def produce_category_focused_pairplot(
corpus,
category,
category_projector=CategoryProjector(projector=TruncatedSVD(20)),
category_projection=None,
**kwargs):
'''
Produces a pair-plot which is focused on a single category.
:param corpus: TermDocMatrix
:param category: str, name of a category in the corpus
:param category_projector: CategoryProjector, a factor analysis of the category/feature vector
:param category_projection: CategoryProjection, None by default. If present, overrides category projector
:param kwargs: remaining kwargs for produce_pairplot
:return: str, HTML
'''
category_num = corpus.get_categories().index(category)
uncorrelated_components_projection = category_projection
if category_projection is None:
if 'use_metadata' in kwargs and kwargs['use_metadata']:
uncorrelated_components_projection = category_projector.project_with_metadata(
corpus)
else:
uncorrelated_components_projection = category_projector.project(
corpus)
distances = cosine_distances(
uncorrelated_components_projection.get_category_embeddings().T)
similarity_to_category_scores = -2 * (rankdata(distances[category_num]) -
0.5)
uncorrelated_components = uncorrelated_components_projection.get_projection(
)
least_correlated_dimension = min([(np.abs(
pearsonr(similarity_to_category_scores, uncorrelated_components.T[i])
[0]), i)] for i in range(uncorrelated_components.shape[1]))[0][1]
projection_to_plot = np.array([
uncorrelated_components.T[least_correlated_dimension],
similarity_to_category_scores
]).T
return produce_pairplot(
corpus,
initial_category=category,
category_projection=uncorrelated_components_projection.
use_alternate_projection(projection_to_plot),
category_focused=True,
**kwargs)
def get_optimal_category_projection(
corpus,
n_dims=3,
n_steps=10,
projector=lambda n_terms, n_dims: CategoryProjector(
AssociationCompactor(n_terms, scorer=RankDifference),
projector=PCA(n_dims)),
verbose=False):
try:
from astropy.stats import RipleysKEstimator
except:
raise Exception("Please install astropy")
ripley = RipleysKEstimator(area=1., x_max=1., y_max=1., x_min=0., y_min=0.)
min_dev = None
best_k = None
best_x = None
best_y = None
best_projector = None
for k in np.power(
2,
np.linspace(
np.log(corpus.get_num_categories()) / np.log(2),
np.log(corpus.get_num_terms()) / np.log(2),
n_steps)).astype(int):
r = np.linspace(0, np.sqrt(2), 100)
category_projector = projector(k, n_dims)
category_projection = category_projector.project(corpus)
for dim_1 in range(0, n_dims):
for dim_2 in range(dim_1 + 1, n_dims):
proj = category_projection.projection[:, [dim_1, dim_2]]
scaled_proj = np.array(
[stretch_0_to_1(proj.T[0]),
stretch_0_to_1(proj.T[1])]).T
dev = np.sum(
np.abs(
ripley(scaled_proj, r, mode='ripley') -
ripley.poisson(r)))
if min_dev is None or dev < min_dev:
min_dev = dev
best_k = k
best_projector = category_projector
best_x, best_y = (dim_1, dim_2)
if verbose:
print(k, dim_1, dim_2, dev, best_k, best_x, best_y,
min_dev)
if verbose:
print(best_k, best_x, best_y)
return best_projector.project(corpus, best_x, best_y)
def get_optimal_category_projection_by_rank(
corpus,
n_dims=2,
n_steps=20,
projector=lambda rank, n_dims: CategoryProjector(
AssociationCompactorByRank(rank), projector=PCA(n_dims)),
verbose=False):
try:
from astropy.stats import RipleysKEstimator
except:
raise Exception("Please install astropy")
ripley = RipleysKEstimator(area=1., x_max=1., y_max=1., x_min=0., y_min=0.)
