# yellowbrick.text.freqdist
# Implementations of frequency distributions for text visualization.
#
# Author: Rebecca Bilbro <rbilbro@districtdatalabs.com>
# Created: 2017-02-08 10:06
#
# Copyright (C) 2017 District Data Labs
# For license information, see LICENSE.txt
#
# ID: fredist.py [] rbilbro@districtdatalabs.com $
"""
Implementations of frequency distributions for text visualization
"""
##########################################################################
## Imports
##########################################################################
import numpy as np
import matplotlib.pyplot as plt
from operator import itemgetter
from yellowbrick.text.base import TextVisualizer
from yellowbrick.exceptions import YellowbrickTypeError
from yellowbrick.style.colors import resolve_colors, get_color_cycle
##########################################################################
## Quick Method
##########################################################################
[docs]def freqdist(X, y=None, ax=None, color=None, N=50, **kwargs):
"""Displays frequency distribution plot for text.
This helper function is a quick wrapper to utilize the FreqDist
Visualizer (Transformer) for one-off analysis.
Parameters
----------
X: ndarray or DataFrame of shape n x m
A matrix of n instances with m features. In the case of text,
X is a list of list of already preprocessed words
y: ndarray or Series of length n
An array or series of target or class values
ax: matplotlib axes
The axes to plot the figure on.
color: string
Specify color for barchart
N: integer
Top N tokens to be plotted.
kwargs: dict
Keyword arguments passed to the super class.
Returns
-------
ax: matplotlib axes
Returns the axes that the plot was drawn on.
"""
# Instantiate the visualizer
visualizer = FreqDistVisualizer(
ax, X, color, **kwargs
)
# Fit and transform the visualizer (calls draw)
visualizer.fit(X, y, **kwargs)
visualizer.transform(X)
# Return the axes object on the visualizer
return visualizer.ax
[docs]class FreqDistVisualizer(TextVisualizer):
"""
A frequency distribution tells us the frequency of each vocabulary
item in the text. In general, it could count any kind of observable
event. It is a distribution because it tells us how the total
number of word tokens in the text are distributed across the
vocabulary items.
Parameters
----------
ax : matplotlib axes
The axes to plot the figure on.
color : list or tuple of colors
Specify color for bars
N: integer
Top N tokens to be plotted.
kwargs : dict
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.
"""
def __init__(self, ax=None, color=None, N=50, **kwargs):
"""
Initializes the base frequency distributions with many
of the options required in order to make this
visualization work.
"""
super(FreqDistVisualizer, self).__init__(ax=ax, **kwargs)
# Visualizer parameters
self.N = 50
# Visual Parameters
self.color = color
[docs] def freq_dist(self):
"""
Called from the fit method, this method gets all the
words from the corpus and their corresponding frequency
counts.
Parameters
----------
kwargs: generic keyword arguments.
"""
counts = np.asarray(self.docs.sum(axis=0)).ravel().tolist()
self.word_freq = list(zip(self.features, counts))
[docs] def get_counts(self):
"""
Called from the fit method, this method sorts the words
from the corpus with their corresponding frequency
counts in reverse order.
Parameters
----------
kwargs: generic keyword arguments.
"""
sorted_word_freq = sorted(self.word_freq,
key=itemgetter(1), reverse=True)
self.words, self.counts = list(zip(*sorted_word_freq))
[docs] def fit(self, docs, features):
"""
The fit method is the primary drawing input for the frequency
distribution visualization. It requires vectorized lists of
documents and a list of features, which are the actual words
from the original corpus (needed to label the x-axis ticks).
Parameters
----------
docs : ndarray or DataFrame of shape n x m
A matrix of n instances with m features representing the corpus of
vectorized documents.
features : list
List of corpus vocabulary words
Text documents must be vectorized before passing to fit()
"""
self.docs = docs
self.features = features
self.freq_dist()
self.get_counts()
self.draw()
[docs] def draw(self, **kwargs):
"""
Called from the fit method, this method creates the canvas and
draws the distribution plot on it.
Parameters
----------
kwargs: generic keyword arguments.
"""
# Create the axis if it doesn't exist
if self.ax is None: self.ax = plt.gca()
# Plot the top 50 most frequent words
y_pos = np.arange(self.N)
self.ax.bar(y_pos, self.counts[:self.N], align='center', alpha=0.5)
# Set the tick marks
self.ax.set_xticks(y_pos)
[docs] def finalize(self, **kwargs):
"""
The finalize method executes any subclass-specific axes
finalization steps. The user calls poof & poof calls finalize.
Parameters
----------
kwargs: generic keyword arguments.
"""
# Set the title
self.set_title(
'Frequency distribution for top {} tokens'.format(self.N)
)
# Rotate tick marks to make words legible
self.ax.set_xticklabels(self.words[:self.N], rotation=90)
# Set the legend and the grid
self.ax.legend(loc='best')
self.ax.grid()