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_max_terms(self):
tdm = build_hamlet_jz_term_doc_mat()
j = (ScatterChart(term_doc_matrix=tdm,
minimum_term_frequency=0,
max_terms=2)
.to_dict('hamlet'))
self.assertEqual(2, len(j['data']))
j = (ScatterChart(term_doc_matrix=tdm,
minimum_term_frequency=0,
max_terms=10)
.to_dict('hamlet'))
self.assertEqual(10, len(j['data']))
j = (ScatterChart(term_doc_matrix=tdm,
minimum_term_frequency=0,
pmi_threshold_coefficient=0,
max_terms=10000)
.to_dict('hamlet'))
self.assertEqual(len(tdm.get_term_freq_df()), len(j['data']))
j = (ScatterChart(term_doc_matrix=tdm,
minimum_term_frequency=0,
pmi_threshold_coefficient=0,
max_terms=None)
.to_dict('hamlet'))
self.assertEqual(len(tdm.get_term_freq_df()), len(j['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 test_multi_categories(self):
corpus = get_test_corpus()
j_vs_all = ScatterChart(term_doc_matrix=corpus, minimum_term_frequency=0) \
.to_dict('hamlet')
j_vs_swift = ScatterChart(term_doc_matrix=corpus, minimum_term_frequency=0) \
.to_dict('hamlet', not_categories=['swift'])
self.assertNotEqual(set(j_vs_all['info']['not_category_internal_names']),
set(j_vs_swift['info']['not_category_internal_names']))
self.assertEqual(j_vs_all['info']['categories'], corpus.get_categories())
self.assertEqual(j_vs_swift['info']['categories'], corpus.get_categories())
def test_title_case_names(self):
tdm = build_hamlet_jz_term_doc_mat()
j = (ScatterChart(term_doc_matrix=tdm,
minimum_term_frequency=0)
.to_dict('hamlet', 'HAMLET', 'NOT HAMLET'))
self.assertEqual(j['info']['category_name'], 'HAMLET')
self.assertEqual(j['info']['not_category_name'], 'NOT HAMLET')
tdm = build_hamlet_jz_term_doc_mat()
j = (ScatterChart(term_doc_matrix=tdm,
minimum_term_frequency=0)
.to_dict('hamlet', 'HAMLET', 'NOT HAMLET', title_case_names=True))
self.assertEqual(j['info']['category_name'], 'Hamlet')
self.assertEqual(j['info']['not_category_name'], 'Not Hamlet')
def produce_scattertext_html(
term_doc_matrix,
category,
category_name,
not_category_name,
protocol='https',
minimum_term_frequency=DEFAULT_MINIMUM_TERM_FREQUENCY,
pmi_threshold_coefficient=DEFAULT_PMI_THRESHOLD_COEFFICIENT,
max_terms=None,
filter_unigrams=False,
height_in_pixels=None,
width_in_pixels=None,
term_ranker=termranking.AbsoluteFrequencyRanker):
'''Returns html code of visualization.
Parameters
----------
term_doc_matrix : TermDocMatrix
Corpus to use
category : str
name of category column
category_name: str
name of category to mine for
not_category_name: str
name of everything that isn't in category
protocol : str
optional, used prototcol of , http or https
minimum_term_frequency : int, optional
Minimum number of times word needs to appear to make it into visualization.
pmi_threshold_coefficient : int, optional
Filter out bigrams with a PMI of < 2 * pmi_threshold_coefficient. Default is 6.
max_terms : int, optional
Maximum number of terms to include in visualization.
filter_unigrams : bool
default False, do we filter unigrams that only occur in one bigram
width_in_pixels: int
width of viz in pixels, if None, default to JS's choice
height_in_pixels: int
height of viz in pixels, if None, default to JS's choice
term_ranker : TermRanker
TermRanker class for determining term frequency ranks.
