def compute_score(self, score_type='association', dim='A'):
"""Compute a score, such as an index or normalization, of the co-occurrence data.
Parameters
----------
score_type : {'association', 'normalize'}, optional
The type of score to apply to the co-occurrence data.
dim : {'A', 'B'}, optional
Which dimension of counts to use to normalize the co-occurence data by.
Only used if 'score' is 'normalize'.
"""
if score_type == 'association':
if self.square:
self.score = compute_association_index(self.counts,
self.terms['A'].counts,
self.terms['A'].counts)
else:
self.score = compute_association_index(self.counts,
self.terms['A'].counts,
self.terms['B'].counts)
elif score_type == 'normalize':
self.score = compute_normalization(self.counts,
self.terms[dim].counts, dim)
else:
raise ValueError('Score type not understood.')
def compute_score(self, score_type='association', dim='A'):
"""Compute a score, such as an index or normalization, of the co-occurrence data.
Parameters
----------
score_type : {'association', 'normalize'}, optional
The type of score to apply to the co-occurrence data.
dim : {'A', 'B'}, optional
Which dimension of counts to use to normalize the co-occurrence data by.
Only used if 'score' is 'normalize'.
Examples
--------
Compute association scores of co-occurrence data collected for two lists of terms:
>>> counts = Counts()
>>> counts.add_terms(['frontal lobe', 'temporal lobe', 'parietal lobe', 'occipital lobe'])
>>> counts.add_terms(['attention', 'perception'], dim='B')
>>> counts.run_collection() # doctest: +SKIP
>>> counts.compute_score() # doctest: +SKIP
Once you have co-occurence scores calculated, you might want to plot this data.
You can plot the results as a matrix, as a clustermap, and/or as a dendrogram:
>>> from lisc.plts.counts import plot_matrix, plot_clustermap, plot_dendrogram # doctest:+SKIP
>>> plot_matrix(counts.score, counts.terms['B'].labels, counts.terms['A'].labels) # doctest:+SKIP
>>> plot_clustermap(counts.score, counts.terms['B'].labels, counts.terms['A'].labels) # doctest:+SKIP
>>> plot_dendrogram(counts.score, counts.terms['B'].labels) # doctest:+SKIP
"""
if score_type == 'association':
if self.square:
self.score = compute_association_index(self.counts,
self.terms['A'].counts,
self.terms['A'].counts)
else:
self.score = compute_association_index(self.counts,
self.terms['A'].counts,
self.terms['B'].counts)
elif score_type == 'normalize':
self.score = compute_normalization(self.counts,
self.terms[dim].counts, dim)
else:
raise ValueError('Score type not understood.')
# Once co-occurrence data has been collected, we often want to compute a normalization # or transform of the data. # # The :func:`~.compute_normalization`, :func:`~.compute_association_index` # and :func:`~.compute_similarity` functions take in co-occurrence data as returned # by the :func:`~.collect_counts` function. # # More details on the measures available in LISC are available in the :class:`~.Counts` # tutorial. When using the functions approach, all implemented scores and transforms # are available in `lisc.analysis`. # ################################################################################################### # Calculate the normalized data measure, normalizing the co-occurrences by the term counts normed_coocs = compute_normalization(coocs, term_counts[0], dim='A') # Check the computed score measure for the co-occurrence collection print(normed_coocs) ################################################################################################### # Compute the association index score, calculating the Jaccard index from the co-occurrences score = compute_association_index(coocs, term_counts[0], term_counts[1]) # Check the computed score measure for the co-occurrence collection print(score) ################################################################################################### # # From here, further analysis of collected co-occurrence data depends on the goal of the analysis.
def compute_score(self,
score_type='association',
dim='A',
return_result=False):
"""Compute a score, such as an index or normalization, of the co-occurrence data.
Parameters
----------
score_type : {'association', 'normalize', 'similarity'}, optional
The type of score to apply to the co-occurrence data.
dim : {'A', 'B'}, optional
Which dimension of counts to use to normalize by or compute similarity across.
Only used if 'score' is 'normalize' or 'similarity'.
return_result : bool, optional, default: False
Whether to return the computed result.
Examples
--------
Compute association scores of co-occurrence data collected for two lists of terms:
>>> counts = Counts()
>>> counts.add_terms(['frontal lobe', 'temporal lobe', 'parietal lobe', 'occipital lobe'])
>>> counts.add_terms(['attention', 'perception'], dim='B')
>>> counts.run_collection() # doctest: +SKIP
>>> counts.compute_score() # doctest: +SKIP
Once you have co-occurrence scores calculated, you might want to plot this data.
You can plot the results as a matrix:
>>> from lisc.plts.counts import plot_matrix # doctest:+SKIP
>>> plot_matrix(counts) # doctest:+SKIP
And/or as a clustermap:
>>> from lisc.plts.counts import plot_clustermap # doctest:+SKIP
>>> plot_clustermap(counts) # doctest:+SKIP
And/or as a dendrogram:
>>> from lisc.plts.counts import plot_dendrogram # doctest:+SKIP
>>> plot_dendrogram(counts) # doctest:+SKIP
"""
if not self.has_data:
raise ValueError('No data is available - cannot proceed.')
if score_type == 'association':
if self.square:
self.score = compute_association_index(self.counts,
self.terms['A'].counts,
self.terms['A'].counts)
else:
self.score = compute_association_index(self.counts,
self.terms['A'].counts,
self.terms['B'].counts)
elif score_type == 'normalize':
self.score = compute_normalization(self.counts,
self.terms[dim].counts, dim)
elif score_type == 'similarity':
self.score = compute_similarity(self.counts, dim=dim)
else:
raise ValueError('Score type not understood.')
self.score_info['type'] = score_type
if score_type in ['normalize', 'similarity']:
self.score_info['dim'] = dim
if return_result:
return deepcopy(self.score)