Welcome to ddop

ddop is a Python library for data-driven operations management. The goal of ddop is to provide well-established data-driven operations management tools within a programming environment that is accessible and easy to use even for non-experts. At the current state ddop contains well known data-driven newsvendor models, a set of performance metrics that can be used for model evaluation and selection, as well as datasets that are useful to quickly illustrate the behavior of the various algorithms implemented in ddop or as benchmark for testing new models. Through its consistent and easy-to-use interface one can run and compare provided models with only a few lines of code.

Install

ddop is available via PyPI using:

pip install ddop2

Quickstart

ddop provides a varity of newsvendor models. The following example shows how to use one of these models for decision making. It assumes a very basic knowledge of data-driven operations management practices.

As first step we initialize the model we want to use. In this example RandomForestWeightedNewsvendor.

from ddop2.newsvendor import RandomForestWeightedNewsvendor
rf_nv = RandomForestWeightedNewsvendor(cu=2, co=1)

2023-08-09 22:10:47.225307: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-08-09 22:10:48.239997: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT

A model can take a set of parameters, each describing the model or the optimization problem it tries to solve. Here we set the underage costs cu to 2 and the overage costs co to 1.

As next step we load the Yaz Dataset and split it into train and test set.

from ddop2.datasets import load_yaz
from sklearn.model_selection import train_test_split
X, y = load_yaz(one_hot_encoding=True, return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, shuffle=False, random_state=0)

After the model is initialized, the fit method can be used to learn a decision model from the training data X_train, y_train.

rf_nv.fit(X_train, y_train)

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We can then use the predict method to make a decision for new data samples.

rf_nv.predict(X_test)

array([[ 5,  4, 14, ..., 23, 35, 22],
       [ 6,  6, 11, ..., 26, 37, 23],
       [ 8,  8, 16, ..., 35, 55, 40],
       ...,
       [ 5,  6, 12, ..., 23, 41, 25],
       [ 6,  6, 13, ..., 24, 41, 32],
       [ 8,  9, 15, ..., 34, 57, 42]])

To get a representation of the model’s decision quality we can use the score function, which takes as input X_test and y_test. The score function makes a decision for each sample in X_test and calculates the negated average costs with respect to the true values y_test and the overage and underage costs.

rf_nv.score(X_test, y_test)

-6.859375000000001