ddom-kit is a Python toolkit for data-driven operations management. The goal of ddom-kit is to provide access to a variety of algorithms for decision making. Thus, one can run and compare the available approaches with only a few lines of code. Moreover ddom-kit's interface allows to easily adapt an algorithm for one task to another.

Currently supported are different algorithms for newsvendor decision making.

ddom-kit is available via PyPI using:

pip install ddop

The package is actively being developed and some features may not be stable yet.

from ddop.datasets.load_datasets import load_data
from ddop.newsvendor import EmpiricalRiskMinimizationNewsvendor
from sklearn.model_selection import train_test_split
data = load_data("yaz_steak.csv")
X = data.iloc[:,0:24]
Y = data.iloc[:,24]
cu,co = 15,10
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.25, shuffle=False)
mdl = EmpiricalRiskMinimizationNewsvendor(cu=15, co=10)
mdl.fit(X_train, Y_train)
mdl.score(X_test, Y_test)

For more details, check out our E-Learning.

• Read the API reference for detailed class and function information.
• Check out our E-Learning.