GofTest

class hyppo.kgof.base.GofTest(p, alpha)

A base class for a discriminability test.

Parameters

compute_distance (str, callable, or None, default: "euclidean" or "gaussian") -- A function that computes the distance among the samples within each data matrix. Valid strings for compute_distance are, as defined in sklearn.metrics.pairwise_distances,

  • From scikit-learn: ["euclidean", "cityblock", "cosine", "l1", "l2", "manhattan"] See the documentation for scipy.spatial.distance for details on these metrics.

  • From scipy.spatial.distance: ["braycurtis", "canberra", "chebyshev", "correlation", "dice", "hamming", "jaccard", "kulsinski", "mahalanobis", "minkowski", "rogerstanimoto", "russellrao", "seuclidean", "sokalmichener", "sokalsneath", "sqeuclidean", "yule"] See the documentation for scipy.spatial.distance for details on these metrics.

Alternatively, this function 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.

Methods Summary

GofTest.statistic(dat)

Calculates the goodness-of-fit test statistic.

GofTest.test(dat)

Perform the goodness-of-fit test and return values computed in a dictionary.
abstract GofTest.statistic(dat)

Calculates the goodness-of-fit test statistic.

Parameters

dat (an instance of Data (observed data)) -- Input data matrices.

abstract GofTest.test(dat)

Perform the goodness-of-fit test and return values computed in a dictionary.

Parameters

dat (an instance of Data (observed data))

Returns

  • { -- alpha: 0.01, pvalue: 0.0002, test_stat: 2.3, h0_rejected: True, time_secs: ...

  • }

Examples using hyppo.kgof.base.GofTest