def test_score_transform(self):
corpus = get_test_corpus()
sc = ScatterChart(term_doc_matrix=corpus, minimum_term_frequency=0)
d1 = sc.to_dict('hamlet')
sc = ScatterChart(term_doc_matrix=corpus, minimum_term_frequency=0, score_transform=lambda x:x)
d2 = sc.to_dict('hamlet')
assert sum([datum['s'] for datum in d1['data']]) != sum([datum['s'] for datum in d2['data']])
def test_score_transform(self):
corpus = get_test_corpus()
sc = ScatterChart(term_doc_matrix=corpus, minimum_term_frequency=0)
d1 = sc.to_dict('hamlet')
sc = ScatterChart(term_doc_matrix=corpus,
minimum_term_frequency=0,
score_transform=lambda x: x)
d2 = sc.to_dict('hamlet')
assert sum([datum['s'] for datum in d1['data']]) != sum(
[datum['s'] for datum in d2['data']])
def to_dict(self,
category,
category_name=None,
not_category_name=None,
scores=None,
metadata=None,
max_docs_per_category=None,
transform=percentile_alphabetical,
alternative_text_field=None,
title_case_names=False):
'''
Parameters
----------
category : str
Category to annotate. Exact value of category.
category_name : str, optional
Name of category which will appear on web site. Default None is same as category.
not_category_name : str, optional
Name of ~category which will appear on web site. Default None is same as "not " + category.
scores : np.array, optional
Scores to use for coloring. Defaults to None, or np.array(self.term_doc_matrix.get_scaled_f_scores(category))
metadata, None or array-like.
List of metadata for each document. Defaults to a list of blank strings.
max_docs_per_category, None or int, optional
Maximum number of documents to store per category. Defaults to 4.
transform : function, optional
Function for ranking terms. Defaults to scattertext.Scalers.percentile_lexicographic.
alternative_text_field : str or None, optional
Field in from dataframe used to make corpus to display in place of parsed text. Only
can be used if corpus is a ParsedCorpus instance.
title_case_names : bool, default False
Should the program title-case the category and not-category names?
Returns
-------
dictionary {info: {category_name: ..., not_category_name},
docs: {'texts': [doc1text, ...],
'labels': [1, 0, ...],
'meta': ['<b>blah</b>', '<b>blah</b>']}
data: {term:, x:frequency [0-1], y:frequency [0-1],
s: score,
bg: background score,
as: association score,
cat25k: freq per 25k in category,
cat: count in category,
ncat: count in non-category,
catdocs: [docnum, ...],
ncatdocs: [docnum, ...]
ncat25k: freq per 25k in non-category}}
'''
j = ScatterChart.to_dict(self,
category,
category_name=category_name,
not_category_name=not_category_name,
scores=scores,
transform=transform,
title_case_names=title_case_names)
docs_getter = self._make_docs_getter(max_docs_per_category, alternative_text_field)
j['docs'] = self._get_docs_structure(docs_getter, metadata)
return j
def test_inject_metadata_term_lists(self):
tdm = build_hamlet_jz_term_doc_mat()
scatter_chart = ScatterChart(term_doc_matrix=tdm,
minimum_term_frequency=0)
with self.assertRaises(TermDocMatrixHasNoMetadataException):
scatter_chart.inject_metadata_term_lists({'blah': ['a', 'adsf', 'asfd']})
scatter_chart = ScatterChart(term_doc_matrix=build_hamlet_jz_corpus_with_meta(),
minimum_term_frequency=0,
