asgl 2.2.0

A regression solver for high dimensional penalized linear, quantile and logistic regression models

asgl

Introduction

asgl fits penalized regression models for high-dimensional variable selection. It supports linear (lm), quantile (qr), and logistic (logit) regression, with a rich menu of penalizations — from plain Lasso to Adaptive Sparse Group Lasso (ASGL) — all through a single scikit-learn compatible Regressor class.

The package is especially useful when:

• Variables have a known group structure (gene pathways, dummy-variable families, …)
• You need simultaneous group- and individual-level sparsity
• You want adaptive weights to improve oracle properties
• Your design matrix X is a scipy.sparse matrix

Based on:

• Adaptive Sparse Group Lasso in Quantile Regression
• asgl: A Python Package for Penalized Linear and Quantile Regression

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Features

Feature	Details
Models	Linear (lm), quantile (qr), logistic binary classification (logit)
Penalizations	lasso, ridge, gl, sgl, alasso, aridge, agl, asgl, or None
Sparse input	Both dense and scipy.sparse matrices accepted.
Multi-output Y	lm and qr accept a 2D y matrix for simultaneous multi-response fitting
Solver fallback	solver accepts a list; falls back through installed CVXPY solvers automatically
Adaptive weights	8 built-in weight techniques: pca_pct, pca_1, pls_pct, pls_1, lasso, ridge, unpenalized, sparse_pca
sklearn API	Full fit / predict / score / GridSearchCV / cross_val_predict support

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Installation

pip install asgl

Requirements: Python >= 3.10, cvxpy >= 1.5.0, numpy >= 1.20.0, scikit-learn >= 1.6, scipy >= 1.1

To run the test suite after installation:

pytest

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Quickstart

import numpy as np
from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
from asgl import Regressor

X, y = make_regression(n_samples=500, n_features=50, n_informative=20,
                       noise=5, random_state=42)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

model = Regressor(model='lm', penalization='lasso', lambda1=0.1)
model.fit(X_train, y_train)

print(model.coef_)
print(mean_squared_error(y_test, model.predict(X_test)))

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API Reference

Regressor

from asgl import Regressor

Regressor(
    model='lm',                  # 'lm' | 'qr' | 'logit'
    penalization='lasso',        # see Penalizations table below, or None
    quantile=0.5,                # quantile level (qr only)
    fit_intercept=True,
    lambda1=0.1,                 # penalization strength
    alpha=0.5,                   # lasso/group-lasso tradeoff for sgl/asgl
    solver='default',            # str or list[str] — CVXPY solver(s)
    canon_backend='CPP',         # 'CPP' | 'SCIPY' | 'COO'
    verbose=False,
    weight_technique='pca_pct',  # adaptive weight method (adaptive penalties only)
    individual_power_weight=1,
    group_power_weight=1,
    variability_pct=0.9,
    lambda1_weights=0.1,
    spca_alpha=1e-5,
    spca_ridge_alpha=1e-2,
    individual_weights=None,     # override weight estimation with custom array
    group_weights=None,
    tol=1e-3,
    weight_tol=1e-4,
)

Penalizations

penalization	Type	Group structure required
None	Unpenalized	No
'lasso'	Individual	No
'ridge'	Individual	No
'gl'	Group	Yes
'sgl'	Individual + Group	Yes
'alasso'	Adaptive individual	No
'aridge'	Adaptive individual	No
'agl'	Adaptive group	Yes
'asgl'	Adaptive individual + Group	Yes

Key methods

Method	Description
fit(X, y, group_index=None)	Fit the model
predict(X)	Predict (regression output or class labels for logit)
predict_proba(X)	Class probabilities (logit only)
decision_function(X)	Raw linear scores
score(X, y)	R² (regression) or accuracy (classifier)

Fitted attributes

Attribute	Description
coef_	(n_features,) or (n_features, n_outputs) coefficient array
intercept_	Intercept scalar
n_features_in_	Number of features seen during fit
solver_stats_	Dict with solver name, iterations, timing

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Examples

1 — Quantile regression with Adaptive Sparse Group Lasso + cross-validation

import numpy as np
from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split, RandomizedSearchCV
from asgl import Regressor

