From 5b6445185bf88d2a9f76c8fe1a8f30a756423813 Mon Sep 17 00:00:00 2001 From: healeyq3 Date: Thu, 25 Jul 2024 17:39:43 -0400 Subject: [PATCH 1/3] some bug patches + torch no_grad forward pass --- cvxpylayers/torch/cvxpylayer.py | 13 +++++-- cvxpylayers/torch/test_cvxpylayer.py | 51 +++++++++++++++++++++++----- 2 files changed, 53 insertions(+), 11 deletions(-) diff --git a/cvxpylayers/torch/cvxpylayer.py b/cvxpylayers/torch/cvxpylayer.py index 1768cd2..d9feeb0 100644 --- a/cvxpylayers/torch/cvxpylayer.py +++ b/cvxpylayers/torch/cvxpylayer.py @@ -283,11 +283,18 @@ def forward(ctx, *params): ctx.shapes.append(A.shape) info['canon_time'] = time.time() - start - # compute solution and derivative function + # compute solution (always) + # and derivative function (if needed for reverse mode) start = time.time() try: - xs, _, _, _, ctx.DT_batch = diffcp.solve_and_derivative_batch( - As, bs, cs, cone_dicts, **solver_args) + if any(p.requires_grad for p in params): + xs, _, _, _, ctx.DT_batch = ( + diffcp.solve_and_derivative_batch( + As, bs, cs, cone_dicts, **solver_args) + ) + else: + xs, _, _ = diffcp.cone_program.solve_only_batch( + As, bs, cs, cone_dicts, **solver_args) except diffcp.SolverError as e: print( "Please consider re-formulating your problem so that " diff --git a/cvxpylayers/torch/test_cvxpylayer.py b/cvxpylayers/torch/test_cvxpylayer.py index adc3f67..69bec54 100755 --- a/cvxpylayers/torch/test_cvxpylayer.py +++ b/cvxpylayers/torch/test_cvxpylayer.py @@ -87,10 +87,9 @@ def test_least_squares(self): def lstsq( A, - b): return torch.solve( - (A_th.t() @ b_th).unsqueeze(1), - A_th.t() @ A_th + - torch.eye(n).double())[0] + b): return torch.linalg.solve( + A.t() @ A + torch.eye(n).double(), + (A.t() @ b).unsqueeze(1)) x_lstsq = lstsq(A_th, b_th) grad_A_cvxpy, grad_b_cvxpy = grad(x.sum(), [A_th, b_th]) @@ -325,10 +324,9 @@ def test_broadcasting(self): def lstsq( A, - b): return torch.solve( - (A.t() @ b).unsqueeze(1), - A.t() @ A + - torch.eye(n).double())[0] + b): return torch.linalg.solve( + A.t() @ A + torch.eye(n).double(), + (A.t() @ b).unsqueeze(1)) x_lstsq = lstsq(A_th, b_th_0) grad_A_cvxpy, grad_b_cvxpy = grad(x.sum(), [A_th, b_th]) @@ -416,6 +414,43 @@ def test_basic_gp(self): "eps": 1e-12, "acceleration_lookback": 0})[0].sum(), (a_tch, b_tch, c_tch), atol=1e-3, rtol=1e-3) + def test_no_grad_context(self): + n, m = 2, 3 + x = cp.Variable(n) + A = cp.Parameter((m, n)) + b = cp.Parameter(m) + constraints = [x >= 0] + objective = cp.Minimize(0.5 * cp.pnorm(A @ x - b, p=1)) + problem = cp.Problem(objective, constraints) + assert problem.is_dpp() + + cvxpylayer = CvxpyLayer(problem, parameters=[A, b], variables=[x]) + A_tch = torch.randn(m, n, requires_grad=True) + b_tch = torch.randn(m, requires_grad=True) + + with torch.no_grad(): + solution, = cvxpylayer(A_tch, b_tch) + + self.assertFalse(solution.requires_grad) + + def test_requires_grad_false(self): + n, m = 2, 3 + x = cp.Variable(n) + A = cp.Parameter((m, n)) + b = cp.Parameter(m) + constraints = [x >= 0] + objective = cp.Minimize(0.5 * cp.pnorm(A @ x - b, p=1)) + problem = cp.Problem(objective, constraints) + assert problem.is_dpp() + + cvxpylayer = CvxpyLayer(problem, parameters=[A, b], variables=[x]) + A_tch = torch.randn(m, n, requires_grad=False) + b_tch = torch.randn(m, requires_grad=False) + + solution, = cvxpylayer(A_tch, b_tch) + + self.assertFalse(solution.requires_grad) + if __name__ == '__main__': unittest.main() From d348a372c047145fff83535cef119ae9ac4be0f8 Mon Sep 17 00:00:00 2001 From: healeyq3 Date: Fri, 9 Aug 2024 22:26:52 -0400 Subject: [PATCH 2/3] learned state estimation example (draft) --- examples/torch/learned_state_estimation.html | 9532 +++++++++++++++++ examples/torch/learned_state_estimation.ipynb | 1831 ++++ 2 files changed, 11363 insertions(+) create mode 100644 examples/torch/learned_state_estimation.html create mode 100644 examples/torch/learned_state_estimation.ipynb diff --git a/examples/torch/learned_state_estimation.html b/examples/torch/learned_state_estimation.html new file mode 100644 index 0000000..b9b45e0 --- /dev/null +++ b/examples/torch/learned_state_estimation.html @@ -0,0 +1,9532 @@ + + + + + +learned_state_estimation + + + + + + + + + + + + +
+ + + + + + + + + + + + + + + + + +
+ + diff --git a/examples/torch/learned_state_estimation.ipynb b/examples/torch/learned_state_estimation.ipynb new file mode 100644 index 0000000..e44cb7e --- /dev/null +++ b/examples/torch/learned_state_estimation.ipynb @@ -0,0 +1,1831 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "from typing import (Tuple, List, Callable)\n", + "\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from scipy import sparse\n", + "\n", + "import cvxpy as cp\n", + "import torch\n", + "import torch.nn.functional as F\n", + "from cvxpylayers.torch import CvxpyLayer\n", + "\n", + "torch.set_default_dtype(torch.float64)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Learning a robust Kalman smoother for vehicle tracking\n", + "\n", + "We will try to recover the state history (*i.e.*, location and velocity trajectories) of a moving vehicle from noisy sensor data. To do this, we'll model the vehicle state as a discrete-time linear dynamical system and use standard and robust **Kalman smoothers** to estimate the state history of this system from the noisy sensor measurements. To construct our smoothers, we'll depart from typical Kalman methodologies in favor of an optimization-based approach. Considering these optimization problems as mutable objects, we then take a machine learning approach to evaluating and tuning the smoothers.\n", + "\n", + "# Background\n", + "\n", + "The primary aim of this notebook is demonstrating how to use `cvxpylayers` to auto-tune parameters in a Kalman smoother. That said, it also contains a broad introduction to Kalman smoothing and a somewhat opinionated tutorial on mathematical optimization based Kalman smoothing. If you're a reader who is familiar with Kalman smoothers and/or just want to focus on the auto-tuning problem, that material starts in the **Background/Learning** section. The entire **Implementation** section is then focused on using `cvxpylayers` to auto-tune vehicle tracking Kalman smoothers. \n", + "\n", + "## Motivating work\n", + "\n", + "We also want to emphasize that this notebook is rather unoriginal. It is an extension of the [robust Kalman filtering for vehicle tracking](https://www.cvxpy.org/examples/applications/robust_kalman.html) example found on the CVXPY website, and it is a watered-down, slight variation of the work in \\[[BB20](https://stanford.edu/~boyd/papers/auto_ks.html)\\] and in \\[[BV18 sec17.2](https://web.stanford.edu/~boyd/vmls/)\\]. All three of these sources are **highly recommended** for anyone wanting to learn more about Kalman smoothers and the Kalman smoother auto-tuning problem.\n", + "\n", + "## System Model\n", + "\n", + "We consider a linear, dynamical system (LDS) governed by\n", + "\n", + "$$\n", + "\\begin{equation}\n", + "x_{t+1} = Ax_t + Bw_t, \\quad t =1, \\ldots, T-1,\n", + "\\tag{1}\n", + "\\end{equation}\n", + "$$\n", + "\n", + "and output or sensor measurements\n", + "\n", + "$$\n", + "\\begin{align}\n", + "y_t = Cx_t + v_t, \\quad t=1, \\ldots, T.\n", + "\\tag{2}\n", + "\\end{align}\n", + "$$\n", + "\n", + "Here $x_t \\in \\mathbf{R}^{n}$ is the state, $w_t \\in \\mathbf{R}^{m}$ is an input to the dynamical system (*e.g.*, a drive force on the vehicle), $y_t \\in \\mathbf{R}^{p}$ is the output or sensor measurement, and $v_t \\in \\mathbf{R}^{p}$ is the sensor noise, at time $t$. The matrix $A \\in \\mathbf{R}^{n \\times n}$ is the state dynamics matrix, $B ∈ \\mathbf{R}^{n \\times m}$ is the input matrix, and $C \\in \\mathbf{R}^{p \\times n}$ is the observation (or output) matrix. \n", + "\n", + "In *state estimation*, **the goal** is to guess the state sequence, $x_1, \\ldots, x_T$, using the observations $y_1, \\ldots, y_T$. In vehicle tracking, $x_t$ could be a four-vector (*i.e.*, $x_t \\in \\mathbf{R}^{4}$), where $\\left(x_t \\right)_{1:2}$ is the position of the vehicle in two dimenions, $\\left(x_t \\right)_{3:4}$ is the vehicle velocity, and $y_t$ could be a five-vector containing measurements of latitude, longitude, heading, speed, and altitude of an iPhone mounted on the vehicle (this is an example in \\[[BB20](https://stanford.edu/~boyd/papers/auto_ks.html)\\]).\n", + "\n", + "Traditionally, the matrices $A$, $B$, and $C$ are assumed fixed, while statistical assumptions are placed on $w_1, \\ldots , w_{T-1}$ and $v_1, \\ldots, v_{T}$. Importantly, whether the lack of knowledge is encoded statistically or not, we do not exactly know these noises. If $w_1, \\ldots, w_{T-1}$ and $v_1, \\ldots, v_T$ were known to us, we would be *solving for* $x_t$, not *estimating* $x_t$. (Sometimes, when arriving at an estimation, $x_t$ is replaced with $\\hat{x}_t$ to clearly mark this distinction.)\n", + "\n", + "## Smoothing\n", + "\n", + "### Overview\n", + "\n", + "Smoothing is an offline (or batch) approach to approximately reconstructing the state sequence, *i.e.*, the measurements $y_1, \\ldots, y_T$ are collected, and *then* smoothing is performed to estimate $x_1, \\ldots, x_T$. There are many approaches to constructing smoothers. The most widely used approach is an extension of **Kalman filtering**. (See \\[[Mur23 sec8.2](https://probml.github.io/pml-book/book2.html)\\] for an overview of the classical Kalman filtering and smoothing methodologies based on the original worked proposed in \\[[Kal60](https://www.unitedthc.com/DSP/Kalman1960.pdf)\\]. But briefly, while smoothing is an offline algorithm used to reconstruct a state trajectory, filtering is an online algorithm used to estimate the current state of the LDS as new measurements arrive. With that being said, the first step in the classical approach to constructing a smoother is to construct a filter.)\n", + "\n", + "In this notebook, however, we consider constructing smoothers by (explicitly) formulating optimization problems. Therefore, we perform the *act* of smoothing, obtaining $\\hat{x}_1, \\ldots, \\hat{x}_T$, by *solving* a smoother's optimization problem. A major advantage to this mathematical optimization approach is the ability to formulate smoothers that handle missing measurements. If a smoother can handle missing measurements, we can take a machine learning approach to evaluating it. That is, to assess how well a smoother *generalizes* to unseen data, we can purposefully withhold measurements when creating the smoother and then compare the smoother's predicted outputs with this unseen (test) data. Additionally, because smoothers are characterized (or flavored) by parameters (hyper-parameters, in machine learning dialect), comparing the *prediction error* between smoothers is one way of determining whether one smoother is \"better\" than another.\n", + "\n", + "There are also a couple, less technically deep, advantages to this mathematical optimization approach to smoothing. Firstly, smoothers formulated as optimization problems are incredibly easy to interpret. In fact, the idea of a smoother \"object\" only arises in an optimization context. In typical literature, Kalman filtering (recalling that smoothing is filtering with some additional adjustments) is just a clever method for computing estimates of the LDS state and state uncertainty recursively. Consequently, understanding what a Kalman filter does in a traditional sense requires understanding some ideas from probability and statistics which are not usually seen in first courses on the subjects. In comparison, even if we were to incorporate statistical assumptions into our optimization models, such as those considered in \\[[BB20](https://stanford.edu/~boyd/papers/auto_ks.html)\\], a smoother packaged as an optimization problem can just be \"read off\" as a state estimator.\n", + "\n", + "It also is far easier to extend and solve smoothers in a (convex) optimization approach than in a statistical approach. For instance, in the sequel, we'll construct a robust smoother by swapping quadratic penalty terms in the non-robust smoother's optimization problem's objective function with Huber penalty terms. This replacement is obvious to anyone familiar with convex optimization (particularly the ideas in \\[[BV04 ch6](https://stanford.edu/~boyd/cvxbook/)\\]), and because the resulting problem is convex, it is readily solved. However, making such a swap in the traditional statisical approach would be less trivial.\n", + "\n", + "Using an out-of-the-box Kalman filter or smoother is certainly not difficult. Because the update equations which define a filter/smoother are already derived, Kalman filtering/smoothing in practice is simply an exercise in sparse linear algebra. However, because the traditional filter is constructed as a mean squared error minimizer (or through maximum likelihood statistics), adjusting the filter to be more robust would require re-constructing the filter to be the minimizer of the expectation of some other error function. Considering the effort required to derive the typical Kalman filter, this approach is not too appealing. This work would only be further complicated if the typical Gaussian assumptions were replaced with fatter-tailed distributions (an idea proposed in \\[[BB20](https://stanford.edu/~boyd/papers/auto_ks.html)\\]). However, if we were to add statistical assumptions to our approach, such a distribution swap would be trivial so long as it maintained problem convexity. We could even add probabilistic restrictions or desired attributes to the filter/smoother via problem constraints.\n", + "\n", + "All of this is to say that an optimization approach to smoothing is **declarative.** Put glibly, you hand some observed measurements to a smoother problem and say (through a `.solve()` call) \"find a state sequence that is consistent with these measurements and some *a priori* knowledge of the system dynamics.\" The solver will then return to you (assuming feasibility) **the best possible state sequence matching the smoother's requirements**, and it will do so reliably and efficiently. (The idea of optimization being a declarative manner of solving problems is explored [here](https://www.youtube.com/watch?v=EwYgjTAagXQ&t=1370s) in the context of control.)\n", + "\n", + "Before detailing our optimization approach, it is worth noting that the typical Kalman filter and smoother are still terrific approaches to state estimation. The traditional approach to Kalman *filtering* actually has a major advantage over this optimization approach: once a state estimate is computed, all observations can be thrown away. Therefore, it's less memory intensive than an optimization approach, and thus more desirable in an online setting (one might argue that an optimization approach is also too computationally expensive in an online setting, but this is a non-issue. See \\[[01 p4](https://web.stanford.edu/~boyd/papers/learning_cocps.html)\\]).\n", + "\n", + "### Approach\n", + "\n", + "We'll consider the main problem formulation ideas here. The conclusion of these ideas will be a basic smoother problem, but the optimization problems used to construct the smoothers that we'll consider in the vehicle tracking example will be stated later in this notebook.\n", + "\n", + "Looking again at (1) and (2), the components of our optimization problem arise quite naturally. Because we are trying to determine the state sequence $x_1, \\ldots, x_T$, these are optimization variables; they need to be decided upon. However, these variables are subject to the requirement that they satisfy the dynamic system model (1), so\n", + "\n", + "$$\n", + "x_{t+1} = Ax_t + Bw_t, \\quad t =1, \\ldots, T-1,\n", + "$$\n", + "\n", + "must be constraints in the formulation.\n", + "\n", + "A fundamental assumption in Kalman filtering and smoothing is that the input and output noises are not too large. Or, to rely on (basic) statistics just briefly, *that the means of both the input noise and measurement noise are known.* This assumption can be understood intuitively thinking through what a Kalman smoother (or filter) is doing. \n", + "\n", + "We are *assuming* that we have two sources of (informally) information about the state in some system of interest. The first is the mathematical model, (1), which dictates how the state should evolve in time. The second is sensor measurements of the state as it does evolve in time. However, both sources include some form of randomness. The purpose of the Kalman smoother (or filter) is to merge these two somewhat reliable state estimators into a far more reliable state estimator. Furthermore, if $w_t$ is not small (or rather, if $w_t$ less the mean input noise is not small), then the first source of information becomes unreliable. Likewise, if the sensor noise is not \"small\" (there's no structure to its randomness), then there's no reason to believe that the measurements $y_t$ provide any useful information about the state.\n", + "\n", + "This fundamental assumption leads us to a bi-criterion optimization problem where we wish to minimize both the sensor noise (or equivalently, the size of the discrepancy between sensor measurements and predicted outputs) and the input noise. Mathematically, a good (and pervasive across Kalman smoothing/filtering) characterization of the total sensor noise is the sum of squares of the norms of the measurement residuals,\n", + "\n", + "$$\n", + "J_{\\text{meas}} = \\left\\lVert v_1 \\right\\rVert_{2}^2 + \\cdots + \\left\\lVert v_T \\right\\rVert_{2}^2 = \\left\\lVert Cx_1 - y_1 \\right\\rVert_{2}^2 + \\cdots + \\left\\lVert Cx_T - y_T \\right\\rVert_{2}^2.\n", + "$$\n", + "\n", + "Likewise, we'll take the total process noise to be the sum of squares of the norms of the process noise,\n", + "\n", + "$$\n", + "J_{\\text{proc}} = \\left\\lVert w_1 \\right\\rVert_{2}^2 + \\cdots + \\left\\lVert w_T \\right\\rVert_{2}^2.\n", + "$$ \n", + "\n", + "Combining this bi-criterion objective with the aforementioned constraints leads us to the simple smoother formulation\n", + "\n", + "$$\n", + "\\begin{equation}\n", + "\\begin{array}{lll}\n", + "\\text{minimize} \\; & \\tau J_{\\text{proc}} + J_{\\text{meas}} & \\\\\n", + "\\text{subject to} & x_{t+1} = Ax_t + Bw_t, \\; & t=1, \\ldots, T-1,\n", + "\\end{array}\n", + "\\tag{3}\n", + "\\end{equation}\n", + "$$\n", + "\n", + "where $x_t \\in \\mathbf{R}^{n}$ and $w_t \\in \\mathbf{R}^{m}, \\, t=1, \\ldots, T,$ are optimization variables and $\\tau > 0$ is a **tuning parameter** in the problem which (roughly) determines whether we trust the measurements more, or the dynamics more. (Throughout this notebook, we'll refer to parameters in a smoother's defining optimization problem as the \"smoother's parameters.\" We'll also refer to a smoother as it's defining optimization problem, *e.g.*, \"consider the smoother (3).\") \n", + "\n", + "Clearly, the choice of $\\tau$ can drastically affect the estimated state that the smoother produces. The focus of this notebook is demonstrating **how to use `cvxpylayers` to auto-tune smoother parameters** to construct \"good\" smoothers.\n", + "\n", + "## Missing Measurements \n", + "\n", + "As the LDS evolves, all updates we have about the system come from the sequence $y_1, \\ldots, y_T$. However, it may be the case that the full output sequence is not available to us. Or put another way, the output sequence could have missing measurements (*e.g.*, over a $T=10$ horizon where $y_t \\in \\mathbf{R}^{2}$, perhaps at the second time step we are missing measurement one, and then at the ninth time step we are missing measurement two). To model this, we modify the output equation (2) so that $y_t \\in (\\mathbf{R} ∪ \\left\\{ ? \\right\\})^p$, where ? denotes a missing value. So (2) becomes\n", + "\n", + "$$\n", + "(y_t)_i = (Cx_t + v_t)_i, \\quad (t, i) \\in \\mathcal{K},\n", + "$$\n", + "\n", + "where $\\mathcal{K} \\subseteq \\left\\{ 1, \\ldots, T \\right\\} \\times \\left\\{ 1, \\ldots, p \\right\\}$ is the set of (scalar) outputs that are available. For $(t, i) \\not \\in \\mathcal{K}$, we take $(y_t)_i = \\,?