Source code for clusterking.maths.metric

#!/usr/bin/env python3

# 3rd
import numpy as np

# ours
from clusterking.maths.metric_utils import condense_distance_matrix
from import DataWithErrors

[docs]def chi2( n1: np.ndarray, n2: np.ndarray, cov1: np.ndarray, cov2: np.ndarray, normalize=False, ) -> np.ndarray: """ Args: n1: n_obs x n_bins n2: Either n_obs x n_bins or just nbins if we're testing against a constant histogram cov1: Either n_obs x n_bins x n_bins or n_bins x n_bins cov2: Either n_obs x n_bins x n_bins or n_bins x n_bins normalize: Returns: n_obs vector of chi2 test results (degrees of freedom not yet divided out) """ assert n1.ndim == 2 n_obs, n_bins = n1.shape if n2.shape == (n_obs, n_bins): pass elif n2.shape == (n_bins,): n2 = n2.reshape((1, n_bins)) else: raise ValueError("Invalid shape of n2: {}.".format(n2.shape)) for _cov in [cov1, cov2]: if _cov.shape == (n_obs, n_bins, n_bins): pass elif _cov.shape == (n_bins, n_bins): pass else: raise ValueError( "Invalid shape of covariance matrix: {}".format(_cov.shape) ) if normalize: if cov1.ndim == 2: cov1 = np.tile(cov1, (n_obs, 1, 1)) if cov2.ndim == 2: cov2 = np.tile(cov2, (n_obs, 1, 1)) norm1 = n1.sum(axis=1) norm2 = n2.sum(axis=1) n1 = n1.copy() / norm1.reshape((norm1.size, 1)) n2 = n2.copy() / norm2.reshape((norm2.size, 1)) cov1 = cov1.copy() / np.square(norm1).reshape((norm1.size, 1, 1)) cov2 = cov2.copy() / np.square(norm2).reshape((norm2.size, 1, 1)) diff = n1 - n2 cov = cov1 + cov2 if cov.ndim == 3: return np.einsum("ni,nij,nj->n", diff, np.linalg.inv(cov), diff) elif cov.ndim == 2: return np.einsum("ni,ij,nj->n", diff, np.linalg.inv(cov), diff) else: raise ValueError( "Invalid dimensionality of covariance matrix." " This is likely a bug in the package. Please" " report it." )
# todo: unittest
[docs]def chi2_metric(dwe: DataWithErrors, output="condensed"): """ Returns the chi2/ndf values of the comparison of a datasets. Args: dwe: :py:class:`` object output: 'condensed' (condensed distance matrix) or 'full' (full distance matrix) Returns: Condensed distance matrix or full distance matrix """ if not isinstance(dwe, DataWithErrors): raise TypeError( "In order to use chi2 metric, you have to use a DataWithErrors " "object with added errors, however you supplied an object of type " "{type}. ".format(type=type(dwe)) ) d = n_obs, n_bins = d.shape cov = dwe.cov(relative=False) assert cov.shape == (n_obs, n_bins, n_bins) # n x n chi2s = np.full((n_obs, n_obs), np.nan) # todo: this calculates the full n x n matrix, even though it's symmetric # so we could likely optimize this if we wanted for i in range(n_obs): chi2s[i, :] = chi2(d, d[i], cov, cov[i], normalize=True) # todo: check for symmetry and vanishing diagonal of matrix here ndf = n_bins - 1 chi2ndf = chi2s / np.full((1, 1), ndf) if output == "condensed": return condense_distance_matrix(chi2ndf) elif output == "full": return chi2ndf else: raise ValueError("Unknown argument '{}'.".format(output))