Python Sensitivity Analysis - Gradient DataFrames and Hex-Bin Plots
It is common in financial modeling to conduct a sensitivity analysis on the model. This analysis runs the model changing the inputs values and collecting the outputs. Then the modeler can examine how the outputs change in response to the inputs changing. This library was created to ease this process, especially around visualization of the results.
While it was developed for financial modeling, it can be used with any function to understand how changing the inputs of the function affect the outputs.
Install sensitivity:
pip install sensitivity
A simple example:
from sensitivity import SensitivityAnalyzer
def my_model(x_1, x_2):
return x_1 ** x_2
sensitivity_dict = {
'x_1': [10, 20, 30],
'x_2': [1, 2, 3]
}
sa = SensitivityAnalyzer(sensitivity_dict, my_model)
plot = sa.plot()
styled_df = sa.styled_dfs()
See the documentation here.
Created by Nick DeRobertis. MIT License.
FAQs
Python Sensitivity Analysis - Gradient DataFrames and Hex-Bin Plots
We found that sensitivity demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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