KMERF

class hyppo.independence.KMERF(forest='regressor', ntrees=500, **kwargs)

Kernel Mean Embedding Random Forest (KMERF) test statistic and p-value.

The KMERF test statistic is a kernel method for calculating independence by using a random forest induced similarity matrix as an input, and has been shown to have especially high gains in finite sample testing power in high dimensional settings 1.

Parameters

  • forest ("regressor", "classifier", default: "regressor") -- Type of forest used when running the independence test. If the y input in test is categorial, use the "classifier" keyword.

  • ntrees (int, default: 500) -- The number of trees used in the random forest.

  • compute_distance (str, callable, or None, default: "euclidean") -- A function that computes the distance among the samples for y. 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.

    Set to None or "precomputed" if y is already a distance matrices. To call a custom function, either create the distance matrix before-hand or create a function of the form metric(x, **kwargs) where x is the data matrix for which pairwise distances are calculated and **kwargs are extra arguements to send to your custom function.

  • distance_kwargs (dict) -- Arbitrary keyword arguments for compute_distance.

  • **kwargs -- Additional arguments used for the forest (see sklearn.ensemble.RandomForestClassifier or sklearn.ensemble.RandomForestRegressor)

Notes

Note

This algorithm is currently under review at a peer-reviewed conference.

A description of KMERF in greater detail can be found in 1. It is computed using the following steps:

Let \(x\) and \(y\) be \((n, p)\) and \((n, 1)\) samples of random variables \(X\) and \(Y\).

  • Run random forest with \(m\) trees. Independent bootstrap samples of size \(n_{b} \leq n\) are drawn to build a tree each time; each tree structure within the forest is denoted as \(\phi_w \in \mathbf{P}\), \(w \in \{ 1, \ldots, m \}\); \(\phi_w(x_i)\) denotes the partition assigned to \(x_i\).

  • Calculate the proximity kernel:

    \[\mathbf{K}^{\mathbf{x}}_{ij} = \frac{1}{m} \sum_{w = 1}^{m} I(\phi_w(x_i) = \phi_w(x_j))\]

    where \(I(\cdot)\) is the indicator function for how often two observations lie in the same partition.

  • Compute the induced kernel correlation: Let

    \[\begin{split}\mathbf{L}^{\mathbf{x}}_{ij}= \begin{cases} \mathbf{K}^{\mathbf{x}}_{ij} - \frac{1}{n-2} \sum_{t=1}^{n} \mathbf{K}^{\mathbf{x}}_{it} - \frac{1}{n-2} \sum_{s=1}^{n} \mathbf{K}^{\mathbf{x}}_{sj} + \frac{1}{(n-1)(n-2)} \sum_{s,t=1}^{n} \mathbf{K}^{\mathbf{x}}_{st} & \mbox{when} \ i \neq j \\ 0 & \mbox{ otherwise} \end{cases}\end{split}\]

  • Then let \(\mathbf{K}^{\mathbf{y}}\) be the Euclidean distance induced kernel, and similarly compute \(\mathbf{L}^{\mathbf{y}}\) from \(\mathbf{K}^{\mathbf{y}}\). The unbiased kernel correlation equals

    \[\mathrm{KMERF}_n(\mathbf{x}, \mathbf{y}) = \frac{1}{n(n-3)} \mathrm{tr} \left( \mathbf{L}^{\mathbf{x}} \mathbf{L}^{\mathbf{y}} \right)\]

The p-value returned is calculated using a permutation test using hyppo.tools.perm_test.

References

1(1,2)

Cencheng Shen, Sambit Panda, and Joshua T. Vogelstein. Learning Interpretable Characteristic Kernels via Decision Forests. arXiv:1812.00029 [cs, stat], September 2020. arXiv:1812.00029.

Methods Summary

KMERF.statistic(x, y)

Helper function that calculates the KMERF test statistic.

KMERF.test(x, y[, reps, workers, auto, ...])

Calculates the KMERF test statistic and p-value.
KMERF.statistic(x, y)

Helper function that calculates the KMERF test statistic.

Parameters

x,y (ndarray of float) -- Input data matrices. x and y must have the same number of samples. That is, the shapes must be (n, p) and (n, 1) where n is the number of samples and p is the number of dimensions.

Returns

stat (float) -- The computed KMERF statistic.

KMERF.test(x, y, reps=1000, workers=1, auto=True, random_state=None)

Calculates the KMERF test statistic and p-value.

Parameters

  • x,y (ndarray of float) -- Input data matrices. x and y must have the same number of samples. That is, the shapes must be (n, p) and (n, 1) where n is the number of samples and p is 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.

  • auto (bool, default: True) -- Automatically uses fast approximation when n and size of array is greater than 20. If True, and sample size is greater than 20, then hyppo.tools.chi2_approx will be run. Parameters reps and workers are irrelevant in this case. Otherwise, hyppo.tools.perm_test will be run.

Returns

  • stat (float) -- The computed KMERF statistic.

  • pvalue (float) -- The computed KMERF p-value.

  • kmerf_dict (dict) --

    Contains additional useful returns containing the following keys:

    • feat_importancendarray of float

      An array containing the importance of each dimension

Examples

>>> import numpy as np
>>> from hyppo.independence import KMERF
>>> x = np.arange(100)
>>> y = x
>>> '%.1f, %.2f' % KMERF().test(x, y)[:1] 
'1.0, 0.001'

Examples using hyppo.independence.KMERF

Feature Importances

Feature Importances

Independence Testing

Independence Testing

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