RandomNormal¶
Predicts random values sampled from a normal distribution.
The parameters of the normal distribution are fitted with running statistics. They parameters are independent of the user, the item, or the context, and are instead fitted globally. This recommender therefore acts as a dummy model that any serious model should easily outperform.
Parameters¶
-
seed – defaults to
None
Random number generation seed. Set this for reproducibility.
Attributes¶
-
mean
stats.Mean
-
variance
stats.Var
Examples¶
>>> from river import reco
>>> dataset = (
... ({'user': 'Alice', 'item': 'Superman'}, 8),
... ({'user': 'Alice', 'item': 'Terminator'}, 9),
... ({'user': 'Alice', 'item': 'Star Wars'}, 8),
... ({'user': 'Alice', 'item': 'Notting Hill'}, 2),
... ({'user': 'Alice', 'item': 'Harry Potter'}, 5),
... ({'user': 'Bob', 'item': 'Superman'}, 8),
... ({'user': 'Bob', 'item': 'Terminator'}, 9),
... ({'user': 'Bob', 'item': 'Star Wars'}, 8),
... ({'user': 'Bob', 'item': 'Notting Hill'}, 2)
... )
>>> model = reco.RandomNormal(seed=42)
>>> for x, y in dataset:
... _ = model.learn_one(**x, y=y)
>>> model.predict_one(user='Bob', item='Harry Potter')
6.147299621751425
Methods¶
clone
Return a fresh estimator with the same parameters.
The clone has the same parameters but has not been updated with any data. This works by looking at the parameters from the class signature. Each parameter is either - recursively cloned if it's a River classes. - deep-copied via copy.deepcopy
if not. If the calling object is stochastic (i.e. it accepts a seed parameter) and has not been seeded, then the clone will not be idempotent. Indeed, this method's purpose if simply to return a new instance with the same input parameters.
learn_one
Fits a user
-item
pair and a real-valued target y
.
Parameters
- user (Union[str, int])
- item (Union[str, int])
- y (Union[numbers.Number, bool])
- x (dict) – defaults to
None
predict_one
Predicts the target value of a set of features x
.
Parameters
- user (Union[str, int])
- item (Union[str, int])
- x (dict) – defaults to
None
Returns
typing.Union[numbers.Number, bool]: The predicted preference from the user for the item.
rank
Rank models by decreasing order of preference for a given user.
Parameters
- user (Union[str, int])
- items (Set[Union[str, int]])
- x (dict) – defaults to
None