$. We refer to the real entries of $y_t$ as *known measurements* and the entries of $y_t$ that have the value $?$ as *missing measurements.*\n", + "\n", + "**Importantly**, for the remainder of this notebook, we assume that the full output sequence, $y_1, \\ldots, y_T$, is collected. That is, $\\mathcal{K} = \\left\\{ 1, \\ldots, T \\right\\} \\times \\left\\{ 1, \\ldots, p \\right\\}$. Nonetheless, we'll need the machinery presented here when we separate the measurements into training and testing datasets (or cross-validation folds), masking measurements $\\mathcal{M} \\subset \\mathcal{K}$ and constructing smoothers with $\\mathcal{K} \\setminus \\mathcal{M}$.\n", + "\n", + "## The smoothing problems\n", + "\n", + "### Initial formulations\n", + "\n", + "Consider again the first proposed smoother, (3). Making the typical statistical assumptions about the noises $w_1, \\ldots, w_T$ and $v_1, \\ldots, v_T$ and considering the problem as $T → ∞$, it turns out that (3) characterizes the Kalman filter. Or rather, the optimal solution to (3) is the recursive, closed-form update equations that appear in Kalman filtering. This observation is useful because it gives insight into a potential failure of this smoother. Namely, it performs well when $w_t$ and $v_t$ are Gaussian, but large outliers in the measurements will significantly influence the smoother's quadratic objective, thus degrading the accuracy of the smoother's state recovery.\n", + "\n", + "To improve estimation in the presence of outliers, we propose a **robust Kalman smoother,** which is formulated by simply replacing\n", + "\n", + "$$\n", + "J_{\\text{meas}} = \\left\\lVert Cx_1 - y_1 \\right\\rVert_{2} + \\cdots + \\left\\lVert Cx_T - y_T \\right\\rVert_{2}^2\n", + "$$\n", + "\n", + "in (3) with\n", + "\n", + "$$\n", + "J_{\\text{meas}} = \\phi_{\\rho}\\left(Cx_1 - y_1 \\right) + \\cdots + \\phi_{\\rho}\\left(Cx_T - y_T \\right),\n", + "$$\n", + "\n", + "where \n", + "\n", + "$ϕ_{ρ}: \\mathbf{R}^{n} \\to \\mathbf{R}$, defined as\n", + "\n", + "$$\n", + "\\phi_{\\rho}(u) = \\begin{cases}\n", + "\\left\\lVert u \\right\\rVert_{2}^2 & \\left\\lVert u \\right\\rVert_{2} \\le \\rho \\\\\n", + "\\rho(2 \\left\\lVert u \\right\\rVert_{2} - \\rho) & \\left\\lVert u \\right\\rVert_{2} > ρ,\n", + "\\end{cases}\n", + "$$\n", + "\n", + "is the Huber penalty function. This function is \"robust\" as it penalizes estimation errors linearly outside of a ball of radius $ρ$. Consequently, large measurement outliers will only (roughly) influence the objective function linearly. (See \\[[BV04 p298-300](https://stanford.edu/~boyd/cvxbook/)\\] for intuition-building visualizations of the Huber function and the induced residual behavior.)\n", + "\n", + "Making this measurement error term replacement (and dropping the $J\\text{s}$ for clarity), the robust smoother problem is\n", + "\n", + "$$\n", + "\\begin{equation}\n", + "\\begin{array}{lll}\n", + "\\text{minimize} \\; & \\sum_{t=1}^{T} \\left(\\tau \\left\\lVert w_t \\right\\rVert_{2}^2 + \\phi_{\\rho}(Cx_t - y_t) \\right) & \\\\\n", + "\\text{subject to} & x_{t+1} = Ax_t + Bw_t, \\; & t = 1, \\ldots, T-1,\n", + "\\end{array}\n", + "\\tag{4}\n", + "\\end{equation}\n", + "$$\n", + "\n", + "where again $x_t$ and $w_t, \\, t=1, \\ldots, T$, are optimization variables, but now both $\\tau >0$ and $\\rho>0$ are tuning parameters. Specifically, $\\tau$ still is a measure of whether we trust the dynamics or the observations more, while $\\rho$ (roughly) determines the size of the ball we believe reasonable observations should lie in.\n", + "\n", + "### Handling missing measurements\n", + "\n", + "Consider masking some fraction (*e.g.*, 20%) of the measurements, denoted by the set $\\mathcal{M} \\subset \\mathcal{K}$, resulting in a *masked trajectory* $\\tilde{y}_1, \\ldots, \\tilde{y}_T$. That is, we let $(\\tilde{y}_t)_i = \\, ?$ for $(t, i) \\in \\mathcal{M}$ and $(\\tilde{y}_t)_i = y_i$ for $(t, i) \\in \\mathcal{K} \\setminus \\mathcal{M}$.\n", + "\n", + "To construct smoothers with this masked trajectory, *i.e.,* (in ML dialect) smoothers *trained on* the non-masked measurements, (3) and (4) must be reformulated. To do this, we introduce $T$ output optimization variables, $\\hat{y}_1, \\ldots \\hat{y}_T \\in \\mathbf{R}^{p}$, which take the place of $y_t$ in (3) and (4)'s objective functions, subject to the constraint that the outputs match the non-masked observations, *i.e.*,\n", + "\n", + "$$\\left(\\hat{y}_t \\right)_i = \\left(y_t \\right)_i, \\quad (t, i) \\in \\mathcal{K} \\setminus \\mathcal{M}.$$\n", + "\n", + "Let $N=Tp$ and define the vector $z \\in \\mathbf{R}^{N}$ as $z = (\\hat{y}_1, \\ldots, \\hat{y}_T)$. Using $z$, (3) and (4) become\n", + "\n", + "$$\n", + "\\begin{equation}\n", + "\\begin{array}{lll}\n", + "\\text{minimize} \\; & \\sum_{t=1}^{T} \\left(\\tau \\left\\lVert w_t \\right\\rVert_{2}^2 + \\left\\lVert Cx_t - \\hat{y}_t \\right\\rVert_{2}^{2} \\right) & \\\\\n", + "\\text{subject to} & x_{t+1} = Ax_t + Bw_t, \\; & t = 1, \\ldots, T-1 \\\\\n", + "& S z = c & \n", + "\\end{array}\n", + "\\tag{5}\n", + "\\end{equation}\n", + "$$\n", + "\n", + "and\n", + "\n", + "$$\n", + "\\begin{equation}\n", + "\\begin{array}{lll}\n", + "\\text{minimize} \\; & \\sum_{t=1}^{T} \\left(\\tau \\left\\lVert w_t \\right\\rVert_{2}^2 + \\phi_{\\rho}(Cx_t - \\hat{y}_t) \\right) & \\\\\n", + "\\text{subject to} & x_{t+1} = Ax_t + Bw_t, \\; & t = 1, \\ldots, T-1 \\\\\n", + "& S z = c, & \n", + "\\end{array}\n", + "\\tag{6}\n", + "\\end{equation}\n", + "$$\n", + "\n", + "where $S \\in \\mathbf{R}^{ \\left| \\mathcal{K} ∖ \\mathcal{M} \\right| \\times N}$ is a selector matrix and $c ∈ \\mathbf{R}^{\\left| \\mathcal{K} \\setminus \\mathcal{M} \\right|}$ contains the corresponding entries of $y_t$. Concretely, if we denote the elements in $\\mathcal{K} ∖ \\mathcal{M}$ as $\\left(\\mathcal{K} ∖ \\mathcal{M} \\right)_1, \\ldots, \\left(\\mathcal{K} ∖ \\mathcal{M} \\right)_{\\left| \\mathcal{K} ∖ \\mathcal{M} \\right|}$, then if $\\left(\\mathcal{K} ∖ \\mathcal{M} \\right)_j = (t, i)$, the $j\\text{th}$ row of $S$ is $e_{tp + i}$ and the $j\\text{th}$ entry of $c$ is $(y_t)_i$.\n", + "\n", + "### Solving the Kalman smoothing problems\n", + "\n", + "Since we wish to use `cvxpylayers` to learn smoother parameters, our smoothers must be [DPP-compliant](https://www.cvxpy.org/tutorial/dpp/index.html). (It is worth noting that more efficient, non-`cvxpylayer` solutions can be developed by exploiting a particular smoother's problem structure. Again, refer to \\[[BB20](https://stanford.edu/~boyd/papers/auto_ks.html)\\] for an example of this.)\n", + "\n", + "In the above formulations, (5) can immediately be used to construct a `CvxpyLayer` object. However, (6) is not DPP-compliant. Furthermore, to construct robust smoothers, we reformulate (6) as\n", + "\n", + "$$\n", + "\\begin{equation}\n", + "\\begin{array}{lll}\n", + "\\text{minimize} \\; & h & \\\\\n", + "\\text{subject to} & \\tau\\left( \\sum_{t=1}^{T} \\left\\lVert w_t \\right\\rVert_{2}^2 \\right) +\n", + "\\left( \\sum_{t=1}^{T} u_t^2 + 2\\rho q_t \\right) \\le h & \\\\\n", + "& \\left\\lVert Cx_t - \\hat{y}_t \\right\\rVert_{2} \\le u_t + q_t & t=1, \\ldots, T \\\\\n", + "& 0 \\preceq u \\preceq \\rho \\mathbf{1} & \\\\\n", + "& q \\succeq 0 & \\\\\n", + "& x_{t+1} = Ax_t + Bw_t, & t = 1, \\ldots, T-1 \\\\\n", + "& S z = c, \n", + "\\end{array}\n", + "\\tag{7}\n", + "\\end{equation}\n", + "$$\n", + "\n", + "where $u, q \\in \\mathbf{R}^{T}$ and $h \\in \\mathbf{R}$ are auxilary optimization variables. The equivalence of (6) and (7) is based on a result shown in the appendix (bottom of the notebook). To see the reformulation (roughly, and implicitly using (6) & (7)'s, not (A2)'s symbols), simply separate the summand in the objective function of (6), replace the summation of Huber penalties with the objective function in (A2), and then append (A2)'s constraints to this new problem. Because (A2) is shown equivalent to (A1) by fixing the $x_t$ variables, the constraints in (6) and total process noise term in (6)'s objective function can be ignored when reformulating the Huber measurement penalties since fixing $x_t$ decouples $\\hat{y}_t$ and $w_t$.\n", + "\n", + "Finally, (7) is obtained by placing this transformed problem into epigraph form. This last step is not required for DPP-compliance, but if it is **not** done, `diffcp` is unable to differentiate through the problem.\n", + "\n", + "\n", + "## Evaluating a Kalman smoother\n", + "\n", + "The first step in judging a Kalman smoother is to mask some fraction (*e.g.*, 10% or 20%) of the observations, denoted by the set $\\mathcal{M} ⊂ \\left\\{1, \\ldots, T \\right\\} \\times \\left\\{ 1, \\ldots, p \\right\\}$. We then use the known set $\\mathcal{K} ∖ \\mathcal{M}$ to construct the selector matrix $S$ and the non-masked observation vector $c$. \n", + "\n", + "We then solve that smoother problem with $S$ and $c$, resulting in a predicted output trajectory $\\hat{y}_1, \\ldots \\hat{y}_T$.\n", + "\n", + "To judge the Kalman smoother, we calculate the *prediction error*, which we'll take to be the mean squared difference between the predicted output trajectory and the actual trajectory in the entries that we masked,\n", + "\n", + "$$\n", + "\\begin{equation}\n", + "L(\\theta) = \\frac{1}{\\left| \\mathcal{M} \\right|} ∑_{(t, i) \\in \\mathcal{M}} \\left( \\left( \\hat{y}_t \\right)_i - \\left( y_t \\right)_i \\right)^2. \n", + "\\tag{8}\n", + "\\end{equation}\n", + "$$\n", + "\n", + "We'll refer to this prediction error as the mean squared error (MSE) of this smoother (*i.e.* the smoother trained on $\\mathcal{K} ∖ \\mathcal{M}$ parameterized by $\\theta$). Importantly, because a smoother's predicted output trajectory is a function of the smoother's parameters, the prediction error $L$ is also a function of the parameters.\n", + "\n", + "The goal in the sequel will be to adjust the parameters to minimize this error.\n", + "\n", + "## Learning \n", + "\n", + "In this section we consider the Kalman smoother automatic parameter-tuning problem. We then present how to use `cvxpylayers` to solve this auto-tuning problem for (5) and (7), as well as the use of cross-validation to assess the generalizability of the tuned smoothers.\n", + "\n", + "Before moving forward however, **a disclaimer.** This is an **explanatory notebook.** Consequently, barely any effort was spent considering how to *solve* the parameter-tuning problem (or equivalently, the `tune_kalman_smoother` function). This is for a reader to consider in the context of their own smoother problem.\n", + "\n", + "\n", + "### Auto-tuning (learning) problem\n", + "\n", + "To tune a Kalman smoother parameterized by $\\theta$, we wish to minimize the prediction error, $L(\\theta)$, on some held out measurements, $\\mathcal{M}$. That is, we wish to solve the problem\n", + "\n", + "$$\n", + "\\text{minimize} \\quad L(\\theta),\n", + "$$\n", + "\n", + "with the variable $\\theta$. **This is actually a difficult problem** since $L$ is not necessarily convex (and almost certainly isn't) and while the problem appears unconstrained, there are *implicit* problem constraints.\n", + "\n", + "Specifically, define $\\Theta$ to be the set of parameters that the smoother optimization problem is defined for (*e.g.*, (7) is parameterized by $\\theta = (\\tau, \\rho) ∈ \\mathbf{R} × \\mathbf{R}$ but only defined for $\\Theta = \\mathbf{R}_{++} \\times \\mathbf{R}_{++}$). Since evaluating the prediction loss at some point $\\theta$ implicitly requires solving a smoother problem parameterized by $\\theta$, the prediction error function is only defined for $\\theta \\in \\Theta$. Concretely, $\\textbf{dom} \\, L = \\Theta$. Furthermore, $\\theta \\in \\Theta$ is an *implicit constraint* in the above optimization problem (equivalently, the *problem's domain* is $\\Theta$). \n", + "\n", + "The arising complication is that $\\textbf{dom} \\, L = \\Theta$ **does not** constrain $\\theta \\in \\Theta$. Because $L$ does not have a natural extended-value extension (convexity would be a sufficient condition for defining such an extension), $\\theta \\to \\textbf{bd}\\,\\Theta$ does not imply that $L \\to \\infty$, so iterative optimization algorithms can converge to $\\theta \\not \\in \\Theta$. This is problematic as evaluating $L$ with an invalid parameter will result in a `.solve()` call to an optimization problem which has \"been told\" will only need to solve problems for $\\theta \\in \\Theta$. The iterative method finding a locally optimal $\\theta$ will then terminate prematurely with a nasty error message from the smoother problem.\n", + "\n", + "To address this issue, we propose \n", + "\n", + "$$\n", + "\\begin{equation}\n", + "\\text{minimize} \\quad F(\\theta) = L(\\theta) + r(\\theta)\n", + "\\tag{9}\n", + "\\end{equation}\n", + "$$\n", + "\n", + "with variable $\\theta$ as the **Kalman smoother auto-tuning problem**, where $r$ is a regularization function such that $\\textbf{dom} \\, r = \\Theta$ and $r(\\theta) = +\\infty$ for $\\theta \\not \\in \\Theta$. \n", + "\n", + "Note that $\\Theta$ can also include constraints on what parameters are allowed to change or constraints on what parameters are desirable. Making these restrictions adds more nuance to the above solver-error-throw argument, but the formulation of (9) does not change. \n", + "\n", + "\n", + "### Solving the auto-tuning problem for non-robust and robust smoothing \n", + "\n", + "The process of using `cvxpylayers` to construct Kalman smoothers and auto-tune their parameters simply requires:\n", + "1. Formulating the smoother problems with `cvxpy`.\n", + "2. Using these smoother objects to construct `CvxpyLayer` objects.\n", + "3. Defining a regularization function for each smoother to concretely define $F(\\theta)$.\n", + "3. Constructing a `tune_kalman_smoother` function to solve (9) using whatever underlying framework was chosen when the layer was constructed (*i.e.*, PyTorch, TensorFlow, or JAX).\n", + "\n", + "Most of this notebook was concerned with (1) and (2), and (4) (as mentioned at the beginning of this **Background/Learning** section) is beyond the scope of this notebook (or rather, best considered for specific, non-toy smoothers). Furthermore, we just consider (3). \n", + "\n", + "The non-robust Kalman smoother, (5), has one parameter, which we denote by $\\theta = \\tau \\in \\mathbf{R}$. The robust smoother, (7), has two parameters, which we denote by $\\theta = \\left(\\tau, \\rho \\right) \\in \\mathbf{R} \\times \\mathbf{R}$. Both smoothers have the allowable set\n", + "\n", + "$$\n", + "\\Theta = \\left\\{ \\theta \\in \\mathbf{R}^{k} \\mid \\theta \\succ 0 \\right\\},\n", + "$$\n", + "\n", + "where $k=1$ and $k=2$, respectively. (Note that for our parameter/allowable parameter notation we're adopting the function notation style found in \\[[BV04](https://stanford.edu/~boyd/cvxbook/)\\], where a function may be declared over some space, but only defined for a subset of that space.) While there are many possible regularization functions we could choose to define (9), we'll auto-tune our smoothers using the logarithmic barrier function. That is, we'll define\n", + "\n", + "$$\n", + "r(\\theta) = ∑_{i=1}^{k} - \\log θ_i.\n", + "$$\n", + "\n", + "### Judging tuned smoothers\n", + "\n", + "#### Update to out-of-sample notation\n", + "\n", + "Once a $\\theta$ is obtained via auto-tuning (*i.e.*, once a smoother is tuned), the prediction loss (8) is no longer a valid measurement of that smoother's performance. To evaluate this tuned smoother, we need to test it on another (unseen) output sequence. To reserve a final output prediction error test dataset, we still mask some fraction of the measurements (now more likely 20% than 10%) and then split that mask (rougly) in half to obtain $\\mathcal{M} \\subset \\mathcal{K}$ and $\\mathcal{T} ⊂ \\mathcal{K}$ such that $\\mathcal{M} ∩ \\mathcal{T} = ∅$. The Kalman smoother is then formulated (or \"trained\") on $\\mathcal{K} ∖ \\left( \\mathcal{M} ∪ \\mathcal{T} \\right)$, tuned on $\\mathcal{M}$, and tested on $\\mathcal{T}$. (The selector matrix, measurement vector, etc. are all updated accordingly.)\n", + "\n", + "#### Cross-validation\n", + "\n", + "We perform $\\mathcal{N}\\text{-fold}$ cross-validation by dividing $\\mathcal{K}$ into $\\mathcal{N}$ sets (folds), and then constructing $\\mathcal{N}$ smoothers on $\\mathcal{N}-1$ folds, using half of the measurements of the withheld fold to tune the smoother's parameters and the other half to evaluate the performance of the smoother. Concretely, we generate\n", + "\n", + "$$\n", + "\\mathcal{M}_n ⊂ \\mathcal{K} \\quad \\text{and} \\quad \\mathcal{T}_n ⊂ \\mathcal{K}, \\quad \\mathcal{M}_n ∩ \\mathcal{T}_n = ∅, \\quad n =1, \\ldots \\mathcal{N},\n", + "$$\n", + "\n", + "where for each $n$\n", + "\n", + "$$\n", + "\\left| \\mathcal{M}_n \\right| + \\left| \\mathcal{T}_n \\right| ≈ \\frac{1}{\\mathcal{N}} \\left| \\mathcal{K} \\right|.\n", + "$$\n", + "\n", + "Without loss of generality, we construct the first of our $\\mathcal{N}$ smoothers with $\\mathcal{K} ∖ (\\mathcal{M}_\\mathcal{N} ∪ \\mathcal{T}_{\\mathcal{N}})$, use $\\mathcal{M}_\\mathcal{N}$ to tune the smoother's parameters, and $\\mathcal{T}_{\\mathcal{N}}$ to compute the smoother's mean squared prediction error." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Implementation (vehicle tracking example)\n", + "\n", + "We consider the vehicle tracking example stated above, where $x_t \\in \\mathbf{R}^{4}$ contains the vehicle's " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plotting helper functions" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_state(t, actual, estimated=None):\n", + " '''\n", + " plot position, speed, and input in the x and y coordinates for\n", + " the actual data, and optionally for the estimated data\n", + " '''\n", + " trajectories = [actual]\n", + " labels = ['Actual']\n", + " colors = ['#1f77b4']\n", + "\n", + " if estimated is not None:\n", + " trajectories.append(estimated)\n", + " labels.append('Estimated')\n", + " colors.append('red')\n", + "\n", + " fig, ax = plt.subplots(3, 2, sharex='col', sharey='row', figsize=(12,8))\n", + " \n", + " for idx, (data, label) in enumerate(zip(trajectories, labels)):\n", + " x, w = data\n", + " ax[0,0].plot(t, x[0,:-1], color=colors[idx], label=label)\n", + " ax[0,1].plot(t, x[1,:-1], color=colors[idx])\n", + " ax[1,0].plot(t, x[2,:-1], color=colors[idx])\n", + " ax[1,1].plot(t, x[3,:-1], color=colors[idx])\n", + " ax[2,0].plot(t, w[0,:], color=colors[idx])\n", + " ax[2,1].plot(t, w[1,:], color=colors[idx])\n", + "\n", + " ax[0,0].set_ylabel('$x$ position')\n", + " ax[1,0].set_ylabel('$x$ velocity')\n", + " ax[2,0].set_ylabel('$x$ input')\n", + "\n", + " ax[0,1].set_ylabel('$y$ position')\n", + " ax[1,1].set_ylabel('$y$ velocity')\n", + " ax[2,1].set_ylabel('$y$ input')\n", + "\n", + " ax[0,1].yaxis.tick_right()\n", + " ax[1,1].yaxis.tick_right()\n", + " ax[2,1].yaxis.tick_right()\n", + "\n", + " ax[0,1].yaxis.set_label_position(\"right\")\n", + " ax[1,1].yaxis.set_label_position(\"right\")\n", + " ax[2,1].yaxis.set_label_position(\"right\")\n", + "\n", + " ax[2,0].set_xlabel('$t$')\n", + " ax[2,1].set_xlabel('$t$')\n", + "\n", + " # Add a single legend\n", + " fig.legend(labels, loc='upper right', ncol=2)\n", + "\n", + "\n", + "def plot_positions(traj: List[List[np.ndarray]],\n", + " titles: List[str],\n", + " legends: List[List[str]],\n", + " plot_args: List[List[str]],\n", + " axis=None,\n", + " filename=None):\n", + " matplotlib.rcParams.update({'font.size': 14})\n", + " n = len(traj)\n", + " \n", + " assert len(traj) == len(titles) == len(legends) == len(plot_args), \"Input lists must have the same length.