c-lasso is a Python package that enables sparse and robust linear regression and classification with linear equality constraints on the model parameters. For detailed info, one can check the documentation.
The forward model is assumed to be:
Here, y and X are given outcome and predictor data. The vector y can be continuous (for regression) or binary (for classification). C is a general constraint matrix. The vector β comprises the unknown coefficients and σ an unknown scale.
The package handles several different estimators for inferring β (and σ), including the constrained Lasso, the constrained scaled Lasso, sparse Huber M-estimation with linear equality constraints, and regularized Support Vector Machines. Several different algorithmic strategies, including path and proximal splitting algorithms, are implemented to solve the underlying convex optimization problems.
We also include two model selection strategies for determining the sparsity of the model parameters: k-fold cross-validation and stability selection.
This package is intended to fill the gap between popular python tools such as scikit-learn which CANNOT solve sparse constrained problems and general-purpose optimization solvers that do not scale well or are inaccurate (see benchmarks) for the considered problems. In its current stage, however, c-lasso is not yet compatible with the scikit-learn API but rather a stand-alone tool.
Below we show several use cases of the package, including an application of sparse log-contrast regression tasks for compositional microbiome data.
The code builds on results from several papers which can be found in the References. We also refer to the accompanying JOSS paper submission, also available on arXiv.
c-lasso is available on pip. You can install the package in the shell using
pip install c-lasso
To use the c-lasso package in Python, type
from classo import classo_problem
# one can add auxiliary functions as well such as random_data or csv_to_np
The c-lasso package depends on the following Python packages:
numpy;matplotlib;scipy;pandas;pytest (for tests)The c-lasso package can solve six different types of estimation problems: four regression-type and two classification-type formulations.
This is the standard Lasso problem with linear equality constraints on the β vector. The objective function combines Least-Squares for model fitting with l1 penalty for sparsity.
This regression problem uses the Huber loss as objective function for robust model fitting with l1 and linear equality constraints on the β vector. The parameter ρ=1.345.
This formulation is similar to [R1] but allows for joint estimation of the (constrained) β vector and the standard deviation σ in a concomitant fashion (see References [4,5] for further info). This is the default problem formulation in c-lasso.
This formulation combines [R2] and [R3] to allow robust joint estimation of the (constrained) β vector and the scale σ in a concomitant fashion (see References [4,5] for further info).
where the xi are the rows of X and l is defined as:
This formulation is similar to [R1] but adapted for classification tasks using the Square Hinge loss with (constrained) sparse β vector estimation.
where the xi are the rows of X and lρ is defined as:
This formulation is similar to [C1] but uses the Huberized Square Hinge loss for robust classification with (constrained) sparse β vector estimation.
We begin with a basic example that shows how to run c-lasso on synthetic data. This example and the next one can be found on the notebook 'Synthetic data Notebook.ipynb'
The c-lasso package includes
the routine random_data that allows you to generate problem instances using normally distributed data.
m, d, d_nonzero, k, sigma = 100, 200, 5, 1, 0.5
(X, C, y), sol = random_data(m, d, d_nonzero, k, sigma, zerosum=True, seed=1)
This code snippet generates a problem instance with sparse β in dimension
d=100 (sparsity d_nonzero=5). The design matrix X comprises n=100 samples generated from an i.i.d standard normal
distribution. The dimension of the constraint matrix C is d x k matrix. The noise level is σ=0.5.
The input zerosum=True implies that C is the all-ones vector and Cβ=0. The n-dimensional outcome vector y
and the regression vector β is then generated to satisfy the given constraints.
Next we can define a default c-lasso problem instance with the generated data:
problem = classo_problem(X, y, C)
You can look at the generated problem instance by typing:
print(problem)
This gives you a summary of the form:
FORMULATION: R3
MODEL SELECTION COMPUTED:
Stability selection
STABILITY SELECTION PARAMETERS:
numerical_method : not specified
method : first
B = 50
q = 10
percent_nS = 0.5
threshold = 0.7
lamin = 0.01
Nlam = 50
As we have not specified any problem, algorithm, or model selection settings, this problem instance represents the default settings for a c-lasso instance:
You can solve the corresponding c-lasso problem instance using
problem.solve()
After completion, the results of the optimization and model selection routines can be visualized using
print(problem.solution)
The command shows the running time(s) for the c-lasso problem instance, and the selected variables for sability selection
STABILITY SELECTION :
Selected variables : 7 63 148 164 168
Running time : 1.546s
Here, we only used stability selection as default model selection strategy. The command also allows you to inspect the computed stability profile for all variables at the theoretical λ
The refitted β values on the selected support are also displayed in the next plot
In the next example, we show how one can specify different aspects of the problem formulation and model selection strategy.
