DVariateTest

class hyppo.d_variate.base.DVariateTest(compute_kernel=None, **kwargs)

A base class for a \(d\)-variate independence test.

Parameters

• compute_kernel (str, callable, or None, default: "gaussian") -- A function that computes the kernel similarity among the samples within each data matrix. Valid strings for compute_kernel are, as defined in sklearn.metrics.pairwise.pairwise_kernels,

  ["additive_chi2", "chi2", "linear", "poly", "polynomial", "rbf", "laplacian", "sigmoid", "cosine"]

  Note "rbf" and "gaussian" are the same metric. Set to None or "precomputed" if args are already similarity matrices. To call a custom function, either create the similarity matrix before-hand or create a function of the form metric(x, **kwargs) where x is the data matrix for which pairwise kernel similarity matrices are calculated and kwargs are extra arguments to send to your custom function.

• **kwargs -- Arbitrary keyword arguments for multi_compute_kern.

Methods Summary

DVariateTest.statistic(*args)	Calculates the \(d\)-variate independence test statistic.
DVariateTest.test(*args[, reps, workers])	Calculates the d_variate independence test statistic and p-value.

---

abstract DVariateTest.statistic(*args)

Calculates the \(d\)-variate independence test statistic.

Parameters

*args (ndarray of float) -- Variable length input data matrices. All inputs must have the same number of samples. That is, the shapes must be (n, p), (n, q), etc., where n is the number of samples and p and q are the number of dimensions.

abstract DVariateTest.test(*args, reps=1000, workers=1)

Calculates the d_variate independence test statistic and p-value.

Parameters

• *args (ndarray of float) -- Variable length input data matrices. All inputs must have the same number of samples. That is, the shapes must be (n, p), (n, q), etc., where n is the number of samples and p and q are the number of dimensions.

• reps (int, default: 1000) -- The number of replications used to estimate the null distribution when using the permutation test used to calculate the p-value.

• workers (int, default: 1) -- The number of cores to parallelize the p-value computation over. Supply -1 to use all cores available to the Process.

Returns

• stat (float) -- The computed \(d\)-variate independence test statistic.

• pvalue (float) -- The computed \(d\)-variate independence p-value.

Examples using hyppo.d_variate.base.DVariateTest

D-Variate Independence Testing

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KSampleTest