min_dev = None
best_rank = None
best_x = None
best_y = None
best_projector = None
for rank in np.linspace(1,
TermCategoryRanker().get_max_rank(corpus),
n_steps):
r = np.linspace(0, np.sqrt(2), 100)
category_projector = projector(rank, n_dims)
category_projection = category_projector.project(corpus)
for dim_1 in range(0, n_dims):
for dim_2 in range(dim_1 + 1, n_dims):
proj = category_projection.projection[:, [dim_1, dim_2]]
scaled_proj = np.array(
[stretch_0_to_1(proj.T[0]),
stretch_0_to_1(proj.T[1])]).T
dev = np.sum(
np.abs(
ripley(scaled_proj, r, mode='ripley') -
ripley.poisson(r)))
if min_dev is None or dev < min_dev:
min_dev = dev
best_rank = rank
best_projector = category_projector
best_x, best_y = (dim_1, dim_2)
if verbose:
print('rank', rank, 'dims', dim_1, dim_2, 'K', dev)
print(' best rank', best_rank, 'dims', best_x, best_y,
'K', min_dev)
if verbose:
print(best_rank, best_x, best_y)
return best_projector.project(corpus, best_x, best_y)
def get_optimal_category_projection(
corpus,
n_dims=3,
n_steps=10,
projector=lambda n_terms, n_dims: CategoryProjector(
selector=AssociationCompactor(n_terms, scorer=RankDifference),
projector=PCA(n_dims)),
optimizer=ripley_poisson_difference,
verbose=False):
min_dev = None
best_k = None
best_x = None
best_y = None
best_projector = None
for k in np.power(
2,
np.linspace(
np.log(corpus.get_num_categories()) / np.log(2),
np.log(corpus.get_num_terms()) / np.log(2),
n_steps)).astype(int):
category_projector = projector(k, n_dims)
category_projection = category_projector.project(corpus)
for dim_1 in range(0, n_dims):
for dim_2 in range(dim_1 + 1, n_dims):
proj = category_projection.projection[:, [dim_1, dim_2]]
scaled_proj = np.array(
[stretch_0_to_1(proj.T[0]),
stretch_0_to_1(proj.T[1])]).T
dev = optimizer(scaled_proj)
#dev = np.sum(np.abs(ripley(scaled_proj, r, mode='ripley') - ripley.poisson(r)))
if min_dev is None or dev < min_dev:
min_dev = dev
best_k = k
best_projector = category_projector
best_x, best_y = (dim_1, dim_2)
if verbose:
print(k, dim_1, dim_2, dev, best_k, best_x, best_y,
min_dev)
if verbose:
print(best_k, best_x, best_y)
return best_projector.project(corpus, best_x, best_y)
def produce_pairplot(corpus,
asian_mode=False,
category_width_in_pixels=500,
category_height_in_pixels=700,
term_width_in_pixels=500,
term_height_in_pixels=700,
terms_to_show=3000,
scaler=scale_neg_1_to_1_with_zero_mean,
term_ranker=AbsoluteFrequencyRanker,
use_metadata=False,
category_projector=CategoryProjector(),
category_projection=None,
topic_model_term_lists=None,
topic_model_preview_size=10,
metadata_descriptions=None,
initial_category=None,
x_dim=0,
y_dim=1,
show_halo=True,
num_terms_in_halo=5,
category_color_func='(function(x) {return "#5555FF"})',
protocol='https',
d3_url_struct=D3URLs(),
**kwargs):
if category_projection is None:
if use_metadata:
category_projection = category_projector.project_with_metadata(
corpus, x_dim=x_dim, y_dim=y_dim)
term_projection = category_projector
else:
category_projection = category_projector.project(corpus,
x_dim=x_dim,
y_dim=y_dim)
if initial_category is None:
initial_category = corpus.get_categories()[0]
category_scatter_chart_explorer = ScatterChartExplorer(
category_projection.category_corpus,
minimum_term_frequency=0,
minimum_not_category_term_frequency=0,
pmi_threshold_coefficient=0,
filter_unigrams=False,
jitter=0,
max_terms=None,
term_ranker=term_ranker,
use_non_text_features=True,
term_significance=None,
terms_to_include=None)
proj_df = category_projection.get_pandas_projection()
category_scatter_chart_explorer.inject_coordinates(
x_coords=scaler(proj_df['x']),
y_coords=scaler(proj_df['y']),
original_x=proj_df['x'],
original_y=proj_df['y'])
category_scatter_chart_data = category_scatter_chart_explorer.to_dict(
category=initial_category,
max_docs_per_category=0,
)
category_tooltip_func = '(function(d) {return d.term})'
category_scatterplot_structure = ScatterplotStructure(
VizDataAdapter(category_scatter_chart_data),
width_in_pixels=category_width_in_pixels,
height_in_pixels=category_height_in_pixels,
asian_mode=asian_mode,
use_non_text_features=True,
show_top_terms=False,
show_characteristic=False,
get_tooltip_content=category_tooltip_func,
color_func=category_color_func,