m, d, d_nonzero, k, sigma = 100, 200, 5, 0, 0.5
(X, C, y), sol = random_data(m, d, d_nonzero, k, sigma, zerosum = True, seed = 4)
problem = classo_problem(X, y, C)
problem.formulation.huber = True
problem.formulation.concomitant = False
problem.model_selection.CV = True
problem.model_selection.LAMfixed = True
problem.model_selection.PATH = True
problem.model_selection.StabSelparameters.method = 'max'
problem.model_selection.CVparameters.seed = 1
problem.model_selection.LAMfixedparameters.rescaled_lam = True
problem.model_selection.LAMfixedparameters.lam = .1
problem.solve()
print(problem)
print(problem.solution)
Results :
FORMULATION: R2
MODEL SELECTION COMPUTED:
Lambda fixed
Path
Cross Validation
Stability selection
LAMBDA FIXED PARAMETERS:
numerical_method = Path-Alg
rescaled lam : True
threshold = 0.09
lam = 0.1
theoretical_lam = 0.224
PATH PARAMETERS:
numerical_method : Path-Alg
lamin = 0.001
Nlam = 80
CROSS VALIDATION PARAMETERS:
numerical_method : Path-Alg
one-SE method : True
Nsubset = 5
lamin = 0.001
Nlam = 80
STABILITY SELECTION PARAMETERS:
numerical_method : Path-Alg
method : max
B = 50
q = 10
percent_nS = 0.5
threshold = 0.7
lamin = 0.01
Nlam = 50
LAMBDA FIXED :
Selected variables : 17 59 123
Running time : 0.104s
PATH COMPUTATION :
Running time : 0.638s
CROSS VALIDATION :
Selected variables : 16 17 57 59 64 73 74 76 93 115 123 134 137 181
Running time : 2.1s
STABILITY SELECTION :
Selected variables : 17 59 76 123 137
Running time : 6.062s
In the the accompanying notebook we study several microbiome data sets. We showcase two examples below.
We first consider the COMBO data set and show how to predict Body Mass Index (BMI) from microbial genus abundances and two non-compositional covariates using "filtered_data".
from classo import csv_to_np, classo_problem, clr
# Load microbiome and covariate data X
X0 = csv_to_np('COMBO_data/complete_data/GeneraCounts.csv', begin = 0).astype(float)
X_C = csv_to_np('COMBO_data/CaloriData.csv', begin = 0).astype(float)
X_F = csv_to_np('COMBO_data/FatData.csv', begin = 0).astype(float)
# Load BMI measurements y
y = csv_to_np('COMBO_data/BMI.csv', begin = 0).astype(float)[:, 0]
labels = csv_to_np('COMBO_data/complete_data/GeneraPhylo.csv').astype(str)[:, -1]
# Normalize/transform data
y = y - np.mean(y) #BMI data (n = 96)
X_C = X_C - np.mean(X_C, axis = 0) #Covariate data (Calorie)
X_F = X_F - np.mean(X_F, axis = 0) #Covariate data (Fat)
X0 = clr(X0, 1 / 2).T
# Set up design matrix and zero-sum constraints for 45 genera
X = np.concatenate((X0, X_C, X_F, np.ones((len(X0), 1))), axis = 1) # Joint microbiome and covariate data and offset
label = np.concatenate([labels, np.array(['Calorie', 'Fat', 'Bias'])])
C = np.ones((1, len(X[0])))
C[0, -1], C[0, -2], C[0, -3] = 0., 0., 0.
# Set up c-lassso problem
problem = classo_problem(X, y, C, label = label)
# Use stability selection with theoretical lambda [Combettes & Müller, 2020b]
problem.model_selection.StabSelparameters.method = 'lam'
problem.model_selection.StabSelparameters.threshold_label = 0.5
# Use formulation R3
problem.formulation.concomitant = True
problem.solve()
print(problem)
print(problem.solution)
# Use formulation R4
problem.formulation.huber = True
problem.formulation.concomitant = True
problem.solve()
print(problem)
print(problem.solution)
The next microbiome example considers the 88 soils dataset from Lauber et al., 2009.
The task is to predict pH concentration in the soil from microbial abundance data. A similar analysis is available in Tree-Aggregated Predictive Modeling of Microbiome Data with Central Park soil data from Ramirez et al..
