6.10. Visualization

This section covers some tools to visualize your data and model.

6.10.1. Use Seaborn Style on Matplotlib Plots

Seaborn style looks nicer than the default style of matplotlib, but matplotlib is more customizable. If you want to apply seaborn style on matplotlib plots, use seaborn.set_theme().

import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns

# Set seaborn style
sns.set_theme()

# Data for plotting
t = np.arange(0.0, 2.0, 0.01)
s = 1 + np.sin(2 * np.pi * t)

# Plot the data
plt.figure(figsize=(7, 5))
plt.plot(t, s)

plt.show()

6.10.2. Graphviz: Create a Flowchart to Capture Your Ideas in Python

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!pip install graphviz

Visualizing complex data and systems is difficult. Creating diagrams by hand is slow and prone to mistakes, especially with large datasets.

Graphviz offers an easy method to generate graphs using a declarative language.

Here’s a short code example to demonstrate the utility of Graphviz:

from graphviz import Graph 

# Instantiate a new Graph object
dot = Graph('Data Science Process', format='png')

# Add nodes
dot.node('A', 'Get Data')
dot.node('B', 'Clean, Prepare, & Manipulate Data')
dot.node('C', 'Train Model')
dot.node('D', 'Test Data')
dot.node('E', 'Improve')

# Connect these nodes
dot.edges(['AB', 'BC', 'CD', 'DE'])

# Save chart
dot.render('data_science_flowchart', view=True)

'data_science_flowchart.png'

dot 

Link to graphviz

6.10.3. folium: Create an Interactive Map in Python

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!pip install folium

If you want to create a map provided the location in a few lines of code, try folium. Folium is a Python library that allows you to create an interactive map.

import folium
m = folium.Map(location=[45.5236, -122.6750])

tooltip = 'Click me!'
folium.Marker([45.3288, -121.6625], popup='<i>Mt. Hood Meadows</i>',
              tooltip=tooltip).add_to(m)
m 

Make this Notebook Trusted to load map: File -> Trust Notebook

View the document of folium here.

6.10.4. dtreeviz: Visualize and Interpret a Decision Tree Model

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!pip install dtreeviz

If you want to find an easy way to visualize and interpret a decision tree model, use dtreeviz.

from dtreeviz.trees import dtreeviz
from sklearn import tree
from sklearn.datasets import load_wine

wine = load_wine()
classifier = tree.DecisionTreeClassifier(max_depth=2)
classifier.fit(wine.data, wine.target)

vis = dtreeviz(
    classifier,
    wine.data,
    wine.target,
    target_name="wine_type",
    feature_names=wine.feature_names,
)

vis.view()

The image below shows the output of dtreeviz when applying it on DecisionTreeClassifier.

Link to dtreeviz.

6.10.5. HiPlot - High Dimensional Interactive Plotting

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!pip install hiplot 

If you are tuning hyperparameters of your machine learning model, it can be difficult to understand the relationships between different combinations of hyperparameters and a specific metric.

That is when HiPlot comes in handy. HiPlot allows you to discover patterns in high-dimensional data using parallel plots like below.

import hiplot as hip
data = [{'lr': 0.001, 'loss': 10.0, 'r2': 0.8, 'optimizer': 'SGD'},
        {'lr': 0.01, 'loss': 2.5, 'r2': 0.9, 'optimizer': 'Adam'},
        {'lr': 0.1, 'loss': 4, 'r2': 0.86, 'optimizer': 'Adam'}]
hip.Experiment.from_iterable(data).display()