use_non_text_features=True)
with self.assertRaises(TypeError):
scatter_chart.inject_metadata_term_lists({'blash': [3, 1]})
with self.assertRaises(TypeError):
scatter_chart.inject_metadata_term_lists({3: ['a', 'b']})
with self.assertRaises(TypeError):
scatter_chart.inject_metadata_term_lists({'a': {'a', 'b'}})
with self.assertRaises(TypeError):
scatter_chart.inject_metadata_term_lists(3)
self.assertEqual(type(scatter_chart.inject_metadata_term_lists({'a': ['a', 'b']})), ScatterChart)
j = scatter_chart.to_dict('hamlet')
self.assertEqual(set(j.keys()), set(['info', 'data', 'metalists']))
self.assertEqual(set(j['info'].keys()),
set(['not_category_name',
'category_name',
'category_terms',
'not_category_terms',
'category_internal_name',
'not_category_internal_names',
'extra_category_internal_names',
'neutral_category_internal_names',
'categories']))
def test_inject_metadata_term_lists(self):
tdm = build_hamlet_jz_term_doc_mat()
scatter_chart = ScatterChart(term_doc_matrix=tdm,
minimum_term_frequency=0)
with self.assertRaises(TermDocMatrixHasNoMetadataException):
scatter_chart.inject_metadata_term_lists({'blah': ['a', 'adsf', 'asfd']})
scatter_chart = ScatterChart(term_doc_matrix=build_hamlet_jz_corpus_with_meta(),
minimum_term_frequency=0,
use_non_text_features=True)
with self.assertRaises(TypeError):
scatter_chart.inject_metadata_term_lists({'blash': [3, 1]})
with self.assertRaises(TypeError):
scatter_chart.inject_metadata_term_lists({3: ['a', 'b']})
with self.assertRaises(TypeError):
scatter_chart.inject_metadata_term_lists({'a': {'a', 'b'}})
with self.assertRaises(TypeError):
scatter_chart.inject_metadata_term_lists(3)
self.assertEqual(type(scatter_chart.inject_metadata_term_lists({'a': ['a', 'b']})), ScatterChart)
j = scatter_chart.to_dict('hamlet')
self.assertEqual(set(j.keys()), set(['info', 'data', 'metalists']))
self.assertEqual(set(j['info'].keys()),
set(['not_category_name',
'category_name',
'category_terms',
'not_category_terms',
'category_internal_name',
'not_category_internal_names',
'extra_category_internal_names',
'neutral_category_internal_names',
'categories']))
def test_inject_term_colors(self):
tdm = build_hamlet_jz_corpus_with_meta()
freq_df = tdm.get_term_freq_df()
scatter_chart = ScatterChart(term_doc_matrix=tdm,
minimum_term_frequency=0)
scatter_chart.inject_term_colors({'t1': '00ffee'})
j = scatter_chart.to_dict('hamlet')
self.assertIn('term_colors', j['info'])
def test_inject_term_colors(self):
tdm = build_hamlet_jz_corpus_with_meta()
freq_df = tdm.get_term_freq_df()
scatter_chart = ScatterChart(term_doc_matrix=tdm,
minimum_term_frequency=0)
scatter_chart.inject_term_colors({'t1': '00ffee'})
j = scatter_chart.to_dict('hamlet')
self.assertIn('term_colors', j['info'])
def test_inject_metadata_descriptions(self):
tdm = build_hamlet_jz_corpus_with_meta()
scatter_chart = ScatterChart(term_doc_matrix=tdm, minimum_term_frequency=0)
with self.assertRaises(AssertionError):
scatter_chart.inject_metadata_descriptions(3323)
if (sys.version_info > (3, 0)):
'''
with self.assertRaisesRegex(Exception, 'The following meta data terms are not present: blah'):
scatter_chart.inject_metadata_descriptions({'blah': 'asjdkflasdjklfsadjk jsdkafsd'})