Returns
-------
str, html of visualization
'''
scatter_chart_data = ScatterChart(term_doc_matrix=term_doc_matrix,
minimum_term_frequency=minimum_term_frequency,
pmi_threshold_coefficient=pmi_threshold_coefficient,
filter_unigrams=filter_unigrams,
max_terms=max_terms,
term_ranker=term_ranker) \
.to_dict(category=category,
category_name=category_name,
not_category_name=not_category_name,
transform=percentile_alphabetical)
html = HTMLVisualizationAssembly(
VizDataAdapter(scatter_chart_data), width_in_pixels,
height_in_pixels).to_html(protocol=protocol)
return html
def test_to_dict_without_categories(self):
tdm = get_term_doc_matrix_without_categories()
scatter_chart = ScatterChart(term_doc_matrix=tdm,
minimum_term_frequency=0)
with self.assertRaises(NeedToInjectCoordinatesException):
scatter_chart.to_dict_without_categories()
x_coords = tdm.get_term_doc_mat().sum(axis=0).A1
y_coords = tdm.get_term_doc_mat().astype(bool).astype(int).sum(
axis=0).A1
scatter_chart.inject_coordinates(original_x=x_coords,
original_y=y_coords,
x_coords=scale(x_coords),
y_coords=scale(y_coords))
j = scatter_chart.to_dict_without_categories()
self.assertIsInstance(j, dict)
self.assertEqual(set(j.keys()), set(['data']))
self.assertEqual(len(j['data']), tdm.get_num_terms())
self.assertEqual(
j['data'][-1], {
'cat': 4,
'cat25k': 735,
'ox': 4,
'oy': 3,
'term': 'speak',
'x': 1.0,
'y': 1.0
})
def test_to_json(self):
tdm = build_hamlet_jz_term_doc_mat()
# with self.assertRaises(NoWordMeetsTermFrequencyRequirementsError):
# ScatterChart(term_doc_matrix=tdm).to_dict('hamlet')
j = (ScatterChart(term_doc_matrix=tdm,
minimum_term_frequency=0).to_dict('hamlet'))
self.assertEqual(set(j.keys()), set(['info', 'data']))
self.assertEqual(
set(j['info'].keys()),
set([
'not_category_name', 'category_name', 'category_terms',
'not_category_terms', 'category_internal_name',
'not_category_internal_names', 'categories'
]))
expected = {
"x": 0.0,
"y": 0.42,
'ox': 0,
'oy': 0.42,
"term": "art",
"cat25k": 758,
"ncat25k": 0,
"neut25k": 0,
'neut': 0,
's': 0.5,
'os': 3,
'bg': 3
}
datum = self._get_data_example(j)
for var in ['cat25k', 'ncat25k']:
np.testing.assert_almost_equal(expected[var],
datum[var],
decimal=1)
self.assertEqual(set(expected.keys()), set(datum.keys()))
self.assertEqual(expected['term'], datum['term'])
def test_inject_coordinates(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)
with self.assertRaises(CoordinatesNotRightException):
scatter_chart.inject_coordinates([], [])
with self.assertRaises(CoordinatesNotRightException):
scatter_chart.inject_coordinates(freq_df[freq_df.columns[0]], [])
with self.assertRaises(CoordinatesNotRightException):
scatter_chart.inject_coordinates([], freq_df[freq_df.columns[0]])
x = freq_df[freq_df.columns[1]].astype(np.float)
y = freq_df[freq_df.columns[0]].astype(np.float)
with self.assertRaises(CoordinatesNotRightException):
scatter_chart.inject_coordinates(x, y)
with self.assertRaises(CoordinatesNotRightException):
scatter_chart.inject_coordinates(x, y / y.max())
with self.assertRaises(CoordinatesNotRightException):
scatter_chart.inject_coordinates(x / x.max(), y)
with self.assertRaises(CoordinatesNotRightException):
scatter_chart.inject_coordinates(-x / x.max(), -y / y.max())
with self.assertRaises(CoordinatesNotRightException):
scatter_chart.inject_coordinates(-x / x.max(), y / y.max())
with self.assertRaises(CoordinatesNotRightException):
scatter_chart.inject_coordinates(x / x.max(), -y / y.max())
scatter_chart.inject_coordinates(x / x.max(), y / y.max())
def test_terms_to_include(self):
tdm = build_hamlet_jz_term_doc_mat()
terms_to_include = list(sorted(['both worlds', 'thou', 'the', 'of', 'st', 'returned', 'best', ]))
j = (ScatterChart(term_doc_matrix=tdm,
minimum_term_frequency=0,
terms_to_include=terms_to_include)
.to_dict('hamlet', 'HAMLET', 'NOT HAMLET'))
self.assertEqual(list(sorted(t['term'] for t in j['data'])), terms_to_include)
def test_p_vals(self):
tdm = build_hamlet_jz_term_doc_mat()
j = (ScatterChart(term_doc_matrix=tdm,
minimum_term_frequency=0,
term_significance=LogOddsRatioUninformativeDirichletPrior())
.to_dict('hamlet'))
datum = self._get_data_example(j)
self.assertIn('p', datum.keys())
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 produce_scattertext_html(term_doc_matrix,
category,
category_name,
not_category_name,
protocol='https',
pmi_filter_thresold=2,
minimum_term_frequency=3,
max_terms=None,
filter_unigrams=False,
height_in_pixels=None,
width_in_pixels=None,
term_ranker=termranking.AbsoluteFrequencyRanker):
'''Returns html code of visualization.