show_axes=False,
unified_context=True,
show_category_headings=False,
show_cross_axes=True,
horizontal_line_y_position=0,
vertical_line_x_position=0,
y_label='',
x_label='',
full_data='getCategoryDataAndInfo()',
alternative_term_func=
'(function (termInfo) {termPlotInterface.drawCategoryAssociation(termInfo.i); return false;})',
div_name='cat-plot')
compacted_corpus = AssociationCompactor(terms_to_show).compact(corpus)
terms_to_hide = set(corpus.get_terms()) - set(compacted_corpus.get_terms())
print('num terms to hide', len(terms_to_hide))
print('num terms to show', compacted_corpus.get_num_terms())
term_scatter_chart_explorer = ScatterChartExplorer(
corpus,
minimum_term_frequency=0,
minimum_not_category_term_frequency=0,
pmi_threshold_coefficient=0,
term_ranker=term_ranker,
use_non_text_features=use_metadata,
score_transform=stretch_0_to_1,
).hide_terms(terms_to_hide)
if topic_model_term_lists is not None:
term_scatter_chart_explorer.inject_metadata_term_lists(
topic_model_term_lists)
if metadata_descriptions is not None:
term_scatter_chart_explorer.inject_metadata_descriptions(
metadata_descriptions)
if use_metadata:
tdf = corpus.get_metadata_freq_df('')
else:
tdf = corpus.get_term_freq_df('')
scores = RankDifference().get_scores(
tdf[initial_category],
tdf[[c for c in corpus.get_categories()
if c != initial_category]].sum(axis=1))
term_scatter_chart_data = term_scatter_chart_explorer.to_dict(
category=initial_category,
scores=scores,
include_term_category_counts=True,
transform=dense_rank,
**kwargs)
term_scatterplot_structure = ScatterplotStructure(
VizDataAdapter(term_scatter_chart_data),
width_in_pixels=term_width_in_pixels,
height_in_pixels=term_height_in_pixels,
asian_mode=asian_mode,
use_non_text_features=use_metadata,
show_top_terms=True,
show_characteristic=False,
get_tooltip_content=None,
show_category_headings=False,
use_full_doc=use_metadata,
horizontal_line_y_position=0,
vertical_line_x_position=0,
topic_model_preview_size=topic_model_preview_size,
y_label=initial_category,
x_label='Not ' + initial_category,
full_data='getTermDataAndInfo()',
div_name='d3-div-1',
)
return PairPlotFromScatterplotStructure(category_scatterplot_structure,
term_scatterplot_structure,
category_projection,
category_width_in_pixels,
category_height_in_pixels,
num_terms=num_terms_in_halo,
show_halo=show_halo,
d3_url_struct=d3_url_struct,
x_dim=x_dim,
y_dim=y_dim,
protocol=protocol).to_html()
def produce_pairplot(
corpus,
asian_mode=False,
category_width_in_pixels=500,
category_height_in_pixels=700,
term_width_in_pixels=500,
term_height_in_pixels=700,
terms_to_show=3000,
scaler=scale_neg_1_to_1_with_zero_mean,
term_ranker=AbsoluteFrequencyRanker,
use_metadata=False,
category_projector=CategoryProjector(),
category_projection=None,
topic_model_term_lists=None,
topic_model_preview_size=10,
metadata_descriptions=None,
initial_category=None,
x_dim=0,
y_dim=1,
show_halo=True,
num_terms_in_halo=5,
category_color_func='(function(x) {return "#5555FF"})',
protocol='https',
d3_url_struct=D3URLs(),
category_focused=False,
verbose=False,
use_full_doc=True,
default_to_term_comparison=True,
category_x_label='',
category_y_label='',
category_show_axes_and_cross_hairs=False,
highlight_selected_category=True,
term_x_label=None, # used if default_to_term_comparison
term_y_label=None, # used if default_to_term_comparison
wordfish_style=False,
**kwargs):
if category_projection is None:
if use_metadata:
category_projection = category_projector.project_with_metadata(
corpus, x_dim=x_dim, y_dim=y_dim)
else:
category_projection = category_projector.project(corpus,
x_dim=x_dim,
y_dim=y_dim)
if initial_category is None:
initial_category = corpus.get_categories()[0]
category_scatter_chart_explorer = _get_category_scatter_chart_explorer(
category_projection, scaler, term_ranker, verbose)
category_scatter_chart_data = category_scatter_chart_explorer.to_dict(
category=initial_category,
max_docs_per_category=0,
)
category_tooltip_func = '(function(d) {return d.term})'
initial_category_idx = corpus.get_categories().index(initial_category)
term_plot_change_func = _get_term_plot_change_js_func(
wordfish_style, category_focused, initial_category_idx)
