RandomSampler

Random sampling by mixing under-sampling and over-sampling.

This is a wrapper for classifiers. It will train the provided classifier by both under-sampling and over-sampling the stream of given observations so that the class distribution seen by the classifier follows a given desired distribution.

See Working with imbalanced data for example usage.

Parameters

• classifier (base.Classifier)

• desired_dist (dict)

  The desired class distribution. The keys are the classes whilst the values are the desired class percentages. The values must sum up to 1. If set to None, then the observations will be sampled uniformly at random, which is stricly equivalent to using ensemble.BaggingClassifier.

• sampling_rate – defaults to 1.0

  The desired ratio of data to sample.

• seed (int) – defaults to None

  Random seed for reproducibility.

Examples

>>> from river import datasets
>>> from river import evaluate
>>> from river import imblearn
>>> from river import linear_model
>>> from river import metrics
>>> from river import preprocessing

>>> model = imblearn.RandomSampler(
...     (
...         preprocessing.StandardScaler() |
...         linear_model.LogisticRegression()
...     ),
...     desired_dist={False: 0.4, True: 0.6},
...     sampling_rate=0.8,
...     seed=42
... )

>>> dataset = datasets.CreditCard().take(3000)

>>> metric = metrics.LogLoss()

>>> evaluate.progressive_val_score(dataset, model, metric)
LogLoss: 0.131988

Methods

learn_one

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

Parameters

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

Returns

Classifier: self

predict_one

Predict the label of a set of features x.

Parameters

• x
• kwargs

Returns

The predicted label.

predict_proba_one

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

Parameters

• x
• kwargs

Returns

A dictionary that associates a probability which each label.