Parameters
----------
term_doc_matrix : TermDocMatrix
Corpus to use
category : str
name of category column
category_name: str
name of category to mine for
not_category_name: str
name of everything that isn't in category
protocol : str
optional, used prototcol of , http or https
filter_unigrams : bool
default False, do we filter unigrams that only occur in one bigram
width_in_pixels: int
width of viz in pixels, if None, default to JS's choice
height_in_pixels: int
height of viz in pixels, if None, default to JS's choice
term_ranker : TermRanker
TermRanker class for determining term frequency ranks.
Returns
-------
str, html of visualization
'''
scatter_chart_data = ScatterChart(term_doc_matrix=term_doc_matrix,
minimum_term_frequency=minimum_term_frequency,
pmi_threshold_coefficient=pmi_filter_thresold,
filter_unigrams=filter_unigrams,
max_terms=max_terms,
term_ranker=term_ranker) \
.to_dict(category=category,
category_name=category_name,
not_category_name=not_category_name,
transform=percentile_alphabetical)
html = HTMLVisualizationAssembly(
VizDataAdapter(scatter_chart_data), width_in_pixels,
height_in_pixels).to_html(protocol=protocol)
return html
def test_max_terms(self):
tdm = build_hamlet_jz_term_doc_mat()
# with self.assertRaises(NoWordMeetsTermFrequencyRequirementsError):
# ScatterChart(term_doc_matrix=tdm).to_dict('hamlet')
j = (ScatterChart(term_doc_matrix=tdm,
minimum_term_frequency=0,
max_terms=2).to_dict('hamlet'))
self.assertEqual(2, len(j['data']))
j = (ScatterChart(term_doc_matrix=tdm,
minimum_term_frequency=0,
max_terms=10).to_dict('hamlet'))
self.assertEqual(10, len(j['data']))
j = (ScatterChart(term_doc_matrix=tdm,
minimum_term_frequency=0,
max_terms=10000).to_dict('hamlet'))
self.assertEqual(51, len(j['data']))
j = (ScatterChart(term_doc_matrix=tdm,
minimum_term_frequency=0,
max_terms=None).to_dict('hamlet'))
self.assertEqual(51, len(j['data']))
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_to_json_use_non_text_features(self):
tdm = build_hamlet_jz_corpus_with_meta()
# with self.assertRaises(NoWordMeetsTermFrequencyRequirementsError):
# ScatterChart(term_doc_matrix=tdm).to_dict('hamlet')
j = (ScatterChart(term_doc_matrix=tdm,
minimum_term_frequency=0,
use_non_text_features=True).to_dict('hamlet'))
self.assertEqual(set(j.keys()), set(['info', 'data']))
self.assertEqual(
set(j['info'].keys()),
set([
'not_category_name', 'category_name', 'category_terms',
'not_category_terms', 'category_internal_name'
]))
self.assertEqual(
{t['term']
for t in j['data']},
{'cat6', 'cat4', 'cat9', 'cat5', 'cat1', 'cat3', 'cat2'})
json.dumps(j)
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')