Code to run this application is available in the accompanying notebook under pH data. Below is a summary of a c-lasso problem instance (using the R3 formulation).
FORMULATION: R3
MODEL SELECTION COMPUTED:
Lambda fixed
Path
Stability selection
LAMBDA FIXED PARAMETERS:
numerical_method = Path-Alg
rescaled lam : True
threshold = 0.004
lam : theoretical
theoretical_lam = 0.2182
PATH PARAMETERS:
numerical_method : Path-Alg
lamin = 0.001
Nlam = 80
STABILITY SELECTION PARAMETERS:
numerical_method : Path-Alg
method : lam
B = 50
q = 10
percent_nS = 0.5
threshold = 0.7
lam = theoretical
theoretical_lam = 0.3085
The c-lasso estimation results are summarized below:
LAMBDA FIXED :
Sigma = 0.198
Selected variables : 14 18 19 39 43 57 62 85 93 94 104 107
Running time : 0.008s
PATH COMPUTATION :
Running time : 0.12s
STABILITY SELECTION :
Selected variables : 2 12 15
Running time : 0.287s
The available problem formulations [R1-C2] require different algorithmic strategies for efficiently solving the underlying optimization problem. We have implemented four algorithms (with provable convergence guarantees) that vary in generality and are not necessarily applicable to all problems. For each problem type, c-lasso has a default algorithm setting that proved to be the fastest in our numerical experiments.
This is the default algorithm for non-concomitant problems [R1,R3,C1,C2]. The algorithm uses the fact that the solution path along λ is piecewise- affine (as shown, e.g., in [1]). When Least-Squares is used as objective function, we derive a novel efficient procedure that allows us to also derive the solution for the concomitant problem [R2] along the path with little extra computational overhead.
This algorithm is derived from [2] and belongs to the class of proximal splitting algorithms. It extends the classical Forward-Backward (FB) (aka proximal gradient descent) algorithm to handle an additional linear equality constraint via projection. In the absence of a linear constraint, the method reduces to FB. This method can solve problem [R1]. For the Huber problem [R3], P-PDS can solve the mean-shift formulation of the problem (see [6]).
This algorithm is a special case of an algorithm proposed in [3] (Eq.4.5) and also belongs to the class of proximal splitting algorithms. The algorithm does not require projection operators which may be beneficial when C has a more complex structure. In the absence of a linear constraint, the method reduces to the Forward-Backward-Forward scheme. This method can solve problem [R1]. For the Huber problem [R3], PF-PDS can solve the mean-shift formulation of the problem (see [6]).
This algorithm is the most general algorithm and can solve all regression problems [R1-R4]. It is based on Doulgas Rachford splitting in a higher-dimensional product space. It makes use of the proximity operators of the perspective of the LS objective (see [4,5]) The Huber problem with concomitant scale [R4] is reformulated as scaled Lasso problem with the mean shift (see [6]) and thus solved in (n + d) dimensions.
[1] B. R. Gaines, J. Kim, and H. Zhou, Algorithms for Fitting the Constrained Lasso, J. Comput. Graph. Stat., vol. 27, no. 4, pp. 861–871, 2018.
[2] L. Briceno-Arias and S.L. Rivera, A Projected Primal–Dual Method for Solving Constrained Monotone Inclusions, J. Optim. Theory Appl., vol. 180, Issue 3, March 2019.
[3] P. L. Combettes and J.C. Pesquet, Primal-Dual Splitting Algorithm for Solving Inclusions with Mixtures of Composite, Lipschitzian, and Parallel-Sum Type Monotone Operators, Set-Valued and Variational Analysis, vol. 20, pp. 307-330, 2012.
[4] P. L. Combettes and C. L. Müller, Perspective M-estimation via proximal decomposition, Electronic Journal of Statistics, 2020, Journal version
[5] P. L. Combettes and C. L. Müller, Regression models for compositional data: General log-contrast formulations, proximal optimization, and microbiome data applications, Statistics in Bioscience, 2020.
[6] A. Mishra and C. L. Müller, Robust regression with compositional covariates, arXiv, 2019.
[7] S. Rosset and J. Zhu, Piecewise linear regularized solution paths, Ann. Stat., vol. 35, no. 3, pp. 1012–1030, 2007.
[8] J. Bien, X. Yan, L. Simpson, and C. L. Müller, Tree-Aggregated Predictive Modeling of Microbiome Data, biorxiv, 2020.
FAQs
Algorithms for constrained Lasso problems
We found that c-lasso demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 2 open source maintainers collaborating on the project.
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