demo_ignore_categories.py

import scattertext as st
import numpy as np
import re

df = st.SampleCorpora.ConventionData2012.get_data().assign(
    parse=lambda df: df.text.apply(st.whitespace_nlp_with_sentences)
)

corpus = st.CorpusFromParsedDocuments(
    df, category_col='party', parsed_col='parse'
).build().compact(st.AssociationCompactor(1000))

query = re.compile('.*(obama|barack|romney|mitt).*')
rep_query = re.compile('.*(romney|mitt).*')
dem_query = re.compile('.*(obama|barack).*')

term_metadata_df = corpus.get_term_freq_df('').assign(
    MatchesQuery=lambda df: np.array([query.match(word) is not None for word in df.index]),
    Frequency=lambda df: df.sum(axis=1),
    TextColor=lambda df: [
        'blue' if dem_query.match(term) is not None
        else 'red' if rep_query.match(term) is not None
        else 'rgb(200, 200, 200)'
        for term in df.index
    ],
    SuppressText=lambda df: df.apply(
        lambda row: not (row.MatchesQuery or row.Frequency < 30),
        axis=1
    ),
    PointColor=lambda df: df.TextColor,
    LabelPriority=lambda df: -(df.MatchesQuery).astype(int),
)

html = st.produce_scattertext_explorer(
    corpus,
    category='democrat',
    category_name='Democratic',
    not_category_name='Republican',
    minimum_term_frequency=0,
    pmi_threshold_coefficient=0,
    width_in_pixels=1000,
    metadata=corpus.get_df()['speaker'],
    transform=st.Scalers.dense_rank,
    max_overlapping=3,
    term_metadata_df=term_metadata_df,
    text_color_column='TextColor',
    suppress_text_column='SuppressText',
    color_column='PointColor',
    label_priority_column='LabelPriority',
    ignore_categories=True
)

fn = 'demo_ignore_categories.html'
open(fn, 'w').write(html)
print('open ./%s in Chrome' % fn)