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: 0.130906

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

Returns

The predicted label.

predict_proba_one

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

Parameters

• x

Returns

A dictionary that associates a probability which each label.