def objective(params):
print('###########################################')
params["topK"] = int(params["topK"])
print("Current parameters:")
print(params)
loss = - RunRecommender.evaluate_on_validation_set(ItemCollaborativeFilter, params, Kfold=N_K_FOLD, parallel_fit=False, parallelize_evaluation=True)
return loss
def objective(params):
params["topK"] = int(params["topK"])
print("############ Current parameters ############")
print(params)
loss = -RunRecommender.evaluate_on_validation_set(
RP3betaRecommender,
params,
Kfold=N_KFOLD,
parallelize_evaluation=True,
user_group="warm")
return loss
def objective(params):
print('###########################################')
params["n_factors"] = int(params["n_factors"])
print("Current parameters:")
print(params)
loss = -RunRecommender.evaluate_on_validation_set(
AlternatingLeastSquare,
params,
Kfold=N_K_FOLD,
parallel_fit=False,
parallelize_evaluation=True,
user_group="warm")
return loss
def objective(params):
params["topK"] = int(params["topK"])
params["batch_size"] = int(params["batch_size"])
params["random_seed"] = 1234
params["epochs"] = 30
print("############ Current parameters ############")
print(params)
loss = -RunRecommender.evaluate_on_validation_set(
SLIM_BPR_Cython,
params,
Kfold=N_KFOLD,
parallelize_evaluation=False,
user_group="warm")
return loss
def objective(params):
print('###########################################')
print(params)
params["topK"] = int(params["topK"])
# if type(params["similarity"]) == tuple:
# params["asymmetric_alpha"] = params["similarity"][1]
# params["similarity"] = params["similarity"][0]
loss = -RunRecommender.evaluate_on_validation_set(
HybridUserCBFRegionalTopPop,
params,
Kfold=10,
user_group="cold",
parallel_fit=False,
parallelize_evaluation=True)
return loss
def objective(params):
print('###########################################')
print(params)
loss = - RunRecommender.evaluate_on_validation_set(UserCBF, params, Kfold=N_FOLD, parallel_fit=False, parallelize_evaluation=True, user_group="warm")
return loss
def objective(params):
print('###########################################')
print(params)
loss = - RunRecommender.evaluate_on_validation_set(UserCBF, params, Kfold=4, user_group="cold")
return loss
def objective(params):
print('###########################################')
print(params)
loss = -RunRecommender.evaluate_on_validation_set(
ItemCBF, params, Kfold=4, parallel_fit=True)
return loss
rows.extend(rows_)
cols.extend(cols_)
# generate the sparse weight matrix
print("Now generating W matrix...")
self.W_sparse = sps.csr_matrix((values, (rows, cols)),
shape=(n_items, n_items),
dtype=np.float32)
print("Done!")
if __name__ == '__main__':
from utils.helper import Helper
helper = Helper()
slim = MultiThreadSSLIM_ElasticNet
best = {
'alpha': 0.0024081648139725204,
'l1_ratio': 0.0007553368138338653,
'positive_only': False,
'side_alpha': 3.86358712510434,
'topK': 65,
'random_state': 1234
}
map_10 = RunRecommender.evaluate_on_validation_set(slim, best)
print(map_10)
scores = self.filter_seen(user_id, scores)
recommended_items = np.argsort(scores)
recommended_items = np.flip(recommended_items, axis=0)
return recommended_items[:at]
def filter_seen(self, user_id, scores):
start_pos = self.URM_train.indptr[user_id]
end_pos = self.URM_train.indptr[user_id + 1]
user_profile = self.URM_train.indices[start_pos:end_pos]
scores[user_profile] = -np.inf
return scores
if __name__ == "__main__":
cbf_recommender = ItemCBF
params = {
'normalize': True,
'shrink': 18,
'similarity': 'asymmetric',
'topK': 15
}
RunRecommender.evaluate_on_validation_set(cbf_recommender, params)
RunRecommender.evaluate_on_validation_set(cbf_recommender, params)
# RunRecommender.run(cbf_recommender)
self.filter_seen(user_id, scores)
recommended_items = np.argsort(scores)
recommended_items = np.flip(recommended_items, axis=0)
return recommended_items[0: at]
def filter_seen(self, user_id, scores):
"""Remove items that are in the user profile from recommendations
:param user_id: array of user ids for which to compute recommendations
:param scores: array containing the scores for each object"""
start_pos = self.URM_train.indptr[user_id]
end_pos = self.URM_train.indptr[user_id + 1]
user_profile = self.URM_train.indices[start_pos:end_pos]
scores[user_profile] = -np.inf
return scores
if __name__ == "__main__":
# Train and test data are now loaded by the helper
hybrid_ucficf = AlternatingLeastSquare
als_parameters = {'n_factors': 480, 'regularization': 0.003946413694592164, "iterations": 30}
# Evaluation is performed by RunRecommender
RunRecommender.evaluate_on_validation_set(hybrid_ucficf, als_parameters)
def filter_seen(self, user_id, scores):
"""Remove items that are in the user profile from recommendations
:param user_id: array of user ids for which to compute recommendations
:param scores: array containing the scores for each object"""
start_pos = self.URM_train.indptr[user_id]
end_pos = self.URM_train.indptr[user_id + 1]
user_profile = self.URM_train.indices[start_pos:end_pos]
scores[user_profile] = -np.inf
return scores
if __name__ == "__main__":
# Train and test data are now loaded by the helper
weights = {
'SLIM_weight': 0.6719606935709935,
'item_cf_weight': 0.08340610330630326,
'user_cf_weight': 0.006456181309865703
}
hybrid_ucficf = HybridElasticNetICFUCF
# Evaluation is performed by RunRecommender
RunRecommender.evaluate_on_validation_set(hybrid_ucficf, weights)
# RunRecommender.run(hybrid_ucficf, weights)
def objective(params):
print("Current parameters:")
print(params)
loss = -RunRecommender.evaluate_on_validation_set(P3alphaRecommender,
params)
return loss
def objective(params):
print("Current parameters:")
print(params)
loss = -RunRecommender.evaluate_on_validation_set(
MultiThreadSLIM_ElasticNet, params, Kfold=4)
return loss
def objective(params):
print('###########################################')
print(params)
loss = - RunRecommender.evaluate_on_validation_set(ItemCBF, params)
return loss
from AssetCBF import AssetCBF
from utils.run import RunRecommender
from ItemBasedCBF import ItemCBF
import numpy as np
### Step 1 : defining the objective function
def objective(params):
print('###########################################')
print(params)
loss = - RunRecommender.evaluate_on_validation_set(ItemCBF, params)
return loss
price_cbf_space = {
"topK": hp.hp.choice('topK', np.arange(0, 500, 10)),
"shrink": hp.hp.choice('shrink', np.arange(0, 100, 2))}
if __name__ == '__main__':
### step 3 : storing the results of every iteration
bayes_trials = Trials()
MAX_EVALS = 100
opt = {'topK': 200, 'shrink': 5}
RunRecommender.evaluate_on_validation_set(ItemCBF, opt)
# Optimize
best = fmin(fn=objective, space=price_cbf_space, algo=hp.tpe.suggest,
max_evals=MAX_EVALS, trials=bayes_trials, verbose=True)
### best will the return the the best hyperparameter set
print(best)