This package provides the implementation of the radar-boxplot, a chart created and developed by the author.
The package is available for Python 3 only and can be installed via pip.
python -m pip install radarboxplot
Basically the arguments are a two-dimensional array of x numerical attributes (either continuous and ordinal will make sense), array of y classes (better the class name, because it will be the title of the subplots) and the colNames which are the column names to label the attributes accordingly. They must be in the same order os the columns of the x array.
Other parameters are:
plotMedian: (boolean) if you want the median to be plottedcolor: list of colors to paint the chart. The first will be the 25-75% percentile, the second color the total range and the third color will be the median line.nrows: number of rows to arrange the subplotsncols: number of columns to arrange the subplots
The function returns (matplotlib figure, [matplotlib axes]). The axes represent each of the subplots axes.
It merges the concepts of both radar chart and the boxplot chart, allowing to compare multivariate data for multiple classes/clusters at a time. It provides a intuitive understanding over the data by creating radar polygons which can be compared in terms of shape and thickness, giving a meaningful insight towards identifying high inner variation and similar classes/clusters.
By interpreting the radar-boxplot, it is possible to predict classification confusion over classes and understand why and what could be done to achieve better results.
The radar-boxplot draws two different regions colors representing the same a boxplot would, but for multiple attributes at once. The following example shows an example of the radar-boxplot over Iris Dataset. The inner red region represents the 25-75% percentiles of each attribute, while the blue area represents the total range, excluding the outliers as defined by Tukey, 1977. Outlier appears as whiskers, just like the classic boxplot.
IQR = Q3 - Q1
LOWER_OUTLIER = Q1 - (1.5 x IQR)
UPPER_OUTLIER = Q3 + (1.5 x IQR)
Intuitively you can see that Iris setosa has a significant different distribution of its attributes. Although the radar-boxplot is still useful for this dataset, because it has only 4 variables, this could also be visualized by pairs of two variables or either a 3D scatter plot with 3 variables.
The radar-boxplot is best suited when you have more than 4 relevant variables for your clustering/classification task, because it makes able to represent such dimensionality while still being readable.
Using the iris dataset from sklearn:
from sklearn.datasets import load_iris
from radarboxplot import radarboxplot
import matplotlib.pyplot as plt
iris = load_iris()
x = iris['data']
y = iris["target_names"][iris['target']]
colNames = iris["feature_names"]
# Basic default plot
radarboxplot(x, y, colNames)
plt.show()
# Use orange and green colors, plot median line, all in the same row
radarboxplot(x, y, colNames, plotMedian=True, color=["orange", "green"], nrows=1, ncols=3)
plt.show()