\"\n", + "\n", + " rows = int(np.ceil(n / 2))\n", + " cols = 2 if n > 1 else 1\n", + " \n", + " fig, ax = plt.subplots(rows, cols, sharex=True, sharey=True, figsize=(12, 5 * rows))\n", + " \n", + " # Ensure ax is a 2D array\n", + " if rows == 1:\n", + " ax = np.array([ax])\n", + " if cols == 1:\n", + " ax = ax.reshape(-1, 1)\n", + " \n", + " ax = ax.flatten()\n", + " \n", + " for i, xs in enumerate(traj):\n", + " for j, x in enumerate(xs):\n", + " typ = plot_args[i][j]\n", + " alpha = 0.1 if 'o' in typ else 1\n", + " ax[i].plot(x[0, :], x[1, :], typ, alpha=alpha)\n", + " ax[i].set_title(titles[i])\n", + " ax[i].legend(legends[i], loc='upper right')\n", + " if axis:\n", + " ax[i].axis(axis)\n", + " \n", + " # Hide unused subplots if n is odd\n", + " for j in range(n, rows * cols):\n", + " fig.delaxes(ax[j])\n", + " \n", + " plt.tight_layout()\n", + " \n", + " if filename:\n", + " fig.savefig(filename, bbox_inches='tight')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Problem dynamics\n", + "The matrices for the system dynamics (and output) are\n", + "$$\n", + "A = \\begin{bmatrix}\n", + "1 & 0 & (1- \\frac{\\gamma}{2}\\Delta t) & 0 \\\\\n", + "0 & 1 & 0 & (1- \\frac{\\gamma}{2}\\Delta t) \\\\\n", + "0 & 0 & 1 - \\gamma \\Delta t & 0 \\\\\n", + "0 & 0 & 0 & 1 - \\gamma \\Delta t\n", + "\\end{bmatrix},\n", + "$$\n", + "\n", + "$$\n", + "B = \\begin{bmatrix}\n", + "\\frac{1}{2}\\Delta t^2 & 0 \\\\\n", + "0 & \\frac{1}{2}\\Delta t^2 \\\\\n", + "\\Delta t & 0 \\\\\n", + "0 & \\Delta t,\n", + "\\end{bmatrix}\n", + "$$\n", + "and\n", + "$$\n", + "C = \\begin{bmatrix}\n", + "1 & 0 & 0 & 0 \\\\\n", + "0 & 1 & 0 & 0\n", + "\\end{bmatrix},\n", + "$$\n", + "where $\\gamma$ is a velocity damping parameter." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "n = 4 # number of entries in the state vector\n", + "m = 2 # number of entries in the input vector\n", + "p = 2 # number of entries in the measurement vector\n", + "gamma = 0.05 # velocity damping; 0 is no damping\n", + "\n", + "num_steps = 1000\n", + "T = num_steps\n", + "end_time = 50 # time will move from 0 to end_time with step delt\n", + "times, delt = np.linspace(0, end_time, num_steps, endpoint=True, retstep=True)\n", + "N = T*p # number of total possible measurements\n", + "\n", + "A = np.zeros((n, n))\n", + "B = np.zeros((n, m))\n", + "C = np.zeros((p, n))\n", + "\n", + "# Dynamics for this particular problem\n", + "A[0,0] = 1\n", + "A[1,1] = 1\n", + "A[0,2] = (1-gamma*delt/2)*delt\n", + "A[1,3] = (1-gamma*delt/2)*delt\n", + "A[2,2] = 1 - gamma*delt\n", + "A[3,3] = 1 - gamma*delt\n", + "\n", + "B[0,0] = delt**2/2\n", + "B[1,1] = delt**2/2\n", + "B[2,0] = delt\n", + "B[3,1] = delt\n", + "\n", + "C[0,0] = 1\n", + "C[1,1] = 1\n", + "\n", + "C_tch = torch.tensor(C) # used in loss functions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generating trajectories" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sigma = 20\n", + "num_outliers = .20\n", + "\n", + "x = np.zeros((n,T+1))\n", + "x[:,0] = [0,0,0,0]\n", + "y = np.zeros((p,T))\n", + "\n", + "# generate random input and noise vectors\n", + "np.random.seed(6)\n", + "w = np.random.randn(m,T)\n", + "v = np.random.randn(p,T)\n", + "\n", + "# add outliers to v\n", + "np.random.seed(0)\n", + "inds = np.random.rand(T) <= num_outliers\n", + "v[:,inds] = sigma*np.random.randn(p, T)[:,inds]\n", + "\n", + "# simulate the system forward in time\n", + "for t in range(T):\n", + " x[:, t+1] = A@x[:, t] + B@w[:, t]\n", + " y[:, t] = C@x[:, t] + v[:, t]\n", + "\n", + "x_true = x.copy()\n", + "w_true = w.copy()\n", + "\n", + "plot_state(times,(x_true,w_true))\n", + "plot_positions(traj=[[x_true], [y], [y, x_true]],\n", + " titles=['True',\n", + " 'Observations',\n", + " 'True vs. Observations'],\n", + " legends=[['True'],\n", + " ['Measurements'],\n", + " ['Measurements', 'True']],\n", + " plot_args=[['r'],\n", + " ['ro'],\n", + " ['ro', 'k']],\n", + " axis=[-4,14,-5,20])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Smoother Instantiation\n", + "\n", + "Creating the cvxpylayers " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def create_kalman_smoother(S: sparse.csc_matrix, c: np.ndarray) -> CvxpyLayer: \n", + " \"\"\"Construct a standard Kalman smoother with measurements K setminus M_n.\n", + "\n", + " Args:\n", + " - S: |K setminus M_n| x N selection matrix.\n", + " - c: |K setminus M_n|-vector of measurements.\n", + "\n", + " Returns:\n", + " - A CvxpyLayer which solves a (or a batch of) standard Kalman\n", + " smoothing problem(s) upon receiving a (or a batch of) tuning\n", + " parameter(s), tau.\n", + " \"\"\"\n", + " x = cp.Variable(shape=(n, T+1))\n", + " w = cp.Variable(shape=(m, T))\n", + " # Missing data constraints\n", + " y_hat = cp.Variable(shape=(p, T))\n", + " z = cp.vec(y_hat, order='F')\n", + " \n", + " tau = cp.Parameter(pos=True)\n", + "\n", + " f0 = tau*cp.sum_squares(w) + cp.sum_squares((C@x)[:, :T] - y_hat)\n", + " obj = cp.Minimize(f0)\n", + "\n", + " constr = [ x[:, t+1] == A@x[:, t] + B@w[:, t] for t in range(T) ] \n", + " constr += [ S@z == c ] # missing data constraint\n", + "\n", + " problem = cp.Problem(obj, constr)\n", + "\n", + " layer = CvxpyLayer(\n", + " problem,\n", + " parameters=[tau],\n", + " variables=[x, y_hat, w]\n", + " )\n", + " return layer\n", + "\n", + "\n", + "def create_robust_kalman_smoother(S: sparse.csc_matrix, c: np.ndarray) -> CvxpyLayer:\n", + " \"\"\"Construct a robust Kalman smoother with measurements K setminus M_n.\n", + " \n", + " Args:\n", + " - S: |K setminus M_n| x N selection matrix.\n", + " - c: |K setminus M_n|-vector of measurements.\n", + "\n", + " Returns:\n", + " - A CvxpyLayer which solves a (or a batch of) robust Kalman\n", + " smoothing problem(s) upon receiving a (or a batch of) tuning\n", + " parameter(s), tau and rho.\n", + " \"\"\"\n", + " x = cp.Variable(shape=(n, T+1))\n", + " w = cp.Variable(shape=(m, T))\n", + " h = cp.Variable()\n", + " # Missing data constraints \n", + " y_hat = cp.Variable(shape=(p, T))\n", + " z = cp.vec(y, order='F')\n", + " # Huber auxilary variables\n", + " u = cp.Variable(T) \n", + " q = cp.Variable(T) \n", + "\n", + " tau = cp.Parameter(pos=True) \n", + " rho = cp.Parameter(pos=True)\n", + "\n", + " f0 = tau*cp.sum_squares(w)\n", + " f0 += cp.sum([ cp.square(u[t]) + 2*rho*q[t] for t in range(T)])\n", + " constr = [f0 <= h]\n", + " obj = cp.Minimize(h)\n", + "\n", + " # Huber constraints\n", + " constr += [cp.norm(C@x[:, t] - y_hat[:, t], 2) <= u[t] + q[t] for t in range(T)]\n", + " constr += [u[t] <= rho for t in range(T) ]\n", + " constr += [0 <= u, 0 <= q]\n", + "\n", + " # smoother constraints\n", + " constr += [ x[:, t+1] == A@x[:, t] + B@w[:, t] for t in range(T) ]\n", + " constr += [ S@z == c ] # missing data constraint\n", + " \n", + " problem = cp.Problem(obj, constr)\n", + " layer = CvxpyLayer(\n", + " problem,\n", + " parameters=[tau, rho],\n", + " variables=[x, y_hat, w]\n", + " )\n", + " return layer" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Learning (Auto-Tuning) Functionality\n", + "\n", + "### Data separation functions\n", + "\n", + "Mention difference between $(\\mathcal{K} \\setminus \\mathcal{M})_i$ mathematically and in the code.\n", + "\n", + "Mathematically use cardinality\n", + "\n", + "In code $\\left| \\mathcal{K} \\setminus \\mathcal{M}_i \\right|$ is found by summing all nonzero entries." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def split_data(fraction: float=0.9) -> Tuple[int, np.ndarray]:\n", + " \"\"\"Split data into a training set and a test set.\n", + " \n", + " Args:\n", + " - fraction: Fraction of the measurement dataset\n", + " included in the training dataset.\n", + " \n", + " Returns:\n", + " - The number of measurements in the training set.\n", + " - A T x p mask matrix. The true entries in the matrix\n", + " are the indices of the measurements in the\n", + " training dataset.\n", + " \"\"\"\n", + " # SEED!\n", + " num_train_pts = int( N*fraction )\n", + " K_setminus_M = np.zeros(N, dtype=int)\n", + " K_setminus_M[:num_train_pts] = 1\n", + " np.random.shuffle(K_setminus_M)\n", + " K_setminus_M = K_setminus_M.astype(bool).reshape((T, p))\n", + " return num_train_pts, K_setminus_M" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "def cv_split(num_folds: int=10) -> List[Tuple[int, np.ndarray, np.ndarray]]:\n", + " \"\"\"Split data into folds for cross-validation.\n", + "\n", + " Args:\n", + " - num_folds (optional): The number of folds desired for cross-validation.\n", + " Default number of folds is 10.\n", + "\n", + " Returns:\n", + " - a list such that len(list) == num_folds, where the ith entry\n", + " is the three-tuple (|K setminus M_n|, M_n, K setminus M_n). Both M_n and\n", + " K setminus M_n are T x p mask matrices, whose true entries\n", + " are the indices of the measurements used for model validation and\n", + " model training for the ith fold, respectively.\n", + " \"\"\"\n", + " np.random.seed(1) # generate same CV split every time\n", + "\n", + " indices = np.arange(N)\n", + " np.random.shuffle(indices)\n", + "\n", + " Ms = np.array_split(indices, num_folds)\n", + "\n", + " to_return = []\n", + "\n", + " for i in range(num_folds):\n", + " M: np.ndarray = np.zeros(N, dtype=bool)\n", + " M[Ms[i]] = True\n", + " M = M.reshape((T, p))\n", + " K_setminus_M = ~M\n", + " num_train_pts = np.sum(K_setminus_M)\n", + " to_return.append((num_train_pts, M, K_setminus_M))\n", + " \n", + " return to_return" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "def create_problem_data_params(selected: np.ndarray,\n", + " num_selected: int=None) -> Tuple[sparse.csc_matrix,\n", + " np.ndarray,\n", + " np.ndarray]:\n", + " \"\"\"Turns a mask matrix of known measurement indices into the sparse selection matrix\n", + " needed in the standard and robust Kalman smoothing problems.\n", + " \n", + " Args:\n", + " - selected: T x p mask matrix of known measurement indices.\n", + " - num_selected (optional): The number of known measurement\n", + " indices in the selected numpy array.\n", + "\n", + " Returns:\n", + " - S: num_selected x N sparse selection matrix.\n", + " - c: (num_selected)-vector containing the trajectory\n", + " measurements in the training dataset.\n", + " - c_test: (N - num_selected)-vector containing the trajectory\n", + " measurements not in the training dataset.\n", + " \n", + " \"\"\"\n", + " num_selected = np.sum(selected) if num_selected == None else num_selected\n", + "\n", + " time_indices, measurement_indices = selected.nonzero() # think about looping through rows and keeping track if the position is zero or not\n", + " S = sparse.csc_matrix(\n", + " (\n", + " np.ones(num_selected), # data to fill into the sparse array\n", + " (\n", + " np.arange(num_selected),\n", + " time_indices*p + measurement_indices\n", + " )\n", + " ),\n", + " shape=(num_selected, N)\n", + " )\n", + " c = y.T[selected]\n", + " c_test = y.T[~selected]\n", + " return (S, c, c_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Error Functions" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "def mean_Jmeas_error(y: torch.Tensor, observable_measurements: torch.Tensor,\n", + " layer: CvxpyLayer=None, params: List[torch.Tensor]=None,\n", + " x_hat: torch.Tensor=None, measurement_loss: str='squared') -> torch.Tensor:\n", + " \"\"\"Prediction error used to judge a Kalman smoother.\n", + " \n", + " Specifically, this function computes\n", + " \n", + " avg (J_meas(theta)) = (1 / np.nonzero(observable_measurments))\n", + " * sum measurement_loss( ( (C x_hat_t)_i - (y_t)_i ),\n", + " \n", + " where x_hat implicitly depends on theta.\n", + "\n", + " Used to compute E_train or E_test.\n", + "\n", + " Args:\n", + " - y: The validation subset of the\n", + " actual trajectory to judge smoother\n", + " output against.\n", + " - observable_measurements: T x p mask matrix\n", + " used to select the predictions from x_hat\n", + " that are to be judged against y.\n", + " - layer (conditonally optional)\n", + " - params (conditonally optional)\n", + " - x_hat (conditonally optional)\n", + " - measurement_loss (optional): Whether to penalize the\n", + " differen\n", + "\n", + " Returns:\n", + " - A torch.Tensor containing MSE(x_hat - y)\n", + " \n", + " Raises\n", + " - ValueError if either a layer output is not provided\n", + " - ValueError if |K setminus M_n| != |test|\n", + "\n", + " \"\"\"\n", + "\n", + " if (x_hat == None) and (layer == None or params == None):\n", + " raise ValueError(\"this function requires a layer and parameters\\\n", + " or x_hat = layer(parameters)\") \n", + " \n", + " # can add more checks on x_hat and K setminus M_i\n", + "\n", + " if x_hat == None:\n", + " x_hat = layer(*params)[1]\n", + " \n", + " y_hat_T = y_hat.t()\n", + " y_hat_to_compare = y_hat_T[observable_measurements]\n", + " return F.mse_loss(y_hat_to_compare, y)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "def kalman_recovery_mse(y: torch.Tensor,\n", + " observable_measurements: torch.Tensor,\n", + " layer: CvxpyLayer=None,\n", + " params: List[torch.Tensor]=None,\n", + " y_hat: torch.Tensor=None) -> torch.Tensor:\n", + " \"\"\"Prediction error used to judge a Kalman smoother.\n", + " \n", + " Specifically, this function computes\n", + " \n", + " L(theta) = MSE(y_hat - y),\n", + " \n", + " where y_hat implicitly depends on theta.\n", + "\n", + " Args:\n", + " - y: The validation subset of the\n", + " actual trajectory to judge smoother\n", + " output against.\n", + " - observable_measurements: T x p mask matrix\n", + " used to select the predictions from y_hat\n", + " that are to be judged against y.\n", + " - layer (conditonally optional)\n", + " - params (conditonally optional)\n", + " - y_hat (conditonally optional)\n", + "\n", + " Returns:\n", + " - A torch.Tensor containing MSE(y_hat - y)\n", + " \n", + " Raises\n", + " - ValueError if either a layer output is not provided\n", + " - ValueError if |K setminus M_n| != |test|\n", + "\n", + " \"\"\"\n", + "\n", + " if (y_hat == None) and (layer == None or params == None):\n", + " raise ValueError(\"this function requires a layer and parameters\\\n", + " or x_hat = layer(parameters)\") \n", + " \n", + " # can add more checks on y_hat and K setminus M_i\n", + "\n", + " if y_hat == None:\n", + " y_hat = layer(*params)[1]\n", + " \n", + " y_hat_T = y_hat.t()\n", + " y_hat_to_compare = y_hat_T[observable_measurements]\n", + " return F.mse_loss(y_hat_to_compare, y)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "def learning_error_fn(layer: CvxpyLayer, y: torch.Tensor,\n", + " observable_measurements: torch.Tensor,\n", + " params: List[torch.Tensor]) -> torch.Tensor:\n", + " \"\"\"The learning error used to auto-tune a Kalman smoother.\n", + "\n", + " Specifically, this function computes\n", + "\n", + " F(theta) = E(theta) + r(theta),\n", + "\n", + " where L is the loss computed in the `kalman_recovery_mse` function\n", + " (the test error loss) and r is a regularization term added to \n", + " the prediction loss to encourage the auto-tuning to generate allowable\n", + " hyper-parameters.\n", + "\n", + " This learning error function assumes that all hyper-parameters must be positive,\n", + " and uses a log-barrier regularization function to encourage positive parameter\n", + " selection. Mathematically,\n", + " \n", + " r(theta) = - sum_i log(theta_i).\n", + " \n", + " Args:\n", + " - layer: The CvxpyLayer being trained.\n", + " - y: The training subset of the\n", + " actual trajectory to judge smoother\n", + " output against. \n", + " - observable_measurements: T x p mask matrix\n", + " used to select the predictions from y_hat\n", + " that are to be judged against y.\n", + " - params: A list of the (Tensor, require_grad==True)\n", + " hyper-parameters being auto-tuned.\n", + "\n", + " Returns:\n", + " - A torch.Tensor containing F(theta).\n", + " \"\"\"\n", + " x_hat = layer(*params)[0]\n", + " y_hat = C_tch@ (x_hat[:, :-1])\n", + " y_hat_T = y_hat.t()\n", + " y_hat_to_compare = y_hat_T[observable_measurements]\n", + " mse_loss = F.mse_loss(y_hat_to_compare, y)\n", + " log_sum = 0.0\n", + " for param in params:\n", + " log_sum -= torch.sum(torch.log(torch.abs(param)))\n", + "\n", + " return mse_loss + log_sum" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Parameter finding functions" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "def parameter_sweep(layer: CvxpyLayer,\n", + " num_params: int) -> Tuple[List[torch.Tensor], torch.Tensor]:\n", + " \"\"\"\n", + " TODO: ADD!\n", + "\n", + " The LS and Robust Kalman tuning have 1 and 2 parameters, respectively,\n", + " so the first line will always yield an integer.\n", + "\n", + " theta_init is a list containing equal length torch Tensors\n", + " such that len(theta_init) == num_params,\n", + " and the ith element element in each tensor \n", + " \"\"\"\n", + " steps = 10 // num_params\n", + "\n", + " logspace_values = [torch.logspace(start=-4, end=7, steps=steps, base=2)\n", + " for _ in range(num_params)]\n", + " meshgrids = torch.meshgrid(*logspace_values)\n", + " theta_product = torch.stack(meshgrids,\n", + " dim=-1).reshape(-1,len(logspace_values))\n", + " theta_init = [theta_product[:, i] for i in range(num_params)]\n", + " \n", + " # batch compute solutions\n", + " ys = layer(*theta_init)[1]\n", + "\n", + " return (theta_init, ys)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "def tune_kalman_smoother(train_loss: Callable[[List[torch.Tensor]], torch.Tensor],\n", + " test_loss: Callable[[List[torch.Tensor]], torch.Tensor],\n", + " params: List[torch.Tensor],\n", + " epochs: int=50, lr: float=2e-2,\n", + " verbose: bool=False,\n", + " print_every: int=5) -> Tuple[int, float,\n", + " List[List[float]],\n", + " List[float], List[float]]:\n", + " \"\"\"\n", + " TODO: ADD\n", + " Works for both regular smoother and robust smoother\n", + " \n", + " can mention up above that this is just scaffolding: would go more in depth here for a real problem.\n", + " \"\"\"\n", + "\n", + " # opt = torch.optim.Adam(params, lr=lr)\n", + " opt = torch.optim.SGD(params, lr=lr, momentum=0.9)\n", + "\n", + " best_param_index, best_loss = None, torch.inf\n", + " param_history: List[List[float]] = []\n", + " train_losses, test_losses = [], []\n", + "\n", + " for epoch in range(epochs):\n", + " \n", + " param_history.append([p.item() for p in params])\n", + " \n", + " with torch.no_grad():\n", + " new_loss = test_loss(params).item()\n", + " if (new_loss < best_loss) and epoch != 0:\n", + " best_loss = new_loss\n", + " best_param_index = epoch\n", + " test_losses.append(new_loss)\n", + " if verbose and epoch % print_every == 0:\n", + " print(\"test loss %03d | %3.5f\" % (epoch + 1, test_losses[-1]))\n", + "\n", + " opt.zero_grad()\n", + " loss = train_loss(params)\n", + " loss.backward()\n", + " opt.step()\n", + " \n", + " neg_theta = any([p.item() < 0 for p in params])\n", + " if neg_theta:\n", + " break\n", + " \n", + " train_losses.append(loss.item())\n", + " if verbose and epoch % print_every == 0:\n", + " print(\"train loss %03d | %3.5f\" % (epoch+1, train_losses[-1]))\n", + " \n", + " return (best_param_index, best_loss,\n", + " param_history, train_losses, test_losses)\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Helper data objects" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "class AutoTune_DataObject:\n", + "\n", + " def __init__(self, num_epochs, best_theta_index, best_loss,\n", + " theta_history, train_losses, test_losses):\n", + " self.tune_succeeded: bool = True if best_theta_index > 0 else False\n", + " self.quit_early: bool = False if len(theta_history) == num_epochs else True\n", + " self.best_theta_index: int = best_theta_index\n", + " self.best_loss: float = best_loss\n", + " self.theta_history: List[List[float]] = theta_history\n", + " self.train_losses: List[float] = train_losses\n", + " self.test_losses: List[float] = test_losses\n", + "\n", + "class Fold_DataObject:\n", + " \"\"\"\n", + "\n", + " Contains\n", + " - losses and param values from sweep\n", + " - theta corresponding to lowest test loss\n", + "\n", + " Can determine if autotune failed by observing size of parameter history list\n", + "\n", + " Keep track of which tune object contained the best param?