edi_kernels

Python bindings for EDI's C++ model-fitting kernels, via pybind11. The point of this package is speed: the same C++ solvers used by the R package, called directly from Python with no R/Rcpp dependency, running one to three orders of magnitude faster than the pure-Python canonical fit for the same model.

This package has no R or Rcpp dependency. It compiles the same R/EDI/src/*.cpp model-fitting kernels the R package EDI (not yet on CRAN) uses, built under an EDI_CORE_ONLY preprocessor guard that swaps out RcppEigen/Rmath for a vanilla Eigen + LBFGSpp, both fetched directly from their own upstream repositories (see CMakeLists.txt). Nothing under python/ is a copy of a R/EDI/src/*.cpp or *.h file — the compiled extension #includes them directly from the R package's own source tree.

See ../R/package_metadata/new_feature_plans/python_bindings_package_spec.md for the full design spec, kernel-by-kernel scope, and baseline-benchmarking methodology.

Speed gains

Point-estimate timings (median of 30 cold runs) for every bound kernel that has a canonical Python baseline to compare against, sorted by speedup, descending. Timing Pval for every row here is significant at p < 0.001 (Welch's t-test, EDI vs. canonical replicate distributions). Rows with no canonical Python implementation are omitted from this table — see Models with no Python canonical baseline below for those.

Class Response EDI (ms) Canonical Canonical (ms) Speedup
InferenceSurvivalWeibullRegr survival 0.08 lifelines WeibullAFTFitter 192.55 2423.43x
InferenceSurvivalKMDiff survival 0.02 lifelines KaplanMeierFitter(median) 26.52 1647.81x
InferenceSurvivalRestrictedMeanDiff survival 0.01 lifelines utils.restricted_mean_survival_time 19.30 1460.28x
InferenceSurvivalStratCoxPHRegr survival 0.47 lifelines CoxPHFitter(strata=) 273.61 583.92x
InferenceSurvivalLogRank survival 0.12 lifelines statistics.logrank_test 26.28 212.34x
InferenceIncidBinomialIdentityRiskDiff incidence 0.10 statsmodels GLM(Binomial, identity link) 15.49 149.00x
InferenceOrdinalPropOddsRegr ordinal 0.91 statsmodels OrderedModel(logit) 117.69 129.83x
InferenceOrdinalOrderedProbitRegr ordinal 0.80 statsmodels OrderedModel(probit) 86.01 106.95x
InferenceOrdinalGCompMeanDiff ordinal 1.09 statsmodels OrderedModel(logit)+gcomp 113.58 104.52x
InferenceSurvivalCoxPHRegr survival 0.50 scikit-survival CoxPHSurvivalAnalysis 51.62 102.32x
InferenceContinRobustRegr continuous 0.17 statsmodels RLM 11.92 69.49x
InferenceCountHurdleNegBin count 2.33 statsmodels HurdleCountModel(negbin) 135.15 57.90x
InferenceCountHurdlePoisson count 1.43 statsmodels HurdleCountModel(poisson) 41.83 29.33x
InferenceCountQuasiPoisson count 0.14 statsmodels GLM(Poisson) 3.85 26.88x
InferenceCountZeroInflatedNegBin count 13.96 statsmodels ZeroInflatedNegativeBinomialP 356.91 25.56x
InferenceIncidModifiedPoisson incidence 0.16 statsmodels GLM(Poisson) 3.90 24.25x
InferenceCountRobustPoisson count 0.16 statsmodels GLM(Poisson) 3.80 23.84x
InferenceCountPoisson count 0.18 statsmodels GLM(Poisson) 3.94 22.54x
InferenceCountNegBin count 0.81 statsmodels NegativeBinomial 17.87 22.13x
InferenceIncidProbitRegr incidence 0.44 statsmodels GLM(Binomial, probit link) 9.38 21.34x
InferencePropFractionalLogit proportion 0.15 statsmodels GLM(Binomial, fractional y) 3.08 20.00x
InferenceIncidLogRegr incidence 0.24 statsmodels GLM(Binomial) 4.03 17.04x
InferencePropGCompMeanDiff proportion 0.26 statsmodels GLM(Binomial)+gcomp 4.04 15.60x
InferenceIncidGCompRiskDiff incidence 0.36 statsmodels GLM(Binomial)+gcomp(RD) 4.78 13.16x
InferenceIncidGCompRiskRatio incidence 0.30 statsmodels GLM(Binomial)+gcomp(RR) 3.69 12.21x
InferencePropBetaRegr proportion 1.40 statsmodels BetaModel 16.81 11.99x
InferenceCountZeroInflatedPoisson count 11.91 statsmodels ZeroInflatedPoisson 59.04 4.96x
InferenceAllSimpleWilcox continuous 0.25 numpy median(HL pairwise diff) 1.12 4.57x
InferenceIncidRiskDiff incidence 0.03 numpy linalg.lstsq (LPM) 0.11 4.12x
InferenceContinOLS continuous 0.03 numpy linalg.lstsq 0.11 3.25x
InferenceIncidLogBinomial incidence 1.63 statsmodels GLM(Binomial, log link) 5.13 3.15x