with self.assertRaisesRegex(Exception, 'The following meta data terms are not present: cat2'):
scatter_chart.inject_metadata_descriptions({'cat1': 'asjdkflasdjklfsadjk jsdkafsd', 'cat2': 'asdf'})
'''
assert scatter_chart == scatter_chart.inject_metadata_descriptions({'cat1': 'asjdkflasdjklfsadjk jsdkafsd'})
j = scatter_chart.to_dict('hamlet')
self.assertEqual(set(j.keys()), set(['info', 'data', 'metadescriptions']))
def test_inject_metadata_descriptions(self):
tdm = build_hamlet_jz_corpus_with_meta()
scatter_chart = ScatterChart(term_doc_matrix=tdm, minimum_term_frequency=0)
with self.assertRaises(AssertionError):
scatter_chart.inject_metadata_descriptions(3323)
if (sys.version_info > (3, 0)):
'''
with self.assertRaisesRegex(Exception, 'The following meta data terms are not present: blah'):
scatter_chart.inject_metadata_descriptions({'blah': 'asjdkflasdjklfsadjk jsdkafsd'})
with self.assertRaisesRegex(Exception, 'The following meta data terms are not present: cat2'):
scatter_chart.inject_metadata_descriptions({'cat1': 'asjdkflasdjklfsadjk jsdkafsd', 'cat2': 'asdf'})
'''
assert scatter_chart == scatter_chart.inject_metadata_descriptions({'cat1': 'asjdkflasdjklfsadjk jsdkafsd'})
j = scatter_chart.to_dict('hamlet')
self.assertEqual(set(j.keys()), set(['info', 'data', 'metadescriptions']))
def test_inject_coordinates_original(self):
tdm = build_hamlet_jz_term_doc_mat()
freq_df = tdm.get_term_freq_df()
scatter_chart = ScatterChart(term_doc_matrix=tdm,
minimum_term_frequency=0)
x = freq_df[freq_df.columns[1]].astype(np.float)
y = freq_df[freq_df.columns[0]].astype(np.float)
scatter_chart.inject_coordinates(x / x.max(), y / y.max(), original_x=x, original_y=y)
j = scatter_chart.to_dict('hamlet')
self.assertEqual(j['data'][0].keys(),
{'x', 'os', 'y', 'ncat25k', 'neut', 'cat25k', 'ox', 'neut25k', 'extra25k', 'extra', 'oy',
'term',
's', 'bg'})
and_term = [t for t in j['data'] if t['term'] == 'and'][0]
self.assertEqual(and_term['ox'], 0)
self.assertEqual(and_term['oy'], 1)
def test_inject_coordinates_original(self):
tdm = build_hamlet_jz_term_doc_mat()
freq_df = tdm.get_term_freq_df()
scatter_chart = ScatterChart(term_doc_matrix=tdm,
minimum_term_frequency=0)
x = freq_df[freq_df.columns[1]].astype(np.float)
y = freq_df[freq_df.columns[0]].astype(np.float)
scatter_chart.inject_coordinates(x / x.max(), y / y.max(), original_x=x, original_y=y)
j = scatter_chart.to_dict('hamlet')
self.assertEqual(j['data'][0].keys(),
{'x', 'os', 'y', 'ncat25k', 'neut', 'cat25k', 'ox', 'neut25k', 'extra25k', 'extra', 'oy',
'term',
's', 'bg'})
and_term = [t for t in j['data'] if t['term'] == 'and'][0]
self.assertEqual(and_term['ox'], 0)
self.assertEqual(and_term['oy'], 1)
def to_dict(self,
category,
category_name=None,
not_category_name=None,
scores=None,
metadata=None,
max_docs_per_category=None,
transform=percentile_alphabetical):
'''
:param category: Category to annotate
:param category_name: Name of category which will appear on web site.
:param not_category_name: Name of non-category axis which will appear on web site.
:param scores: Scores to use. Default to Scaled F-Score.
:param metadata: None or array-like. List of metadata for each document.
:param max_docs_per_category: None or int. Maximum number of documents to store per category.
