def save(self, path):
"""
Save a model.
The model can be saved, then reloaded later to provide recommendations.
Parameters
----------
path : str
The path where the model will be saved.
This should refer to a file, not to a directory.
Three items will be stored here: the underlying model parameters, the original ratings,
and the column names. These are stored with suffix '.model', '.ratings', and
'.metadata'.
"""
sc = CommonSparkContext.Instance().sc()
delete_file_or_dir(path)
os.makedirs(path)
model_path, ratings_path, metadata_path = self._file_paths(path)
# save model
self.model.save(sc, model_path)
# save ratings
self.ratings.save(ratings_path)
# save metadata
metadata = [self.user_col, self.item_col, self.rating_col]
with fileio.open_file(metadata_path, 'w') as f:
# TODO detect filesystem errors
pickle.dump(metadata, f)
def load(cls, path):
"""
Load a model that was saved previously.
Parameters
----------
path : str
The path where the model files are stored.
This is the same path that was passed to ``save``.
There are three files/directories based on this path, with
extensions '.model', '.ratings', and '.metadata'.
Returns
-------
out : MatrixFactorizationModel
A model that can be used to predict ratings.
"""
sc = CommonSparkContext.Instance().sc()
model_path, ratings_path, metadata_path = cls._file_paths(path)
# load model
model = recommendation.MatrixFactorizationModel.load(sc, model_path)
# load ratings
ratings = XFrame.load(ratings_path)
# load metadata
with open(metadata_path) as f:
user_col, item_col, rating_col = pickle.load(f)
return cls(model, ratings, user_col, item_col, rating_col)
def train(self):
sc = CommonSparkContext.Instance().sc()
rdd = self.corpus.to_spark_rdd()
model = Word2Vec().setVectorSize(self.vector_size).setSeed(
self.seed).fit(rdd)
return TextModel(model)
