skoot.balance.under_sample_balance

skoot.balance.under_sample_balance(X, y, balance_ratio=0.2, random_state=None, shuffle=True)

Under sample the majority class to a specified ratio.

One strategy for balancing data is to under-sample the majority class until it is represented at the prescribed balance_ratio. This can be effective in cases where the training set is already quite large, and diminishing its size may not prove detrimental.

The under-sampling procedure behaves differently than the over-sampling technique: its objective is only to under-sample the majority class, and will down-sample it until the second-most represented class is present at the prescribed ratio.

Parameters:

X : array-like, shape (n_samples, n_features)

The training array. Samples from this array that correspond to the majority class will be omitted until the minority class is represented at the balance_ratio.

y : array-like, shape (n_samples,)

Training labels corresponding to the samples in X.

balance_ratio : float, optional (default=0.2)

The minimum acceptable ratio of $MINORITY_CLASS : $MAJORITY_CLASS representation, where 0 < ratio <= 1

random_state : int, None or numpy RandomState, optional (default=None)

The seed to construct the random state to generate random selections.

shuffle : bool, optional (default=True)

Whether to shuffle the output.

Notes

You should only use the under sampling method when you have lots of data and can afford to lose some training samples. Moreover, using this in conjunction with a high variance modeling method can pose a higher risk of over-fitting, since they typically require more data.

Examples

>>> from sklearn.datasets import make_classification
>>> X, y = make_classification(n_samples=1000, random_state=42,
...                            n_classes=2, weights=[0.99, 0.01])
>>> X_bal, y_bal = under_sample_balance(X, y, balance_ratio=0.2,
...                                     random_state=42)
>>> ratio = round((y_bal == 1).sum() / float((y_bal == 0).sum()), 1)
>>> assert ratio == 0.2, ratio

Note that the number of samples in the data is now lower than it initially was:

>>> assert X_bal.shape[0] < 1000

Examples using skoot.balance.under_sample_balance

Undersampling minority samples