def __init__(self, data, config, params, *args, **kwargs):
self._random = np.random
self._params_list = [
("_num_neighbors", "neighbors", "nn", 40, int, None),
("_similarity", "similarity", "sim", "cosine", None, None),
("_implementation", "implementation", "imp", "standard", None, None)
]
self.autoset_params()
self._ratings = self._data.train_dict
if self._implementation == "aiolli":
self._model = AiolliSimilarity(self._data, maxk=self._num_neighbors, shrink=100, similarity=self._similarity, normalize=True)
else:
self._model = Similarity(self._data, self._num_neighbors, self._similarity)
class UserKNN(RecMixin, BaseRecommenderModel):
r"""
GroupLens: An Open Architecture for Collaborative Filtering of Netnews
For further details, please refer to the `paper <https://dl.acm.org/doi/10.1145/192844.192905>`_
Args:
neighbors: Number of item neighbors
similarity: Similarity function
implementation: Implementation type ('aiolli', 'classical')
To include the recommendation model, add it to the config file adopting the following pattern:
.. code:: yaml
models:
UserKNN:
meta:
save_recs: True
neighbors: 40
similarity: cosine
implementation: aiolli
"""
@init_charger
def __init__(self, data, config, params, *args, **kwargs):
self._random = np.random
self._params_list = [
("_num_neighbors", "neighbors", "nn", 40, None, None),
("_similarity", "similarity", "sim", "cosine", None, None),
("_implementation", "implementation", "imp", "standard", None,
None)
]
self.autoset_params()
self._ratings = self._data.train_dict
if self._implementation == "aiolli":
self._model = AiolliSimilarity(self._data,
maxk=self._num_neighbors,
shrink=100,
similarity=self._similarity,
normalize=True)
else:
self._model = Similarity(self._data, self._num_neighbors,
self._similarity)
def get_recommendations(self, k: int = 100):
return {
u: self._model.get_user_recs(u, k)
for u in self._ratings.keys()
}
@property
def name(self):
return f"UserKNN_{self.get_params_shortcut()}"
def train(self):
if self._restore:
return self.restore_weights()
start = time.time()
self._model.initialize()
end = time.time()
print(f"The similarity computation has taken: {end - start}")
print(f"Transactions: {self._data.transactions}")
best_metric_value = 0
recs = self.get_recommendations(
self.evaluator.get_needed_recommendations())
result_dict = self.evaluator.eval(recs)
self._results.append(result_dict)
print(f'Finished')
if self._results[-1][self._validation_k]["val_results"][
self._validation_metric] > best_metric_value:
print("******************************************")
if self._save_weights:
with open(self._saving_filepath, "wb") as f:
pickle.dump(self._model.get_model_state(), f)
if self._save_recs:
store_recommendation(
recs,
self._config.path_output_rec_result + f"{self.name}.tsv")
def restore_weights(self):
try:
with open(self._saving_filepath, "rb") as f:
self._model.set_model_state(pickle.load(f))
print(f"Model correctly Restored")
recs = self.get_recommendations(
self.evaluator.get_needed_recommendations())
result_dict = self.evaluator.eval(recs)
self._results.append(result_dict)
print("******************************************")
if self._save_recs:
store_recommendation(
recs,
self._config.path_output_rec_result + f"{self.name}.tsv")
return True
except Exception as ex:
print(f"Error in model restoring operation! {ex}")
return False
class UserKNN(RecMixin, BaseRecommenderModel):
@init_charger
def __init__(self, data, config, params, *args, **kwargs):
self._random = np.random
self._params_list = [
("_num_neighbors", "neighbors", "nn", 40, None, None),
("_similarity", "similarity", "sim", "cosine", None, None),
("_implementation", "implementation", "imp", "standard", None,
None)
]
self.autoset_params()
self._ratings = self._data.train_dict
if self._implementation == "aiolli":
self._model = AiolliSimilarity(self._data,
maxk=self._num_neighbors,
shrink=100,
similarity=self._similarity,
normalize=True)
else:
self._model = Similarity(self._data, self._num_neighbors,
self._similarity)
def get_recommendations(self, k: int = 100):
return {
u: self._model.get_user_recs(u, k)
for u in self._ratings.keys()
}
@property
def name(self):
return f"UserKNN_{self.get_params_shortcut()}"
def train(self):
if self._restore:
return self.restore_weights()
start = time.time()
self._model.initialize()
end = time.time()
print(f"The similarity computation has taken: {end - start}")
print(f"Transactions: {self._data.transactions}")
best_metric_value = 0
recs = self.get_recommendations(
self.evaluator.get_needed_recommendations())
result_dict = self.evaluator.eval(recs)
self._results.append(result_dict)
print(f'Finished')
if self._results[-1][self._validation_k]["val_results"][
self._validation_metric] > best_metric_value:
print("******************************************")
if self._save_weights:
with open(self._saving_filepath, "wb") as f:
pickle.dump(self._model.get_model_state(), f)
if self._save_recs:
store_recommendation(
recs,
self._config.path_output_rec_result + f"{self.name}.tsv")
def restore_weights(self):
try:
with open(self._saving_filepath, "rb") as f:
self._model.set_model_state(pickle.load(f))
print(f"Model correctly Restored")
recs = self.get_recommendations(
self.evaluator.get_needed_recommendations())
result_dict = self.evaluator.eval(recs)
self._results.append(result_dict)
print("******************************************")
if self._save_recs:
store_recommendation(
recs,
self._config.path_output_rec_result + f"{self.name}.tsv")
return True
except Exception as ex:
print(f"Error in model restoring operation! {ex}")
return False