha + (1 - self.alpha) * self.W_sparse_beta
self.matrix_level1 = self.beta * self.W_sparse_Slim + (1 - self.beta) * self.W_sparse_URM_T
self.parameters = "alpha={}, beta={}, gamma={}, theta={}, omega={}, coeff={}".format(self.alpha, self.beta, self.gamma,
self.theta, self.omega, self.coeff)
if save_model:
self.saveModel("saved_models/"+location+"/", file_name=self.RECOMMENDER_NAME)