:param transform: Defaults to percentile_lexicographic
:return: dictionary {info: {category_name: ..., not_category_name},
docs: {'texts': [doc1text, ...],
'labels': [1, 0, ...],
'meta': ['<b>blah</b>', '<b>blah</b>']}
data: {term:, x:frequency [0-1], y:frequency [0-1],
s: score,
bg: background score,
as: association score,
cat25k: freq per 25k in category,
cat: count in category,
ncat: count in non-category,
catdocs: [docnum, ...],
ncatdocs: [docnum, ...]
ncat25k: freq per 25k in non-category}}
'''
j = ScatterChart.to_dict(self,
category,
category_name=category_name,
not_category_name=not_category_name,
scores=scores,
transform=transform)
docs_getter = self._make_docs_getter(max_docs_per_category)
j['docs'] = self._get_docs_structure(docs_getter, metadata)
return j
def to_dict(self,
category,
category_name=None,
not_category_name=None,
scores=None,
metadata=None,
max_docs_per_category=None,
transform=percentile_alphabetical,
alternative_text_field=None,
title_case_names=False,
not_categories=None,
neutral_categories=None,
extra_categories=None,
neutral_category_name=None,
extra_category_name=None,
background_scorer=None,
include_term_category_counts=False):
'''
Parameters
----------
category : str
Category to annotate. Exact value of category.
category_name : str, optional
Name of category which will appear on web site. Default None is same as category.
not_category_name : str, optional
Name of ~category which will appear on web site. Default None is same as "not " + category.
scores : np.array, optional
Scores to use for coloring. Defaults to None, or RankDifference scores
metadata, None or array-like.
List of metadata for each document. Defaults to a list of blank strings.
max_docs_per_category, None or int, optional
Maximum number of documents to store per category. Defaults to 4.
transform : function, optional
Function for ranking terms. Defaults to scattertext.Scalers.percentile_lexicographic.
alternative_text_field : str or None, optional
Field in from dataframe used to make corpus to display in place of parsed text. Only
can be used if corpus is a ParsedCorpus instance.
title_case_names : bool, default False
Should the program title-case the category and not-category names?
not_categories : list, optional
List of categories to use as "not category". Defaults to all others.
neutral_categories : list, optional
List of categories to use as neutral. Defaults [].
extra_categories : list, optional
List of categories to use as extra. Defaults [].
neutral_category_name : str
"Neutral" by default. Only active if show_neutral is True. Name of the neutra l
column.
extra_category_name : str
"Extra" by default. Only active if show_neutral and show_extra are true. Name of the
extra column.
background_scorer : CharacteristicScorer, optional
Used for bg scores
include_term_category_counts : bool, default False
Includes term-category counts in keyed off 'term-category-count'. If use_non_text_features,
use metadata counts instead.
Returns
-------
dictionary {info: {'category_name': full category name, ...},
docs: {'texts': [doc1text, ...],
'labels': [1, 0, ...],
'meta': ['<b>blah</b>', '<b>blah</b>']},
// if include_term_category_counts
termCounts: [term num -> [total occurrences, total documents, variance], ... for the number of categories]
data: {term:term,
x:frequency [0-1],
y:frequency [0-1],
s: score,
bg: background score,
as: association score,
cat25k: freq per 25k in category,
cat: count in category,
ncat: count in non-category,
catdocs: [docnum, ...],
ncatdocs: [docnum, ...]
ncat25k: freq per 25k in non-category}}
'''
json_data = ScatterChart.to_dict(self,
category,
category_name=category_name,
not_category_name=not_category_name,
scores=scores,
transform=transform,
title_case_names=title_case_names,
not_categories=not_categories,
neutral_categories=neutral_categories,
extra_categories=extra_categories,
background_scorer=background_scorer)
docs_getter = self._make_docs_getter(max_docs_per_category,
alternative_text_field)
if neutral_category_name is None:
neutral_category_name = 'Neutral'
if extra_category_name is None:
extra_category_name = 'Extra'
json_data['docs'] = self._get_docs_structure(docs_getter, metadata)
json_data['info']['neutral_category_name'] = neutral_category_name
json_data['info']['extra_category_name'] = extra_category_name
if include_term_category_counts:
terms = np.array(
[term_struct['term'] for term_struct in json_data['data']])
json_data['termCounts'] = self._get_term_doc_counts(terms)
return json_data
def test_resuse_is_disabled(self):
corpus = get_test_corpus()
sc = ScatterChart(term_doc_matrix=corpus, minimum_term_frequency=0)
sc.to_dict('hamlet')
with self.assertRaises(Exception):
sc.to_dict('hamlet')
def to_dict(self,
category,
category_name=None,
not_category_name=None,
scores=None,
metadata=None,
max_docs_per_category=None,
transform=percentile_alphabetical,
alternative_text_field=None,
title_case_names=False,
not_categories=None,
neutral_categories=None,
extra_categories=None,
neutral_category_name=None,
extra_category_name=None,
background_scorer=None,
include_term_category_counts=False):
'''
Parameters
----------
category : str
Category to annotate. Exact value of category.
