def _update_stats_view(self):
if self.statistics_name in GLOBAL_STATS:
stat_func = GLOBAL_STATS[self.statistics_name]
try:
res = stat_func(self.annotations, nclasses=self.nclasses)
except PyannoValueError as e:
logger.info(e)
res = np.nan
self.stats_view = _SingleStatView(value=res,
name=self.statistics_name)
else:
stat_func = PAIRWISE_STATS[self.statistics_name]
try:
res = measures.pairwise_matrix(stat_func,
self.annotations,
nclasses=self.nclasses)
except PyannoValueError as e:
logger.info(e)
nannotators = self.annotations.shape[1]
res = np.empty((nannotators, nannotators))
res.fill(np.nan)
self.stats_view = MatrixPlot(matrix=res,
colormap_low=-1.0,
colormap_high=1.0,
title=self.statistics_name)
def _update_stats_view(self):
if self.statistics_name in GLOBAL_STATS:
stat_func = GLOBAL_STATS[self.statistics_name]
try:
res = stat_func(self.annotations, nclasses=self.nclasses)
except PyannoValueError as e:
logger.info(e)
res = np.nan
self.stats_view = _SingleStatView(value=res,
name=self.statistics_name)
else:
stat_func = PAIRWISE_STATS[self.statistics_name]
try:
res = measures.pairwise_matrix(stat_func, self.annotations,
nclasses=self.nclasses)
except PyannoValueError as e:
logger.info(e)
nannotators = self.annotations.shape[1]
res = np.empty((nannotators, nannotators))
res.fill(np.nan)
self.stats_view = MatrixPlot(matrix=res,
colormap_low=-1.0,
colormap_high=1.0,
title=self.statistics_name)
def plot_pairwise_statistics(stat_func, annotations, nclasses=None, **kwargs):
"""Plot a matrix representation of a statistical function of annotations.
The function `stat_func` is applied to the annotations of all pairs of
annotators, and the resulting matrix is displayed in a window.
Example: :::
>>> plot_pairwise_statistics(pyanno.measures.cohens_kappa, annotations)
See also :func:`pyanno.measures.helpers.pairwise_matrix`,
:func:`~pyanno.plots.matrix_plot.plot_square_matrix`.
Arguments
---------
stat_func : function
Function accepting as first two arguments two 1D array of
annotations, and returning a single scalar measuring some annotations
statistics.
annotations : ndarray, shape = (n_items, n_annotators)
Array of annotations for multiple annotators. 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
kwargs : dictionary
Additional keyword arguments passed to the plot. The argument 'title'
sets the title of the plot.
"""
if nclasses is None:
measures.helpers.compute_nclasses(annotations)
matrix = measures.pairwise_matrix(stat_func, annotations,
nclasses=nclasses)
kwargs_local = {'colormap_low': -1.0,
'colormap_high': 1.0}
# add user kwargs, allowing for overwrite
kwargs_local.update(kwargs)
matrix_view = plot_square_matrix(matrix, **kwargs_local)
return matrix_view
