Source code for mgcpy.independence_tests.abstract_class

    **Main Independence Test Abstract Class**
import time
import warnings
from abc import ABC, abstractmethod

import numpy as np
from scipy.spatial.distance import pdist, squareform
from scipy.stats import kendalltau, pearsonr, spearmanr, t

[docs]def EUCLIDEAN_DISTANCE(x): return squareform(pdist(x, metric='euclidean'))
[docs]class IndependenceTest(ABC): """ IndependenceTest abstract class Specifies the generic interface that must be implemented by all the independence tests in the mgcpy package. :param compute_distance_matrix: a function to compute the pairwise distance matrix, given a data matrix :type compute_distance_matrix: ``FunctionType`` or ``callable()`` """ def __init__(self, compute_distance_matrix=None): self.test_statistic_ = None self.test_statistic_metadata_ = None self.p_value_ = None self.p_value_metadata_ = None self.which_test = None if not compute_distance_matrix: compute_distance_matrix = EUCLIDEAN_DISTANCE self.compute_distance_matrix = compute_distance_matrix super().__init__()
[docs] def get_name(self): ''' :return: the name of the independence test :rtype: string ''' return self.which_test
[docs] @abstractmethod def test_statistic(self, matrix_X, matrix_Y): """ Abstract method to compute the test statistic given two data matrices :param matrix_X: a ``[n*p]`` data matrix, a matrix with n samples in ``p`` dimensions :type matrix_X: 2D `numpy.array` :param matrix_Y: a ``[n*q]`` data matrix, a matrix with n samples in ``q`` dimensions :type matrix_Y: 2D `numpy.array` :return: returns a list of two items, that contains: - :test_statistic_: the test statistic computed using the respective independence test - :test_statistic_metadata_: (optional) metadata other than the test_statistic, that the independence tests computes in the process :rtype: list """ pass
[docs] def p_value(self, matrix_X, matrix_Y, replication_factor=1000): """ Tests independence between two datasets using the independence test and permutation test. :param matrix_X: a ``[n*p]`` matrix, a matrix with n samples in ``p`` dimensions :type matrix_X: 2D `numpy.array` :param matrix_Y: a ``[n*q]`` matrix, a matrix with n samples in ``q`` dimensions :type matrix_Y: 2D `numpy.array` :param replication_factor: specifies the number of replications to use for the permutation test. Defaults to ``1000``. :type replication_factor: integer :return: returns a list of two items, that contains: - :p_value_: P-value - :p_value_metadata_: (optional) a ``dict`` of metadata other than the p_value, that the independence tests computes in the process """ np.random.seed(int(time.time())) # calculte the test statistic with the given data test_statistic, independence_test_metadata = self.test_statistic(matrix_X, matrix_Y) if self.get_name() == "unbiased": ''' for the unbiased centering scheme used to compute unbiased dcorr test statistic we can use a t-test to compute the p-value notation follows from: Székely, Gábor J., and Maria L. Rizzo. "The distance correlation t-test of independence in high dimension." Journal of Multivariate Analysis 117 (2013): 193-213. ''' T, df = self.unbiased_T(matrix_X=matrix_X, matrix_Y=matrix_Y) # p-value is the probability of obtaining values more extreme than the test statistic # under the null if T < 0: p_value = t.cdf(T, df=df) else: p_value = 1 - t.cdf(T, df=df) p_value_metadata = {} elif self.get_name() == "mgc": local_correlation_matrix = independence_test_metadata["local_correlation_matrix"] p_local_correlation_matrix = np.zeros(local_correlation_matrix.shape) p_value = 0 # compute sample MGC statistic and all local correlations for each set of permuted data for _ in range(replication_factor): # use random permutations on the second data set premuted_matrix_Y = np.random.permutation(matrix_Y) temp_mgc_statistic, temp_independence_test_metadata = self.test_statistic( matrix_X, premuted_matrix_Y) temp_local_correlation_matrix = temp_independence_test_metadata["local_correlation_matrix"] p_value += ((temp_mgc_statistic >= test_statistic) * (1/replication_factor)) p_local_correlation_matrix += ((temp_local_correlation_matrix >= local_correlation_matrix) * (1/replication_factor)) p_value_metadata = {"test_statistic": test_statistic, "p_local_correlation_matrix": p_local_correlation_matrix, "local_correlation_matrix": local_correlation_matrix, "optimal_scale": independence_test_metadata["optimal_scale"]} elif self.get_name() == "kendall": p_value = kendalltau(matrix_X, matrix_Y)[1] p_value_metadata = {} elif self.get_name() == "spearman": p_value = spearmanr(matrix_X, matrix_Y)[1] p_value_metadata = {} elif self.get_name() == "pearson": p_value = pearsonr(matrix_X, matrix_Y)[1] p_value_metadata = {} else: # estimate the null by a permutation test test_stats_null = np.zeros(replication_factor) for rep in range(replication_factor): permuted_y = np.random.permutation(matrix_Y) test_stats_null[rep], _ = self.test_statistic(matrix_X=matrix_X, matrix_Y=permuted_y) # p-value is the probability of observing more extreme test statistic under the null p_value = np.where(test_stats_null >= test_statistic)[0].shape[0] / replication_factor p_value_metadata = {} # The results are not statistically significant if p_value > 0.05: warnings.warn("The p-value is greater than 0.05, implying that the results are not statistically significant.\n" + "Use results such as test_statistic and optimal_scale, with caution!") self.p_value_ = p_value self.p_value_metadata_ = p_value_metadata return p_value, p_value_metadata