kde_classifier.py

import numpy as np
from sklearn.base import BaseEstimator, ClassifierMixin
from sklearn.neighbors import KernelDensity
from typing import *


# noinspection PyAttributeOutsideInit,PyPep8Naming
class KDEClassifier(BaseEstimator, ClassifierMixin):
    """Bayesian generative classification based on KDE.

    Code adapted from Chapter 5 of the Python Data Science Handbook by Jake VanderPlas:
    https://jakevdp.github.io/PythonDataScienceHandbook/05.13-kernel-density-estimation.html

    Args:
        bandwidth : float
            the kernel bandwidth within each class
        kernel : str
            the kernel name, passed to KernelDensity
    """

    def __init__(self, bandwidth=1.0, kernel='gaussian'):
        self.bandwidth: float = bandwidth
        self.kernel: str = kernel

    def fit(self, X, y):
        self.classes_ = np.sort(np.unique(y))
        training_sets = [X[y == yi] for yi in self.classes_]
        self.models_ = [KernelDensity(bandwidth=self.bandwidth,
                                      kernel=self.kernel).fit(Xi)
                        for Xi in training_sets]
        self.logpriors_ = [np.log(Xi.shape[0] / X.shape[0])
                           for Xi in training_sets]
        return self

    # noinspection PyUnresolvedReferences
    def predict_proba(self, X):
        logprobs = np.array([model.score_samples(X)
                             for model in self.models_]).T
        result = np.exp(logprobs + self.logpriors_)
        return result / result.sum(1, keepdims=True)

    def predict(self, X):
        return self.classes_[np.argmax(self.predict_proba(X), 1)]