Package metrics

Given a list of reference values and a corresponding list of test probability distributions, return the average log likelihood of the reference values, given the probability distributions.

Parameters:

• reference (list) - A list of reference values
• test (list of ProbDistI) - A list of probability distributions over values to compare against the corresponding reference values.

Given a set of reference values and a set of test values, return the fraction of reference values that appear in the test set. In particular, return |reference∩test|/|reference|. If reference is empty, then return None.

Parameters:

• reference (Set) - A set of reference values.
• test (Set) - A set of values to compare against the reference set.

Returns: float or None

Given a set of reference values and a set of test values, return the fraction of test values that appear in the reference set. In particular, return |reference∩test|/|test|. If test is empty, then return None.

Parameters:

• reference (Set) - A set of reference values.
• test (Set) - A set of values to compare against the reference set.

Returns: float or None

Returns an approximate significance level between two lists of independently generated test values.

Approximate randomization calculates significance by randomly drawing from a sample of the possible permutations. At the limit of the number of possible permutations, the significance level is exact. The approximate significance level is the sample mean number of times the statistic of the permutated lists varies from the actual statistic of the unpermuted argument lists.

Parameters:

• a (list) - a list of test values
• b (list) - another list of independently generated test values

Returns: tuple

a tuple containing an approximate significance level, the count of the number of times the pseudo-statistic varied from the actual statistic, and the number of shuffles

Given a set of reference values and a set of test values, return the f-measure of the test values, when compared against the reference values. The f-measure is the harmonic mean of the precision and recall, weighted by alpha. In particular, given the precision p and recall r defined by:

• p = |reference∩test|/|test|
• r = |reference∩test|/|reference|

The f-measure is:

• 1/(alpha/p + (1-alpha)/r)

If either reference or test is empty, then f_measure returns None.

Parameters:

• reference (Set) - A set of reference values.
• test (Set) - A set of values to compare against the reference set.

Returns: float or None

Given a list of reference values and a corresponding list of test values, return the fraction of corresponding values that are equal. In particular, return the fraction of indices 0<i<=len(test) such that test[i] == reference[i].

Parameters:

• reference (list) - An ordered list of reference values.
• test (list) - A list of values to compare against the corresponding reference values.

Raises:

• ValueError - If reference and length do not have the same length.

Compute the windowdiff score for a pair of segmentations. A segmentation is any sequence over a vocabulary of two items (e.g. "0", "1"), where the specified boundary value is used to mark the edge of a segmentation.

>>> s1 = "00000010000000001000000"
>>> s2 = "00000001000000010000000"
>>> s3 = "00010000000000000001000"
>>> windowdiff(s1, s1, 3)
0
>>> windowdiff(s1, s2, 3)
4
>>> windowdiff(s2, s3, 3)
16

Parameters:

• seg1 (string or list) - a segmentation
• seg2 (string or list) - a segmentation
• k (int) - window width
• boundary (string or int or bool) - boundary value

Returns: int

Calculate the Levenshtein edit-distance between two strings. The edit distance is the number of characters that need to be substituted, inserted, or deleted, to transform s1 into s2. For example, transforming "rain" to "shine" requires three steps, consisting of two substitutions and one insertion: "rain" -> "sain" -> "shin" -> "shine". These operations could have been done in other orders, but at least three steps are needed.

Parameters:

• s1 (string), s2 (string @rtype int) - The strings to be analysed

Krippendorff'1 interval distance metric

>>> interval_distance(1,10)
81

Krippendorff 1980, Content Analysis: An Introduction to its Methodology

Simple equality test.

0.0 if the labels are identical, 1.0 if they are different.

>>> binary_distance(1,1)
0.0

>>> binary_distance(1,3)
1.0

Distance metric that takes into account partial agreement when multiple labels are assigned.

>>> masi_distance(set([1,2]),set([1,2,3,4]))
0.5

Passonneau 2005, Measuring Agreement on Set-Valued Items (MASI) for Semantic and Pragmatic Annotation.

Returns the Spearman correlation coefficient for two rankings, which should be dicts or sequences of (key, rank). The coefficient ranges from -1.0 (ranks are opposite) to 1.0 (ranks are identical), and is only calculated for keys in both rankings (for meaningful results, remove keys present in only one list before ranking).

Given a sequence of (key, score) tuples, yields each key with an increasing rank, tying with previous key's rank if the difference between their scores is less than rank_gap. Suitable for use as an argument to spearman_correlation.