pygsti.data.hypothesistest

Defines HypothesisTest object and supporting functions

Module Contents

Classes

HypothesisTest

A set of statistical hypothesis tests on a set of null hypotheses.

class pygsti.data.hypothesistest.HypothesisTest(hypotheses, significance=0.05, weighting='equal', passing_graph='Holms', local_corrections='Holms')

Bases: object

A set of statistical hypothesis tests on a set of null hypotheses.

This object has not been carefully tested.

Parameters
  • hypotheses (list or tuple) –

    Specifies the set of null hypotheses. This should be a list containing elements that are either

    • A “label” for a hypothesis, which is just some hashable object such as a string.

    • A tuple of “nested hypotheses”, which are also just labels for some null hypotheses.

    The elements of this list are then subject to multi-test correction of the “closed test procedure” type, with the exact correction method specified by passing_graph. For each element that is itself a tuple of hypotheses, these hypotheses are then further corrected using the method specified by local_corrections.

  • significance (float in (0,1), optional) – The global significance level. If either there are no “nested hypotheses” or the correction used for the nested hypotheses will locally control the family-wise error rate (FWER) (such as if local_correction`=’Holms’) then when the hypothesis test encoded by this object will control the FWER to `significance.

  • weighting (string or dict.) – Specifies what proportion of significance is initially allocated to each element of hypotheses. If a string, must be ‘equal’. In this case, the local significance allocated to each element of hypotheses is significance/len(hypotheses). If not a string, a dictionary whereby each key is an element of hypotheses and each value is a non-negative integer (which will be normalized to one inside the function).

  • passing_graph (string or numpy.array) –

    Specifies where the local significance from each test in hypotheses that triggers is passed to. If a string, then must be ‘Holms’. In this case a test that triggers passes it’s local significance to all the remaining hypotheses that have not yet triggered, split evenly over these hypotheses. If it is an array then its value for [i,j] is the proportion of the “local significance” that is passed from hypothesis with index i (in the tuple hypotheses) to the hypothesis with index j if the hypothesis with index i is rejected (and if j hasn’t yet been rejected; otherwise that proportion is re-distributed other the other hypothesis that i is to pass it’s significance to). The only restriction on restriction on this array is that a row must sum to <= 1 (and it is sub-optimal for a row to sum to less than 1).

    Note that a nested hypothesis is not allowed to pass significance out of it, so any rows that request doing this will be ignored. This is because a nested hypothesis represents a set of hypotheses that are to be jointly tested using some multi-test correction, and so this can only pass significance out if all of the hypotheses in that nested hypothesis are rejected. As this is unlikely in most use-cases, this has not been allowed for.

  • local_corrections (str, optional) –

    The type of multi-test correction used for testing any nested hypotheses. After all of the “top level” testing as been implemented on all non-nested hypotheses, whatever the “local” significance is for each of the “nested hypotheses” is multi-test corrected using this procedure. Must be one of:

    • ’Holms’. This implements the Holms multi-test compensation technique. This

    controls the FWER for each set of nested hypotheses (and so controls the global FWER, in combination with the “top level” corrections). This requires no assumptions about the null hypotheses.

    • ’Bonferroni’. This implements the well-known Bonferroni multi-test compensation

    technique. This is strictly less powerful test than the Hochberg correction.

    Note that neither ‘Holms’ nor ‘Bonferronni’ gained any advantage from being implemented using “nesting”, as if all the hypotheses were put into the “top level” the same corrections could be achieved.

    • ’Hochberg’. This implements the Hockberg multi-test compensation technique. It is

    not a “closed test procedure”, so it is not something that can be implemented in the top level. To be provably valid, it is necessary for the p-values of the nested hypotheses to be non-negatively dependent. When that is true, this is strictly better than the Holms and Bonferroni corrections whilst still controlling the FWER.

    • ’none’. This implements no multi-test compensation. This option does not control the

    FWER of the nested hypotheses. So it will generally not control the global FWER as specified.

    -‘Benjamini-Hochberg’. This implements the Benjamini-Hockberg multi-test compensation technique. This does not control the FWER of the nested hypotheses, and instead controls the “False Detection Rate” (FDR); see wikipedia. That means that the global significance is maintained in the sense that the probability of one or more tests triggering is at most significance. But, if one or more tests are triggered in a particular nested hypothesis test we are only guaranteed that (in expectation) no more than a fraction of “local signifiance” of tests are false alarms.This method is strictly more powerful than the Hochberg correction, but it controls a different, weaker quantity.

_initialize_to_weighted_holms_test(self)

Initializes the passing graph to the weighted Holms test.

add_pvalues(self, pvalues)

Insert the p-values for the hypotheses.

Parameters

pvalues (dict) – A dictionary specifying the p-value for each hypothesis.

Returns

None

run(self)

Implements the multiple hypothesis testing routine encoded by this object.

This populates the self.hypothesis_rejected dictionary, that shows which hypotheses can be rejected using the procedure specified.

Returns

None

_implement_nested_hypothesis_test(self, hypotheses, significance, correction='Holms')

Todo