API Documentation

class vfi.VFI(n_bins='auto', strategy='uniform')

Classification by voting feature intervals.

Intervals are constucted around each class for each attribute (basically discretization). Class counts are recorded for each interval on each attribute. Classification is by voting.

Parameters

n_binsint (default=”auto”)

The number of bins to produce. When is set to ‘auto’ the n_bins equals to the double of the number of classes. Raises ValueError if n_bins < 2.

strategy{‘uniform’, ‘quantile’, ‘kmeans’}, (default=’quantile’)

Strategy used to define the widths of the bins.

uniform

All bins in each feature have identical widths.

quantile

All bins in each feature have the same number of points.

kmeans

Values in each bin have the same nearest center of a 1D k-means cluster.

References

Ra8705851e4ae-1

G. Demiroz, A. Guvenir: Classification by voting feature intervals. In: 9th European Conference on Machine Learning, 85-92, 1997.01.

Attributes

classes_array, shape (n_classes,)

The classes.

classes_distribution_array, shape (n_classes,)

The distribution of the classes.

interval_class_counts_array, shape (n_features, n_bins, n_classes,)

Contains the raw class counts per feature and per bin.

n_bins_int

The number of bins used during fit.

n_classes_int

The number of classes.

Methods

fit(self, X, y)	Fit VFI according to X, y.
get_params(self[, deep])	Get parameters for this estimator.
predict(self, X)	Perform classification on an array of test vectors X.
predict_proba(self, X)	Return probability estimates for the test vector X.
score(self, X, y[, sample_weight])	Return the mean accuracy on the given test data and labels.
set_params(self, \*\*params)	Set the parameters of this estimator.

fit(self, X, y)

Fit VFI according to X, y.

Parameters

Xarray-like, shape (n_samples, n_features)

The training input samples.

yarray-like, shape (n_samples,)

The target values. An array of int.

Returns

selfobject

Returns self.

predict(self, X)

Perform classification on an array of test vectors X.

Parameters

Xarray-like, shape (n_samples, n_features)

The input samples.

Returns

yndarray, shape (n_samples,)

Predicted target values for X.

predict_proba(self, X)

Return probability estimates for the test vector X.

Parameters

Xarray-like of shape (n_samples, n_features)

Returns

probasarray-like of shape (n_samples, n_classes)

Returns the probability of the samples for each class in the model. The columns correspond to the classes in sorted order, as they appear in the attribute classes_.