How to use similarize method in ATX

Best Python code snippet using ATX

density.py

Source:density.py

1"""2Measures for estimating the information density of a given sample.3"""4from typing import Callable, Union5import numpy as np6from scipy.spatial.distance import cosine, euclidean7from sklearn.metrics.pairwise import pairwise_distances8from modAL.utils.data import modALinput9def similarize_distance(distance_measure: Callable) -> Callable:10    """11    Takes a distance measure and converts it into a information_density measure.12    Args:13        distance_measure: The distance measure to be converted into information_density measure.14    Returns:15        The information_density measure obtained from the given distance measure.16    """17    def sim(*args, **kwargs):18        return 1/(1 + distance_measure(*args, **kwargs))19    return sim20cosine_similarity = similarize_distance(cosine)21euclidean_similarity = similarize_distance(euclidean)22def information_density(X: modALinput, metric: Union[str, Callable] = 'euclidean') -> np.ndarray:23    """24    Calculates the information density metric of the given data using the given metric.25    Args:26        X: The data for which the information density is to be calculated.27        metric: The metric to be used. Should take two 1d numpy.ndarrays for argument.28    Todo:29        Should work with all possible modALinput.30        Perhaps refactor the module to use some stuff from sklearn.metrics.pairwise31    Returns:32        The information density for each sample.33    """34    # inf_density = np.zeros(shape=(X.shape[0],))35    # for X_idx, X_inst in enumerate(X):36    #     inf_density[X_idx] = sum(similarity_measure(X_inst, X_j) for X_j in X)37    #38    # return inf_density/X.shape[0]39    similarity_mtx = 1/(1+pairwise_distances(X, X, metric=metric))...

information_density.py

Source:information_density.py

1from modAL.density import similarize_distance, information_density2from sklearn.datasets import make_blobs3from scipy.spatial.distance import euclidean4X, y = make_blobs(n_features=2, n_samples=10, centers=3, random_state=0, cluster_std=0.7)5cosine_density = information_density(X)...

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