Feature Interaction Charting

This tool is made for quick interactions visualization between variables in a dataset. User can specify the variables to be plotted on the x, y and hue (color) parameters. The tool automatically picks chart type to render based on the detected variable types and renders 1/2/3-way interactions.

This feature can be useful in exploring patterns, trends, and outliers and potentially identify good predictors for the task.

Using Interaction Charts for Missing Values Filling

Let’s load the titanic dataset:

import pandas as pd

df_train = pd.read_csv('https://autogluon.s3.amazonaws.com/datasets/titanic/train.csv')
df_test = pd.read_csv('https://autogluon.s3.amazonaws.com/datasets/titanic/test.csv')
target_col = 'Survived'

Next we will look at missing data in the variables:

import autogluon.eda.auto as auto

auto.missing_values_analysis(train_data=df_train)

Missing Values Analysis

	missing_count	missing_ratio
Age	177	0.198653
Cabin	687	0.771044
Embarked	2	0.002245

It looks like there are only two null values in the Embarked feature. Let’s see what those two null values are:

df_train[df_train.Embarked.isna()]

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
61	62	1	1	Icard, Miss. Amelie	female	38.0	0	0	113572	80.0	B28	NaN
829	830	1	1	Stone, Mrs. George Nelson (Martha Evelyn)	female	62.0	0	0	113572	80.0	B28	NaN

We may be able to fill these by looking at other independent variables. Both passengers paid a Fare of $80, are of Pclass 1 and female Sex. Let’s see how the Fare is distributed among all Pclass and Embarked feature values:

auto.analyze_interaction(train_data=df_train, x='Embarked', y='Fare', hue='Pclass')

The average Fare closest to $80 are in the C Embarked values where Pclass is 1. Let’s fill in the missing values as C.

Using Interaction Charts To Learn Information About the Data

auto.analyze_interaction(x='Pclass', y='Survived', train_data=df_train, test_data=df_test)

It looks like 63% of first class passengers survived, while; 48% of second class and only 24% of third class passengers survived. Similar information is visible via Fare variable:

auto.analyze_interaction(x='Fare', hue='Survived', train_data=df_train, test_data=df_test, chart_args=dict(fill=True))

auto.analyze_interaction(x='Age', hue='Survived', train_data=df_train, test_data=df_test)

The very left part of the distribution on this chart possibly hints that children and infants were the priority.

auto.analyze_interaction(x='Fare', y='Age', hue='Survived', train_data=df_train, test_data=df_test)

This chart highlights three outliers with a Fare of over $500. Let’s take a look at these:

df_train[df_train.Fare > 400]

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
258	259	1	1	Ward, Miss. Anna	female	35.0	0	0	PC 17755	512.3292	NaN	C
679	680	1	1	Cardeza, Mr. Thomas Drake Martinez	male	36.0	0	1	PC 17755	512.3292	B51 B53 B55	C
737	738	1	1	Lesurer, Mr. Gustave J	male	35.0	0	0	PC 17755	512.3292	B101	C

As you can see all 4 passengers share the same ticket. Per-person fare would be 1/4 of this value. Looks like we can add a new feature to the dataset fare per person; also this allows us to see if some passengers travelled in larger groups. Let’s create two new features and take at the Fare-Age relationship once again.

ticket_to_count = df_train.groupby(by='Ticket')['Embarked'].count().to_dict()
data = df_train.copy()
data['GroupSize'] = data.Ticket.map(ticket_to_count)
data['FarePerPerson'] = data.Fare / data.GroupSize

auto.analyze_interaction(x='FarePerPerson', y='Age', hue='Survived', train_data=data)
auto.analyze_interaction(x='FarePerPerson', y='Age', hue='Pclass', train_data=data)

You can see cleaner separation between Fare, Pclass and Survived now.