Stylometric plots¶

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

Description¶

Plots for stylometry: a dendrogram and multidimensional scaling from the delta distance matrix, a principal component chart from the frequencies of the most frequent words and Mendenhall curves of several texts - the set used in stylo to look at the clustering of texts by author. The functions take axes ax and return Axes.

Dendrogram¶

Hierarchical clustering by scipy.cluster.hierarchy from the distance matrix between texts; Ward's method by default, as in stylo and Evert et al. (2015), leaf labels are the text names from the matrix index.

Parameter	Type	Default	Description
`distances`	DataFrame	`-`	Symmetric distance matrix with text names
`method`	str	`ward`	Cluster linkage method of `scipy.cluster.hierarchy.linkage`
`ax`	Axes	`None`	Axes for the plot

Principal components¶

Principal component analysis of the z-scores of the relative frequencies of the most frequent units (frequency_table, z_scores) via singular value decomposition, like pca.visualization in stylo: the texts on the plane of the first two components with labels, the axis labels show the share of explained variance.

Parameter	Type	Default	Description
`corpus`	dict[str, list[str]]	`-`	Units of the texts by text name
`n_mfw`	int	`100`	Number of the most frequent units; `None` - all
`culling`	float	`0.0`	Minimum share of texts a unit must occur in
`ax`	Axes	`None`	Axes for the plot

Multidimensional scaling¶

Classical multidimensional scaling (Torgerson 1952) from any distance matrix: double centering of the squared distance matrix and the two leading eigenvectors; the distances between the points approximate the distances of the matrix.

Parameter	Type	Default	Description
`distances`	DataFrame	`-`	Symmetric distance matrix with text names
`ax`	Axes	`None`	Axes for the plot

Mendenhall curves¶

The shares of words by length in characters (mendenhall_curve) for every text on one plot - a comparison of author profiles.

Parameter	Type	Default	Description
`corpus`	dict[str, list[str]]	`-`	Words of the texts by text name
`ax`	Axes	`None`	Axes for the plot

Usage example¶

Let us look at the visualizers on 8 texts of the StalinWorks dataset.

Example

Code:

# Import the libraries
import matplotlib.pyplot as plt
from ruts import WordsExtractor
from ruts.corpus import delta
from ruts.datasets import StalinWorks
from ruts.visualizers import dendrogram_plot, mds_plot, mendenhall_plot, pca_plot

# Prepare the data
sw = StalinWorks()
we = WordsExtractor(use_lexemes=True, lowercase=True, filter_nums=True)
corpus = {f"текст {i + 1}": we.extract(text) for i, text in enumerate(sw.get_texts(limit=8))}
distances = delta(corpus, n_mfw=100, variant="cosine")

# Dendrogram and principal components on one figure
fig, (left, right) = plt.subplots(1, 2, figsize=(13, 5))
dendrogram_plot(distances, ax=left)
pca_plot(corpus, n_mfw=100, ax=right)

# Multidimensional scaling and Mendenhall curves
fig, (left, right) = plt.subplots(1, 2, figsize=(13, 5))
mds_plot(distances, ax=left)
mendenhall_plot({name: corpus[name] for name in list(corpus)[:3]}, ax=right)

Result:

ruts