FactoMinePy

⚠️ Experimental — use with caution. This is an independent Python port of the R package FactoMineR. It is not affiliated with or endorsed by the authors of FactoMineR. The port is in early development; APIs may change, edge cases may differ from R, and several FactoMineR methods are not yet implemented (see status table below). For production work or published research, treat results as preliminary and cross-check against the original R package.

A from-primitives reimplementation in pure NumPy/SciPy/Pandas of the R package FactoMineR for multivariate exploratory data analysis (PCA, CA, MCA, HCPC, dimdesc/catdes/condes).

This package is not a wrapper around R; every method is reimplemented from the published FactoMineR documentation and R source, then validated numerically against R FactoMineR (currently 2.14 on CRAN) via a checked-in fixture harness. R FactoMineR remains the canonical reference implementation; this port aims for byte-identical fixture output and column-by-column schema parity, but is not a drop-in replacement.

Status

Early-alpha. The supported-methods table is the source of truth for what works.

FactoMineR method	Python equivalent	Live	R-parity verified	Notes
PCA	factominer.PCA	✅	✅	active + supplementary individuals, quanti.sup, quali.sup
CA	factominer.CA	✅	✅	symmetric biplot, supplementary rows/columns
MCA	factominer.MCA	✅	✅	indicator matrix; Burt option
HCPC	factominer.HCPC	✅	✅	hierarchical clustering on PCA/CA/MCA, k-means consolidation
dimdesc	factominer.dimdesc	✅	✅	quantitative + categorical description per axis
catdes	factominer.catdes	✅	✅	Cla/Mod, Mod/Cla, Global, hypergeometric v-test; quanti_var Eta²; per-level quanti with sd in category / Overall sd / n
condes	factominer.condes	✅	✅	correlation tests for a continuous target
plot.PCA / .CA / .MCA / .HCPC	factominer.plot.plot()	✅	structural	matplotlib backend; factor maps, biplot, scree, contributions, dendrogram, ellipses, habillage
FAMD	factominer.FAMD	🚧 stub	—	Round 2
MFA	factominer.MFA	🚧 stub	—	Round 2
HMFA	factominer.HMFA	🚧 stub	—	Round 2
DMFA	factominer.DMFA	🚧 stub	—	Round 2
GPA	factominer.GPA	🚧 stub	—	Round 2
Plotly backend	factominer.plot.plotly_*	🚧 stub	—	Round 2

Methods marked 🚧 are importable but raise NotImplementedError("deferred — see docs/plans/factominer-python-port.md §2") when called. This is by design so downstream code can from factominer import HMFA without an ImportError.

Install

pip install factominer
# matplotlib backend ships by default; for the optional plotly backend:
pip install 'factominer[plotly]'

Quickstart

from factominer import PCA, HCPC, dimdesc
from factominer.datasets import load_decathlon

decathlon = load_decathlon()
res = PCA(decathlon, scale_unit=True, ncp=5,
          quanti_sup=["Rank", "Points"],
          quali_sup=["Competition"])

print(res.summary())
print(res.eig)             # eigenvalue table (DataFrame)
print(res.ind.coord)       # individual coordinates
print(res.var.contrib)     # variable contributions

# Describe each axis
desc = dimdesc(res, axes=[0, 1])
print(desc[0]["quanti"])

# Cluster on the principal components
clust = HCPC(res, nb_clust=3)
print(clust.data_clust.head())

# Plot
import matplotlib.pyplot as plt
from factominer.plot import plot
fig, ax = plt.subplots(1, 2, figsize=(12, 5))
plot(res, choix="ind", habillage="Competition", ax=ax[0])
plot(res, choix="var", ax=ax[1])
plt.show()

Migrating from R

See docs/migrating-from-r.md for a side-by-side cheat sheet (R call → Python call → result attribute mapping → semantic differences).

The most important semantic differences:

1. Argument names use snake_case. scale.unit=TRUE → scale_unit=True, quanti.sup=11:12 → quanti_sup=[10, 11] (and column names like "Rank" work too).
2. Indices are 0-based. ind.sup=1:3 (R) → ind_sup=[0, 1, 2] (Python).
3. Sign convention. SVD is sign-ambiguous; we apply a deterministic rule (first absolute-max coordinate of each axis is positive). Coordinates may differ from R by a sign; the interpretation (clusters, distances, contributions) is identical. See factominer._sign.
4. Result objects. res$eig (R) → res.eig (Python). res$var$coord → res.var.coord. All result tables are pandas.DataFrame.
5. Plotting is explicit. graph=TRUE does not exist; you call factominer.plot.plot(res, ...) yourself. No magic on print(res).

