Dispersion Plot

A word’s importance can be weighed by its dispersion in a corpus. Lexical dispersion is a measure of a word’s homogeneity across the parts of a corpus.

Lexical dispersion illustrates the homogeneity of a word (or set of words) across the documents of a corpus. DispersionPlot allows for visualization of the lexical dispersion of words in a corpus. This plot illustrates with vertical lines the occurrences of one or more search terms throughout the corpus, noting how many words relative to the beginning of the corpus it appears.

Visualizer	`DispersionPlot`
Quick Method	`dispersion()`
Models	Text Modeling
Workflow	Feature Engineering

from yellowbrick.text import DispersionPlot
from yellowbrick.datasets import load_hobbies

# Load the text data
corpus = load_hobbies()

# Create a list of words from the corpus text
text = [doc.split() for doc in corpus.data]

# Choose words whose occurence in the text will be plotted
target_words = ['Game', 'player', 'score', 'oil', 'Man']

# Create the visualizer and draw the plot
visualizer = DispersionPlot(target_words)
visualizer.fit(text)
visualizer.show()

(Source code, png, pdf)

If the target vector of the corpus documents is provided, the points will be colored with respect to their document category, which allows for additional analysis of relationships in search term homogeneity within and across document categories.

from yellowbrick.text import DispersionPlot
from yellowbrick.datasets import load_hobbies

corpus = load_hobbies()
text = [doc.split() for doc in corpus.data]
y = corpus.target

target_words = ['points', 'money', 'score', 'win', 'reduce']

visualizer = DispersionPlot(
    target_words,
    colormap="Accent",
    title="Lexical Dispersion Plot, Broken Down by Class"
)
visualizer.fit(text, y)
visualizer.show()

(Source code, png, pdf)

Quick Method

The same functionality above can be achieved with the associated quick method dispersion. This method will build the Dispersion Plot object with the associated arguments, fit it, then (optionally) immediately show the visualization.

from yellowbrick.text import DispersionPlot, dispersion
from yellowbrick.datasets import load_hobbies

# Load the text data
corpus = load_hobbies()

# Create a list of words from the corpus text
text = [doc.split() for doc in corpus.data]

# Choose words whose occurence in the text will be plotted
target_words = ['features', 'mobile', 'cooperative', 'competitive', 'combat', 'online']

# Create the visualizer and draw the plot
dispersion(target_words, text, colors=['olive'])

(Source code, png, pdf)

API Reference

Implementation of lexical dispersion for text visualization

class yellowbrick.text.dispersion.DispersionPlot(search_terms, ax=None, colors=None, colormap=None, ignore_case=False, annotate_docs=False, labels=None, **kwargs)[source]

Bases: TextVisualizer

Lexical dispersion illustrates the homogeneity of a word (or set of words) across the documents of a corpus.

DispersionPlot allows for visualization of the lexical dispersion of words in a corpus. This plot illustrates with vertical lines the occurrences of one or more search terms throughout the corpus, noting how many words relative to the beginning of the corpus it appears. If the target vector of the corpus documents is provided, the points will be colored with respect to their document category, which allows for additional analysis of relationships in search term homogeneity within and across document categories. If annotation is requested, document boundaries will be displayed as vertical lines in the plot.

Parameters

search_termslist: A list of search terms whose dispersion across a corpus passed at fit should be visualized.
axmatplotlib axes, default: None: The axes to plot the figure on.
colorslist or tuple of colors: Specify the colors for each individual class. Will override colormap if both are provided.
colormapstring or matplotlib cmap: Qualitative colormap for discrete target
ignore_caseboolean, default: False: Specify whether input will be case-sensitive.
annotate_docsboolean, default: False: Specify whether document boundaries will be displayed. Vertical lines are positioned at the end of each document.
labelslist of strings: The names of the classes in the target, used to create a legend. Labels must match names of classes in sorted order.
kwargsdict: Pass any additional keyword arguments to the super class.

Attributes

self.classes_list: A list of strings representing the unique classes in the target in sorted order. If y is provided, these are extracted from y, unless a list of class labels is provided by the user on instantiation.
self.boundaries_list: A list of integers indicating the document boundaries with respect to word offsets.
self.indexed_words_list: A list of integers indicating the y position for each occurrence of each of the search terms.
self.word_categories_list: A list of strings indicating the corresponding document category of each search term occurrence.

NULL_CLASS = None

draw(points, **kwargs)[source]

Called from the fit method, this method creates the canvas and draws the plot on it.

Parameters

kwargs: generic keyword arguments.

finalize(**kwargs)[source]

Prepares the figure for rendering by adding a title, axis labels, and managing the limits of the text labels. Adds a legend outside of the plot.

Parameters

kwargs: generic keyword arguments.

Notes

Generally this method is called from show and not directly by the user.

fit(X, y=None, **kwargs)[source]

The fit method is the primary drawing input for the dispersion visualization.

Parameters

Xlist or generator: Should be provided as a list of documents or a generator that yields a list of documents that contain a list of words in the order they appear in the document.
yndarray or Series of length n: An optional array or series of target or class values for instances. If this is specified, then the points will be colored according to their class.

Returns

selfinstance: Returns the instance of the transformer/visualizer

Attributes

self.classes_list: A list of strings representing the unique classes in the target in sorted order. If y is provided, these are extracted from y, unless a list of class labels is provided by the user on instantiation.
self.indexed_words_list: A list of integers indicating the y position for each occurrence of each of the search terms.
self.word_categories_list: A list of strings indicating the corresponding document category of each search term occurrence.

yellowbrick.text.dispersion.dispersion(search_terms, corpus, y=None, ax=None, colors=None, colormap=None, annotate_docs=False, ignore_case=False, labels=None, show=True, **kwargs)[source]

Displays lexical dispersion plot for words in a corpus

This helper function is a quick wrapper to utilize the DispersionPlot Visualizer for one-off analysis

Parameters

search_termslist: A list of words whose dispersion will be examined within a corpus
corpuslist: Should be provided as a list of documents that contain a list of words in the order they appear in the document.
yndarray or Series of length n: An optional array or series of target or class values for instances. If this is specified, then the points will be colored according to their class.
axmatplotlib axes, default: None: The axes to plot the figure on.
colorslist or tuple of colors: Specify the colors for each individual class. Will override colormap if both are provided.
colormapstring or matplotlib cmap: Qualitative colormap for discrete target
annotate_docsboolean, default: False: Specify whether document boundaries will be displayed. Vertical lines are positioned at the end of each document.
ignore_caseboolean, default: False: Specify whether input will be case-sensitive.
labelslist of strings: The names of the classes in the target, used to create a legend. Labels must match names of classes in sorted order.
showbool, default: True: If True, calls show(), which in turn calls plt.show() however you cannot call plt.savefig from this signature, nor clear_figure. If False, simply calls finalize()
kwargsdict: Pass any additional keyword arguments to the super class.

Returns

viz: DispersionPlot: Returns the fitted, finalized visualizer