def _fleiss_kappa_nannotations(nannotations):
"""Compute Fleiss' kappa gien number of annotations per class format.
This is a testable helper for fleiss_kappa.
"""
nitems = nannotations.shape[0]
# check that all rows are annotated by the same number of annotators
_nanno_sum = nannotations.sum(1)
nannotations_per_item = _nanno_sum[0]
if not np.all(_nanno_sum == nannotations_per_item):
raise PyannoValueError("Number of annotations per item is not constant.")
# empirical frequency of categories
freqs = nannotations.sum(0) / (nitems * nannotations_per_item)
chance_agreement = (freqs ** 2.0).sum()
# annotator agreement for i-th item, relative to possible annotators pairs
agreement_rate = ((nannotations ** 2.0).sum(1) - nannotations_per_item) / (
nannotations_per_item * (nannotations_per_item - 1.0)
)
observed_agreement = agreement_rate.mean()
return chance_adjusted_agreement(observed_agreement, chance_agreement)
def _fleiss_kappa_nannotations(nannotations):
"""Compute Fleiss' kappa gien number of annotations per class format.
This is a testable helper for fleiss_kappa.
"""
nitems = nannotations.shape[0]
# check that all rows are annotated by the same number of annotators
_nanno_sum = nannotations.sum(1)
nannotations_per_item = _nanno_sum[0]
if not np.all(_nanno_sum == nannotations_per_item):
raise PyannoValueError(
'Number of annotations per item is not constant.'
)
# empirical frequency of categories
freqs = nannotations.sum(0) / (nitems*nannotations_per_item)
chance_agreement = (freqs**2.).sum()
# annotator agreement for i-th item, relative to possible annotators pairs
agreement_rate = (((nannotations**2.).sum(1) - nannotations_per_item)
/ (nannotations_per_item*(nannotations_per_item-1.)))
observed_agreement = agreement_rate.mean()
return chance_adjusted_agreement(observed_agreement, chance_agreement)
def cohens_kappa(annotations1, annotations2, nclasses=None):
"""Compute Cohen's kappa for two annotators.
Assumes that the annotators draw annotations at random with different but
constant frequencies.
See also :func:`~pyanno.measures.helpers.pairwise_matrix`.
**References:**
* Cohen, Jacob (1960). A coefficient of agreement for nominal scales.
Educational and Psychological Measurement, 20, 37--46.
* `Wikipedia entry <http://en.wikipedia.org/wiki/Cohen%27s_kappa>`_
Arguments
---------
annotations1 : ndarray, shape = (n_items, )
Array of annotations for a single annotator. Missing values should be
indicated by :attr:`pyanno.util.MISSING_VALUE`
annotations2 : ndarray, shape = (n_items, )
Array of annotations for a single annotator. Missing values should be
indicated by :attr:`pyanno.util.MISSING_VALUE`
nclasses : int
Number of annotation classes. If None, `nclasses` is inferred from the
values in the annotations
Returns
-------
stat : float
The value of the statistics
"""
if all_invalid(annotations1, annotations2):
logger.debug('No valid annotations')
return np.nan
if nclasses is None:
nclasses = compute_nclasses(annotations1, annotations2)
chance_agreement = chance_agreement_different_frequency(annotations1,
annotations2,
nclasses)
observed_agreement = observed_agreement_frequency(annotations1,
annotations2,
nclasses)
return chance_adjusted_agreement(observed_agreement.sum(),
chance_agreement.sum())
def scotts_pi(annotations1, annotations2, nclasses=None):
"""Return Scott's pi statistic for two annotators.
Assumes that the annotators draw random annotations with the same
frequency as the combined observed annotations.
See also :func:`~pyanno.measures.helpers.pairwise_matrix`.
**References:**
* Scott, W. (1955). "Reliability of content analysis: The case of nominal
scale coding." Public Opinion Quarterly, 19(3), 321-325.
