Hypothesis testing

For examples and interpretation, see my notebook on hypothesis test explanations.

gshap.hypothesis.HypothesisTest

class gshap.hypothesis.HypothesisTest(test, bootstrap_samples=1000) [source]

This class measures how likely a hypothesis is to be true of an output vector. It uses a bootstrap analysis to compute the probability that a hypothesis is true of a population from a sample output vector.

Parameters:	test : callable Takes an output vector and returns a boolean indicator that the hypothesis is true of the output vector. This will usually involve computing a sample statistic of the output vector, then returning an indicator that the sample statistic fell within a certain range. bootstrap_samples : int, default=1000 Number of bootstrap samples for hypothesis testing.
Attributes:	test : callable Set from the test parameter. bootstrap_samples : int Set from the bootstrap_samples parameter.

Examples

import gshap
from gshap.hypothesis import HypothesisTest

from sklearn.datasets import load_diabetes
from sklearn.linear_model import Lasso

X, y = load_diabetes(return_X_y=True)
reg = Lasso(alpha=.1).fit(X, y)

test = lambda y_pred: y_pred.mean() > 155
g = HypothesisTest(test, bootstrap_samples=100)
explainer = gshap.KernelExplainer(reg.predict, X, g)
# artifically select a sample which with higher-than-average y
explainer.gshap_values(X[y > 70], nsamples=100)

Out:

array([-0.0069,  0.0253,  0.2572,  0.1112, -0.0108, -0.0105,  0.0317,
    0.0009,  0.1415,  0.0071])

Methods

__call__(self, output) [source]

Computes the probablity of the hypothesis being true of the population from which the sample was drawn.

Parameters:	output : numpy.array (# observations, # targets) vector of model outputs.
Returns:	probability : scalar between 0 and 1 Probability that the hypothesis is true of the population from which the sample was drawn.