\n", + "\n", + " \"\"\"\n", + " def __init__(self, num_train_pts, layer):\n", + " self.num_train_pts: int = num_train_pts\n", + " self.layer: CvxpyLayer = layer\n", + " \n", + " self.param_sweep_result = None\n", + "\n", + " self.auto_tunes = []\n", + " self.theta: List[float] = None\n", + " self.auto_tune_succeeded = False\n", + " self.train_mse = torch.inf\n", + " self.test_mse = torch.inf\n", + "\n", + " def add_tune_result(self, to_add: AutoTune_DataObject,\n", + " train_loss: Callable[[List[torch.Tensor]], torch.Tensor]):\n", + " self.auto_tunes.append(to_add)\n", + "\n", + " best_theta_index = to_add.best_theta_index\n", + " \n", + " if to_add.tune_succeeded:\n", + " self.auto_tune_succeeded = True\n", + " \n", + " if to_add.best_loss < self.test_mse:\n", + " self.test_mse = to_add.best_loss\n", + " # if the first gradient step fails, then we don't have the training loss, so need to compute.\n", + " self.train_mse = to_add.train_history[best_theta_index] if best_theta_index != 0\\\n", + " else train_loss(to_add.theta_history[0])\n", + " self.theta = to_add.theta_history[best_theta_index]\n", + "\n", + " \n", + "\n", + "class CV_DataObject:\n", + " \"\"\"\n", + " Contains\n", + " - number of folds\n", + " - CV-MSE error\n", + " - each fold\n", + " \n", + " TODOS:\n", + " - make sure to serialize/save after running cross_validate\n", + "\n", + " if size of fold list == num_folds, automatically compute final statistics.\n", + " \"\"\"\n", + " def __init__(self, num_params, num_folds):\n", + " self.num_params = num_params\n", + " self.num_folds = num_folds\n", + " self.folds: List[Fold_DataObject] = []\n", + "\n", + " self.mse_cv_error = torch.inf\n", + "\n", + " def add_fold(self, to_add: Fold_DataObject):\n", + " self.folds.append(to_add)\n", + " \n", + " if len(self.folds) == self.num_folds:\n", + " self.compute_cv_errors()\n", + "\n", + " def compute_cv_error(self):\n", + " sum_square = 0\n", + " for fold in self.folds:\n", + " sum_square += fold.test_mse**2\n", + " return sum_square / len(self.folds)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Cross-validation instantiation" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "def cross_validate(create_smoother: Callable[[sparse.csc_matrix, np.ndarray],\n", + " CvxpyLayer],\n", + " num_params: int,\n", + " num_folds: int=10,\n", + " num_st_pts: int=3,\n", + " num_epochs: int=50,\n", + " verbose: bool=False,\n", + " double_verbose: bool=False) -> CV_DataObject:\n", + " \n", + " cv_result: CV_DataObject = CV_DataObject(num_params, num_folds)\n", + " \n", + " data_folds = cv_split(num_folds)\n", + " \"\"\"\n", + " to think about:\n", + " - how to handle failures?\n", + " \"\"\"\n", + " for i, fold in enumerate(data_folds):\n", + " if verbose:\n", + " print(\"=== Starting fold %02d | %02d ===\" % (i, num_folds))\n", + " \n", + " # === initialize fold's layer & data ===\n", + " num_pts, M, K_setminus_M = fold\n", + "\n", + " K_setminus_M_tch, M_tch = torch.tensor(K_setminus_M), torch.tensor(M)\n", + " S, c, c_test = create_problem_data_params(K_setminus_M, num_pts)\n", + " c_tch, c_test_tch = torch.tensor(c), torch.tensor(c_test)\n", + " \n", + " layer = create_smoother(S, c)\n", + "\n", + " fold_to_add: Fold_DataObject = Fold_DataObject(num_pts, layer)\n", + " \n", + " train_loss = lambda parameters : learning_error_fn(layer,\n", + " c_tch,\n", + " K_setminus_M_tch,\n", + " parameters)\n", + " test_loss = lambda parameters : kalman_recovery_mse(y=c_test_tch,\n", + " observable_measurements=M_tch,\n", + " layer=layer,\n", + " params=parameters)\n", + "\n", + " # === parameter sweep ===\n", + " if verbose:\n", + " print(\"starting parameter sweep\")\n", + " \n", + " theta_inits, xs = parameter_sweep(layer, num_params)\n", + " sweep_loss = lambda x_val : kalman_recovery_mse(y=c_test_tch,\n", + " observable_measurements=M_tch,\n", + " x_hat = x_val)\n", + " \n", + " # losses is a list containing tuples of length 2\n", + " # 0th entry in the tuple is the list of tensor parameters\n", + " # ex: if num_params==2, then \n", + " # losses[0][0] == [torch.tensor(val1), torch.tensor(val2)] =: theta\n", + " # 1st entry is the test loss of the smoother instantiated with \n", + " # the parameters in the 0th entry of that tuple.\n", + " # ex: losses[0][1] has test mse loss corresponding\n", + " # to layer(*losses[0][0])\n", + " losses: List[Tuple[\n", + " List[torch.Tensor],\n", + " float\n", + " ]] = [\n", + " (\n", + " [theta_inits[j][i] for j in range(num_params)],\n", + " sweep_loss(xs[i]).item()\n", + " )\n", + " for i in range(xs.shape[0])\n", + " ]\n", + " losses.sort(key=lambda x: x[1])\n", + "\n", + " fold_to_add.param_sweep_result = losses\n", + " \n", + " # === auto-tuning ===\n", + " if verbose:\n", + " print(\"starting auto_tuning\")\n", + " \n", + " starting_thetas = [theta for theta, _ in losses[0:num_st_pts]]\n", + "\n", + " for j, theta_0 in enumerate(starting_thetas):\n", + " if verbose:\n", + " print(\"auto-tuning starting point %02d | %02d\" % (j, num_st_pts))\n", + " \n", + " result = tune_kalman_smoother(train_loss, test_loss,\n", + " params=theta_0, epochs=num_epochs,\n", + " verbose=double_verbose)\n", + " \n", + " fold_to_add.add_tune_result(AutoTune_DataObject(num_epochs, *result), train_loss)\n", + " \n", + " cv_result.add_fold(fold_to_add)\n", + " \n", + " return cv_result" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Cross-validation helper functions" + ] + }, + { + "cell_type": "code", + "execution_count": 197, + "metadata": {}, + "outputs": [], + "source": [ + "# print results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Least Squares Smoothing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Perform cross-validation" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "# initialize parameter values\n", + "# note difference between torch parameters and cvxpylayer parameters\n", + "# SEED HERE (or wherever it needs to go for the shuffle that happens above\n", + "num_train_pts, K_setminus_M = split_data(fraction=0.9) # will still use this after CV\n", + "S, c, c_test = create_problem_data_params(K_setminus_M, num_train_pts)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "layer = create_kalman_smoother(S, c)\n", + "tau_tch = torch.tensor(.08, requires_grad=True)\n", + "x_hat_orig, w_hat_orig, v_hat_orig = layer(tau_tch)" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "K_setminus_M_tch, c_tch, c_test_tch = torch.tensor(K_setminus_M), torch.tensor(c), torch.tensor(c_test)\n", + "train_loss = lambda parameters : learning_error_fn(layer=layer, y=c_tch,\n", + " observable_measurements=K_setminus_M_tch,\n", + " params=parameters)\n", + "M = ~K_setminus_M\n", + "M_tch = torch.tensor(M)\n", + "test_loss = lambda parameters : kalman_recovery_mse(y=c_test_tch, observable_measurements=M_tch,\n", + " layer=layer, params=parameters)\n", + "params = [tau_tch]\n", + "initial_params = [p.item() for p in params]" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor(83.5806, grad_fn=)" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test_loss(params)" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "test loss 001 | 84.34669\n", + "train loss 001 | 69.49468\n", + "test loss 002 | 83.57898\n", + "train loss 002 | 69.37068\n", + "test loss 003 | 83.55436\n", + "train loss 003 | 68.31472\n", + "test loss 004 | 83.41125\n", + "train loss 004 | 67.38767\n", + "test loss 005 | 83.58059\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/q3/opt/anaconda3/envs/deep-learning-env/lib/python3.12/site-packages/diffcp/cone_program.py:154: SyntaxWarning: invalid escape sequence '\\i'\n", + " \"\"\"Solves a cone program, returns its derivative as an abstract linear map.\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[51], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m train_losses, test_losses \u001b[38;5;241m=\u001b[39m tune_kalman_smoother(train_loss, test_loss, params,epochs\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m25\u001b[39m, verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, print_every\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n", + "Cell \u001b[0;32mIn[32], line 38\u001b[0m, in \u001b[0;36mtune_kalman_smoother\u001b[0;34m(train_loss, test_loss, params, epochs, lr, verbose, print_every)\u001b[0m\n\u001b[1;32m 36\u001b[0m opt\u001b[38;5;241m.\u001b[39mzero_grad()\n\u001b[1;32m 37\u001b[0m loss \u001b[38;5;241m=\u001b[39m train_loss(params)\n\u001b[0;32m---> 38\u001b[0m loss\u001b[38;5;241m.\u001b[39mbackward()\n\u001b[1;32m 39\u001b[0m opt\u001b[38;5;241m.\u001b[39mstep()\n\u001b[1;32m 41\u001b[0m neg_theta \u001b[38;5;241m=\u001b[39m \u001b[38;5;28many\u001b[39m([p\u001b[38;5;241m.\u001b[39mitem() \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m0\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m p \u001b[38;5;129;01min\u001b[39;00m params])\n", + "File \u001b[0;32m~/opt/anaconda3/envs/deep-learning-env/lib/python3.12/site-packages/torch/_tensor.py:522\u001b[0m, in \u001b[0;36mTensor.backward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m 512\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m has_torch_function_unary(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 513\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m handle_torch_function(\n\u001b[1;32m 514\u001b[0m Tensor\u001b[38;5;241m.\u001b[39mbackward,\n\u001b[1;32m 515\u001b[0m (\u001b[38;5;28mself\u001b[39m,),\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 520\u001b[0m inputs\u001b[38;5;241m=\u001b[39minputs,\n\u001b[1;32m 521\u001b[0m )\n\u001b[0;32m--> 522\u001b[0m torch\u001b[38;5;241m.\u001b[39mautograd\u001b[38;5;241m.\u001b[39mbackward(\n\u001b[1;32m 523\u001b[0m \u001b[38;5;28mself\u001b[39m, gradient, retain_graph, create_graph, inputs\u001b[38;5;241m=\u001b[39minputs\n\u001b[1;32m 524\u001b[0m )\n", + "File \u001b[0;32m~/opt/anaconda3/envs/deep-learning-env/lib/python3.12/site-packages/torch/autograd/__init__.py:266\u001b[0m, in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[1;32m 261\u001b[0m retain_graph \u001b[38;5;241m=\u001b[39m create_graph\n\u001b[1;32m 263\u001b[0m \u001b[38;5;66;03m# The reason we repeat the same comment below is that\u001b[39;00m\n\u001b[1;32m 264\u001b[0m \u001b[38;5;66;03m# some Python versions print out the first line of a multi-line function\u001b[39;00m\n\u001b[1;32m 265\u001b[0m \u001b[38;5;66;03m# calls in the traceback and some print out the last line\u001b[39;00m\n\u001b[0;32m--> 266\u001b[0m Variable\u001b[38;5;241m.\u001b[39m_execution_engine\u001b[38;5;241m.\u001b[39mrun_backward( \u001b[38;5;66;03m# Calls into the C++ engine to run the backward pass\u001b[39;00m\n\u001b[1;32m 267\u001b[0m tensors,\n\u001b[1;32m 268\u001b[0m grad_tensors_,\n\u001b[1;32m 269\u001b[0m retain_graph,\n\u001b[1;32m 270\u001b[0m create_graph,\n\u001b[1;32m 271\u001b[0m inputs,\n\u001b[1;32m 272\u001b[0m allow_unreachable\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m 273\u001b[0m accumulate_grad\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m 274\u001b[0m )\n", + "File \u001b[0;32m~/opt/anaconda3/envs/deep-learning-env/lib/python3.12/site-packages/torch/autograd/function.py:289\u001b[0m, in \u001b[0;36mBackwardCFunction.apply\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m 283\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[1;32m 284\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mImplementing both \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbackward\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m and \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mvjp\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m for a custom \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 285\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFunction is not allowed. You should only implement one \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 286\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mof them.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 287\u001b[0m )\n\u001b[1;32m 288\u001b[0m user_fn \u001b[38;5;241m=\u001b[39m vjp_fn \u001b[38;5;28;01mif\u001b[39;00m vjp_fn \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m Function\u001b[38;5;241m.\u001b[39mvjp \u001b[38;5;28;01melse\u001b[39;00m backward_fn\n\u001b[0;32m--> 289\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m user_fn(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs)\n", + "File \u001b[0;32m~/opt/anaconda3/envs/deep-learning-env/lib/python3.12/site-packages/cvxpylayers/torch/cvxpylayer.py:339\u001b[0m, in \u001b[0;36m_CvxpyLayerFn.._CvxpyLayerFnFn.backward\u001b[0;34m(ctx, *dvars)\u001b[0m\n\u001b[1;32m 336\u001b[0m dys\u001b[38;5;241m.\u001b[39mappend(np\u001b[38;5;241m.\u001b[39mzeros(ctx\u001b[38;5;241m.\u001b[39mshapes[i][\u001b[38;5;241m0\u001b[39m]))\n\u001b[1;32m 337\u001b[0m dss\u001b[38;5;241m.\u001b[39mappend(np\u001b[38;5;241m.\u001b[39mzeros(ctx\u001b[38;5;241m.\u001b[39mshapes[i][\u001b[38;5;241m0\u001b[39m]))\n\u001b[0;32m--> 339\u001b[0m dAs, dbs, dcs \u001b[38;5;241m=\u001b[39m ctx\u001b[38;5;241m.\u001b[39mDT_batch(dxs, dys, dss)\n\u001b[1;32m 341\u001b[0m \u001b[38;5;66;03m# differentiate from cone problem data to cvxpy parameters\u001b[39;00m\n\u001b[1;32m 342\u001b[0m start \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n", + "File \u001b[0;32m~/opt/anaconda3/envs/deep-learning-env/lib/python3.12/site-packages/diffcp/cone_program.py:114\u001b[0m, in \u001b[0;36msolve_and_derivative_batch..DT_batch\u001b[0;34m(dxs, dys, dss, **kwargs)\u001b[0m\n\u001b[1;32m 112\u001b[0m dAs, dbs, dcs \u001b[38;5;241m=\u001b[39m [], [], []\n\u001b[1;32m 113\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(batch_size):\n\u001b[0;32m--> 114\u001b[0m dA, db, dc \u001b[38;5;241m=\u001b[39m DTs[i](dxs[i], dys[i], dss[i], \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 115\u001b[0m dAs \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m [dA]\n\u001b[1;32m 116\u001b[0m dbs \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m [db]\n", + "File \u001b[0;32m~/opt/anaconda3/envs/deep-learning-env/lib/python3.12/site-packages/diffcp/cone_program.py:471\u001b[0m, in \u001b[0;36msolve_and_derivative_internal..adjoint_derivative\u001b[0;34m(dx, dy, ds, **kwargs)\u001b[0m\n\u001b[1;32m 469\u001b[0m r \u001b[38;5;241m=\u001b[39m _diffcp\u001b[38;5;241m.\u001b[39m_solve_adjoint_derivative_dense(M, MT, dz)\n\u001b[1;32m 470\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m mode \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlsqr\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m--> 471\u001b[0m r \u001b[38;5;241m=\u001b[39m _diffcp\u001b[38;5;241m.\u001b[39mlsqr(MT, dz)\u001b[38;5;241m.\u001b[39msolution\n\u001b[1;32m 472\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m mode \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlsmr\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 473\u001b[0m MT_sp \u001b[38;5;241m=\u001b[39m sparse\u001b[38;5;241m.\u001b[39mlinalg\u001b[38;5;241m.\u001b[39mLinearOperator(dz\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m2\u001b[39m, matvec\u001b[38;5;241m=\u001b[39mMT\u001b[38;5;241m.\u001b[39mmatvec, rmatvec\u001b[38;5;241m=\u001b[39mMT\u001b[38;5;241m.\u001b[39mrmatvec)\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "\n", + "train_losses, test_losses = tune_kalman_smoother(train_loss, test_loss, params,epochs=25, verbose=True, print_every=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 153, + "metadata": {}, + "outputs": [], + "source": [ + "x_hat, w_hat, v_hat = layer(tau_tch)" + ] + }, + { + "cell_type": "code", + "execution_count": 154, + "metadata": {}, + "outputs": [], + "source": [ + "x_new, _, _ = layer(torch.tensor(0.08))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Final estimations" + ] + }, + { + "cell_type": "code", + "execution_count": 155, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot_positions([x_true,x_hat_orig.detach().numpy()], ['True', 'KF orig. recovery'], [-4,14,-5,20])\n", + "plot_positions([x_true,x_new.detach().numpy()], ['True', 'KF tuned recovery'], [-4,14,-5,20])\n", + "plot_positions([x_true,x_hat.detach().numpy()], ['True', 'KF tuned recovery'], [-4,14,-5,20])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Robust Smoothing" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# initialize parameter values\n", + "# note difference between torch parameters and cvxpylayer parameters\n", + "num_train_pts, K_setminus_M = split_data(fraction=1.0)\n", + "M = ~K_setminus_M\n", + "K_setminus_M_tch, M_tch = torch.tensor(K_setminus_M), torch.tensor(M)\n", + "S, c, c_test = create_problem_data_params(K_setminus_M, num_train_pts)\n", + "S_tch = torch.tensor(S.todense(out=np.zeros((num_train_pts, N))))\n", + "c_tch, c_test_tch = torch.tensor(c), torch.tensor(c_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "eta_tch = torch.tensor(0.5, requires_grad=True)\n", + "rho_tch = torch.tensor(2.0, requires_grad=True)\n", + "params = [eta_tch, rho_tch] \n", + "robust_layer = create_robust_kalman_smoother(S, c)\n", + "x_hat_orig, w_hat_orig, v_hat_orig = robust_layer(*params)\n", + "train_loss = lambda parameters : learning_error_fn(layer=robust_layer, y=c_tch,\n", + " observable_measurements=K_setminus_M_tch,\n", + " params=parameters)\n", + "test_loss = lambda parameters : kalman_recovery_mse(y=c_test_tch, observable_measurements=M_tch,\n", + " layer=robust_layer, params=parameters)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "batch_robust_results = robust_layer(torch.tensor([0.5, 1.0], requires_grad=True), torch.tensor([2.0, 3.0], requires_grad=True))" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "test loss 001 | nan\n" + ] + }, + { + "ename": "AttributeError", + "evalue": "'csr_matrix' object has no attribute 'A'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[18], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m train_losses, test_losses \u001b[38;5;241m=\u001b[39m tune_kalman_smoother(train_loss, test_loss, params, verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, print_every\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5\u001b[39m)\n", + "Cell \u001b[0;32mIn[14], line 38\u001b[0m, in \u001b[0;36mtune_kalman_smoother\u001b[0;34m(train_loss, test_loss, params, epochs, lr, verbose, print_every)\u001b[0m\n\u001b[1;32m 36\u001b[0m opt\u001b[38;5;241m.\u001b[39mzero_grad()\n\u001b[1;32m 37\u001b[0m loss \u001b[38;5;241m=\u001b[39m train_loss(params)\n\u001b[0;32m---> 38\u001b[0m loss\u001b[38;5;241m.\u001b[39mbackward()\n\u001b[1;32m 39\u001b[0m opt\u001b[38;5;241m.\u001b[39mstep()\n\u001b[1;32m 41\u001b[0m neg_theta \u001b[38;5;241m=\u001b[39m \u001b[38;5;28many\u001b[39m([p\u001b[38;5;241m.\u001b[39mitem() \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m0\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m p \u001b[38;5;129;01min\u001b[39;00m params])\n", + "File \u001b[0;32m~/opt/anaconda3/envs/deep-learning-env/lib/python3.12/site-packages/torch/_tensor.py:522\u001b[0m, in \u001b[0;36mTensor.backward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m 512\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m has_torch_function_unary(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 513\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m handle_torch_function(\n\u001b[1;32m 514\u001b[0m Tensor\u001b[38;5;241m.\u001b[39mbackward,\n\u001b[1;32m 515\u001b[0m (\u001b[38;5;28mself\u001b[39m,),\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 520\u001b[0m inputs\u001b[38;5;241m=\u001b[39minputs,\n\u001b[1;32m 521\u001b[0m )\n\u001b[0;32m--> 522\u001b[0m torch\u001b[38;5;241m.