* `Wikipedia entry <http://en.wikipedia.org/wiki/Scott%27s_Pi>`_
Arguments
---------
annotations1 : ndarray, shape = (n_items, )
Array of annotations for a single annotator. Missing values should be
indicated by :attr:`pyanno.util.MISSING_VALUE`
annotations2 : ndarray, shape = (n_items, )
Array of annotations for a single annotator. Missing values should be
indicated by :attr:`pyanno.util.MISSING_VALUE`
nclasses : int
Number of annotation classes. If None, `nclasses` is inferred from the
values in the annotations
Returns
-------
stat : float
The value of the statistics
"""
if all_invalid(annotations1, annotations2):
logger.debug('No valid annotations')
return np.nan
if nclasses is None:
nclasses = compute_nclasses(annotations1, annotations2)
chance_agreement = chance_agreement_same_frequency(annotations1,
annotations2,
nclasses)
observed_agreement = observed_agreement_frequency(annotations1,
annotations2,
nclasses)
return chance_adjusted_agreement(observed_agreement.sum(),
chance_agreement.sum())
def cohens_kappa(annotations1, annotations2, nclasses=None):
"""Compute Cohen's kappa for two annotators.
Assumes that the annotators draw annotations at random with different but
constant frequencies.
See also :func:`~pyanno.measures.helpers.pairwise_matrix`.
**References:**
* Cohen, Jacob (1960). A coefficient of agreement for nominal scales.
Educational and Psychological Measurement, 20, 37--46.
* `Wikipedia entry <http://en.wikipedia.org/wiki/Cohen%27s_kappa>`_
Arguments
---------
annotations1 : ndarray, shape = (n_items, )
Array of annotations for a single annotator. Missing values should be
indicated by :attr:`pyanno.util.MISSING_VALUE`
annotations2 : ndarray, shape = (n_items, )
Array of annotations for a single annotator. Missing values should be
indicated by :attr:`pyanno.util.MISSING_VALUE`
nclasses : int
Number of annotation classes. If None, `nclasses` is inferred from the
values in the annotations
Returns
-------
stat : float
The value of the statistics
"""
if all_invalid(annotations1, annotations2):
logger.debug("No valid annotations")
return np.nan
if nclasses is None:
nclasses = compute_nclasses(annotations1, annotations2)
chance_agreement = chance_agreement_different_frequency(annotations1, annotations2, nclasses)
observed_agreement = observed_agreement_frequency(annotations1, annotations2, nclasses)
return chance_adjusted_agreement(observed_agreement.sum(), chance_agreement.sum())
def scotts_pi(annotations1, annotations2, nclasses=None):
"""Return Scott's pi statistic for two annotators.
Assumes that the annotators draw random annotations with the same
frequency as the combined observed annotations.
See also :func:`~pyanno.measures.helpers.pairwise_matrix`.
**References:**
* Scott, W. (1955). "Reliability of content analysis: The case of nominal
scale coding." Public Opinion Quarterly, 19(3), 321-325.
* `Wikipedia entry <http://en.wikipedia.org/wiki/Scott%27s_Pi>`_
Arguments
---------
annotations1 : ndarray, shape = (n_items, )
Array of annotations for a single annotator. Missing values should be
indicated by :attr:`pyanno.util.MISSING_VALUE`
annotations2 : ndarray, shape = (n_items, )
Array of annotations for a single annotator. Missing values should be
indicated by :attr:`pyanno.util.MISSING_VALUE`
nclasses : int
Number of annotation classes. If None, `nclasses` is inferred from the
values in the annotations
Returns
-------
stat : float
The value of the statistics
"""
if all_invalid(annotations1, annotations2):
logger.debug("No valid annotations")
return np.nan
if nclasses is None:
nclasses = compute_nclasses(annotations1, annotations2)
chance_agreement = chance_agreement_same_frequency(annotations1, annotations2, nclasses)
observed_agreement = observed_agreement_frequency(annotations1, annotations2, nclasses)
return chance_adjusted_agreement(observed_agreement.sum(), chance_agreement.sum())