ebemse | Entropy-Based Ensemble Members SElection

ebemse is a Python library for the selection of a set of mutually exclusive, collectivelly exaustive (MECE) ensemble members.

The library implements the approach presented by Darbandsari and Coulibaly (2021) as step that antecedes the further merging of a set of ensemble forecasts.

Installing

The library can be installed using the traditional pip:

pip install ebemse

And is listed on the Python Package Index (pypi) at .

Using

Suppose you have a file named example.csv with the following content:

Date,       Memb_A, Memb_B, ...,  Memb_Z, Obsv
2020/05/15, 1.12,   1.05,   ...,  0.5,    1.01
2020/05/16, 1.15,   1.12,   ...,  0.9,    1.10
2020/05/17, 1.13,   1.32,   ...,  1.1,    1.29
...         ...     ...     ...,  ...,    ...
2020/11/30, 1.22,   0.95,   ...,  0.3,    0.87

In which the columns starting with "Memb_" hold the realization of one ensemble member for the time interval and "Obsv" holds the observed values for the same time interval.

If your our objective is to select a MECE set considering obaservations, it can be done using the standard parameters by:

import pandas as pd
import ebemse

# read file
data_ensemble = pd.read_csv("example.csv").to_dict('list')
data_obsv = data_ensemble["Obsv"]
del data_ensemble["Obsv"], data_ensemble["Date"]

# perform selection
selected_members = ebemse.select_ensemble_members(data_ensemble, data_obsv)

The variable selected_members will be a dictionary with the following keys and values:

• history: dictionary with the following additional information related with the selection process:
  • total_correlation: list of floats
  • joint_entropy: list of floats
  • transinformation: list of floats or None
• selected_members: list of string with the labels of the selected elements
• original_ensemble_joint_entropy: float

Further information

select_ensemble_members()

Arguments:

• all_ensemble_members: dict
• observations: Union[list, tuple, np.array, None] (default: None)
• n_bins: Union[int, None] (default: 10)
• bin_by: str (default: "quantile_individual")
• beta_threshold: float (default: 0.9)
• n_processes: int (default: 1)
• minimum_n_members: int (default: 2)
• verbose: bool (default: False)