\u001b[39mautograd\u001b[38;5;241m.\u001b[39mbackward(\n\u001b[1;32m 523\u001b[0m \u001b[38;5;28mself\u001b[39m, gradient, retain_graph, create_graph, inputs\u001b[38;5;241m=\u001b[39minputs\n\u001b[1;32m 524\u001b[0m )\n", + "File \u001b[0;32m~/opt/anaconda3/envs/deep-learning-env/lib/python3.12/site-packages/torch/autograd/__init__.py:266\u001b[0m, in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[1;32m 261\u001b[0m retain_graph \u001b[38;5;241m=\u001b[39m create_graph\n\u001b[1;32m 263\u001b[0m \u001b[38;5;66;03m# The reason we repeat the same comment below is that\u001b[39;00m\n\u001b[1;32m 264\u001b[0m \u001b[38;5;66;03m# some Python versions print out the first line of a multi-line function\u001b[39;00m\n\u001b[1;32m 265\u001b[0m \u001b[38;5;66;03m# calls in the traceback and some print out the last line\u001b[39;00m\n\u001b[0;32m--> 266\u001b[0m Variable\u001b[38;5;241m.\u001b[39m_execution_engine\u001b[38;5;241m.\u001b[39mrun_backward( \u001b[38;5;66;03m# Calls into the C++ engine to run the backward pass\u001b[39;00m\n\u001b[1;32m 267\u001b[0m tensors,\n\u001b[1;32m 268\u001b[0m grad_tensors_,\n\u001b[1;32m 269\u001b[0m retain_graph,\n\u001b[1;32m 270\u001b[0m create_graph,\n\u001b[1;32m 271\u001b[0m inputs,\n\u001b[1;32m 272\u001b[0m allow_unreachable\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m 273\u001b[0m accumulate_grad\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m 274\u001b[0m )\n", + "File \u001b[0;32m~/opt/anaconda3/envs/deep-learning-env/lib/python3.12/site-packages/torch/autograd/function.py:289\u001b[0m, in \u001b[0;36mBackwardCFunction.apply\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m 283\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[1;32m 284\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mImplementing both \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbackward\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m and \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mvjp\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m for a custom \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 285\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFunction is not allowed. You should only implement one \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 286\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mof them.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 287\u001b[0m )\n\u001b[1;32m 288\u001b[0m user_fn \u001b[38;5;241m=\u001b[39m vjp_fn \u001b[38;5;28;01mif\u001b[39;00m vjp_fn \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m Function\u001b[38;5;241m.\u001b[39mvjp \u001b[38;5;28;01melse\u001b[39;00m backward_fn\n\u001b[0;32m--> 289\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m user_fn(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs)\n", + "File \u001b[0;32m~/opt/anaconda3/envs/deep-learning-env/lib/python3.12/site-packages/cvxpylayers/torch/cvxpylayer.py:345\u001b[0m, in \u001b[0;36m_CvxpyLayerFn.._CvxpyLayerFnFn.backward\u001b[0;34m(ctx, *dvars)\u001b[0m\n\u001b[1;32m 343\u001b[0m grad \u001b[38;5;241m=\u001b[39m [[] \u001b[38;5;28;01mfor\u001b[39;00m _ \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(param_ids))]\n\u001b[1;32m 344\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(ctx\u001b[38;5;241m.\u001b[39mbatch_size):\n\u001b[0;32m--> 345\u001b[0m del_param_dict \u001b[38;5;241m=\u001b[39m compiler\u001b[38;5;241m.\u001b[39mapply_param_jac(\n\u001b[1;32m 346\u001b[0m dcs[i], \u001b[38;5;241m-\u001b[39mdAs[i], dbs[i])\n\u001b[1;32m 347\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m j, pid \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(param_ids):\n\u001b[1;32m 348\u001b[0m grad[j] \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m [to_torch(del_param_dict[pid],\n\u001b[1;32m 349\u001b[0m ctx\u001b[38;5;241m.\u001b[39mdtype, ctx\u001b[38;5;241m.\u001b[39mdevice)\u001b[38;5;241m.\u001b[39munsqueeze(\u001b[38;5;241m0\u001b[39m)]\n", + "File \u001b[0;32m~/opt/anaconda3/envs/deep-learning-env/lib/python3.12/site-packages/cvxpy/reductions/dcp2cone/cone_matrix_stuffing.py:252\u001b[0m, in \u001b[0;36mParamConeProg.apply_param_jac\u001b[0;34m(self, delc, delA, delb, active_params)\u001b[0m\n\u001b[1;32m 247\u001b[0m del_param_vec \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39msqueeze(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mA\u001b[38;5;241m.\u001b[39mT\u001b[38;5;241m.\u001b[39mdot(delAb\u001b[38;5;241m.\u001b[39mtoarray()))\n\u001b[1;32m 248\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 249\u001b[0m \u001b[38;5;66;03m# slow path.\u001b[39;00m\n\u001b[1;32m 250\u001b[0m \u001b[38;5;66;03m# TODO: make this faster by intelligently operating on the\u001b[39;00m\n\u001b[1;32m 251\u001b[0m \u001b[38;5;66;03m# sparse matrix data / making use of reduced_A\u001b[39;00m\n\u001b[0;32m--> 252\u001b[0m del_param_vec \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39msqueeze((delAb\u001b[38;5;241m.\u001b[39mT \u001b[38;5;241m@\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mA)\u001b[38;5;241m.\u001b[39mA)\n\u001b[1;32m 253\u001b[0m del_param_vec \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39msqueeze(del_param_vec)\n\u001b[1;32m 255\u001b[0m param_id_to_delta_param \u001b[38;5;241m=\u001b[39m {}\n", + "\u001b[0;31mAttributeError\u001b[0m: 'csr_matrix' object has no attribute 'A'" + ] + } + ], + "source": [ + "train_losses, test_losses = tune_kalman_smoother(train_loss, test_loss, params, verbose=True, print_every=5)" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_positions([x_true,x_hat_orig.detach().numpy()], ['True', 'KF orig. recovery'], [-4,14,-5,20])\n", + "# plot_positions([x_true,x_hat.detach().numpy()], ['True', 'KF tuned recovery'], [-4,14,-5,20])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# References\n", + "- \\[[BB20](https://stanford.edu/~boyd/papers/auto_ks.html)\\] S. Barratt and S. Boyd. \"Fitting a kalman smoother to data.\" 2019.\n", + "- \\[[BV04](https://stanford.edu/~boyd/cvxbook/)\\] S. Boyd and L. Vandenberghe. *Convex Optimization*. Cambridge University Press, 2004\n", + "- \\[[BV18](https://web.stanford.edu/~boyd/vmls/)\\] S. Boyd and L. Vandenberghe. *Introduction to applied linear algebra: vectors, matrices, and least squares.* Cambridge University Press, 2018.\n", + "- \\[[Kal60](https://www.unitedthc.com/DSP/Kalman1960.pdf)\\] R. Kalman. A new approach to linear filtering and prediction problems. *Journal Basic Engineering*, 82(1):35-45, 1960.\n", + "- \\[[Mur23](https://probml.github.io/pml-book/book2.html)\\] K. Murphy. *Probabilistic Machine Learning: Advanced Topics.* MIT Press, 2023.\n", + "- \\[[01](https://web.stanford.edu/~boyd/papers/learning_cocps.html)\\] A. Agrawal, S. Barratt, S. Boyd, and B. Stellato, \"Learning convex optimization control policies,\" in *Proc. 2nd Annu. Conf. Learning for Dynamics and Control, 2020*\n", + "# Appendix\n", + "## Huber reformulation\n", + "\n", + "(The following is a variation of \\[[BV04 ex4.8](https://stanford.edu/~boyd/cvxbook/)\\].)\n", + "\n", + "Consider the unconstrained optimization problem\n", + "\n", + "$$\n", + "\\begin{equation}\n", + "\\text{minimize} \\; \\sum_{t = 1}^{T} \\phi_M(Ax_t - b_t),\n", + "\\tag{A1}\n", + "\\end{equation}\n", + "$$\n", + "\n", + "where $x_1, \\ldots, x_T \\in \\mathbf{R}^{n}$ are the optimization variables, and\n", + "$\\phi_M : \\mathbf{R}^{n} \\to \\mathbf{R}$ given by\n", + "\n", + "$$\n", + "\\phi_M(u) = \\begin{cases}\n", + "\\left\\lVert u \\right\\rVert_{2}^2 & \\left\\lVert u \\right\\rVert_{2} \\le M \\\\\n", + "M(2\\left\\lVert u \\right\\rVert_{2} - M) & \\left\\lVert u \\right\\rVert_{2} > M.\n", + "\\end{cases}\n", + "$$\n", + "is the *Huber penalty function.*\n", + "\n", + "The reformulation of (6) to (7) requires that we show equivalence between (A1) and\n", + "\n", + "$$\n", + "\\begin{equation}\n", + "\\begin{array}{lll}\n", + "\\text{minimize} \\; & \\sum_{i = 1}^{n} \\left(u_t^2 + 2Mv_t \\right) & \\\\\n", + "\\text{subject to} & \\left\\lVert Ax_t - b_t \\right\\rVert_{2} \\le u_t + v_t, \\; & t = 1, \\ldots, T \\\\\n", + "& 0 \\preceq u \\preceq M \\mathbf{1} \\; & \\\\\n", + "& v \\succeq 0,\n", + "\\end{array}\n", + "\\tag{A2}\n", + "\\end{equation}\n", + "$$\n", + "where $x_1, \\ldots, x_T \\in \\mathbf{R}^{n}$, $u \\in \\mathbf{R}^{T}$,\n", + "and $v \\in \\mathbf{R}^{T}$ are the optimization variables.\n", + "\n", + "We make the (reasonable) assumption that there exists a $t \\in \\left\\{ 1, \\ldots, T \\right\\}$ such that $Ax_t \\not = b_t$.\n", + "Now, consider (A2) and fix $x_t$. At the optimum, it is necessary that $u_t + v_t = \\left\\lVert Ax_t - b_t \\right\\rVert_{2}$, otherwise,\n", + "since $u_t$ and $v_t$ are not simultaneously zero, we could decrease one (or both) and see a reduction in the objective function.\n", + "\n", + "Therefore, we can eliminate $v_t$ from the original problem, (A2), using the substitution\n", + "\n", + "$$\n", + "v_t = \\left\\lVert Ax_t - b_t \\right\\rVert_{2} - u_t,\n", + "$$\n", + "\n", + "with the additional constraints\n", + "\n", + "$$\n", + "u_t \\le \\left\\lVert Ax_t - b_t \\right\\rVert_{2},\n", + "$$\n", + "which ensure the substition does not violate $v \\succeq 0$, to form the equivalent problem\n", + "\n", + "$$\n", + "\\begin{array}{lll}\n", + "\\text{minimize} \\; & f_0(u) = \\sum_{t=1}^{T} \\left(u_t^2 - 2Mu_t + 2M\\left\\lVert Ax_t - b_t \\right\\rVert_{2} \\right) & \\\\\n", + "\\text{subject to} & 0 \\le u_t \\le \\min \\left\\{ M, \\left\\lVert Ax_t - b_t \\right\\rVert_{2}\\right\\}, \\quad t=1, \\ldots, T. & \\\\\n", + "\\end{array}\n", + "$$\n", + "\n", + "Considering solely the objective function, a sum of (convex) quadratic functions, calculus tells us that $u_t = M$ minimizes each summand of $f_0$. However, if $\\left\\lVert Ax_t - b_t \\right\\rVert_{2} < M$ for some $t \\in \\left\\{1, \\ldots, T \\right\\}$, then $u_t = \\left\\lVert Ax_t - b_t \\right\\rVert_{2}$ becomes the minimizer of that particular summand. \n", + "\n", + "Therefore, when $\\left\\lVert Ax_t - b_t \\right\\rVert_{2} \\le M$, we choose $u_t = \\left\\lVert Ax_t - b_t \\right\\rVert_{2}$ and the $t$ th summand simplifies to\n", + "$$\n", + "\\left\\lVert Ax_t - b_t \\right\\rVert_{2}^{2} - 2M\\left\\lVert Ax_t - b_t \\right\\rVert_{2}^{2} + 2M \\left\\lVert Ax_t - b_t \\right\\rVert_{2}^{2} = \\left\\lVert Ax_t - b_t \\right\\rVert_{2}^{2}.\n", + "$$\n", + "\n", + "Otherwise, if $\\left\\lVert Ax_t - b_t \\right\\rVert_{2} > M$, we choose $u_t = M$ and the $t$ th summand becomes\n", + "\n", + "$$\n", + "M^2 - 2M^2 + 2M \\left\\lVert Ax_t - b_t \\right\\rVert_{2} = M(2\\left\\lVert Ax_t - b_t \\right\\rVert_{2} - M).\n", + "$$\n", + "\n", + "We conclude that for a fixed $x$, the optimal value of (A2) is given by\n", + "\n", + "$$\n", + "∑_{t=1}^T ϕ_M(Ax_t - b_t).\n", + "$$\n", + "\n", + "## Selection matrix construction\n", + "\n", + "rows, cols = matrix.nonzero() are constructed by looping row wise through the matrix, recording the row and column value, respectively, whenever the matrix has a nonzero value" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cp.sum(cp.vec(y).value == c).value # check if c is ordered correctly as column majored" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "c_test = y[K_setminus_M.T]" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "nm, KM = split_data(1.0)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-1.26099346, -1.02476223, -0.18265832, ..., 18.51687772,\n", + " 19.28085045, 19.33229155])" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y[KM.T]" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-1.26099346, -1.02476223, -0.18265832, ..., 4.69332517,\n", + " 2.68689846, 2.3267197 ],\n", + " [ 0.13240848, -1.65302845, -1.50460709, ..., 18.51687772,\n", + " 19.28085045, 19.33229155]])" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-1.26099346, 0.13240848, -1.02476223, ..., 19.28085045,\n", + " 2.3267197 , 19.33229155])" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y.T[KM]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "deep-learning-env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From c91b1fa8fdcef78e7f344777b8e42e61eb1a0a2e Mon Sep 17 00:00:00 2001 From: healeyq3 Date: Wed, 14 Aug 2024 23:21:19 -0400 Subject: [PATCH 3/3] requested changes and example update --- cvxpylayers/torch/cvxpylayer.py | 2 +- cvxpylayers/torch/test_cvxpylayer.py | 2 +- examples/torch/learned_state_estimation.html | 1597 ++++++++++------- examples/torch/learned_state_estimation.ipynb | 1499 ++++++++++------ 4 files changed, 1885 insertions(+), 1215 deletions(-) diff --git a/cvxpylayers/torch/cvxpylayer.py b/cvxpylayers/torch/cvxpylayer.py index d9feeb0..32d0b7c 100644 --- a/cvxpylayers/torch/cvxpylayer.py +++ b/cvxpylayers/torch/cvxpylayer.py @@ -293,7 +293,7 @@ def forward(ctx, *params): As, bs, cs, cone_dicts, **solver_args) ) else: - xs, _, _ = diffcp.cone_program.solve_only_batch( + xs, _, _ = diffcp.solve_only_batch( As, bs, cs, cone_dicts, **solver_args) except diffcp.SolverError as e: print( diff --git a/cvxpylayers/torch/test_cvxpylayer.py b/cvxpylayers/torch/test_cvxpylayer.py index 69bec54..044636d 100755 --- a/cvxpylayers/torch/test_cvxpylayer.py +++ b/cvxpylayers/torch/test_cvxpylayer.py @@ -88,7 +88,7 @@ def test_least_squares(self): def lstsq( A, b): return torch.linalg.solve( - A.t() @ A + torch.eye(n).double(), + A.t() @ A + torch.eye(n, dtype=torch.float64), (A.t() @ b).unsqueeze(1)) x_lstsq = lstsq(A_th, b_th) diff --git a/examples/torch/learned_state_estimation.html b/examples/torch/learned_state_estimation.html index b9b45e0..6f02ed0 100644 --- a/examples/torch/learned_state_estimation.html +++ b/examples/torch/learned_state_estimation.html @@ -7331,12 +7331,11 @@ if (!diagrams.length) { return; } - const mermaid = (await import("https://cdnjs.cloudflare.com/ajax/libs/mermaid/10.7.0/mermaid.esm.min.mjs")).default; + const mermaid = (await import("https://cdnjs.cloudflare.com/ajax/libs/mermaid/10.6.0/mermaid.esm.min.mjs")).default; const parser = new DOMParser(); mermaid.initialize({ maxTextSize: 100000, - maxEdges: 100000, startOnLoad: false, fontFamily: window .getComputedStyle(document.body) @@ -7407,8 +7406,7 @@ let results = null; let output = null; try { - let { svg } = await mermaid.render(id, raw, el); - svg = cleanMermaidSvg(svg); + const { svg } = await mermaid.render(id, raw, el); results = makeMermaidImage(svg); output = document.createElement("figure"); results.map(output.appendChild, output); @@ -7423,38 +7421,6 @@ parent.appendChild(output); } - - /** - * Post-process to ensure mermaid diagrams contain only valid SVG and XHTML. - */ - function cleanMermaidSvg(svg) { - return svg.replace(RE_VOID_ELEMENT, replaceVoidElement); - } - - - /** - * A regular expression for all void elements, which may include attributes and - * a slash. - * - * @see https://developer.mozilla.org/en-US/docs/Glossary/Void_element - * - * Of these, only `
` is generated by Mermaid in place of `\n`, - * but _any_ "malformed" tag will break the SVG rendering entirely. - */ - const RE_VOID_ELEMENT = - /<\s*(area|base|br|col|embed|hr|img|input|link|meta|param|source|track|wbr)\s*([^>]*?)\s*>/gi; - - /** - * Ensure a void element is closed with a slash, preserving any attributes. - */ - function replaceVoidElement(match, tag, rest) { - rest = rest.trim(); - if (!rest.endsWith('/')) { - rest = `${rest} /`; - } - return `<${tag} ${rest}>`; - } - void Promise.all([...diagrams].map(renderOneMarmaid)); }); @@ -7518,6 +7484,7 @@ @@ -7742,7 +7714,32 @@

Cross-validation @@ -7867,31 +7864,8 @@

Plotting helper functions @@ -7947,7 +7921,8 @@

Problem dynamics @@ -7985,16 +7960,10 @@

Generating trajectoriesw_true = w.copy() plot_state(times,(x_true,w_true)) -plot_positions(traj=[[x_true], [y], [y, x_true]], - titles=['True', - 'Observations', - 'True vs. Observations'], - legends=[['True'], - ['Measurements'], - ['Measurements', 'True']], - plot_args=[['r'], - ['ro'], - ['ro', 'k']], +plot_positions(traj=[[y, x_true]], + titles=['True vs. Observations'], + legends=[['Measurements', 'True']], + plot_args=[['ro', 'k']], axis=[-4,14,-5,20]) @@ -8008,13 +7977,13 @@

Generating trajectories @@ -8026,7 +7995,20 @@

Generating trajectories @@ -8038,16 +8020,24 @@

Smoother InstantiationIn [ ]: @@ -8925,7 +9042,33 @@

Perform cross-validationIn [ ]: + + + - + + + diff --git a/examples/torch/learned_state_estimation.ipynb b/examples/torch/learned_state_estimation.ipynb index e44cb7e..1cc972a 100644 --- a/examples/torch/learned_state_estimation.ipynb +++ b/examples/torch/learned_state_estimation.ipynb @@ -2,11 +2,12 @@ "cells": [ { "cell_type": "code", - "execution_count": 19, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from typing import (Tuple, List, Callable)\n", + "from functools import partial\n", "\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", @@ -15,7 +16,7 @@ "\n", "import cvxpy as cp\n", "import torch\n", - "import torch.nn.functional as F\n", + "import torch.nn.functional as torch_functional\n", "from cvxpylayers.torch import CvxpyLayer\n", "\n", "torch.set_default_dtype(torch.float64)" @@ -216,7 +217,7 @@ "\\end{equation}\n", "$$\n", "\n", - "where $S \\in \\mathbf{R}^{ \\left| \\mathcal{K} ∖ \\mathcal{M} \\right| \\times N}$ is a selector matrix and $c ∈ \\mathbf{R}^{\\left| \\mathcal{K} \\setminus \\mathcal{M} \\right|}$ contains the corresponding entries of $y_t$. Concretely, if we denote the elements in $\\mathcal{K} ∖ \\mathcal{M}$ as $\\left(\\mathcal{K} ∖ \\mathcal{M} \\right)_1, \\ldots, \\left(\\mathcal{K} ∖ \\mathcal{M} \\right)_{\\left| \\mathcal{K} ∖ \\mathcal{M} \\right|}$, then if $\\left(\\mathcal{K} ∖ \\mathcal{M} \\right)_j = (t, i)$, the $j\\text{th}$ row of $S$ is $e_{tp + i}$ and the $j\\text{th}$ entry of $c$ is $(y_t)_i$.\n", + "where $S \\in \\mathbf{R}^{ \\left| \\mathcal{K} ∖ \\mathcal{M} \\right| \\times N}$ is a selector matrix and $c ∈ \\mathbf{R}^{\\left| \\mathcal{K} \\setminus \\mathcal{M} \\right|}$ contains the corresponding entries of $y_t$. Concretely, if we denote the elements in $\\mathcal{K} ∖ \\mathcal{M}$ as $\\left(\\mathcal{K} ∖ \\mathcal{M} \\right)_1, \\ldots, \\left(\\mathcal{K} ∖ \\mathcal{M} \\right)_{\\left| \\mathcal{K} ∖ \\mathcal{M} \\right|}$, then if $\\left(\\mathcal{K} ∖ \\mathcal{M} \\right)_j = (t, i)$, the $j\\text{th}$ row of $S$ is $e_{tp + i}$ and the $j\\text{th}$ entry of $c$ is $(y_t)_i$. Evidently, $S$ is very sparse.\n", "\n", "### Solving the Kalman smoothing problems\n", "\n", @@ -315,7 +316,7 @@ "\\Theta = \\left\\{ \\theta \\in \\mathbf{R}^{k} \\mid \\theta \\succ 0 \\right\\},\n", "$$\n", "\n", - "where $k=1$ and $k=2$, respectively. (Note that for our parameter/allowable parameter notation we're adopting the function notation style found in \\[[BV04](https://stanford.edu/~boyd/cvxbook/)\\], where a function may be declared over some space, but only defined for a subset of that space.) While there are many possible regularization functions we could choose to define (9), we'll auto-tune our smoothers using the logarithmic barrier function. That is, we'll define\n", + "where $k=1$ and $k=2$, respectively. (Note that for our parameter/allowable parameter notation we're adopting the function notation style found in \\[[BV04](https://stanford.edu/~boyd/cvxbook/)\\], where a function may be declared over some space, but only defined for a subset of that space.) While there are many possible regularization functions we could choose to define $F(\\theta)$, we'll auto-tune our smoothers using the logarithmic barrier function. That is, we'll define\n", "\n", "$$\n", "r(\\theta) = ∑_{i=1}^{k} - \\log θ_i.