PWKCLF#

class mlquantify.classification.methods.PWKCLF(alpha=1, n_neighbors=10, algorithm='auto', metric='euclidean', leaf_size=30, p=2, metric_params=None, n_jobs=None)[source]#

Learner based on k-Nearest Neighbors (KNN) to use in the PWK method.

This classifier adjusts the influence of neighbors using class weights derived from the alpha parameter. The alpha parameter controls the influence of class imbalance.

Parameters:

alphafloat, default=1: Controls the influence of class imbalance. Must be >= 1.
n_neighborsint, default=10: Number of neighbors to use.
algorithm{‘auto’, ‘ball_tree’, ‘kd_tree’, ‘brute’}, default=’auto’: Algorithm to compute nearest neighbors.
metricstr, default=’euclidean’: Distance metric to use.
leaf_sizeint, default=30: Leaf size passed to the tree-based algorithms.
pint, default=2: Power parameter for the Minkowski metric.
metric_paramsdict, optional: Additional keyword arguments for the metric function.
n_jobsint, optional: Number of parallel jobs to run for neighbors search.

Examples

>>> from sklearn.datasets import load_breast_cancer
>>> from sklearn.model_selection import train_test_split
>>> from mlquantify.methods.aggregative import PWK
>>> from mlquantify.utils.general import get_real_prev
>>> from mlquantify.classification import PWKCLF
>>> 
>>> # Load dataset
>>> features, target = load_breast_cancer(return_X_y=True)
>>> 
>>> # Split into training and testing sets
>>> X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.3, random_state=32)
>>> 
>>> # Create and configure the PWKCLF learner
>>> learner = PWKCLF(alpha=1, n_neighbors=10)
>>> 
>>> # Create the PWK quantifier
>>> model = PWK(learner=learner)
>>> 
>>> # Train the model
>>> model.fit(X_train, y_train)
>>> 
>>> # Predict prevalences
>>> y_pred = model.predict(X_test)
>>> 
>>> # Display results
>>> print("Real:", get_real_prev(y_test))
>>> print("PWK:", y_pred)

fit(X, y)[source]#

Fit the PWKCLF model to the training data.

Parameters:

Xarray-like of shape (n_samples, n_features): Training features.
yarray-like of shape (n_samples,): Training labels.

Returns:

selfobject: The fitted instance.

get_metadata_routing()[source]#

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)[source]#

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.

predict(X)[source]#

Predict class labels for samples in X.

Parameters:

Xarray-like of shape (n_samples, n_features): Input data to predict.

Returns:

y_predarray of shape (n_samples,): Predicted class labels.

set_params(**params)[source]#

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.

PWKCLF#

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