【Python】 主双対アルゴリズムを用いた LASSO 回帰

主双対アルゴリズム¹を使用して LASSO (least absolute shrinkage and selection operator) 回帰を実装してみた．

扱う問題

\min_{w}{\frac1n\|Xw-y\|_1 + \alpha\|w\|_1}

ここで $X\in\mathbb{R}^{n\times m},\ y\in\mathbb{R}^n$ は入力データセット，$w\in\mathbb{R}^m$ は決定変数，$\alpha$ は正則化パラメータである．

\|w\|_1 = \max_{-1\leq p\leq1}\left<w, p\right>

より，先の最適化問題は，

\min_w{\max_{-1 \leq (p, q) \leq 1}{\frac1n\left<Xw-y, q\right> + \alpha\left<w, p\right>}}

とかける．

主双対アルゴリズム

近接点法を用いて交互に最小化・最大化問題を解く．

\left\{
\begin{align}
&w^{k+1} = \mathop{\mathrm{arg~min}}\limits_{w}{\left\{\frac1n\left<Xw-y, q^k\right> + \alpha\left<w, p^k\right> + \frac12\|w - w^k\|_X^2\right\}}\\
&\bar{w}^{k+1} = 2w^{k+1} - w^k\\
&(p^{k+1}, q^{k+1}) = \mathop{\mathrm{arg~max}}\limits_{-1 \leq (p, q) \leq 1}{\left\{\frac1n\left<X\bar{w}^{k+1}-y, q\right> + \alpha\left<\bar{w}^{k+1}, p\right> - \frac12\|(p, q) - (p^k, q^k)\|_Y^2\right\}}
\end{align}
\right.

ここで，

K = \left(\frac1nX^\top\hspace{-1pt}, \alpha I\right)^\top\hspace{-1pt}\\
\|x\|_X^2 = \left<x, T^{-1}x\right>\\
T = \mathrm{diag}(\tau)\quad \mathrm{where}\quad \tau_j=\frac{1}{\sum_{i}|K_{i,j}|^{2-\beta}}\\
\|y\|_Y^2 = \left<y, \Sigma^{-1}y\right>\\
\Sigma = \mathrm{diag}(\sigma)\quad \mathrm{where}\quad \sigma_i=\frac{1}{\sum_{j}|K_{i,j}|^{\beta}}

である．これより，更新式は

\left\{
\begin{align}
&w^{k+1} = w^k - T\left(\frac1nX^\top\hspace{-1pt}q^k + \alpha p^k\right)\\
&\bar{w}^{k+1} = 2w^{k+1} - w^k\\
&(p^{k+1}, q^{k+1}) = \mathrm{proj}_{[-1, 1]}\left((p^k, q^k) + \Sigma\left(\alpha\bar{w}^{k+1}, \frac1n(X\bar{w}^{k+1} - y)\right)\right)
\end{align}
\right.

となる．

ソースコード

Python で実装．

• MacOS Mojave : 10.14.3
• Python : 3.7.3
• numpy : 1.16.4

ソースコードや実行した notebook は Github に．

from typing import Tuple, List

import numpy as np
from tqdm import trange


class Lasso:
    """
    Lasso regression model using the preconditioned primal dual algorithm
    """
    def __init__(self,
            alpha: float = 1.0,
            beta: float = 0.5,
            max_iter: int = 1000,
            extended_output: bool = False):
        """
        Parameters
        ----------
        alpha : float
            A regularization parameter.
        beta : float in [0.0, 2.0]
            A parameter used in step size.
        max_iter : int
            The maximum number of iterations.
        extended_output : bool
            If True, return the value of the objective function of each iteration.
        """
        np.random.seed(0)
        self.alpha = alpha
        self.beta = beta
        self.max_iter = max_iter
        self.extended_output = extended_output

        self.coef_ = None
        self.objective_function = list()

    def _step_size(self, X: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
        n_samples, n_features = X.shape
        abs_X = np.abs(X)

        tau = np.sum(abs_X ** self.beta, axis=0)
        tau += self.alpha ** self.beta
        tau = 1 / tau

        sigma = np.empty(n_samples + n_features, dtype=X.dtype)
        sigma[:n_samples] = np.sum(abs_X ** (2 - self.beta), axis=1)
        sigma[n_samples:] = self.alpha ** (2 - self.beta)
        sigma = 1. / sigma

        return tau, sigma

    def fit(self, X: np.ndarray, y: np.ndarray) -> None:
        """
        Parameters
        ----------
        X : array, shape = (n_samples, n_features)
        y : array, shape = (n_samples, )
        """
        n_samples, n_features = X.shape
        n_samples_inv = 1 / n_samples
        bar_X = X * n_samples_inv
        bar_y = y * n_samples_inv
        tau, sigma = self._step_size(bar_X)

        # initialize
        res = np.zeros(n_features, dtype=X.dtype)
        dual = np.zeros(n_samples + n_features, dtype=X.dtype)
        dual[:n_samples] = np.clip(-sigma[:n_samples] * y * n_samples_inv, -1, 1)

        # objective function
        if self.extended_output:
            self.objective_function.append(
                np.sum(np.abs(bar_y)) + self.alpha * np.sum(np.abs(res))
            )

        # main loop
        for _ in trange(self.max_iter):
            w = res - tau * (bar_X.T.dot(dual[:n_samples]) + self.alpha * dual[n_samples:])
            bar_w = 2 * w - res
            dual[:n_samples] = dual[:n_samples] + sigma[:n_samples] * (bar_X.dot(bar_w) - bar_y)
            dual[n_samples:] = dual[n_samples:] + sigma[n_samples:] * bar_w * self.alpha
            dual = np.clip(dual, -1, 1)
            res = w
            if self.extended_output:
                self.objective_function.append(
                    np.sum(np.abs(bar_X.dot(w) - bar_y)) + self.alpha * np.sum(np.abs(res))
                )
        self.coef_ = res

実行結果．
$X,\ w$ を乱数で与え，$w$ の復元を見ている．復元誤差 (error) は $\alpha$ に依存するが，実装した Lasso は収束が遅く scikit-learn のが良さそう．

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1. Thomas Pock and Antonin Chambolle 2012 "Diagonal preconditioning for first order primal-dual algorithms in convex optimization" 2011 International Conference on Computer Vision https://ieeexplore.ieee.org/abstract/document/6126441 ↩