def item_features(self):
"""
The item features.
Underlying model parameters.
"""
return XArray.from_rdd(self.model.productFeatures(), list)
def user_features(self):
"""
The user features.
Underlying model parameters.
"""
return XArray.from_rdd(self.model.userFeatures(), list)
def _base_predict(self, data):
"""
Call the model's predict function.
Data can be a single item or a collection of items.
"""
features = self.make_features(data)
if isinstance(features, DenseVector):
return self.model.predict(features)
if isinstance(features, XArray) and issubclass(features.dtype(), DenseVector):
res = self.model.predict(features.to_spark_rdd())
return XArray.from_rdd(res, float)
raise TypeError('must pass a DenseVector or XArray of DenseVector')