\n", @@ -323,16 +324,16 @@ "\n", "### Judging tuned smoothers\n", "\n", - "#### Update to out-of-sample notation\n", + "#### Update to notation for out-of-sample validation\n", "\n", "Once a $\\theta$ is obtained via auto-tuning (*i.e.*, once a smoother is tuned), the prediction loss (8) is no longer a valid measurement of that smoother's performance. To evaluate this tuned smoother, we need to test it on another (unseen) output sequence. To reserve a final output prediction error test dataset, we still mask some fraction of the measurements (now more likely 20% than 10%) and then split that mask (rougly) in half to obtain $\\mathcal{M} \\subset \\mathcal{K}$ and $\\mathcal{T} ⊂ \\mathcal{K}$ such that $\\mathcal{M} ∩ \\mathcal{T} = ∅$. The Kalman smoother is then formulated (or \"trained\") on $\\mathcal{K} ∖ \\left( \\mathcal{M} ∪ \\mathcal{T} \\right)$, tuned on $\\mathcal{M}$, and tested on $\\mathcal{T}$. (The selector matrix, measurement vector, etc. are all updated accordingly.)\n", "\n", "#### Cross-validation\n", "\n", - "We perform $\\mathcal{N}\\text{-fold}$ cross-validation by dividing $\\mathcal{K}$ into $\\mathcal{N}$ sets (folds), and then constructing $\\mathcal{N}$ smoothers on $\\mathcal{N}-1$ folds, using half of the measurements of the withheld fold to tune the smoother's parameters and the other half to evaluate the performance of the smoother. Concretely, we generate\n", + "We perform $\\mathcal{N}\\text{-fold}$ cross-validation by dividing $\\mathcal{K}$ into $\\mathcal{N}$ sets (folds), and then constructing $\\mathcal{N}$ smoothers each with $\\mathcal{N}-1$ folds, using half of the measurements of the withheld fold to tune the smoother's parameters and the other half to evaluate the performance of the smoother. Concretely, we generate\n", "\n", "$$\n", - "\\mathcal{M}_n ⊂ \\mathcal{K} \\quad \\text{and} \\quad \\mathcal{T}_n ⊂ \\mathcal{K}, \\quad \\mathcal{M}_n ∩ \\mathcal{T}_n = ∅, \\quad n =1, \\ldots \\mathcal{N},\n", + "\\mathcal{M}_n ⊂ \\mathcal{K} \\quad \\text{and} \\quad \\mathcal{T}_n ⊂ \\mathcal{K}, \\quad \\text{such that} \\quad \\mathcal{M}_n ∩ \\mathcal{T}_n = ∅, \\quad n =1, \\ldots \\mathcal{N},\n", "$$\n", "\n", "where for each $n$\n", @@ -341,7 +342,15 @@ "\\left| \\mathcal{M}_n \\right| + \\left| \\mathcal{T}_n \\right| ≈ \\frac{1}{\\mathcal{N}} \\left| \\mathcal{K} \\right|.\n", "$$\n", "\n", - "Without loss of generality, we construct the first of our $\\mathcal{N}$ smoothers with $\\mathcal{K} ∖ (\\mathcal{M}_\\mathcal{N} ∪ \\mathcal{T}_{\\mathcal{N}})$, use $\\mathcal{M}_\\mathcal{N}$ to tune the smoother's parameters, and $\\mathcal{T}_{\\mathcal{N}}$ to compute the smoother's mean squared prediction error." + "Without loss of generality, the first of the $\\mathcal{N}$ smoothers is constructed using the observations in $\\mathcal{K} ∖ \\left( \\mathcal{M}_{1} ∪ \\mathcal{T}_{1} \\right)$, tuned by solving (9) where $\\mathcal{M}_{1}$ are the observed outputs in $L$, and evaluated with (8) where $\\mathcal{T}_{1}$ are the observed outputs in $L$.\n", + "\n", + "We'll define the $\\mathcal{N}\\text{-fold}$ cross-validation error as\n", + "\n", + "$$\n", + "\\text{CV}_{\\mathcal{N}} = \\sum_{n=1}^{\\mathcal{N}} \\frac{\\left| \\mathcal{T}_n \\right|}{ \\left| \\mathcal{T} \\right| } L_n(\\hat{\\theta}_n)\n", + "$$\n", + "\n", + "where $L_n(\\hat{\\theta}_n)$ is the prediction error between the output *observations* in $\\mathcal{T}_n$ and the output *predictions* of the smoother constructed with the observations $\\mathcal{K} ∖ \\left(\\mathcal{M}_n ∪ \\mathcal{T}_n \\right)$ and defined with the parameters $\\hat{\\theta}_n$, which were found by tuning the smoother with observations $\\mathcal{M}_n$. Finally, $\\mathcal{T} =⋃_{n=1}^{\\mathcal{N}}\\mathcal{T}_n$." ] }, { @@ -349,8 +358,34 @@ "metadata": {}, "source": [ "# Implementation (vehicle tracking example)\n", + "We'll apply standard and robust Kalman smoothing to recover the state history, $x_1, \\ldots, x_T$, of a vehicle. The state, $x_t \\in \\mathbf{R}^{4}$, is as described in Background/System Model: the first two components of the state vector is the position of the vehicle in two dimensions, and the second two components is the vehicle velocity. The vehicle has unknown drive force $w_t$, and we observe noisy measurements of the vehicle position, $y_t \\in \\mathbf{R}^{2}$.\n", + "\n", + "The matrices for the system dynamics (and output) are\n", + "$$\n", + "A = \\begin{bmatrix}\n", + "1 & 0 & (1- \\frac{\\gamma}{2}\\Delta t)\\Delta t & 0 \\\\\n", + "0 & 1 & 0 & (1- \\frac{\\gamma}{2}\\Delta t)\\Delta t \\\\\n", + "0 & 0 & 1 - \\gamma \\Delta t & 0 \\\\\n", + "0 & 0 & 0 & 1 - \\gamma \\Delta t\n", + "\\end{bmatrix},\n", + "$$\n", "\n", - "We consider the vehicle tracking example stated above, where $x_t \\in \\mathbf{R}^{4}$ contains the vehicle's " + "$$\n", + "B = \\begin{bmatrix}\n", + "\\frac{1}{2}\\Delta t^2 & 0 \\\\\n", + "0 & \\frac{1}{2}\\Delta t^2 \\\\\n", + "\\Delta t & 0 \\\\\n", + "0 & \\Delta t,\n", + "\\end{bmatrix}\n", + "$$\n", + "and\n", + "$$\n", + "C = \\begin{bmatrix}\n", + "1 & 0 & 0 & 0 \\\\\n", + "0 & 1 & 0 & 0\n", + "\\end{bmatrix},\n", + "$$\n", + "where $\\gamma$ is a velocity damping parameter." ] }, { @@ -362,7 +397,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -462,38 +497,15 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Problem dynamics\n", - "The matrices for the system dynamics (and output) are\n", - "$$\n", - "A = \\begin{bmatrix}\n", - "1 & 0 & (1- \\frac{\\gamma}{2}\\Delta t) & 0 \\\\\n", - "0 & 1 & 0 & (1- \\frac{\\gamma}{2}\\Delta t) \\\\\n", - "0 & 0 & 1 - \\gamma \\Delta t & 0 \\\\\n", - "0 & 0 & 0 & 1 - \\gamma \\Delta t\n", - "\\end{bmatrix},\n", - "$$\n", + "## Problem data\n", + "We generate the data for the vehicle tracking problem. We'll have $T=1000$, $w_t$ a standard Gaussian, and $v_t$ a standard Gaussian, except $20\\%$ of the points will be outliers with $σ=20$.\n", "\n", - "$$\n", - "B = \\begin{bmatrix}\n", - "\\frac{1}{2}\\Delta t^2 & 0 \\\\\n", - "0 & \\frac{1}{2}\\Delta t^2 \\\\\n", - "\\Delta t & 0 \\\\\n", - "0 & \\Delta t,\n", - "\\end{bmatrix}\n", - "$$\n", - "and\n", - "$$\n", - "C = \\begin{bmatrix}\n", - "1 & 0 & 0 & 0 \\\\\n", - "0 & 1 & 0 & 0\n", - "\\end{bmatrix},\n", - "$$\n", - "where $\\gamma$ is a velocity damping parameter." + "Below, we set the problem parameters and define the matrices $A$, $B$, and $C$." ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -535,7 +547,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Generating trajectories" + "## Simulation\n", + "\n", + "We seed $x_0 = 0$ (starting at the origin with zero velocity) and simulate the system forward in time. The results are the true vehicle positions `x_true` (which we'll use to judge our recovery) and the observed positions `y`. (Note that $C$ only \"observes\" the first two components of a state vector.)\n", + "\n", + "We plot the position, velocity, and system input $w$ in both dimensions as a function of time. We also plot the sets of true and observed vehicle positions." ] }, { @@ -545,7 +561,7 @@ "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -555,9 +571,9 @@ }, { "data": { - "image/png": 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", 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", 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" ] }, "metadata": {}, @@ -591,16 +607,10 @@ "w_true = w.copy()\n", "\n", "plot_state(times,(x_true,w_true))\n", - "plot_positions(traj=[[x_true], [y], [y, x_true]],\n", - " titles=['True',\n", - " 'Observations',\n", - " 'True vs. Observations'],\n", - " legends=[['True'],\n", - " ['Measurements'],\n", - " ['Measurements', 'True']],\n", - " plot_args=[['r'],\n", - " ['ro'],\n", - " ['ro', 'k']],\n", + "plot_positions(traj=[[y, x_true]],\n", + " titles=['True vs. Observations'],\n", + " legends=[['Measurements', 'True']],\n", + " plot_args=[['ro', 'k']],\n", " axis=[-4,14,-5,20])" ] }, @@ -610,7 +620,29 @@ "source": [ "## Smoother Instantiation\n", "\n", - "Creating the cvxpylayers " + "Before moving into the main functionality of this vehicle path reconstruction example, we define some additional notation and comment on how this notation and the notation from the Background section is adapted to the code. (Note that exposition on *all* the code is not given here. What is not explained here can be extrapolated from the following main ideas.)\n", + "\n", + "Firstly, \n", + "\n", + "$$\n", + "\\mathcal{F} = \\mathcal{K} ∖ \\left( \\mathcal{M} ∪ \\mathcal{T} \\right)\n", + "$$\n", + "\n", + "are the observations that a smoother is trained on in an out-of-sample validation setting. Similarily, when using $\\mathcal{N}\\text{-fold}$ cross-validation to evaluate smoothers, \n", + "\n", + "$$\n", + "\\mathcal{F}_n = \\mathcal{K} ∖ \\left( \\mathcal{M}_n ∪ \\mathcal{T}_n \\right)\n", + "$$\n", + "\n", + "are the observations that the $n\\text{th}$ smoother is trained on. Except in the cross-validation related functionality, the non-subscripted notation will be used.\n", + "\n", + "The code is very consistent with this set notation. The set of all observation, $\\mathcal{K}$, is contained in `y`, a $p \\times T$ `np.ndarray` whose $(i, t)\\text{th}$ entry is the $i\\text{th}$ component of the observation at time $\\text{t}$. `F` and `M` will (almost always) be used to represent $\\mathcal{F}$ and $\\mathcal{M}$. However, these will be $T \\times p$ *mask matrices* whose $(t, i)\\text{th}$ entry is a boolean representing whether the $(i, t)\\text{th}$ entry in `y` is contained in that respective set. (The transposition is required for ease of indexing. See the Appendix for more on this.) Because $T$ is used pervasively throughout the Kalman smoothing mathematics, the set $\\mathcal{T}$ will be represented in the code more bluntly by the $T \\times p$ mask matrix `test_mask`.\n", + "\n", + "Keeping with this set notation, in the code's docstrings and comments, the concatenated symbol of absolute value bars around a mask matrix refers to the cardinality of the set that matrix represents (*e.g.*, `|F|` should be understood as $\\left| \\mathcal{F} \\right|$).\n", + "\n", + "As mentioned above, `F` and `M` will not always represent $\\mathcal{F}$ and $\\mathcal{M}$. Specifically, because the problem (9) is concerned with minimizing $F(\\theta)$ with $\\theta$, whenever the set $\\mathcal{F}$ needs to exist in the same scope as this tuning function, $\\mathcal{F}$ and $\\mathcal{M}$ will be represented by the mask matrices `train_mask` and `tune_mask`, respectively.\n", + "\n", + "Finally, these prediction error and tuning functions (*i.e.,* $L$ and $F$) will be defined in the code as `L` and `F`. However, like in the Background/Cross-validation section, when in the cross-validation setting and referring to a specific smoother, $L_n$ and $F_n$ will be used mathematically with the corresponding variable names `Ln` and `Fn`." ] }, { @@ -619,16 +651,24 @@ "metadata": {}, "outputs": [], "source": [ - "def create_kalman_smoother(S: sparse.csc_matrix, c: np.ndarray) -> CvxpyLayer: \n", - " \"\"\"Construct a standard Kalman smoother with measurements K setminus M_n.\n", - "\n", - " Args:\n", - " - S: |K setminus M_n| x N selection matrix.\n", - " - c: |K setminus M_n|-vector of measurements.\n", - "\n", - " Returns:\n", - " - A CvxpyLayer which solves a (or a batch of) standard Kalman\n", - " smoothing problem(s) upon receiving a (or a batch of) tuning\n", + "def create_kalman_smoother(S: sparse.csc_matrix,\n", + " c: np.ndarray) -> CvxpyLayer: \n", + " \"\"\"\n", + " Construct a standard Kalman smoother with\n", + " measurements F.\n", + "\n", + " Arguments\n", + " ---------\n", + " S : sparse.csc_matrix\n", + " |F| x N selection matrix.\n", + " c : np.ndarray\n", + " |F|-vector of measurements.\n", + " \n", + " Returns\n", + " --------\n", + " CvxpyLayer\n", + " Solves a (or a batch of) standard Kalman smoothing\n", + " problem(s) upon receiving a (or a batch of) tuning\n", " parameter(s), tau.\n", " \"\"\"\n", " x = cp.Variable(shape=(n, T+1))\n", @@ -645,34 +685,42 @@ " constr = [ x[:, t+1] == A@x[:, t] + B@w[:, t] for t in range(T) ] \n", " constr += [ S@z == c ] # missing data constraint\n", "\n", - " problem = cp.Problem(obj, constr)\n", + " smoother_problem = cp.Problem(obj, constr)\n", "\n", " layer = CvxpyLayer(\n", - " problem,\n", + " smoother_problem,\n", " parameters=[tau],\n", " variables=[x, y_hat, w]\n", " )\n", " return layer\n", "\n", "\n", - "def create_robust_kalman_smoother(S: sparse.csc_matrix, c: np.ndarray) -> CvxpyLayer:\n", - " \"\"\"Construct a robust Kalman smoother with measurements K setminus M_n.\n", + "def create_robust_kalman_smoother(S: sparse.csc_matrix,\n", + " c: np.ndarray) -> CvxpyLayer:\n", + " \"\"\"\n", + " Construct a robust Kalman smoother with\n", + " measurements F.\n", + "\n", + " Arguments\n", + " ---------\n", + " S : sparse.csc_matrix\n", + " |F| x N selection matrix.\n", + " c : np.ndarray\n", + " |F|-vector of measurements.\n", " \n", - " Args:\n", - " - S: |K setminus M_n| x N selection matrix.\n", - " - c: |K setminus M_n|-vector of measurements.\n", - "\n", - " Returns:\n", - " - A CvxpyLayer which solves a (or a batch of) robust Kalman\n", - " smoothing problem(s) upon receiving a (or a batch of) tuning\n", - " parameter(s), tau and rho.\n", + " Returns\n", + " --------\n", + " CvxpyLayer\n", + " Solves a (or a batch of) robust Kalman smoothing\n", + " problem(s) upon receiving a (or a batch of) tuning\n", + " parameter(s), tau.\n", " \"\"\"\n", " x = cp.Variable(shape=(n, T+1))\n", " w = cp.Variable(shape=(m, T))\n", " h = cp.Variable()\n", " # Missing data constraints \n", " y_hat = cp.Variable(shape=(p, T))\n", - " z = cp.vec(y, order='F')\n", + " z = cp.vec(y_hat, order='F')\n", " # Huber auxilary variables\n", " u = cp.Variable(T) \n", " q = cp.Variable(T) \n", @@ -683,7 +731,7 @@ " f0 = tau*cp.sum_squares(w)\n", " f0 += cp.sum([ cp.square(u[t]) + 2*rho*q[t] for t in range(T)])\n", " constr = [f0 <= h]\n", - " obj = cp.Minimize(h)\n", + " obj = cp.Minimize(h) # epigraph form\n", "\n", " # Huber constraints\n", " constr += [cp.norm(C@x[:, t] - y_hat[:, t], 2) <= u[t] + q[t] for t in range(T)]\n", @@ -694,9 +742,10 @@ " constr += [ x[:, t+1] == A@x[:, t] + B@w[:, t] for t in range(T) ]\n", " constr += [ S@z == c ] # missing data constraint\n", " \n", - " problem = cp.Problem(obj, constr)\n", + " smoother_problem = cp.Problem(obj, constr)\n", + " \n", " layer = CvxpyLayer(\n", - " problem,\n", + " smoother_problem,\n", " parameters=[tau, rho],\n", " variables=[x, y_hat, w]\n", " )\n", @@ -709,13 +758,9 @@ "source": [ "## Learning (Auto-Tuning) Functionality\n", "\n", - "### Data separation functions\n", - "\n", - "Mention difference between $(\\mathcal{K} \\setminus \\mathcal{M})_i$ mathematically and in the code.\n", + "Before implementing the `tune_kalman_smoother` and `cross_validate` functions, we implement the following boilerplate code. That is, the functionality required to split measurements into training, tuning, and validation sets (or into training, tuning, and validation cross-validation folds), and the penalty functions $L$ and $F$.\n", "\n", - "Mathematically use cardinality\n", - "\n", - "In code $\\left| \\mathcal{K} \\setminus \\mathcal{M}_i \\right|$ is found by summing all nonzero entries." + "### Data separation functions" ] }, { @@ -724,26 +769,60 @@ "metadata": {}, "outputs": [], "source": [ - "def split_data(fraction: float=0.9) -> Tuple[int, np.ndarray]:\n", - " \"\"\"Split data into a training set and a test set.\n", + "def split_data(fraction: float=0.8) -> Tuple[int,\n", + " np.ndarray,\n", + " np.ndarray,\n", + " np.ndarray]:\n", + " \"\"\"\n", + " Split data into a training set,\n", + " tuning set, and test set.\n", " \n", - " Args:\n", - " - fraction: Fraction of the measurement dataset\n", - " included in the training dataset.\n", + " Arguments\n", + " ---------\n", + " fraction : float, optional\n", + " Fraction of the measurement dataset included\n", + " in the training dataset.\n", + " Default fraction is 0.8.\n", " \n", - " Returns:\n", - " - The number of measurements in the training set.\n", - " - A T x p mask matrix. The true entries in the matrix\n", - " are the indices of the measurements in the\n", - " training dataset.\n", + " Returns\n", + " -------\n", + " |F| : int\n", + " Number of measurements in the training dataset\n", + " F : np.ndarray\n", + " T x p mask matrix whose True entries are the\n", + " indices of the measurements used for model\n", + " training.\n", + " M : np.ndarray\n", + " T x p mask matrix whose True entries are the\n", + " indices of the measurements used for model\n", + " tuning.\n", + " test_mask : np.ndarray\n", + " T x p mask matrix whose True entries are the\n", + " indices of the measurements used for model\n", + " validation.\n", " \"\"\"\n", - " # SEED!\n", " num_train_pts = int( N*fraction )\n", - " K_setminus_M = np.zeros(N, dtype=int)\n", - " K_setminus_M[:num_train_pts] = 1\n", - " np.random.shuffle(K_setminus_M)\n", - " K_setminus_M = K_setminus_M.astype(bool).reshape((T, p))\n", - " return num_train_pts, K_setminus_M" + " num_tune_pts = int( (N - num_train_pts)/2 )\n", + " \n", + " # generate same out-of-sample validation split every time\n", + " np.random.seed(1) \n", + " \n", + " indices = np.arange(N)\n", + " np.random.shuffle(indices)\n", + "\n", + " train_indices = indices[:num_train_pts]\n", + " tune_indices = indices[num_train_pts:(num_train_pts+num_tune_pts)]\n", + " test_indices = indices[(num_train_pts+num_tune_pts):]\n", + "\n", + " F = np.zeros((T, p), dtype=bool)\n", + " M = np.zeros((T, p), dtype=bool)\n", + " test_mask = np.zeros((T, p), dtype=bool)\n", + "\n", + " F.flat[train_indices] = True\n", + " M.flat[tune_indices] = True\n", + " test_mask.flat[test_indices] = True\n", + "\n", + " return num_train_pts, F, M, test_mask" ] }, { @@ -752,36 +831,61 @@ "metadata": {}, "outputs": [], "source": [ - "def cv_split(num_folds: int=10) -> List[Tuple[int, np.ndarray, np.ndarray]]:\n", - " \"\"\"Split data into folds for cross-validation.\n", - "\n", - " Args:\n", - " - num_folds (optional): The number of folds desired for cross-validation.\n", - " Default number of folds is 10.\n", - "\n", - " Returns:\n", - " - a list such that len(list) == num_folds, where the ith entry\n", - " is the three-tuple (|K setminus M_n|, M_n, K setminus M_n). Both M_n and\n", - " K setminus M_n are T x p mask matrices, whose true entries\n", - " are the indices of the measurements used for model validation and\n", - " model training for the ith fold, respectively.\n", + "def cv_split(num_folds: int=5) -> List[Tuple\n", + " [int,\n", + " np.ndarray,\n", + " np.ndarray,\n", + " np.ndarray]\n", + " ]:\n", + " \"\"\"\n", + " Split data into folds for cross-validation.\n", + "\n", + " Arguments\n", + " ---------\n", + " num_folds : int, optional\n", + " The number of folds desired for\n", + " cross-validation.\n", + " Default number of folds is 5.\n", + "\n", + " Returns\n", + " -------\n", + " List\n", + " The nth entry of the list is the four-\n", + " tuple `(|Fn|, Fn, Mn, test_mask_n)`. \n", + " `Fn`, `Mn`, and `test_mask_n` are T x p mask matrices,\n", + " whose true entries are the indices of the\n", + " measurements used for model training, tuning, and\n", + " validation for the nth fold, respectively.\n", + " The list has length equal to `num_folds`.\n", " \"\"\"\n", " np.random.seed(1) # generate same CV split every time\n", "\n", " indices = np.arange(N)\n", " np.random.shuffle(indices)\n", "\n", - " Ms = np.array_split(indices, num_folds)\n", + " # List of Fn^c, where Fn^c does contain indices (it is not mask matrix).\n", + " # Specifically, Fn^c contains measurement indices in M U test_mask.