Parallel coordinates plot

Market Basket Analysis in Python

Isaiah Hull

Visiting Associate Professor of Finance, BI Norwegian Business School

What is a parallel coordinates plot?

The plot provides an example of a parallel coordinates plot using the MovieLens dataset.

When to use parallel coordinate plots

Parallel coordinates vs. heatmap.
- Don't need intensity information.
- Only want to know whether rule exists.
- Want to reduce visual clutter.

Parallel coordinates vs. scatterplot.
- Want individual rule information.
- Not interested in multiple metrics.
- Only want to examine final rules.

Preparing the data

from mlxtend.frequent_patterns import association_rules, apriori

# Load the one-hot encoded data
onehot = pd.read_csv('datasets/movies_onehot.csv')

# Generate frequent itemsets
frequent_itemsets = apriori(onehot, min_support = 0.10, use_colnames = True, max_len = 2)

# Generate association rules
rules = association_rules(frequent_itemsets, metric = 'support', min_threshold = 0.00)

Converting rules to coordinates

# Convert rules to coordinates.
rules['antecedent'] = rules['antecedents'].apply(lambda antecedent: list(antecedent)[0])
rules['consequent'] = rules['consequents'].apply(lambda consequent: list(consequent)[0])
rules['rule'] = rules.index

# Define coordinates and label
coords = rules[['antecedent','consequent','rule']]

# Print example
print(coords.head(1))

                antecedent        consequent  rule
0  Dark Knight, The (2008)  Inception (2010)     0

Generating a parallel coordinates plot

from pandas.plotting import parallel_coordinates

# Generate parallel coordinates plot
parallel_coordinates(coords, 'rule', colormap = 'ocean')

Generating a parallel coordinates plot

The figure shows a parallel coordinates plot using the MovieLens data.

Refining a parallel coordinates plot

# Generate frequent itemsets
frequent_itemsets = apriori(onehot, min_support = 0.01, use_colnames = True, max_len = 2)

# Generate association rules
rules = association_rules(frequent_itemsets, metric = 'lift', min_threshold = 1.00)

# Generate coordinates and print example
coords = rules_to_coordinates(rules)

# Generate parallel coordinates plot
parallel_coordinates(coords, 'rule')

Refining a parallel coordinates plot

The figure shows a parallel coordinates plot using the MovieLens data.

Let's practice!

Market Basket Analysis in Python