`mlresearch.synthetic_data`.GeometricSMOTE¶

class mlresearch.synthetic_data.GeometricSMOTE(sampling_strategy='auto', random_state=None, truncation_factor=1.0, deformation_factor=0.0, selection_strategy='combined', k_neighbors=5, categorical_features=None, n_jobs=1)[source]¶

Class to to perform over-sampling using Geometric SMOTE.

This algorithm is an implementation of Geometric SMOTE, a geometrically enhanced drop-in replacement for SMOTE as presented in [1].

Parameters:

categorical_featuresndarray of shape (n_cat_features,) or (n_features,)

Specified which features are categorical. Can either be:

array of indices specifying the categorical features;
mask array of shape (n_features, ) and bool dtype for which True indicates the categorical features.

sampling_strategyfloat, str, dict or callable, default=’auto’

Sampling information to resample the data set.

When float, it corresponds to the desired ratio of the number of samples in the minority class over the number of samples in the majority class after resampling. Therefore, the ratio is expressed as \(\alpha_{os} = N_{rm} / N_{M}\) where \(N_{rm}\) is the number of samples in the minority class after resampling and \(N_{M}\) is the number of samples in the majority class.

Warning

float is only available for binary classification. An error is raised for multi-class classification.
When str, specify the class targeted by the resampling. The number of samples in the different classes will be equalized. Possible choices are:

'minority': resample only the minority class;

'not minority': resample all classes but the minority class;

'not majority': resample all classes but the majority class;

'all': resample all classes;

'auto': equivalent to 'not majority'.
When dict, the keys correspond to the targeted classes. The values correspond to the desired number of samples for each targeted class.
When callable, function taking y and returns a dict. The keys correspond to the targeted classes. The values correspond to the desired number of samples for each class.

random_stateint, RandomState instance, default=None

Control the randomization of the algorithm.

If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used by np.random.

truncation_factorfloat, optional (default=0.0)

The type of truncation. The values should be in the [-1.0, 1.0] range.

deformation_factorfloat, optional (default=0.0)

The type of geometry. The values should be in the [0.0, 1.0] range.

selection_strategystr, optional (default=’combined’)

The type of Geometric SMOTE algorithm with the following options: 'combined', 'majority', 'minority'.

k_neighborsint or object, optional (default=5)

If int, number of nearest neighbours to use when synthetic samples are constructed for the minority method. If object, an estimator that inherits from sklearn.neighbors.base.KNeighborsMixin that will be used to find the k_neighbors.

n_jobsint, optional (default=1)

The number of threads to open if possible.

Notes

See the original paper: [1] for more details.

Supports multi-class resampling. A one-vs.-rest scheme is used as originally proposed in [2].

References

[1] (1,2)

G. Douzas, F. Bacao, “Geometric SMOTE: a geometrically enhanced drop-in replacement for SMOTE”, Information Sciences, vol. 501, pp. 118-135, 2019.

[2]

N. V. Chawla, K. W. Bowyer, L. O. Hall, W. P. Kegelmeyer, “SMOTE: synthetic minority over-sampling technique”, Journal of Artificial Intelligence Research, vol. 16, pp. 321-357, 2002.

Examples

>>> from collections import Counter
>>> from sklearn.datasets import make_classification
>>> from gsmote import GeometricSMOTE 
>>> X, y = make_classification(n_classes=2, class_sep=2,
... weights=[0.1, 0.9], n_informative=3, n_redundant=1, flip_y=0,
... n_features=20, n_clusters_per_class=1, n_samples=1000, random_state=10)
>>> print('Original dataset shape %s' % Counter(y))
Original dataset shape Counter({1: 900, 0: 100})
>>> gsmote = GeometricSMOTE(random_state=1)
>>> X_res, y_res = gsmote.fit_resample(X, y)
>>> print('Resampled dataset shape %s' % Counter(y_res))
Resampled dataset shape Counter({0: 900, 1: 900})

Attributes:

sampling_strategy_dict: Dictionary containing the information to sample the dataset. The keys corresponds to the class labels from which to sample and the values are the number of samples to sample.
n_features_in_int: Number of features in the input dataset.
nns_pos_estimator object: Validated k-nearest neighbours created from the k_neighbors parameter. It is used to find the nearest neighbors of the same class of a selected observation.
nn_neg_estimator object: Validated k-nearest neighbours created from the k_neighbors parameter. It is used to find the nearest neighbor of the remaining classes (k=1) of a selected observation.
random_state_instance of RandomState: If the random_state parameter is None, it is a RandomState singleton used by np.random. If random_state is an int, it is a RandomState instance seeded with seed. If random_state is already a RandomState instance, it is the same object.

fit(X, y)¶

Check inputs and statistics of the sampler.

You should use fit_resample in all cases.

Parameters:

X{array-like, dataframe, sparse matrix} of shape (n_samples, n_features): Data array.
yarray-like of shape (n_samples,): Target array.

Returns:

selfobject: Return the instance itself.

fit_resample(X, y, sample_weight=None)[source]¶

Resample the dataset.

Parameters:

X{array-like, sparse matrix} of shape (n_samples, n_features): Matrix containing the data which have to be sampled.
yarray-like of shape (n_samples,): Corresponding label for each sample in X.
sample_weightarray-like of shape (n_samples,), default=None: Individual weights for each sample. Assigns probabilities for selecting a sample as a center point.

Returns:

X_resampled{array-like, sparse matrix} of shape (n_samples_new, n_features): The array containing the resampled data.
y_resampledarray-like of shape (n_samples_new,): The corresponding label of X_resampled.

get_feature_names_out(input_features=None)¶

Get output feature names for transformation.

Parameters:

input_featuresarray-like of str or None, default=None

Input features.

If input_features is None, then feature_names_in_ is used as feature names in. If feature_names_in_ is not defined, then the following input feature names are generated: [“x0”, “x1”, …, “x(n_features_in_ - 1)”].
If input_features is an array-like, then input_features must match feature_names_in_ if feature_names_in_ is defined.

Returns:

feature_names_outndarray of str objects: Same as input features.

get_metadata_routing()¶

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest: A MetadataRequest encapsulating routing information.

get_params(deep=True)¶

Get parameters for this estimator.

Parameters:

deepbool, default=True: If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

paramsdict: Parameter names mapped to their values.

set_params(**params)¶

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**paramsdict: Estimator parameters.

Returns:

selfestimator instance: Estimator instance.

mlresearch.synthetic_data.GeometricSMOTE¶

`mlresearch.synthetic_data`.GeometricSMOTE¶