DiscrimOneSample¶

class
hyppo.discrim.
DiscrimOneSample
(is_dist=False, remove_isolates=True)¶ 1 Sample Discriminability test statistic and pvalue.
Discriminability index is a measure of whether a data acquisition and preprocessing pipeline is more discriminable among different subjects. The key insight is that each repeated mesurements of the same item should be the more similar to one another than measurements between different items. The one sample test measures whether the discriminability for a dataset differs from random chance. More details are in [1].
With \(D_x\) as the sample discriminability of \(x\), one sample test performs the following test,
\[\begin{split}H_0: D_x &= D_0 \\ H_A: D_x &> D_0\end{split}\]where \(D_0\) is the discriminability that would be observed by random chance.
Methods Summary
Helper function that calculates the discriminability test statistics. 


Calculates the test statistic and pvalue for Discriminability one sample test. 

DiscrimOneSample.
statistic
(x, y)¶ Helper function that calculates the discriminability test statistics.
 Parameters
x,y (
ndarray
)  Input data matrices.x
andy
must have the same number of samples. That is, the shapes must be(n, p)
and(n, q)
where n is the number of samples and p and q are the number of dimensions. Alternatively,x
andy
can be distance matrices, where the shapes must both be(n, n)
. Returns
stat (
float
)  The computed two sample discriminability statistic.

DiscrimOneSample.
test
(x, y, reps=1000, workers=1)¶ Calculates the test statistic and pvalue for Discriminability one sample test.
 Parameters
x (
ndarray
)  Input data matrices.x
must have shape(n, p)
where n is the number of samples and p are the number of dimensions. Alternatively,x
can be distance matrices, where the shape must be(n, n)
, andis_dist
must set toTrue
in this case.y (
ndarray
)  A vector containing the sample ids for our n samples.reps (
int
, default:1000
)  The number of replications used to estimate the null distribution when using the permutation test used to calculate the pvalue.workers (
int
, default:1
)  The number of cores to parallelize the pvalue computation over. Supply1
to use all cores available to the Process.
 Returns
Examples
>>> import numpy as np >>> from hyppo.discrim import DiscrimOneSample >>> x = np.concatenate([np.zeros((50, 2)), np.ones((50, 2))], axis=0) >>> y = np.concatenate([np.zeros(50), np.ones(50)], axis=0) >>> '%.1f, %.2f' % DiscrimOneSample().test(x, y) '1.0, 0.00'