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NoChangeClassifier

Dummy classifier which returns the last class seen.

The predict_one method will output the last class seen whilst predict_proba_one will return 1 for the last class seen and 0 for the others.

Attributes

  • last_class

    The last class seen.

  • classes

    The set of classes seen.

Examples

Taken from example 2.1 from this page.

>>> import pprint
>>> from river import dummy

>>> sentences = [
...     ('glad happy glad', '+'),
...     ('glad glad joyful', '+'),
...     ('glad pleasant', '+'),
...     ('miserable sad glad', '−')
... ]

>>> model = dummy.NoChangeClassifier()

>>> for sentence, label in sentences:
...     model = model.learn_one(sentence, label)

>>> new_sentence = 'glad sad miserable pleasant glad'
>>> model.predict_one(new_sentence)
'−'

>>> pprint.pprint(model.predict_proba_one(new_sentence))
{'+': 0, '−': 1}

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

Update the model with a set of features x and a label y.

Parameters

  • x (dict)
  • y (Union[bool, str, int])

Returns

Classifier: self

predict_one

Predict the label of a set of features x.

Parameters

  • x (dict)

Returns

typing.Union[bool, str, int]: The predicted label.

predict_proba_one

Predict the probability of each label for a dictionary of features x.

Parameters

  • x (dict)

Returns

typing.Dict[typing.Union[bool, str, int], float]: A dictionary that associates a probability which each label.