fuzzy-rough-learn API

This is the full API documentation of fuzzy-rough-learn.

Classifiers

FRNN	Implementation of Fuzzy Rough Nearest Neighbour (FRNN) classification.
FROVOCO	Implementation of the Fuzzy Rough OVO COmbination (FROVOCO) ensemble classifier [Rcffc929d1d8e-1].
FRONEC	Implementation of the Fuzzy ROugh NEighbourhood Consensus (FRONEC) multilabel classifier.
NN	Implementation of Nearest Neighbour (NN) classification.

Data descriptors

ALP	Implementation of the Average Localised Proximity (ALP) data descriptor [R4d6affda87e1-1].
CD	Implementation of the Centre Distance (CD) data descriptor.
EIF	Wrapper for the Extended Isolation Forest (IF) data descriptor [R4b2ca5e1909e-1].
IF	Wrapper for the Isolation Forest (IF) data descriptor [R288ff40200ea-1] implemented in scikit-learn.
LNND	Implementation of the Localised Nearest Neighbour Distance (LNND) data descriptor [Rdc09924e667a-1]_[Rdc09924e667a-2]_.
LOF	Implementation of the Local Outlier Factor (LOF) data descriptor [Race47188aa49-1].
MD	Implementation of the Mahalanobis Distance (MD) data descriptor [Rddee2b422011-1].
NND	Implementation of the Nearest Neighbour Distance (NND) data descriptor, which goes back to at least [R2f1514802a03-1].
SVM	Wrapper for the Support Vector Machine (SVM) data descriptor [R52cd54939bff-1] with gaussian kernel, implemented in scikit-learn.

Regressors

FRNN	Implementation of the Fuzzy Rough Nearest Neighbour (FRNN) regressor [R889f5cf58f8d-1].

Feature preprocessors

Linear normalisers

LinearNormaliser	Linearly transforms all features by normalising a measure of dispersion and a measure of location, ensuring that for each feature, that measure of dispersion becomes 1 and that measure of location becomes 0.
IQRNormaliser	Implementation of the interquartile range (IQR) normaliser.
MaxAbsNormaliser	Implementation of the maximum absolute value normaliser.
RangeNormaliser	Implementation of the range normaliser.
Standardiser	Implementation of the standard deviation normaliser, or standardiser.

Other

FRFS	Implementation of the Fuzzy Rough Feature Selection (FRFS) preprocessor.
VectorSizeNormaliser	Rescales each instance (seen as a vector) to a fixed size.

Instance preprocessors

FRPS	Implementation of the Fuzzy Rough Prototype Selection (FRPS) preprocessor.

Other

Array functions

div_or	Divides x by y, replacing np.nan values with fallback.
first	Returns the k first values of a along the specified axis.
greatest	Returns the k greatest values of a along the specified axis, in order.
last	Returns the k last values of a along the specified axis, in reverse order.
least	Returns the k least values of a along the specified axis, in order.
remove_diagonal	Remove the diagonal from a square array.
soft_head	Calculates the soft head of an array.
soft_max	Calculates the soft maximum of an array.
soft_min	Calculates the soft minimum of an array.
soft_tail	Calculates the soft tail of an array.

Dispersion measures

interquartile_range	Distance between the first and the third quartile; range of the central half of the data.
maximum_absolute_value	Maximum distance from 0.
standard_deviation	Square root of the sum of the squared distances to the mean.
total_range	Distance between the smallest and largest value.

Location measures

maximum	Greatest value.
mean	Sum of all values divided by the number of values.
median	Middle value after sorting all values by size, or mean of the two middle values.
midhinge	Mean of the first and third quartiles.
midrange	Mean of the minimum and maximum.
minimum	Least value.

Nearest neighbour search algorithms

BallTree	Nearest neighbour search with a Ball tree.
KDTree	Nearest neighbour search with a KD-tree.

Parametrisations

log_multiple	Function to obtain multiples of the logarithm of other numbers.
multiple	Function to obtain multiples of other numbers.

T-norms

goguen_t_norm	x * y; also known as product t-norm.
heyting_t_norm	min(x, y); also known as Gödel or minimum t-norm.
lukasiewicz_t_norm	max(x + y - 1, 0)

Transformations

contract	Strictly order-preserving function from [-∞, ∞] to [0, 1] that sends -∞, -c, 0, c, ∞ to 0, 0.25, 0.5, 0.75, 1, respectively.
shifted_reciprocal	Order-reversing function from [0, ∞) to [0, 1] that sends x to 1/(1 + x/c).
truncated_complement	Order-reversing function from [0, ∞) to [0, 1] that sends x to max(0, 1 - x).

Vector size measures

MinkowskiSize

Family of vector size measures of the form `(x1**p + x2**p + .

Weights

Weights	Abstract base class for parametrisable weights functions.
ConstantWeights	(1/4, 1/4, 1/4, 1/4) Also known as mean weights, as they compute the unweighted mean.
ExponentialWeights	(8/15, 4/15, 2/15, 1/15) Exponentially decreasing weights with parametrisable base.
LinearWeights	(4/10, 3/10, 2/10, 1/10) Also known as additive weights.
QuantifierWeights	Weights that encode a regular non-decreasing quantifier [Rcc56311c3f1f-1].
ReciprocallyLinearWeights	(12/25, 12/50, 12/75, 12/100) Also known as inverse additive weights.