category_scatterplot_structure = ScatterplotStructure(
VizDataAdapter(category_scatter_chart_data),
width_in_pixels=category_width_in_pixels,
height_in_pixels=category_height_in_pixels,
asian_mode=asian_mode,
use_non_text_features=True,
show_characteristic=False,
x_label=category_x_label,
y_label=category_y_label,
show_axes_and_cross_hairs=category_show_axes_and_cross_hairs,
full_data='getCategoryDataAndInfo()',
show_top_terms=False,
get_tooltip_content=category_tooltip_func,
color_func=category_color_func,
show_axes=False,
horizontal_line_y_position=0,
vertical_line_x_position=0,
unified_context=True,
show_category_headings=False,
show_cross_axes=True,
div_name='cat-plot',
alternative_term_func=term_plot_change_func,
highlight_selected_category=highlight_selected_category)
compacted_corpus = AssociationCompactor(
terms_to_show, use_non_text_features=use_metadata).compact(corpus)
terms_to_hide = set(corpus.get_terms()) - set(compacted_corpus.get_terms())
if verbose:
print('num terms to hide', len(terms_to_hide))
print('num terms to show', compacted_corpus.get_num_terms())
term_scatter_chart_explorer = ScatterChartExplorer(
category_projection.get_corpus(),
minimum_term_frequency=0,
minimum_not_category_term_frequency=0,
pmi_threshold_coefficient=0,
term_ranker=term_ranker,
use_non_text_features=use_metadata,
score_transform=stretch_0_to_1,
verbose=verbose).hide_terms(terms_to_hide)
if default_to_term_comparison:
if topic_model_term_lists is not None:
term_scatter_chart_explorer.inject_metadata_term_lists(
topic_model_term_lists)
if metadata_descriptions is not None:
term_scatter_chart_explorer.inject_metadata_descriptions(
metadata_descriptions)
if use_metadata:
tdf = corpus.get_metadata_freq_df('')
else:
tdf = corpus.get_term_freq_df('')
scores = RankDifference().get_scores(
tdf[initial_category],
tdf[[c for c in corpus.get_categories()
if c != initial_category]].sum(axis=1))
term_scatter_chart_data = term_scatter_chart_explorer.to_dict(
category=initial_category,
scores=scores,
include_term_category_counts=True,
transform=dense_rank,
**kwargs)
y_label = initial_category,
x_label = 'Not ' + initial_category,
color_func = None
show_top_terms = True
show_axes = False
else:
term_projection = category_projection.get_term_projection()
original_x = term_projection['x']
original_y = term_projection['y']
x_coords = scaler(term_projection['x'])
y_coords = scaler(term_projection['y'])
x_label = term_x_label if term_x_label is not None else ''
y_label = term_y_label if term_y_label is not None else ''
show_axes = True
horizontal_line_y_position = 0
vertical_line_x_position = 0
term_scatter_chart_explorer.inject_coordinates(x_coords,
y_coords,
original_x=original_x,
original_y=original_y)
if topic_model_term_lists is not None:
term_scatter_chart_explorer.inject_metadata_term_lists(
topic_model_term_lists)
if metadata_descriptions is not None:
term_scatter_chart_explorer.inject_metadata_descriptions(
metadata_descriptions)
term_scatter_chart_data = term_scatter_chart_explorer.to_dict(
category=initial_category,
category_name=initial_category,
include_term_category_counts=True,
# transform=dense_rank,
)
color_func = '(function(x) {return "#5555FF"})'
show_top_terms = False
term_scatterplot_structure = ScatterplotStructure(
VizDataAdapter(term_scatter_chart_data),
width_in_pixels=term_width_in_pixels,
height_in_pixels=term_height_in_pixels,
use_full_doc=use_metadata or use_full_doc,
asian_mode=asian_mode,
use_non_text_features=use_metadata,
show_characteristic=False,
x_label=x_label,
y_label=y_label,
full_data='getTermDataAndInfo()',
show_top_terms=show_top_terms,
get_tooltip_content=None,
color_func=color_func,
# horizontal_line_y_position=0,
# vertical_line_x_position=0,
show_axes=show_axes,
topic_model_preview_size=topic_model_preview_size,
show_category_headings=False,
div_name='d3-div-1',
unified_context=True,
highlight_selected_category=highlight_selected_category)
return PairPlotFromScatterplotStructure(category_scatterplot_structure,
term_scatterplot_structure,
category_projection,
category_width_in_pixels,
category_height_in_pixels,
num_terms=num_terms_in_halo,
show_halo=show_halo,
d3_url_struct=d3_url_struct,
x_dim=x_dim,
y_dim=y_dim,
protocol=protocol).to_html()