I'm using a Parallel Coordinates plot, and I wish to increase the font size of the Axis Labels, Legend and Title, can someone help me out? Here's what I have:
from sklearn import datasets
from yellowbrick.features import ParallelCoordinates
iris = datasets.load_iris()
X = iris.data[:, :]
y = iris.target
features = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width']
classes = ['Iris-setosa', 'Iris-versicolor', 'Iris-virginica']
title = "Plot over Iris Data"
# Instantiate the visualizer
visualizer = ParallelCoordinates(
classes=classes, features=features, fast=False, alpha=.40, title=title)
# Fit the visualizer and display it
visualizer.fit_transform(X, y)
visualizer.show()
I saw another post here where they did this:
for label in viz.ax.texts:
label.set_size(12)
But this does not change anything for me, and I can't seem to find an alternative that would work for the labels, title and legend.
As explained in the linked post, the matplotlib ax
elements can be accessed directly to change their properties (color, fontsize, ...). That post also suggests replacing visualizer.show()
by visualizer.finalize()
(which adds a.o. the title and the legend), so the elements can be updated before showing the plot.
from sklearn import datasets
from yellowbrick.features import ParallelCoordinates
iris = datasets.load_iris()
X = iris.data[:, :]
y = iris.target
features = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width']
classes = ['Iris-setosa', 'Iris-versicolor', 'Iris-virginica']
title = "Plot over Iris Data"
# Instantiate the visualizer
visualizer = ParallelCoordinates(
classes=classes, features=features, fast=False, alpha=.40, title=title)
# Fit the visualizer and display it
visualizer.fit_transform(X, y)
visualizer.finalize() # creates title, legend, etc.
visualizer.ax.tick_params(labelsize=22) # change size of tick labels
visualizer.ax.title.set_fontsize(30) # change size of title
for text in visualizer.ax.legend_.texts: # change size of legend texts
text.set_fontsize(20)
visualizer.fig.tight_layout() # fit all texts nicely into the surrounding figure
visualizer.fig.show()