\n", + " Fn_c_s = np.array_split(indices, num_folds)\n", "\n", " to_return = []\n", "\n", - " for i in range(num_folds):\n", - " M: np.ndarray = np.zeros(N, dtype=bool)\n", - " M[Ms[i]] = True\n", - " M = M.reshape((T, p))\n", - " K_setminus_M = ~M\n", - " num_train_pts = np.sum(K_setminus_M)\n", - " to_return.append((num_train_pts, M, K_setminus_M))\n", + " for n in range(num_folds):\n", + " Fn_c_indices = Fn_c_s[n]\n", + " \n", + " num_tune_pts_n = int( len(Fn_c_indices)/2 )\n", + " tune_indices_n = Fn_c_indices[:num_tune_pts_n]\n", + " test_indices_n = Fn_c_indices[num_tune_pts_n:]\n", + "\n", + " Mn = np.zeros((T, p), dtype=bool)\n", + " test_mask_n = np.zeros((T, p), dtype=bool)\n", + " \n", + " Mn.flat[tune_indices_n] = True\n", + " test_mask_n.flat[test_indices_n] = True\n", + "\n", + " Fn = ~(Mn + test_mask_n)\n", + " num_train_pts_n = np.sum(Fn)\n", + "\n", + " to_return.append((num_train_pts_n, Fn, Mn, test_mask_n))\n", " \n", " return to_return" ] @@ -792,49 +896,74 @@ "metadata": {}, "outputs": [], "source": [ - "def create_problem_data_params(selected: np.ndarray,\n", - " num_selected: int=None) -> Tuple[sparse.csc_matrix,\n", - " np.ndarray,\n", - " np.ndarray]:\n", - " \"\"\"Turns a mask matrix of known measurement indices into the sparse selection matrix\n", - " needed in the standard and robust Kalman smoothing problems.\n", - " \n", - " Args:\n", - " - selected: T x p mask matrix of known measurement indices.\n", - " - num_selected (optional): The number of known measurement\n", - " indices in the selected numpy array.\n", - "\n", - " Returns:\n", - " - S: num_selected x N sparse selection matrix.\n", - " - c: (num_selected)-vector containing the trajectory\n", - " measurements in the training dataset.\n", - " - c_test: (N - num_selected)-vector containing the trajectory\n", - " measurements not in the training dataset.\n", - " \n", + "def create_problem_vectorized_data(F: np.ndarray,\n", + " M: np.ndarray,\n", + " test_mask: np.ndarray,\n", + " num_measurements: int=None) -> Tuple[sparse.csc_matrix,\n", + " np.ndarray,\n", + " np.ndarray,\n", + " np.ndarray]:\n", + " \"\"\"\n", + " Turns a mask matrix of known measurement indices\n", + " into the sparse selection matrix needed in the\n", + " standard and robust Kalman smoothing problems.\n", + " Also produces the corresponding measurement\n", + " vector, and the tuning and testing measurement\n", + " vectors.\n", + "\n", + " Arguments\n", + " ---------\n", + " F : np.ndarray\n", + " T x p mask matrix of measurement indices.\n", + " M : np.ndarray\n", + " T x p mask matrix of tuning indices.\n", + " test_mask : np.ndarray\n", + " T x p mask matrix of validation indices.\n", + " num_measurements : int, optional\n", + " The number of known measurement indices in\n", + " the selected numpy array. Mathematically,\n", + " |F|.\n", + "\n", + " Returns\n", + " -------\n", + " S : sparse.csc_matrix\n", + " |F| x N sparse selection matrix.\n", + " c : np.ndarray\n", + " |F|-vector containing the output\n", + " measurements in the training dataset\n", + " c_tune : np.ndarray\n", + " |M|-vector containing the output\n", + " measurements in the tuning dataset.\n", + " c_test : np.ndarray\n", + " |test_mask|-vector containing the output\n", + " measurements in the validation dataset. \n", " \"\"\"\n", - " num_selected = np.sum(selected) if num_selected == None else num_selected\n", + " num_measurements = np.sum(F) if num_measurements == None else num_measurements\n", "\n", - " time_indices, measurement_indices = selected.nonzero() # think about looping through rows and keeping track if the position is zero or not\n", + " time_indices, measurement_indices = F.nonzero()\n", " S = sparse.csc_matrix(\n", " (\n", - " np.ones(num_selected), # data to fill into the sparse array\n", + " np.ones(num_measurements), # data filled into the sparse array\n", " (\n", - " np.arange(num_selected),\n", + " np.arange(num_measurements),\n", " time_indices*p + measurement_indices\n", " )\n", " ),\n", - " shape=(num_selected, N)\n", + " shape=(num_measurements, N)\n", " )\n", - " c = y.T[selected]\n", - " c_test = y.T[~selected]\n", - " return (S, c, c_test)" + " c = y.T[F]\n", + " c_tune = y.T[M]\n", + " c_test = y.T[test_mask]\n", + " return (S, c, c_tune, c_test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Error Functions" + "### Error Functions\n", + "\n", + "The following are the implementations of $L$ and $F$, respectively." ] }, { @@ -843,159 +972,79 @@ "metadata": {}, "outputs": [], "source": [ - "def mean_Jmeas_error(y: torch.Tensor, observable_measurements: torch.Tensor,\n", - " layer: CvxpyLayer=None, params: List[torch.Tensor]=None,\n", - " x_hat: torch.Tensor=None, measurement_loss: str='squared') -> torch.Tensor:\n", - " \"\"\"Prediction error used to judge a Kalman smoother.\n", - " \n", - " Specifically, this function computes\n", - " \n", - " avg (J_meas(theta)) = (1 / np.nonzero(observable_measurments))\n", - " * sum measurement_loss( ( (C x_hat_t)_i - (y_t)_i ),\n", - " \n", - " where x_hat implicitly depends on theta.\n", - "\n", - " Used to compute E_train or E_test.\n", - "\n", - " Args:\n", - " - y: The validation subset of the\n", - " actual trajectory to judge smoother\n", - " output against.\n", - " - observable_measurements: T x p mask matrix\n", - " used to select the predictions from x_hat\n", - " that are to be judged against y.\n", - " - layer (conditonally optional)\n", - " - params (conditonally optional)\n", - " - x_hat (conditonally optional)\n", - " - measurement_loss (optional): Whether to penalize the\n", - " differen\n", - "\n", - " Returns:\n", - " - A torch.Tensor containing MSE(x_hat - y)\n", - " \n", - " Raises\n", - " - ValueError if either a layer output is not provided\n", - " - ValueError if |K setminus M_n| != |test|\n", - "\n", + "def L(y: torch.Tensor,\n", + " measurement_mask: np.ndarray,\n", + " y_hat: torch.Tensor) -> torch.Tensor:\n", " \"\"\"\n", - "\n", - " if (x_hat == None) and (layer == None or params == None):\n", - " raise ValueError(\"this function requires a layer and parameters\\\n", - " or x_hat = layer(parameters)\") \n", - " \n", - " # can add more checks on x_hat and K setminus M_i\n", - "\n", - " if x_hat == None:\n", - " x_hat = layer(*params)[1]\n", - " \n", - " y_hat_T = y_hat.t()\n", - " y_hat_to_compare = y_hat_T[observable_measurements]\n", - " return F.mse_loss(y_hat_to_compare, y)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "def kalman_recovery_mse(y: torch.Tensor,\n", - " observable_measurements: torch.Tensor,\n", - " layer: CvxpyLayer=None,\n", - " params: List[torch.Tensor]=None,\n", - " y_hat: torch.Tensor=None) -> torch.Tensor:\n", - " \"\"\"Prediction error used to judge a Kalman smoother.\n", - " \n", - " Specifically, this function computes\n", + " Prediction error used to evaluate a\n", + " Kalman smoother.\n", " \n", - " L(theta) = MSE(y_hat - y),\n", - " \n", - " where y_hat implicitly depends on theta.\n", - "\n", - " Args:\n", - " - y: The validation subset of the\n", - " actual trajectory to judge smoother\n", - " output against.\n", - " - observable_measurements: T x p mask matrix\n", - " used to select the predictions from y_hat\n", - " that are to be judged against y.\n", - " - layer (conditonally optional)\n", - " - params (conditonally optional)\n", - " - y_hat (conditonally optional)\n", - "\n", - " Returns:\n", - " - A torch.Tensor containing MSE(y_hat - y)\n", - " \n", - " Raises\n", - " - ValueError if either a layer output is not provided\n", - " - ValueError if |K setminus M_n| != |test|\n", - "\n", + " Specifically, this function computes (8),\n", + " i.e., L(theta),\n", + " for a provided y, y_hat, and measurement\n", + " selection set.\n", + "\n", + " Recall that y_hat implicitly depends on theta.\n", + "\n", + " Arguments\n", + " ---------\n", + " y : torch.Tensor\n", + " An observed output subset to judge smoother\n", + " output against. Should satisfy\n", + " y.shape == (|measurement_mask|,).\n", + " measurement_mask : np.ndarray\n", + " T x p mask matrix used to select the subet\n", + " of y_hat to be judged against y.\n", + " y_hat : np.ndarray\n", + " p x T output matrix \"predicted\" by a smoother.\n", + "\n", + " Returns\n", + " -------\n", + " torch.Tensor\n", + " containing L(theta)\n", " \"\"\"\n", - "\n", - " if (y_hat == None) and (layer == None or params == None):\n", - " raise ValueError(\"this function requires a layer and parameters\\\n", - " or x_hat = layer(parameters)\") \n", " \n", - " # can add more checks on y_hat and K setminus M_i\n", + " return torch_functional.mse_loss(y_hat.t()[measurement_mask], y)\n", "\n", - " if y_hat == None:\n", - " y_hat = layer(*params)[1]\n", - " \n", - " y_hat_T = y_hat.t()\n", - " y_hat_to_compare = y_hat_T[observable_measurements]\n", - " return F.mse_loss(y_hat_to_compare, y)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "def learning_error_fn(layer: CvxpyLayer, y: torch.Tensor,\n", - " observable_measurements: torch.Tensor,\n", - " params: List[torch.Tensor]) -> torch.Tensor:\n", - " \"\"\"The learning error used to auto-tune a Kalman smoother.\n", - "\n", - " Specifically, this function computes\n", - "\n", - " F(theta) = E(theta) + r(theta),\n", + "def F(y: torch.Tensor,\n", + " measurement_mask: np.ndarray,\n", + " y_hat: torch.Tensor,\n", + " params: List[torch.Tensor]) -> torch.Tensor:\n", + " \"\"\"\n", + " The learning function used to auto-tune\n", + " a Kalman smoother.\n", "\n", - " where L is the loss computed in the `kalman_recovery_mse` function\n", - " (the test error loss) and r is a regularization term added to \n", - " the prediction loss to encourage the auto-tuning to generate allowable\n", - " hyper-parameters.\n", + " Specifically, this function computes F(theta)\n", + " found in (9) by adding \n", "\n", - " This learning error function assumes that all hyper-parameters must be positive,\n", - " and uses a log-barrier regularization function to encourage positive parameter\n", - " selection. Mathematically,\n", - " \n", - " r(theta) = - sum_i log(theta_i).\n", + " r(theta) = sum_i -log(theta_i)\n", " \n", - " Args:\n", - " - layer: The CvxpyLayer being trained.\n", - " - y: The training subset of the\n", - " actual trajectory to judge smoother\n", - " output against. \n", - " - observable_measurements: T x p mask matrix\n", - " used to select the predictions from y_hat\n", - " that are to be judged against y.\n", - " - params: A list of the (Tensor, require_grad==True)\n", - " hyper-parameters being auto-tuned.\n", - "\n", - " Returns:\n", - " - A torch.Tensor containing F(theta).\n", + " to the prediction error computed by `L`.\n", + "\n", + " Arguments\n", + " ---------\n", + " y : torch.Tensor\n", + " An observed output subset to judge smoother output\n", + " against. Should satisfy\n", + " y.shape == (|measurement_mask|,)\n", + " measurement_mask : np.ndarray\n", + " T x p mask matrix used to select subet of y_hat\n", + " to be judged against y. \n", + " y_hat : torch.Tensor\n", + " p x T output matrix \"predicted\" by a smoother.\n", + " params : List[torch.Tensor]\n", + " A list of the Tensor hyper-parameters needed to\n", + " solve the smoother defining the layer object.\n", + " \n", + " Returns\n", + " -------\n", + " torch.Tensor\n", + " containing F(theta)\n", " \"\"\"\n", - " x_hat = layer(*params)[0]\n", - " y_hat = C_tch@ (x_hat[:, :-1])\n", - " y_hat_T = y_hat.t()\n", - " y_hat_to_compare = y_hat_T[observable_measurements]\n", - " mse_loss = F.mse_loss(y_hat_to_compare, y)\n", - " log_sum = 0.0\n", - " for param in params:\n", - " log_sum -= torch.sum(torch.log(torch.abs(param)))\n", - "\n", - " return mse_loss + log_sum" + " \n", + " mse_loss = L(y, measurement_mask, y_hat)\n", + " r_theta = sum(-torch.log(param) for param in params)\n", + " return mse_loss + r_theta" ] }, { @@ -1007,21 +1056,45 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "def parameter_sweep(layer: CvxpyLayer,\n", - " num_params: int) -> Tuple[List[torch.Tensor], torch.Tensor]:\n", + " num_params: int) -> Tuple[List[torch.Tensor],\n", + " torch.Tensor]:\n", " \"\"\"\n", - " TODO: ADD!\n", - "\n", - " The LS and Robust Kalman tuning have 1 and 2 parameters, respectively,\n", - " so the first line will always yield an integer.\n", - "\n", - " theta_init is a list containing equal length torch Tensors\n", - " such that len(theta_init) == num_params,\n", - " and the ith element element in each tensor \n", + " Solve a batch of smoother problems with\n", + " log-spaced (or Cartesian product of log-spaced)\n", + " parameter values.\n", + "\n", + " Used in the `cross_validate` function to find\n", + " initial theta values to use in `tune_kalman_smoother`.\n", + "\n", + " Arguments\n", + " ---------\n", + " layer : CvxpyLayer\n", + " The Kalman smoother to perform the batch\n", + " solving with.\n", + " num_params : int\n", + " Number of parameters needed as arguments\n", + " for the provided layer.\n", + "\n", + " Returns\n", + " -------\n", + " theta_init : List[torch.Tensor]\n", + " Contains a tensor for each parameter\n", + " needed for the smoother\n", + " (i.e., `len(theta_init) == num_params`).\n", + " Taking the jth entry of each tensor yields\n", + " the jth theta the smoother was solved with.\n", + " ys : torch.Tensor\n", + " A 3-D tensor containing the predicted\n", + " outputs that correspond to solving\n", + " a smoother with each combination of\n", + " log-spaced parameters in a batched fashion.\n", + " More specifically, the tensor has shape\n", + " len(theta_init[0].shape[0]) x p x T\n", " \"\"\"\n", " steps = 10 // num_params\n", "\n", @@ -1040,61 +1113,144 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ - "def tune_kalman_smoother(train_loss: Callable[[List[torch.Tensor]], torch.Tensor],\n", - " test_loss: Callable[[List[torch.Tensor]], torch.Tensor],\n", + "def tune_kalman_smoother(smoother_layer: CvxpyLayer,\n", + " Fn: Callable[[torch.Tensor,\n", + " List[torch.Tensor]],\n", + " torch.Tensor],\n", + " Ln: Callable[[torch.Tensor],\n", + " torch.Tensor],\n", " params: List[torch.Tensor],\n", - " epochs: int=50, lr: float=2e-2,\n", + " num_iters: int=25,\n", + " lr: float=2e-2,\n", " verbose: bool=False,\n", - " print_every: int=5) -> Tuple[int, float,\n", + " print_every: int=5) -> Tuple[int,\n", + " float,\n", " List[List[float]],\n", - " List[float], List[float]]:\n", - " \"\"\"\n", - " TODO: ADD\n", - " Works for both regular smoother and robust smoother\n", - " \n", - " can mention up above that this is just scaffolding: would go more in depth here for a real problem.\n", + " List[float],\n", + " List[float]]:\n", " \"\"\"\n", + " Auto-tunes a Kalman smoother.\n", + "\n", + " Specifically, attempts to tune the provided Kalman\n", + " smoother by solving (9) using PyTorch's\n", + " implementation of Adam.\n", "\n", - " # opt = torch.optim.Adam(params, lr=lr)\n", - " opt = torch.optim.SGD(params, lr=lr, momentum=0.9)\n", + " Along with recording the minimizing sequence for\n", + " solving\n", + " \n", + " minimize F(theta)\n", + "\n", + " with theta, this function records the prediction\n", + " error for each parameter element in the sequence.\n", + "\n", + " (Note that \"prediction error\" is the value of Ln(theta)\n", + " over the validation dataset.)\n", + "\n", + " Auto-tuning will terminate early if an invalid theta\n", + " is produced as a minimizing iterate. See the above\n", + " background section for information on what makes a theta\n", + " invalid. Also note that this can occurr despite the use\n", + " of the regularization function, r(theta), since\n", + " no care has been taken in choosing a solution method.\n", + "\n", + " Arguments\n", + " ---------\n", + " smoother_layer : CvxpyLayer\n", + " The Kalman smoother to tune.\n", + " (Recall that the smoother opt. problem\n", + " is embedded in a CvxpyLayer so that auto-\n", + " tuning can be performed.)\n", + " Fn : Callable[[torch.Tensor, List[torch.Tensor]], torch.Tensor]\n", + " The learning function F in (9) defined over some\n", + " tuning dataset. This Fn is the `F` above\n", + " with a `y` and `measurement_mask` already provided,\n", + " so the only arguments it needs to be provided during\n", + " runtime are the p x T y_hat output matrix returned\n", + " from passing the provided layer a parameter iterate,\n", + " and that parameter iterate.\n", + " Ln : Callable[[torch.Tensor], torch.Tensor]\n", + " The prediction error L from (8) defined over\n", + " some testing dataset. This Ln is the `L`\n", + " above with a `y` and `measurement_mask`\n", + " already provided, so the only argument it needs\n", + " to be provided during runtime is the p x T\n", + " y_hat output matrix returned from passing\n", + " the provided layer a parameter iterate.\n", + " params : List[torch.Tensor]\n", + " The scalar parameter(s) needed to define\n", + " a standard or robust Kalman smoother.\n", + " Used as the starting point for\n", + " solving min. F(theta).\n", + " num_iters : int, optional\n", + " Number of iterations to run Adam on\n", + " min. F(theta). \n", + " Default is 25 iterations.\n", + " lr : float, optional\n", + " Learning rate to instatiate Adam with.\n", + " Default is 2e-2.\n", + " verbose : bool, optional\n", + " Default is False.\n", + " print_every : int, optional\n", + " Default is every 5 iterations.\n", + "\n", + " Returns\n", + " -------\n", + " best_param_index : int\n", + " The index in `param_history` corresponding\n", + " to the value of theta that produced the\n", + " smallest (test) prediction error.\n", + " best_loss : float\n", + " The smallest prediction error seen during\n", + " the attempt to minimize F(theta).\n", + " param_history : List[List[float]] \n", + " The minimzing sequence of F(theta).\n", + " train_losses : List[float]\n", + " F(theta_i) for theta_i in param_history.\n", + " test_losses : List[float]\n", + " L(theta_i) for theta_i in param_history. \n", + " \"\"\"\n", + " opt = torch.optim.Adam(params, lr=lr)\n", "\n", " best_param_index, best_loss = None, torch.inf\n", " param_history: List[List[float]] = []\n", " train_losses, test_losses = [], []\n", "\n", - " for epoch in range(epochs):\n", - " \n", + " for k in range(num_iters):\n", + "\n", " param_history.append([p.item() for p in params])\n", " \n", + " y_hat_k = smoother_layer(*params)[1]\n", + "\n", " with torch.no_grad():\n", - " new_loss = test_loss(params).item()\n", - " if (new_loss < best_loss) and epoch != 0:\n", - " best_loss = new_loss\n", - " best_param_index = epoch\n", - " test_losses.append(new_loss)\n", - " if verbose and epoch % print_every == 0:\n", - " print(\"test loss %03d | %3.5f\" % (epoch + 1, test_losses[-1]))\n", + " curr_test_loss = Ln(y_hat_k)\n", + " if curr_test_loss < best_loss:\n", + " best_loss = curr_test_loss\n", + " best_param_index = k\n", + " test_losses.append(curr_test_loss)\n", + " if verbose and k % print_every == 0:\n", + " print(\"test loss %03d | %3.5f\" % (k + 1, test_losses[-1]))\n", "\n", " opt.zero_grad()\n", - " loss = train_loss(params)\n", - " loss.backward()\n", + " curr_train_loss = Fn(y_hat=y_hat_k, params=params)\n", + " curr_train_loss.backward()\n", " opt.step()\n", - " \n", + "\n", " neg_theta = any([p.item() < 0 for p in params])\n", " if neg_theta:\n", + " # despite r(theta), out-of-box opt. algo.\n", + " # could result in theta_i < 0\n", " break\n", - " \n", - " train_losses.append(loss.item())\n", - " if verbose and epoch % print_every == 0:\n", - " print(\"train loss %03d | %3.5f\" % (epoch+1, train_losses[-1]))\n", + "\n", + " train_losses.append(curr_train_loss.item())\n", + " if verbose and k % print_every == 0:\n", + " print(\"train loss %03d | %3.5f\" % (k+1, train_losses[-1]))\n", " \n", " return (best_param_index, best_loss,\n", - " param_history, train_losses, test_losses)\n", - " " + " param_history, train_losses, test_losses)" ] }, { @@ -1106,7 +1262,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -1205,100 +1361,129 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "def cross_validate(create_smoother: Callable[[sparse.csc_matrix, np.ndarray],\n", " CvxpyLayer],\n", " num_params: int,\n", - " num_folds: int=10,\n", + " num_folds: int=5,\n", " num_st_pts: int=3,\n", - " num_epochs: int=50,\n", + " num_iters: int=25,\n", " verbose: bool=False,\n", " double_verbose: bool=False) -> CV_DataObject:\n", + " \"\"\"\n", + " Creates a Kalman smoother and auto-tunes it ov\n", " \n", - " cv_result: CV_DataObject = CV_DataObject(num_params, num_folds)\n", + " NEED TO MENTION TUNING\n", + " Arguments\n", + " ---------\n", + " create_smoother : Callable[[sparse.csc_matrix, np.ndarray], CvxpyLayer]\n", + " Pass in `create_kalman_smoother` to perform cross-validation\n", + " on a standard Kalman smoother or `create_robust_kalman_smoother`\n", + " to perform cross-validation on a robust Kalman smoother.