Context-dependent DEA

Preparing…

Import modules and prepare data.

import matplotlib.pyplot as plt
import pandas as pd

from Pyfrontier.frontier_model import EnvelopDEA, HierarchalDEA

df = pd.DataFrame(
    {
        "input_1": [4, 2, 1, 1, 5, 2.5, 1.5, 5, 4, 2.5],
        "input_2": [1, 1.5, 3, 4, 2, 2.5, 5, 3, 3, 4.5],
        "output": [3, 3, 3, 3, 3, 3, 3, 3, 3, 3],
    }
)

h_dea = HierarchalDEA(EnvelopDEA("CRS", "in"))

h_dea.fit(df[["input_1", "input_2"]].to_numpy(), df[["output"]].to_numpy())

h_dea.result[0]

plt.figure()
for res in h_dea.result:
    plt.plot(
        [r.dmu.input[0] for r in res],
        [r.dmu.input[1] for r in res],
        "o-",
        color="C0",
    )

References

Author Lawrence M. Seiford and Joe Zhu. Title Context-dependent data envelopment analysis—Measuring attractiveness and progress, Omega-international Journal of Management Science, 2003. :numref:https://EconPapers.repec.org/RePEc:eee:jomega:v:31:y:2003:i:5:p:397-408.