RandomUnderSampler¶
Random under-sampling.
This is a wrapper for classifiers. It will train the provided classifier by under-sampling the stream of given observations so that the class distribution seen by the classifier follows a given desired distribution. The implementation is a discrete version of rejection sampling.
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.
-
seed (int) – defaults to
NoneRandom 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.RandomUnderSampler(
... (
... preprocessing.StandardScaler() |
... linear_model.LogisticRegression()
... ),
... desired_dist={False: 0.4, True: 0.6},
... seed=42
... )
>>> dataset = datasets.CreditCard().take(3000)
>>> metric = metrics.LogLoss()
>>> evaluate.progressive_val_score(dataset, model, metric)
LogLoss: 0.07292
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_many
Predict the labels of a DataFrame X.
Parameters
- X (pandas.core.frame.DataFrame)
Returns
Series: Series of predicted labels.
predict_one
Predict the label of a set of features x.
Parameters
- x
Returns
The predicted label.
predict_proba_many
Predict the labels of a DataFrame X.
Parameters
- X (pandas.core.frame.DataFrame)
Returns
DataFrame: DataFrame that associate probabilities which each label as columns.
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.