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PredClipper

Clips the target after predicting.

Parameters

  • regressor (base.Regressor)

    Regressor model for which to clip the predictions.

  • y_min (float)

    minimum value.

  • y_max (float)

    maximum value.

Examples

>>> from river import linear_model
>>> from river import meta

>>> dataset = (
...     ({'a': 2, 'b': 4}, 80),
...     ({'a': 3, 'b': 5}, 100),
...     ({'a': 4, 'b': 6}, 120)
... )

>>> model = meta.PredClipper(
...     regressor=linear_model.LinearRegression(),
...     y_min=0,
...     y_max=200
... )

>>> for x, y in dataset:
...     _ = model.learn_one(x, y)

>>> model.predict_one({'a': -100, 'b': -200})
0

>>> model.predict_one({'a': 50, 'b': 60})
200

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 to a set of features x and a real-valued target y.

Parameters

  • x (dict)
  • y (numbers.Number)

Returns

Regressor: self

predict_one

Predicts the target value of a set of features x.

Parameters

  • x (dict)

Returns

Number: The prediction.