utils.desirability.DCategorical(values, tol=None, missing=None)Implements a desirability function for categorical inputs.
This class allows users to define desirability values for specific categorical inputs.
| Name | Type | Description |
|---|---|---|
| values | dict | A dictionary where keys are category names (strings) and values are desirability scores (floats). |
| tol | float | A tolerance value to replace desirability values of 0. Defaults to None. |
| missing | float | The value to use for missing inputs. Defaults to a non-informative value. |
Predicts the desirability values for the given categorical input data.
Plots the desirability function for the categorical inputs.
Many thanks to Max Kuhn for his implementation of the ‘desirability’ package in R. This class is based on the ‘desirability’ package in R, see: https://cran.r-project.org/package=desirability
from spotdesirability import DCategorical
import matplotlib.pyplot as plt
# Define desirability values for categories
values = {"A": 0.1, "B": 0.9, "C": 0.5}
# Create a DCategorical object
dcat = DCategorical(values)
# Predict desirability for a list of categories
inputs = ["A", "B", "C"]
desirability = dcat.predict(inputs)
print(desirability)
# [0.1 0.9 0.5]
# Plot the desirability function
dcat.plot()[0.1 0.9 0.5]
| Name | Description |
|---|---|
plot |
Plots the desirability function for the categorical inputs. |
predict |
Predicts the desirability values for the given categorical input data. |