# Simplex Regression with Trend Filtering¶

This notebook showcase a structured regression method which defines a predictor as an average of several values (e.g. baseline methods), with a temporal regularization which enforces piecewise constant choices (often called "trend filtering"). $\newcommand{\dotp}[2]{\langle #1, #2 \rangle}$ $\newcommand{\enscond}[2]{\lbrace #1, #2 \rbrace}$ $\newcommand{\pd}[2]{ \frac{ \partial #1}{\partial #2} }$ $\newcommand{\umin}[1]{\underset{#1}{\min}\;}$ $\newcommand{\umax}[1]{\underset{#1}{\max}\;}$ $\newcommand{\umin}[1]{\underset{#1}{\min}\;}$ $\newcommand{\uargmin}[1]{\underset{#1}{argmin}\;}$ $\newcommand{\norm}[1]{\|#1\|}$ $\newcommand{\abs}[1]{\left|#1\right|}$ $\newcommand{\choice}[1]{ \left\{ \begin{array}{l} #1 \end{array} \right. }$ $\newcommand{\pa}[1]{\left(#1\right)}$ $\newcommand{\diag}[1]{{diag}\left( #1 \right)}$ $\newcommand{\qandq}{\quad\text{and}\quad}$ $\newcommand{\qwhereq}{\quad\text{where}\quad}$ $\newcommand{\qifq}{ \quad \text{if} \quad }$ $\newcommand{\qarrq}{ \quad \Longrightarrow \quad }$ $\newcommand{\ZZ}{\mathbb{Z}}$ $\newcommand{\CC}{\mathbb{C}}$ $\newcommand{\RR}{\mathbb{R}}$ $\newcommand{\EE}{\mathbb{E}}$ $\newcommand{\Zz}{\mathcal{Z}}$ $\newcommand{\Ww}{\mathcal{W}}$ $\newcommand{\Vv}{\mathcal{V}}$ $\newcommand{\Nn}{\mathcal{N}}$ $\newcommand{\NN}{\mathcal{N}}$ $\newcommand{\Hh}{\mathcal{H}}$ $\newcommand{\Bb}{\mathcal{B}}$ $\newcommand{\Ee}{\mathcal{E}}$ $\newcommand{\Cc}{\mathcal{C}}$ $\newcommand{\Gg}{\mathcal{G}}$ $\newcommand{\Ss}{\mathcal{S}}$ $\newcommand{\Pp}{\mathcal{P}}$ $\newcommand{\Ff}{\mathcal{F}}$ $\newcommand{\Xx}{\mathcal{X}}$ $\newcommand{\Mm}{\mathcal{M}}$ $\newcommand{\Ii}{\mathcal{I}}$ $\newcommand{\Dd}{\mathcal{D}}$ $\newcommand{\Ll}{\mathcal{L}}$ $\newcommand{\Tt}{\mathcal{T}}$ $\newcommand{\si}{\sigma}$ $\newcommand{\al}{\alpha}$ $\newcommand{\la}{\lambda}$ $\newcommand{\ga}{\gamma}$ $\newcommand{\Ga}{\Gamma}$ $\newcommand{\La}{\Lambda}$ $\newcommand{\si}{\sigma}$ $\newcommand{\Si}{\Sigma}$ $\newcommand{\be}{\beta}$ $\newcommand{\de}{\delta}$ $\newcommand{\De}{\Delta}$ $\newcommand{\phi}{\varphi}$ $\newcommand{\th}{\theta}$ $\newcommand{\om}{\omega}$ $\newcommand{\Om}{\Omega}$

You need to install CVXPY. Warning: seems to not be working on Python 3.7, use rather 3.6.

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import numpy as np
import matplotlib.pyplot as plt
import cvxpy as cp


# Simplex Regression¶

In the following $n$ is the number of "temporal" samples (for instance angle) and $m$ the number of methods.

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n = 360  # angles
m = 4 # methods


The set of regression weights is $W \in \RR^{n \times m}$ and the linear regression of $y_i$ at time (or angle $i$) reads $$y_i \approx \sum_{j} M_{i,j} W_{i,j}.$$ Here $M_{i,j}$ is the output of method $j$ at time $i$.

The constraint is that for each time $i$, the set of weights $W_{i,\cdot} = (W_{i,j})_{j=1}^m$ is in the probability simplex $$W_{i,j} \geq 0 \qandq \sum_j W_{i,j}=1.$$ We re-write this constraint conveniently as $W \geq 0, W 1_m = 1_n$.

We use a $\ell^2$ regression penalized by the sum of absolute value of temporal differences $$\min_{W \geq 0, W 1_m = 1_n} \sum_i \Big( y_i - \sum_{j} M_{i,j} W_{i,j} \Big)^2 + \lambda \sum_{i,j} |W_{i+1,j}-W_{i,j}|$$

Generate random data $M$ and $y$.

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M = np.random.randn(n,m)
y = np.random.randn(n)


Define the optimized over variable $W$.

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W = cp.Variable((n,m))


Define the set constraints (vectors in the simplex).

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u = np.ones((m,1))
v = np.ones((n,1))
U = [0 <= W, cp.matmul(W,u)==v]


Regularization parameter $\lambda$. Increase this value makes the resulting weights more and more constant.

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Lambda = 10


Solve the minimization using CVXPY

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objective = cp.Minimize( cp.sum( ( cp.sum(cp.multiply(M,W), axis=1) - y )**2 ) + Lambda * cp.sum( cp.abs(W[1:,:] - W[:-1,:]) )  )
prob = cp.Problem(objective, U)
result = prob.solve()


Display the evolution of the weights associated to the three first variables as function of time.

In [8]:
Wm = W.value
plt.plot(Wm[:,0])
plt.plot(Wm[:,1])
plt.plot(Wm[:,2]);


# Group-Lasso Regularization¶

It is possible to enforce that breakpoints in time are somehow "synchronized" by using a group lasso regularization. $$\min_{W \geq 0, W 1_m = 1_n} \sum_i \Big( yi - \sum{j} M{i,j} W{i,j} \Big)^2 + \lambda \sum_{i} \sqrt{ \sum_j|W_{i+1,j}-W_{i,j}|^2 } $$

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Lambda = 20
objective = cp.Minimize( cp.sum( ( cp.sum(cp.multiply(M,W), axis=1) - y )**2 ) + Lambda * cp.mixed_norm( W[1:,:] - W[:-1,:], 2, 1 ) )
prob = cp.Problem(objective, U)
result = prob.solve()

In [10]:
Wm = W.value
plt.plot(Wm[:,0])
plt.plot(Wm[:,1])
plt.plot(Wm[:,2]);


We now show the evolution of the solution for varying $\lambda$.

In [11]:
Lambda_list = np.array([2, 5, 10, 20])
for i in np.arange(0,4):
Lambda = Lambda_list[i];
objective = cp.Minimize( cp.sum( ( cp.sum(cp.multiply(M,W), axis=1) - y )**2 ) + Lambda * cp.mixed_norm( W[1:,:] - W[:-1,:], 2, 1 ) )
prob = cp.Problem(objective, U)
result = prob.solve()
Wm = W.value
ax1=plt.subplot(2, 2, i+1)
plt.plot(Wm[:,0])
plt.plot(Wm[:,1])
plt.plot(Wm[:,2])
plt.title('$\lambda$='+str(Lambda))
ax1.set_xticklabels([])

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