SmoothCFTest¶

class hyppo.ksample.SmoothCFTest(num_randfreq=5)¶

Smooth Characteristic Function test statistic and p-value

The Smooth Characteristic Function test is a two-sample test that uses differences in the smoothed (analytic) characteristic function of two data distributions in order to determine how different the two data are 1.

Parameters: num_randfreq (int) -- Used to construct random array with size (p, q) where p is the number of dimensions of the data and q is the random frequency at which the test is performed. These are the random test points at which test occurs (see notes).

Notes

The test statistic takes on the following form:

\[nW_n\Sigma_n^{-1}W_n\]

As seen in the above formulation, this test-statistic takes the same form as the Hotelling \(T^2\) statistic. However, the components are defined differently in this case. Given data sets X and Y, define the following as \(Z_i\), the vector of differences:

\[Z_i = (k(X_i, T_1) - k(Y_i, T_1), \ldots, k(X_i, T_J) - k(Y_i, T_J)) \in \mathbb{R}^J\]

The above is the vector of differences between kernels at test points, \(T_j\). This same formulation is used in the Mean Embedding Test. Moving forward, \(W_n\) can be defined:

\[W_n = \frac{1}{n} \sum_{i = 1}^n Z_i\]

This leaves \(\Sigma_n\), the covariance matrix as:

\[\Sigma_n = \frac{1}{n}ZZ^T\]

In the specific case of the Smooth Characteristic function test, the vector of differences can be defined as follows:

\[Z_i = (f(X_i)\sin(X_iT_1) - f(Y_i)\sin(Y_iT_1), f(X_i)\cos(X_iT_1) - f(Y_i)\cos(Y_iT_1),\cdots) \in \mathbb{R}^{2J}\]

Once \(S_n\) is calculated, a threshold \(r_{\alpha}\) corresponding to the \(1 - \alpha\) quantile of a Chi-squared distribution w/ J degrees of freedom is chosen. Null is rejected if \(S_n\) is larger than this threshold.

References

1: Kacper P Chwialkowski, Aaditya Ramdas, Dino Sejdinovic, and Arthur Gretton. Fast two-sample testing with analytic representations of probability measures. Advances in Neural Information Processing Systems, 2015.

Methods Summary

`SmoothCFTest.statistic`(x, y, random_state)	Calculates the smooth CF test statistic.
`SmoothCFTest.test`(x, y[, random_state])	Calculates the smooth CF test statistic and p-value.

SmoothCFTest.statistic(x, y, random_state)¶

Calculates the smooth CF test statistic.

Parameters

x,y (ndarray of float) -- Input data matrices. x and y must have the same number of dimensions. That is, the shapes must be (n, p) and (m, p) where n is the number of samples and p and q are the number of dimensions.
random_state (int) -- Set random seed for generation of test points

Returns

stat (float) -- The computed Smooth CF statistic.

SmoothCFTest.test(x, y, random_state=None)¶

Calculates the smooth CF test statistic and p-value.

Parameters

x,y (ndarray of float) -- Input data matrices. x and y must have the same number of dimensions. That is, the shapes must be (n, p) and (m, p) where n is the number of samples and p and q are the number of dimensions.
random_state (int) -- Set random seed for generation of test points

Returns

stat (float) -- The computed Smooth CF statistic.
pvalue (float) -- The computed smooth CF p-value.

Examples

>>> import numpy as np
>>> from hyppo.ksample import SmoothCFTest
>>> np.random.seed(1234)
>>> x = np.random.randn(500, 10)
>>> y = np.random.randn(500, 10)
>>> stat, pvalue = SmoothCFTest().test(x, y, random_state=1234)
>>> '%.2f, %.3f' % (stat, pvalue)
'4.70, 0.910'

Examples using `hyppo.ksample.SmoothCFTest`¶

Hotelling

MeanEmbeddingTest

SmoothCFTest¶

Examples using hyppo.ksample.SmoothCFTest¶

Examples using `hyppo.ksample.SmoothCFTest`¶