Numerical fidelity

For every live method, the package ships parity tests that assert column-by-column equivalence against R FactoMineR 2.14 (current CRAN) within tight tolerances:

• Eigenvalues to 1e-10 absolute
• Coordinates / cos² / correlations / eta² to 1e-9 after sign alignment
• Contributions to 1e-8
• v-tests to 1e-6
• p-values to 1e-5 relative
• HCPC partitions to ARI ≥ 0.999 (k-means consolidation can swap a couple of individuals)

Fixtures are JSON dumps of R FactoMineR results, generated by tools/refresh_r_fixtures.R and committed under tests/fixtures/r_outputs/. The Python tests load them without needing R at test time. Every fixture in the repo is byte-identical to what live R FactoMineR 2.14 emits on a Linux GitHub runner with R 4.6.0 (verified by the rpy2-parity CI job, which is triggerable on-demand via workflow_dispatch and runs on a weekly cron).

To regenerate fixtures locally (requires R + FactoMineR + jsonlite):

Rscript tools/refresh_r_fixtures.R
pytest -q

Known limitations / use with caution

This port targets the most common FactoMineR API surface and is rigorously validated on the bundled datasets, but the following caveats apply:

• Several methods are stubs. FAMD, MFA, HMFA, DMFA, GPA are importable but raise NotImplementedError when called.
• Parity is empirical, not exhaustive. The 83 parity tests cover the active + supplementary blocks for PCA / CA / MCA / HCPC and the full output schemas of dimdesc / catdes / condes on standard fixtures (decathlon, children, tea). Behavior with row weights, missing values, very small samples, or method="burt" MCA has not been independently verified.
• Sign of axes is arbitrary. SVD is sign-ambiguous; we apply a deterministic rule that may give the opposite sign from R on a given axis. Distances, clusters, contributions, and cos² are sign-invariant; coordinates may need a flip to align visually with R output.
• HCPC partitions can differ by one or two individuals. K-means consolidation is sensitive to initialization; the adjusted Rand index against R is ≥ 0.999 on the decathlon test fixture but not exactly 1.0.
• No plotly backend yet. Only matplotlib is implemented; the plotly module's functions raise NotImplementedError.

For production analyses, journal submissions, or any use where reproducibility against R FactoMineR is load-bearing, cross-check results against the original R package.

Datasets

Bundled datasets under factominer.datasets:

Loader	Source	Use case
load_decathlon()	IAAF 2004 Athens Olympic + Décastar 2004, re-derived from public results	PCA, dimdesc, HCPC
load_children()	FactoMineR's children (children's worries by socio-educational category)	CA
load_tea()	FactoMineR's tea (300-person tea-consumption survey)	MCA, catdes
load_poison()	FactoMineR's poison (food-poisoning outbreak survey)	mixed quantitative + categorical

See factominer/datasets/data/PROVENANCE.md for each dataset's origin and licensing notes.

Contributing

See CONTRIBUTING.md for dev setup, parity-bar expectations, and the PR / issue workflow. Bug reports and feature requests are welcome — please use the issue templates so we have the reproducer / R-side context up front. For security issues, see SECURITY.md and email [email protected] rather than filing a public issue.

Citing

If you use FactoMinePy in published work, please cite both this package and the original R FactoMineR (Lê, Josse, Husson, J. Stat. Softw. 2008, doi:10.18637/jss.v025.i01). A CITATION.cff is included for tools that consume it automatically.

License

MIT for code. Bundled datasets carry their original licensing — see factominer/datasets/data/PROVENANCE.md. The package does not redistribute R FactoMineR source (GPL); everything is reimplemented from the published documentation and validated against R outputs.

Acknowledgments

• The R FactoMineR package by Sébastien Lê, Julie Josse, François Husson (and many contributors) defines the API surface this package targets.
• factoextra for the visualization patterns that the matplotlib backend reproduces.
• scientisttools and prince for prior Python ports that informed the API shape.