plot_wordcloud

---

method plot_wordcloud(index=None, title=None, legend=None, figsize=(900, 600), filename=None, display=True, **kwargs)[source]

Plot a wordcloud from the corpus.

The text for the plot is extracted from the column named corpus. If there is no column with that name, an exception is raised.

Parameters

index: int, str, slice, sequence or None, default=None Documents in the corpus to include in the wordcloud. If None, it selects all documents in the dataset. title: str, dict or None, default=None Title for the plot. If None, no title is shown. If str, text for the title. If dict, title configuration. legend: str, dict or None, default=None Does nothing. Implemented for continuity of the API. figsize: tuple, default=(900, 600) Figure's size in pixels, format as (x, y). filename: str or None, default=None Save the plot using this name. Use "auto" for automatic naming. The type of the file depends on the provided name (.html, .png, .pdf, etc...). If filename has no file type, the plot is saved as html. If None, the plot is not saved. display: bool or None, default=True Whether to render the plot. If None, it returns the figure. **kwargs Additional keyword arguments for the Wordcloud object.

Returns

go.Figure or None Plot object. Only returned if display=None.

See Also

plot_ngrams

Plot n-gram frequencies.

plot_pipeline

Plot a diagram of the pipeline.

Example

>>> from atom import ATOMClassifier
>>> from sklearn.datasets import fetch_20newsgroups

>>> X, y = fetch_20newsgroups(
...     return_X_y=True,
...     categories=[
...         'alt.atheism',
...         'sci.med',
...         'comp.windows.x',
...     ],
...     shuffle=True,
...     random_state=1,
... )
>>> X = np.array(X).reshape(-1, 1)

>>> atom = ATOMClassifier(X, y)
>>> atom.textclean()
>>> atom.textnormalize()
>>> atom.plot_wordcloud()