mcda-core 0.3.0

Foundation data structures and interfaces for the MCDA Python ecosystem

mcda-core

Foundation library for the MCDA Python ecosystem. Provides the shared data structures, abstract interfaces, and data transformations that all other libraries in the ecosystem build on.

Installation

pip install mcda-core

Python ≥ 3.9, NumPy ≥ 1.25, pandas ≥ 2.0.

Quick start

import numpy as np
import pandas as pd
from mcda_core import PerformanceTable, minmax, zscore

# Build a performance table from raw values
table = PerformanceTable(
    values=np.array([
        [80.0, 20.0, 10.0],
        [65.0, 35.0, 18.0],
        [90.0, 15.0, 14.0],
    ]),
    alternatives=["A", "B", "C"],
    criteria=["cost", "quality", "CO2"],
    directions=[-1, 1, -1],  # minimise cost and CO2, maximise quality
)

# Or load from a CSV
df = pd.read_csv("data.csv", index_col="alternative")
table = PerformanceTable.from_dataframe(df, directions=[-1, 1, -1])

# Normalise — returns a new (immutable) PerformanceTable
norm = minmax(table)

# Convert minimisation criteria to maximisation before passing to a method
table_max = table.as_maximization()

What's included

PerformanceTable

The central data structure — a frozen dataclass wrapping a (n, c) float array of n alternatives × c criteria, together with:

attribute	type	default
values	NDArray shape (n, c)	required
alternatives	list[str]	None
criteria	list[str]	None
criteria_types	list[str]	all "cardinal"
directions	list[int]	all +1 (maximise)

Key methods: from_dataframe(), as_maximization(), to_dataframe(), .shape.

Interfaces

class	purpose
MCDAMethod[P]	Abstract base for all MCDA methods — requires fit() and result()
MCDAParameters	Abstract base for method parameters

KEY_WEIGHTS is the shared string constant for the weights key.

Transformations

All transformations take a PerformanceTable and return a new one (metadata preserved):

function	description
minmax(table)	(x − min) / (max − min) → [0, 1]
zscore(table)	(x − μ) / σ
sum_normalization(table)	x / Σx
max_scaling(table)	x / max(|x|)
invert_criteria(table)	Flip criteria with direction = −1; method: "range" (default) or "sign"
scores_to_ranking(scores)	Indices of alternatives sorted best → worst; higher_is_better=True (default)

All table transformations accept an optional criteria argument (list of column indices) to apply the transformation to a subset of criteria.

Ecosystem

This library is the foundation of a modular MCDA ecosystem:

library	role	repository
mcda-core	← you are here	gitlab.ulb.be/smg/mcda-core
promethee	PROMETHEE I/II/Gamma/Sort	gitlab.ulb.be/smg/promethee
mcda-methods	ELECTRE I, MAUT	gitlab.ulb.be/smg/mcda-methods
mcda-viz	Visualization dashboards (Dash/Plotly)	gitlab.ulb.be/smg/mcda-viz

New method libraries only need to depend on mcda-core and inherit from MCDAMethod to be fully compatible with the rest of the ecosystem.

License

MIT — see LICENSE.