Data and target formats

Feature matrix X

X must be a two-dimensional numeric matrix with shape (n_samples, n_features). Values must be finite, and prediction data must have the same number of features used during fitting.

Backend	Dense NumPy-like X	SciPy sparse X	Feature range
MiniBatchKMeans	Yes	Yes	Finite; scale consistently
KMeans	Yes	Yes	Finite; scale consistently
BallCover	Yes	No	Finite; scale consistently
Fuzzy ARTMAP	Yes	No	Must be in [0, 1]
Hypersphere ARTMAP	Yes	No	Must be in [0, 1]

Sparse inputs are normalized to CSR internally and remain sparse through fitting, slicing, prediction, multi-label expansion, and chunked scoring. The centroid and bounded score blocks are dense.

Single-label targets

A one-dimensional sequence assigns one label to each row:

y = ["cat", "cat", "dog", "dog"]
oi.fit(X, y)

Labels may be strings, integers, or other hashable scalar values. None, NaN, unhashable values, and ambiguous array-like labels are rejected. Every fit must have the same number of target entries as feature rows.

Multi-label target collections

A one-dimensional outer sequence may contain a collection of positive labels for each row:

y = [
    {"animal", "cat"},
    {"animal", "dog"},
    {"vehicle", "car"},
]
oi.fit(X, y)

Every sample must have at least one positive label. Duplicate labels within a sample are removed. See Multi-label data for scoring semantics and competitor selection.

Binary indicator matrices

Dense NumPy arrays and SciPy sparse matrices with shape (n_samples, n_labels) are accepted. They must contain only 0 and 1. Positive column indices become integer labels; original external class names are not inferred.

import numpy as np
from scipy import sparse

Y_dense = np.array([
    [1, 1, 0],
    [1, 0, 0],
    [0, 1, 1],
])
oi.fit(X, Y_dense)       # labels are 0, 1, and 2
oi.fit(X, sparse.csr_matrix(Y_dense))

Caution

A rectangular Python list such as [[1, 0], [0, 1]] retains sequence-of-label-collections semantics and emits a warning when it looks binary. Convert it to a NumPy or SciPy matrix to explicitly request indicator semantics.

Continuous targets

overlapindex.ContinuousOverlapIndex accepts a one-dimensional numeric target or a two-dimensional numeric matrix for multivariate regression. All values must be finite. It does not accept categorical or multi-label targets.

from overlapindex import ContinuousOverlapIndex

coi.fit(X, y_regression)                  # shape (n_samples,)
coi.fit(X, Y_multivariate_regression)     # shape (n_samples, n_targets)

The continuous estimator currently supports only the three offline backends. See Continuous targets for target scaling, target cells, and null calibration.

Empty and degenerate inputs

An empty dataset or a dataset with only one observed discrete class cannot provide a class-overlap comparison. The estimator emits RuntimeWarning and leaves index at its default 1.0; this value should not be interpreted as evidence of separation. Continuous fitting behaves similarly when it forms only one target cell or one prototype.