Pre-release
This release is a pre-release and may not be stable for production use.
A Python Package for Quantification
Website: https://luizfernandolj.github.io/mlquantify/
mlquantify is a Python library for quantification, also known as supervised prevalence estimation, designed to estimate the distribution of classes within datasets. It offers a range of tools for various quantification methods, model selection tailored for quantification tasks, evaluation metrics, and protocols to assess quantification performance. Additionally, mlquantify includes calibration tools, confidence region estimation, pluggable solvers and representations, and visualization utilities to help analyze and interpret results.
Installation
To install mlquantify, run the following command:
pip install mlquantify
If you only want to update, run the code below:
pip install --upgrade mlquantify
Contents
| Section | Description |
|---|---|
| 33 Quantification Methods | Counting (CC, PCC, ACC, TAC, TX, TMAX, T50, MS, MS2, FM, GACC, GPACC), Matching (DyS, HDy, HDx, SORD, SMM, MMD_RKHS, KDEyML, KDEyHD, KDEyCS, GHDy, GHDx, GKDEyML, EDy, EDx), Likelihood (EMQ, CDE, MLPE), Neighbors (PWK), Meta (EnsembleQ, AggregativeBootstrap, QuaDapt). |
| Dynamic class management | All methods are dynamic, and handle multiclass and binary problems; in the binary case, One-Vs-All (OVA) is applied automatically. |
| Solvers | Modular optimization backends: BinarySolver, LeastSquaresSolver, SimplexSolver. |
| Representations | Pluggable feature representations: HistogramRepresentation, KDERepresentation, DistanceRepresentation, KernelMeanRepresentation, PredictionRepresentation. |
| Losses | Composable loss functions (distance-based and likelihood-based) shared across quantifier families. |
| Calibration | ClassifierCalibrator and QuantifierCalibrator for post-hoc calibration of classifiers and quantifiers. |
| Confidence Regions | ConfidenceInterval, ConfidenceEllipseSimplex, ConfidenceEllipseCLR for uncertainty estimation on prevalence predictions. |
| Model Selection | GridSearchQ and evaluation protocols (APP, NPP, UPP, PPP) tailored for quantification tasks. |
| Evaluation Metrics | Metrics for quantification performance: AE, MAE, NAE, SE, MSE, KLD, RAE, NRAE, NKLD, NMD, RNOD, VSE, CvM_L1. |
| Visualization | scikit-learn-style Display classes for both single- and multiple-sample results: DiagonalDisplay, BiasDisplay, ErrorByShiftDisplay, PrevalenceDisplay, ConfidenceRegionDisplay. |
| Comprehensive Documentation | Full API reference and user guide covering all modules and methods. |
Quick example:
This snippet builds a synthetic 3-class dataset that combines prior and covariate shift with make_quantification, then evaluates a quantifier across the Artificial Prevalence Protocol (APP) with apply_protocol — which trains the model on a held-out split and scores it on many test samples drawn at controlled prevalences.
import numpy as np
from sklearn.ensemble import RandomForestClassifier
from mlquantify.datasets import make_quantification
from mlquantify.model_selection import apply_protocol
from mlquantify.counting import ACC
# Synthetic 3-class data with a complex (stacked prior + covariate) shift
Xs, ys, _ = make_quantification(
n_classes=5,
shift_type=["prior", "covariate"], # compose two kinds of dataset shift
covariate_scale=1.0,
random_state=0,
)
X, y = np.concatenate(Xs), np.concatenate(ys) # pool the bags into one dataset
# Evaluate ACC under the Artificial Prevalence Protocol. apply_protocol fits the
# quantifier on a train split and scores it on many held-out test samples drawn
# at controlled prevalences (the test instances never overlap with training).
results = apply_protocol(
ACC(RandomForestClassifier(random_state=0)), X, y,
protocol="app", batch_size=[100, 500], scoring=["mae", "rae"], random_state=0, verbose=1
)
print(f"APP over {results['n_batches']} test samples")
print(f" mean MAE -> {results['MAE'].mean():.4f}")
print(f" mean RAE -> {results['RAE'].mean():.4f}")
- In case you need any help, refer to the User Guide.
- Explore the API documentation for detailed developer information.
- See also the library in the pypi site in pypi mlquantify
- Check the CHANGELOG to see what's currently beign developed!
Requirements
Core dependencies (installed automatically with pip install mlquantify):
- scikit-learn
- numpy
- scipy
- pandas
- joblib
- tqdm
Optional extras:
pip install mlquantify[viz]— plotting viamlquantify.visualization(adds matplotlib)pip install mlquantify[neural]— neural quantifiers such as QuaNet (adds PyTorch)pip install mlquantify[all]— everything above
2 maintainers
Metadata
2 maintainers
Release files for mlquantify 0.5.2b2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
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|---|---|---|---|
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Built distributions (wheels)
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twine/7.0.0 CPython/3.13.14
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Release files / mlquantify-0.5.2b2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_28_x86_64.whl
| Download URL | mlquantify-0.5.2b2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_28_x86_64.whl |
|---|---|
| Size | 806.5 kB |
| Tags | CPython 3.9 Linux glibc 2.17+ x86-64 Linux glibc 2.28+ x86-64 |
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No |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
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Release files / mlquantify-0.5.2b2-cp39-cp39-macosx_11_0_arm64.whl
| Download URL | mlquantify-0.5.2b2-cp39-cp39-macosx_11_0_arm64.whl |
|---|---|
| Size | 435.1 kB |
| Tags | CPython 3.9 macOS 11.0+ ARM64 |
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SHA-256 checksum How to use checksums |
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twine/7.0.0 CPython/3.13.14
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Release files / mlquantify-0.5.2b2-cp39-cp39-macosx_10_9_x86_64.whl
| Download URL | mlquantify-0.5.2b2-cp39-cp39-macosx_10_9_x86_64.whl |
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| Size | 438.2 kB |
| Tags | CPython 3.9 macOS 10.9+ x86-64 |
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SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
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