X, y = make_regression(n_samples=1000, n_features=50, n_informative=25,
                       noise=5, random_state=42)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)

group_index = np.repeat(np.arange(1, 11), 5)   # 10 groups of 5 features each

model = Regressor(model='qr', penalization='asgl', quantile=0.5)

param_grid = {
    'lambda1': [1e-3, 1e-2, 1e-1, 1.0],
    'alpha':   [0.0, 0.5, 1.0],
}
cv = RandomizedSearchCV(model, param_grid, scoring='neg_median_absolute_error',
                        n_iter=12, cv=5)
cv.fit(X_train, y_train, **{'group_index': group_index})
print(cv.best_params_)
print(cv.score(X_test, y_test))

2 — Sparse input (scipy.sparse)

import scipy.sparse as sp
from sklearn.datasets import make_regression
from asgl import Regressor

X_dense, y = make_regression(n_samples=500, n_features=200, n_informative=30,
                              random_state=0)
X = sp.random(500, 200, density=0.05, format='csr')  # or your real sparse matrix

model = Regressor(model='lm', penalization='lasso', lambda1=0.05)
model.fit(X, y)
print(f"Non-zero coefficients: {(model.coef_ != 0).sum()}")

3 — Multi-output regression

import numpy as np
from sklearn.datasets import make_regression
from asgl import Regressor

X, y_1d = make_regression(n_samples=300, n_features=30, n_informative=10,
                           noise=3, random_state=7)
y = np.column_stack([y_1d, y_1d * 0.5 + np.random.randn(300) * 2])  # 2 outputs

group_index = np.repeat(np.arange(1, 6), 6)   # 5 groups

model = Regressor(model='lm', penalization='gl', lambda1=0.1)
model.fit(X, y, group_index=group_index)
print(model.coef_.shape)   # (n_features, 2)

4 — Solver fallback

from asgl import Regressor

# Try CLARABEL first, then SCS, then let cvxpy choose
model = Regressor(model='lm', penalization='lasso',
                  solver=['CLARABEL', 'SCS', 'default'])
model.fit(X_train, y_train)
print(model.solver_stats_['solver_name'])

5 — Logistic regression with custom decision threshold

import numpy as np
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split, cross_val_predict
from sklearn.metrics import accuracy_score
from asgl import Regressor

X, y = make_classification(n_samples=1000, n_features=100, random_state=42)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)

model = Regressor(model='logit', penalization='ridge')
proba_cv = cross_val_predict(model, X_train, y_train, method='predict_proba', cv=5)

# Find threshold that maximises CV accuracy
thresholds = np.linspace(0.01, 0.99, 99)
best_thr = thresholds[np.argmax(
    [accuracy_score(y_train, (proba_cv[:, 1] >= t).astype(int)) for t in thresholds]
)]

model.fit(X_train, y_train)
test_preds = (model.predict_proba(X_test)[:, 1] >= best_thr).astype(int)
print(f"Test accuracy: {accuracy_score(y_test, test_preds):.3f}")

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Citation

If you use asgl in a scientific publication, please cite:

@article{mendez2022adaptive,
  title   = {Adaptive sparse group lasso in quantile regression},
  author  = {M{\'e}ndez-Civieta, {\'A}lvaro and Aguilera-Morillo, M Carmen and Lillo, Rosa E},
  journal = {Advances in Data Analysis and Classification},
  year    = {2021},
  doi     = {10.1007/s11634-020-00413-8}
}

Full paper | Package paper | Towards Data Science walkthrough

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Contributions

Contributions are welcome! Please open an issue to discuss ideas or submit a pull request.

See CONTRIBUTORS.md for a full list of contributors.

Acknowledgments

v2.2.0 incorporates a major contribution from zeyuz35: sparse matrix support, multi-output Y regression, solver fallbacks, performance improvements (vectorized group weights, PLS optimization), and an expanded test suite. See CONTRIBUTORS.md for details.

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What's new?

2.2.0

• Sparse matrix (scipy.sparse) input support throughout
• Multivariate Y (multi-output) for lm and qr models
• solver accepts a list of names with automatic fallback
• New parameters: verbose, canon_backend
• Performance: vectorized group weights, PLS without refitting
• Internal refactor: skmodels.py → 5 focused modules
• Test suite: 24 → 96 test functions
• Requires Python >= 3.10

2.1.4

• scikit-learn estimator tag compliance
• Quantile loss optimized via residual-splitting LP

2.1.3

• Logistic model rewritten: predict_proba, decision_function added
• logit_proba and logit_raw model types removed

2.1.0

• Ridge and adaptive ridge penalizations added ('ridge', 'aridge')

2.0.0

• Regressor class introduced with full scikit-learn compatibility

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License

GPL-3.0 — open source, modifications must be redistributed under the same license. See LICENSE for full text.