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. This plot notes the occurrences of a word and how many words from the beginning of the corpus it appears.
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()
Implementation of lexical dispersion for text visualization
DispersionPlot(target_words, ax=None, colors=None, ignore_case=False, annotate_docs=False, labels=None, colormap=None, **kwargs)¶
DispersionPlotVisualizer allows for visualization of the lexical dispersion of words in a corpus. Lexical dispersion is a measure of a word’s homeogeneity across the parts of a corpus. This plot notes the occurences of a word and how many words from the beginning it appears.
A list of target words whose dispersion across a corpus passed at fit will be visualized.
- axmatplotlib axes, default: None
The axes to plot the figure on.
- 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.
- colorslist or tuple of colors
Specify the colors for each individual class
- 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.
Pass any additional keyword arguments to the super class.
- These parameters can be influenced later on in the visualization
- process, but can and should be set as early as possible.
draw(self, points, target=None, **kwargs)¶
Called from the fit method, this method creates the canvas and draws the plot on it. Parameters ———- kwargs: generic keyword arguments.
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.
- kwargs: generic keyword arguments.
Generally this method is called from show and not directly by the user.
fit(self, X, y=None, **kwargs)¶
The fit method is the primary drawing input for the dispersion visualization.
- 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.
Pass generic arguments to the drawing method
Returns the instance of the transformer/visualizer