def _word_cloud(self, *, ids: torch.LongTensor,
id_to_label: Mapping[int, str], top: int):
try:
from word_cloud.word_cloud_generator import WordCloud
except ImportError:
logger.warning(
"Could not import module `word_cloud`. "
"Try installing it with `pip install git+https://github.com/kavgan/word_cloud.git`",
)
return
# pre-filter to keep only topk
uniq, counts = ids.view(-1).unique(return_counts=True)
# if top is larger than the number of available options
top = min(top, uniq.numel())
top_counts, top_ids = counts.topk(k=top, largest=True)
# generate text
text = list(
itertools.chain(
*(itertools.repeat(id_to_label[e_id], count)
for e_id, count in zip(top_ids.tolist(), top_counts.tolist())
)))
from IPython.core.display import HTML
word_cloud = WordCloud()
return HTML(word_cloud.get_embed_code(text=text, topn=top))
def _word_cloud(self, *, text: List[str], top: int):
try:
from word_cloud.word_cloud_generator import WordCloud
except ImportError:
logger.warning(
'Could not import module `word_cloud`. '
'Try installing it with `pip install git+https://github.com/kavgan/word_cloud.git`',
)
return
from IPython.core.display import HTML
word_cloud = WordCloud()
return HTML(word_cloud.get_embed_code(text=text, topn=top))
def word_cloud(self, text_field='text', top_n=40):
from word_cloud.word_cloud_generator import WordCloud
wc = WordCloud(stopwords=self.colador.STOP_WORDS_ES)
texts = [m[text_field] for m in self.messages]
try:
embed_code = wc.get_embed_code(text=texts,
random_color=True,
topn=top_n)
except ValueError as e:
embed_code = str(e)
from IPython.core.display import HTML
return HTML(embed_code)