category_name : str, optional
Name of category which will appear on web site. Default None is same as category.
not_category_name : str, optional
Name of ~category which will appear on web site. Default None is same as "not " + category.
scores : np.array, optional
Scores to use for coloring. Defaults to None, or RankDifference scores
metadata, None or array-like.
List of metadata for each document. Defaults to a list of blank strings.
max_docs_per_category, None or int, optional
Maximum number of documents to store per category. Defaults to 4.
transform : function, optional
Function for ranking terms. Defaults to scattertext.Scalers.percentile_lexicographic.
alternative_text_field : str or None, optional
Field in from dataframe used to make corpus to display in place of parsed text. Only
can be used if corpus is a ParsedCorpus instance.
title_case_names : bool, default False
Should the program title-case the category and not-category names?
not_categories : list, optional
List of categories to use as "not category". Defaults to all others.
neutral_categories : list, optional
List of categories to use as neutral. Defaults [].
extra_categories : list, optional
List of categories to use as extra. Defaults [].
neutral_category_name : str
"Neutral" by default. Only active if show_neutral is True. Name of the neutra l
column.
extra_category_name : str
"Extra" by default. Only active if show_neutral and show_extra are true. Name of the
extra column.
background_scorer : CharacteristicScorer, optional
Used for bg scores
include_term_category_counts : bool, default False
Includes term-category counts in keyed off 'term-category-count'. If use_non_text_features,
use metadata counts instead.
Returns
-------
dictionary {info: {'category_name': full category name, ...},
docs: {'texts': [doc1text, ...],
'labels': [1, 0, ...],
'meta': ['<b>blah</b>', '<b>blah</b>']},
// if include_term_category_counts
termCounts: [term num -> [total occurrences, total documents, variance], ... for the number of categories]
data: {term:term,
x:frequency [0-1],
y:frequency [0-1],
s: score,
bg: background score,
as: association score,
cat25k: freq per 25k in category,
cat: count in category,
ncat: count in non-category,
catdocs: [docnum, ...],
ncatdocs: [docnum, ...]
ncat25k: freq per 25k in non-category}
etc: term specific dictionary (if inject_term_metadata is called and contains terms)}
'''
json_data = ScatterChart.to_dict(self,
category,
category_name=category_name,
not_category_name=not_category_name,
scores=scores,
transform=transform,
title_case_names=title_case_names,
not_categories=not_categories,
neutral_categories=neutral_categories,
extra_categories=extra_categories,
background_scorer=background_scorer)
docs_getter = self._make_docs_getter(max_docs_per_category, alternative_text_field)
if neutral_category_name is None:
neutral_category_name = 'Neutral'
if extra_category_name is None:
extra_category_name = 'Extra'
json_data['docs'] = self._get_docs_structure(docs_getter, metadata)
json_data['info']['neutral_category_name'] = neutral_category_name
json_data['info']['extra_category_name'] = extra_category_name
if include_term_category_counts:
terms = np.array([term_struct['term'] for term_struct in json_data['data']])
json_data['termCounts'] = self._get_term_doc_counts(terms)
return json_data
def test_resuse_is_disabled(self):
corpus = get_test_corpus()
sc = ScatterChart(term_doc_matrix=corpus, minimum_term_frequency=0)
sc.to_dict('hamlet')
with self.assertRaises(Exception):
sc.to_dict('hamlet')