→ Full results (benchmark_model_fits_python.html) — includes the Wald/full-inference table (standard errors + p-values, not just the point estimate), the utility/math-kernel table, dataset spec, and methodology notes. Same table shape as the R package's own benchmark_model_fits_R.html.

Models with no Python canonical baseline

Fourteen EDI estimators have no clean, actively-maintained Python package to benchmark against (an absent comparison is more honest than a mismatched substitute baseline). They're bound in edi_kernels regardless — EDI is still the only fast way to fit them in Python — they just don't appear in the speed-gains table above since there's nothing to compare to:

EDI class What it fits Why there's no Python baseline
InferenceContinGLMM (pairs) Gaussian LMM, random intercept, fit via MLE statsmodels' MixedLM uses REML/a different estimation path by default — not a like-for-like comparison
InferenceCountGLMM (pairs) Poisson GLMM, random intercept, adaptive Gauss-Hermite quadrature + MLE no pure-Python package offers ML (not variational/Bayesian) GLMM fitting
InferenceCountHurdlePoisson (pairs) Hurdle-Poisson GLMM, random intercept no pure-Python package combines a hurdle count model with adaptive-quadrature GLMM fitting
InferenceCountKKCondPoissonOneLik KK combined (matched-pair + reservoir) joint-likelihood Poisson estimator no canonical analog in either R or Python
InferenceIncidKKCondLogitGLMMOneLik KK combined (matched-pair + reservoir) joint-likelihood conditional-logit/GLMM estimator no canonical analog in either R or Python
InferenceOrdinalAdjCatLogitRegr Adjacent-category logit ordinal regression no identified Python package implements this link (R uses VGAM::vglm(acat()))
InferenceOrdinalCauchitRegr Ordinal regression, cauchit link statsmodels' OrderedModel(distr=) only documents 'probit'/'logit'
InferenceOrdinalCloglogRegr Ordinal regression, complementary log-log link same — distr= doesn't officially support cloglog
InferenceOrdinalContRatioRegr Continuation-ratio ordinal regression no identified Python package implements this link (R uses VGAM::vglm(cratio()))
InferenceOrdinalCLMM (pairs) Ordinal cumulative-link mixed model (logit/probit/cauchit/cloglog), random intercept no pure-Python package offers ML ordinal-GLMM fitting with adaptive quadrature
InferenceOrdinalGLMM (pairs) Proportional-odds ordinal GLMM, random intercept same — no pure-Python ML ordinal-GLMM fitter
InferenceOrdinalStereotypeLogitRegr Stereotype logit ordinal regression R uses VGAM::vglm(multinomial(...))-style fitting; no Python package implements this link
InferencePropZeroOneInflatedBetaRegr Zero-one-inflated beta regression (proportions) no canonical package in either R or Python
InferenceSurvivalKKWeibullFrailtyOneLik Weibull AFT with shared log-normal frailty no clean Python package (even the R side only has a partial/PH-parameterized frailtypack analog)

Install

pip install .

Usage

import numpy as np
from edi_kernels import fast_ols

X = np.column_stack([np.ones(100), np.random.default_rng(0).normal(size=100)])
y = X @ [1.0, 0.5] + np.random.default_rng(1).normal(size=100)
result = fast_ols(X, y)
print(result["b"])

Every bound kernel returns a dict (see python/cpp/result_map_pybind.h) mirroring the R package's own edi::ResultMap -> list conversion field for field.

Key dates

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  • Released:
Newer release available (1.0.0.post5)

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License expression

GPL-3.0-only
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Requires

Python >=3.9

Provides Extra

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1.0.0.post5

21 files

1.0.0.post4

21 files

1.0.0.post3

21 files

1.0.0.post2

21 files

1.0.0.post1

21 files

This release

1.0.0 This release

21 files

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