Stylometric plots

anyts.visualizers.dendrogram_plot(), anyts.visualizers.pca_plot(), anyts.visualizers.mds_plot(), anyts.visualizers.mendenhall_plot()

Description

Plots for stylometry: a dendrogram and the multidimensional scaling of the matrix of distances of delta, the principal components of the frequencies of the most frequent words and the Mendenhall curves of several texts. The functions take the axes ax and return Axes.

Dendrogram

Hierarchical clustering by scipy.cluster.hierarchy over the matrix of distances between texts; Ward's method by default, as in Evert et al. (2015); the labels of the leaves are the names of the texts of the index of the matrix. The columns are taken in the order of the rows; a matrix with other texts in the columns than in the rows, fewer than two texts, an infinite or negative distance, asymmetry or a distance on the diagonal raises SourceError before a figure is created.

Parameter	Type	Default	Description
distances	DataFrame	-	Symmetric matrix of distances with the names of the texts in the rows and the columns, in any order
method	str	ward	Method of scipy.cluster.hierarchy.linkage to join the clusters: single, complete, average, weighted, centroid, median or ward
ax	Axes	None	Axes for the plot
labels	dict[str, str]	None	Labels over the defaults: title, xlabel

Principal components

Principal component analysis of the z-scores of the relative frequencies of the most frequent units (frequency_table, z_scores): the texts on the plane of the first two components with their names, the shares of the explained variance in the labels of the axes.

Parameter	Type	Default	Description
corpus	dict[str, list[str]]	-	Units of the texts by the names of the texts
n_mfw	int	100	Number of the most frequent units; None - all of them
culling	float	0.0	Smallest share of the texts a unit occurs in
ax	Axes	None	Axes for the plot
labels	dict[str, str]	None	Labels over the defaults: title, xlabel and ylabel (format strings with the explained share)

Multidimensional scaling

Classical multidimensional scaling (Torgerson 1952) of any matrix of distances: the double centering of the matrix of squared distances and the two leading eigenvectors; the distances between the points approximate the distances of the matrix. The columns are taken in the order of the rows; a matrix with other texts in the columns than in the rows, fewer than two texts, an infinite or negative distance, asymmetry or a distance on the diagonal raises SourceError before a figure is created.

Parameter	Type	Default	Description
distances	DataFrame	-	Symmetric matrix of distances with the names of the texts in the rows and the columns, in any order
ax	Axes	None	Axes for the plot
labels	dict[str, str]	None	Labels over the defaults: title, xlabel, ylabel

Mendenhall curves

The shares of the words by length in characters (mendenhall_curve) of every text on one plot; a length no word has is drawn at zero.

Parameter	Type	Default	Description
corpus	dict[str, list[str]]	-	Words of the texts by the names of the texts
ax	Axes	None	Axes for the plot
labels	dict[str, str]	None	Labels over the defaults: title, xlabel, ylabel

Usage example

Example

import matplotlib.pyplot as plt

from anyts.corpus import delta
from anyts.visualizers import dendrogram_plot, mds_plot, mendenhall_plot, pca_plot

corpus = {
    "A": "the cat sat on the mat and the cat slept".split(),
    "B": "a dog lay on a rug and a dog barked".split(),
    "C": "the cat and a dog sat on the rug".split(),
}
distances = delta(corpus, n_mfw=5)
fig, axes = plt.subplots(2, 2, figsize=(12, 10))
dendrogram_plot(distances, ax=axes[0, 0])
mds_plot(distances, ax=axes[0, 1])
pca_plot(corpus, n_mfw=5, ax=axes[1, 0])
mendenhall_plot(corpus, ax=axes[1, 1])