\n", + " num_params : int\n", + " Number of parameters needed as arguments in the smoother layer\n", + " returned by `created_smoother`. \n", + " num_folds : int, optional\n", + " The \n", + " Default is 5 folds.\n", + " num_st_pts : int, optional\n", + " Default is three starting points.\n", + " num_iters: int, optional\n", + " Number of iterations to run Adam\n", + " on min. F(theta).\n", + " Default is 25 iterations.\n", + " verbose : bool, optional\n", + " Default is False.\n", + " double_verbose : bool, optional\n", + " Default is False.\n", + "\n", + " Returns\n", + " -------\n", + " CV_DataObject\n", + " The aggregate and granular results of running this function.\n", + " See the docstrings of the above helper data functions for\n", + " more information on what results are captured.\n", + " \"\"\"\n", " \n", + " cv_result: CV_DataObject = CV_DataObject(num_params, num_folds)\n", + "\n", " data_folds = cv_split(num_folds)\n", - " \"\"\"\n", - " to think about:\n", - " - how to handle failures?\n", - " \"\"\"\n", - " for i, fold in enumerate(data_folds):\n", + "\n", + " for n, fold in enumerate(data_folds):\n", " if verbose:\n", - " print(\"=== Starting fold %02d | %02d ===\" % (i, num_folds))\n", + " print(\"=== Starting fold %02d | %02d ===\" % (n, num_folds))\n", " \n", " # === initialize fold's layer & data ===\n", - " num_pts, M, K_setminus_M = fold\n", + " num_train_pts, train_mask, tune_mask, test_mask = fold\n", "\n", - " K_setminus_M_tch, M_tch = torch.tensor(K_setminus_M), torch.tensor(M)\n", - " S, c, c_test = create_problem_data_params(K_setminus_M, num_pts)\n", - " c_tch, c_test_tch = torch.tensor(c), torch.tensor(c_test)\n", + " S, c, c_tune, c_test = create_problem_vectorized_data(train_mask,\n", + " tune_mask,\n", + " test_mask,\n", + " num_train_pts)\n", " \n", - " layer = create_smoother(S, c)\n", + " c_tune_tch, c_test_tch = torch.tensor(c_tune), torch.tensor(c_test)\n", "\n", - " fold_to_add: Fold_DataObject = Fold_DataObject(num_pts, layer)\n", - " \n", - " train_loss = lambda parameters : learning_error_fn(layer,\n", - " c_tch,\n", - " K_setminus_M_tch,\n", - " parameters)\n", - " test_loss = lambda parameters : kalman_recovery_mse(y=c_test_tch,\n", - " observable_measurements=M_tch,\n", - " layer=layer,\n", - " params=parameters)\n", + " smoother_layer = create_smoother(S, c)\n", "\n", + " fold_to_add: Fold_DataObject = Fold_DataObject(num_train_pts,\n", + " smoother_layer)\n", + " \n", + " Fn = partial(F, y=c_tune_tch,\n", + " measurement_mask=tune_mask)\n", + " Ln = lambda y_hat : L(y=c_test_tch,\n", + " measurement_mask=test_mask,\n", + " y_hat=y_hat)\n", + " \n", " # === parameter sweep ===\n", " if verbose:\n", " print(\"starting parameter sweep\")\n", + "\n", + " theta_inits, ys = parameter_sweep(smoother_layer,\n", + " num_params)\n", " \n", - " theta_inits, xs = parameter_sweep(layer, num_params)\n", - " sweep_loss = lambda x_val : kalman_recovery_mse(y=c_test_tch,\n", - " observable_measurements=M_tch,\n", - " x_hat = x_val)\n", - " \n", - " # losses is a list containing tuples of length 2\n", - " # 0th entry in the tuple is the list of tensor parameters\n", - " # ex: if num_params==2, then \n", - " # losses[0][0] == [torch.tensor(val1), torch.tensor(val2)] =: theta\n", - " # 1st entry is the test loss of the smoother instantiated with \n", - " # the parameters in the 0th entry of that tuple.\n", - " # ex: losses[0][1] has test mse loss corresponding\n", - " # to layer(*losses[0][0])\n", " losses: List[Tuple[\n", " List[torch.Tensor],\n", " float\n", " ]] = [\n", " (\n", " [theta_inits[j][i] for j in range(num_params)],\n", - " sweep_loss(xs[i]).item()\n", + " Ln(ys[i]).item()\n", " )\n", - " for i in range(xs.shape[0])\n", + " for i in range(ys.shape[0])\n", " ]\n", " losses.sort(key=lambda x: x[1])\n", "\n", " fold_to_add.param_sweep_result = losses\n", - " \n", + "\n", " # === auto-tuning ===\n", " if verbose:\n", " print(\"starting auto_tuning\")\n", - " \n", + "\n", " starting_thetas = [theta for theta, _ in losses[0:num_st_pts]]\n", "\n", " for j, theta_0 in enumerate(starting_thetas):\n", + " # make sure thetas are torch tensors\n", + " # well, param sweeep uses tensors, but don't declare grad\n", + " # also now I recombine\n", + " # perhaps use numpy and then only create torch tensors here when need to.\n", " if verbose:\n", " print(\"auto-tuning starting point %02d | %02d\" % (j, num_st_pts))\n", + "\n", + " auto_tune_result = tune_kalman_smoother(smoother_layer,\n", + " Fn, Ln,\n", + " params=theta_0,\n", + " num_iters=num_iters,\n", + " verbose=double_verbose)\n", " \n", - " result = tune_kalman_smoother(train_loss, test_loss,\n", - " params=theta_0, epochs=num_epochs,\n", - " verbose=double_verbose)\n", - " \n", - " fold_to_add.add_tune_result(AutoTune_DataObject(num_epochs, *result), train_loss)\n", - " \n", + " fold_to_add.add_tune_result(AutoTune_DataObject(num_iters,\n", + " *auto_tune_result),\n", + " Fn)\n", " cv_result.add_fold(fold_to_add)\n", - " \n", - " return cv_result" + "\n", + " return cv_result " ] }, { @@ -1328,168 +1513,205 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Perform cross-validation" + "### Test basic functionality" ] }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 77, "metadata": {}, "outputs": [], "source": [ - "# initialize parameter values\n", - "# note difference between torch parameters and cvxpylayer parameters\n", - "# SEED HERE (or wherever it needs to go for the shuffle that happens above\n", - "num_train_pts, K_setminus_M = split_data(fraction=0.9) # will still use this after CV\n", - "S, c, c_test = create_problem_data_params(K_setminus_M, num_train_pts)" + "num_train_pts, train_mask, tune_mask, test_mask = split_data() # will still use this after CV\n", + "S, c, c_tune, c_test = create_problem_vectorized_data(train_mask, tune_mask, test_mask, num_train_pts)\n", + "c_test_tch = torch.tensor(c_test) \n", + "c_tune_tch = torch.tensor(c_tune)" ] }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 78, "metadata": {}, "outputs": [], "source": [ "layer = create_kalman_smoother(S, c)\n", "tau_tch = torch.tensor(.08, requires_grad=True)\n", - "x_hat_orig, w_hat_orig, v_hat_orig = layer(tau_tch)" + "x_hat_orig, y_hat_orig, w_hat_orig = layer(tau_tch)" ] }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 79, "metadata": {}, "outputs": [], "source": [ - "K_setminus_M_tch, c_tch, c_test_tch = torch.tensor(K_setminus_M), torch.tensor(c), torch.tensor(c_test)\n", - "train_loss = lambda parameters : learning_error_fn(layer=layer, y=c_tch,\n", - " observable_measurements=K_setminus_M_tch,\n", - " params=parameters)\n", - "M = ~K_setminus_M\n", - "M_tch = torch.tensor(M)\n", - "test_loss = lambda parameters : kalman_recovery_mse(y=c_test_tch, observable_measurements=M_tch,\n", - " layer=layer, params=parameters)\n", - "params = [tau_tch]\n", - "initial_params = [p.item() for p in params]" + "Fn = partial(F, y=c_tune_tch, measurement_mask=tune_mask)\n", + "Ln = lambda y_hat : L(y=c_test_tch, measurement_mask=test_mask, y_hat=y_hat)" ] }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 80, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "tensor(83.5806, grad_fn=)" + "tensor(54.9676, grad_fn=)" ] }, - "execution_count": 54, + "execution_count": 80, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "test_loss(params)" + "Ln(y_hat_orig)" ] }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 81, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "test loss 001 | 84.34669\n", - "train loss 001 | 69.49468\n", - "test loss 002 | 83.57898\n", - "train loss 002 | 69.37068\n", - "test loss 003 | 83.55436\n", - "train loss 003 | 68.31472\n", - "test loss 004 | 83.41125\n", - "train loss 004 | 67.38767\n", - "test loss 005 | 83.58059\n" - ] - }, + "data": { + "text/plain": [ + "tensor(97.2082, grad_fn=)" + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Fn(y_hat=y_hat_orig, params=[tau_tch])" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [ { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "/Users/q3/opt/anaconda3/envs/deep-learning-env/lib/python3.12/site-packages/diffcp/cone_program.py:154: SyntaxWarning: invalid escape sequence '\\i'\n", - " \"\"\"Solves a cone program, returns its derivative as an abstract linear map.\n" + "test loss 001 | 54.96757\n", + "train loss 001 | 97.20817\n", + "test loss 002 | 54.94265\n", + "train loss 002 | 96.94404\n", + "test loss 003 | 55.01819\n", + "train loss 003 | 96.85116\n", + "test loss 004 | 54.98556\n", + "train loss 004 | 96.65761\n", + "test loss 005 | 54.86169\n", + "train loss 005 | 96.31496\n", + "test loss 006 | 54.89240\n", + "train loss 006 | 96.26523\n", + "test loss 007 | 54.89424\n", + "train loss 007 | 96.15142\n", + "test loss 008 | 54.87469\n", + "train loss 008 | 96.03847\n", + "test loss 009 | 54.86414\n", + "train loss 009 | 95.93140\n", + "test loss 010 | 54.87093\n", + "train loss 010 | 95.86843\n", + "test loss 011 | 54.85492\n", + "train loss 011 | 95.77033\n", + "test loss 012 | 54.82499\n", + "train loss 012 | 95.64594\n", + "test loss 013 | 54.81648\n", + "train loss 013 | 95.56851\n", + "test loss 014 | 54.80896\n", + "train loss 014 | 95.49646\n", + "test loss 015 | 54.79921\n", + "train loss 015 | 95.42338\n" ] - }, + } + ], + "source": [ + "result = tune_kalman_smoother(layer, Fn, Ln, [tau_tch], num_iters=15, verbose=True, print_every=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [ { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[51], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m train_losses, test_losses \u001b[38;5;241m=\u001b[39m tune_kalman_smoother(train_loss, test_loss, params,epochs\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m25\u001b[39m, verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, print_every\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n", - "Cell \u001b[0;32mIn[32], line 38\u001b[0m, in \u001b[0;36mtune_kalman_smoother\u001b[0;34m(train_loss, test_loss, params, epochs, lr, verbose, print_every)\u001b[0m\n\u001b[1;32m 36\u001b[0m opt\u001b[38;5;241m.\u001b[39mzero_grad()\n\u001b[1;32m 37\u001b[0m loss \u001b[38;5;241m=\u001b[39m train_loss(params)\n\u001b[0;32m---> 38\u001b[0m loss\u001b[38;5;241m.\u001b[39mbackward()\n\u001b[1;32m 39\u001b[0m opt\u001b[38;5;241m.\u001b[39mstep()\n\u001b[1;32m 41\u001b[0m neg_theta \u001b[38;5;241m=\u001b[39m \u001b[38;5;28many\u001b[39m([p\u001b[38;5;241m.\u001b[39mitem() \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m0\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m p \u001b[38;5;129;01min\u001b[39;00m params])\n", - "File \u001b[0;32m~/opt/anaconda3/envs/deep-learning-env/lib/python3.12/site-packages/torch/_tensor.py:522\u001b[0m, in \u001b[0;36mTensor.backward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m 512\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m has_torch_function_unary(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 513\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m handle_torch_function(\n\u001b[1;32m 514\u001b[0m Tensor\u001b[38;5;241m.\u001b[39mbackward,\n\u001b[1;32m 515\u001b[0m (\u001b[38;5;28mself\u001b[39m,),\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 520\u001b[0m inputs\u001b[38;5;241m=\u001b[39minputs,\n\u001b[1;32m 521\u001b[0m )\n\u001b[0;32m--> 522\u001b[0m torch\u001b[38;5;241m.\u001b[39mautograd\u001b[38;5;241m.\u001b[39mbackward(\n\u001b[1;32m 523\u001b[0m \u001b[38;5;28mself\u001b[39m, gradient, retain_graph, create_graph, inputs\u001b[38;5;241m=\u001b[39minputs\n\u001b[1;32m 524\u001b[0m )\n", - "File \u001b[0;32m~/opt/anaconda3/envs/deep-learning-env/lib/python3.12/site-packages/torch/autograd/__init__.py:266\u001b[0m, in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[1;32m 261\u001b[0m retain_graph \u001b[38;5;241m=\u001b[39m create_graph\n\u001b[1;32m 263\u001b[0m \u001b[38;5;66;03m# The reason we repeat the same comment below is that\u001b[39;00m\n\u001b[1;32m 264\u001b[0m \u001b[38;5;66;03m# some Python versions print out the first line of a multi-line function\u001b[39;00m\n\u001b[1;32m 265\u001b[0m \u001b[38;5;66;03m# calls in the traceback and some print out the last line\u001b[39;00m\n\u001b[0;32m--> 266\u001b[0m Variable\u001b[38;5;241m.\u001b[39m_execution_engine\u001b[38;5;241m.\u001b[39mrun_backward( \u001b[38;5;66;03m# Calls into the C++ engine to run the backward pass\u001b[39;00m\n\u001b[1;32m 267\u001b[0m tensors,\n\u001b[1;32m 268\u001b[0m grad_tensors_,\n\u001b[1;32m 269\u001b[0m retain_graph,\n\u001b[1;32m 270\u001b[0m create_graph,\n\u001b[1;32m 271\u001b[0m inputs,\n\u001b[1;32m 272\u001b[0m allow_unreachable\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m 273\u001b[0m accumulate_grad\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m 274\u001b[0m )\n", - "File \u001b[0;32m~/opt/anaconda3/envs/deep-learning-env/lib/python3.12/site-packages/torch/autograd/function.py:289\u001b[0m, in \u001b[0;36mBackwardCFunction.apply\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m 283\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[1;32m 284\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mImplementing both \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbackward\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m and \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mvjp\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m for a custom \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 285\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFunction is not allowed. You should only implement one \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 286\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mof them.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 287\u001b[0m )\n\u001b[1;32m 288\u001b[0m user_fn \u001b[38;5;241m=\u001b[39m vjp_fn \u001b[38;5;28;01mif\u001b[39;00m vjp_fn \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m Function\u001b[38;5;241m.\u001b[39mvjp \u001b[38;5;28;01melse\u001b[39;00m backward_fn\n\u001b[0;32m--> 289\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m user_fn(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs)\n", - "File \u001b[0;32m~/opt/anaconda3/envs/deep-learning-env/lib/python3.12/site-packages/cvxpylayers/torch/cvxpylayer.py:339\u001b[0m, in \u001b[0;36m_CvxpyLayerFn.._CvxpyLayerFnFn.backward\u001b[0;34m(ctx, *dvars)\u001b[0m\n\u001b[1;32m 336\u001b[0m dys\u001b[38;5;241m.\u001b[39mappend(np\u001b[38;5;241m.\u001b[39mzeros(ctx\u001b[38;5;241m.\u001b[39mshapes[i][\u001b[38;5;241m0\u001b[39m]))\n\u001b[1;32m 337\u001b[0m dss\u001b[38;5;241m.\u001b[39mappend(np\u001b[38;5;241m.\u001b[39mzeros(ctx\u001b[38;5;241m.\u001b[39mshapes[i][\u001b[38;5;241m0\u001b[39m]))\n\u001b[0;32m--> 339\u001b[0m dAs, dbs, dcs \u001b[38;5;241m=\u001b[39m ctx\u001b[38;5;241m.\u001b[39mDT_batch(dxs, dys, dss)\n\u001b[1;32m 341\u001b[0m \u001b[38;5;66;03m# differentiate from cone problem data to cvxpy parameters\u001b[39;00m\n\u001b[1;32m 342\u001b[0m start \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n", - "File \u001b[0;32m~/opt/anaconda3/envs/deep-learning-env/lib/python3.12/site-packages/diffcp/cone_program.py:114\u001b[0m, in \u001b[0;36msolve_and_derivative_batch..DT_batch\u001b[0;34m(dxs, dys, dss, **kwargs)\u001b[0m\n\u001b[1;32m 112\u001b[0m dAs, dbs, dcs \u001b[38;5;241m=\u001b[39m [], [], []\n\u001b[1;32m 113\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(batch_size):\n\u001b[0;32m--> 114\u001b[0m dA, db, dc \u001b[38;5;241m=\u001b[39m DTs[i](dxs[i], dys[i], dss[i], \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 115\u001b[0m dAs \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m [dA]\n\u001b[1;32m 116\u001b[0m dbs \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m [db]\n", - "File \u001b[0;32m~/opt/anaconda3/envs/deep-learning-env/lib/python3.12/site-packages/diffcp/cone_program.py:471\u001b[0m, in \u001b[0;36msolve_and_derivative_internal..adjoint_derivative\u001b[0;34m(dx, dy, ds, **kwargs)\u001b[0m\n\u001b[1;32m 469\u001b[0m r \u001b[38;5;241m=\u001b[39m _diffcp\u001b[38;5;241m.\u001b[39m_solve_adjoint_derivative_dense(M, MT, dz)\n\u001b[1;32m 470\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m mode \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlsqr\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m--> 471\u001b[0m r \u001b[38;5;241m=\u001b[39m _diffcp\u001b[38;5;241m.\u001b[39mlsqr(MT, dz)\u001b[38;5;241m.\u001b[39msolution\n\u001b[1;32m 472\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m mode \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlsmr\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 473\u001b[0m MT_sp \u001b[38;5;241m=\u001b[39m sparse\u001b[38;5;241m.\u001b[39mlinalg\u001b[38;5;241m.\u001b[39mLinearOperator(dz\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m2\u001b[39m, matvec\u001b[38;5;241m=\u001b[39mMT\u001b[38;5;241m.\u001b[39mmatvec, rmatvec\u001b[38;5;241m=\u001b[39mMT\u001b[38;5;241m.\u001b[39mrmatvec)\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " - ] + "data": { + "text/plain": [ + "14" + ] + }, + "execution_count": 83, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "\n", - "train_losses, test_losses = tune_kalman_smoother(train_loss, test_loss, params,epochs=25, verbose=True, print_every=1)" + "result[0]" ] }, { "cell_type": "code", - "execution_count": 153, + "execution_count": 84, "metadata": {}, "outputs": [], "source": [ - "x_hat, w_hat, v_hat = layer(tau_tch)" + "theta_star = result[2][14]" ] }, { "cell_type": "code", - "execution_count": 154, + "execution_count": 85, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[0.328879177216338]" + ] + }, + "execution_count": 85, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "theta_star" + ] + }, + { + "cell_type": "code", + "execution_count": 86, "metadata": {}, "outputs": [], "source": [ - "x_new, _, _ = layer(torch.tensor(0.08))" + "theta_star_tch = torch.tensor(theta_star[0])" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 87, "metadata": {}, + "outputs": [], "source": [ - "### Final estimations" + "x_hat_star, y_hat_star, v_hat_star = layer(theta_star_tch)" ] }, { "cell_type": "code", - "execution_count": 155, + "execution_count": 100, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -1497,9 +1719,11 @@ } ], "source": [ - "# plot_positions([x_true,x_hat_orig.detach().numpy()], ['True', 'KF orig. recovery'], [-4,14,-5,20])\n", - "plot_positions([x_true,x_new.detach().numpy()], ['True', 'KF tuned recovery'], [-4,14,-5,20])\n", - "plot_positions([x_true,x_hat.detach().numpy()], ['True', 'KF tuned recovery'], [-4,14,-5,20])" + "plot_positions(traj=[[y, x_true, x_hat_orig.detach().numpy(), x_hat_star.numpy()]],\n", + " titles=['Observations vs. True vs. Recovery vs. Tuned Recovery'],\n", + " legends=[['Measurements', 'True', 'Recovery', 'Tuned Recovery']],\n", + " plot_args=[['ro', 'g', 'k', 'b']],\n", + " axis=[-4,14,-5,20])" ] }, { @@ -1511,91 +1735,156 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ - "# initialize parameter values\n", - "# note difference between torch parameters and cvxpylayer parameters\n", - "num_train_pts, K_setminus_M = split_data(fraction=1.0)\n", - "M = ~K_setminus_M\n", - "K_setminus_M_tch, M_tch = torch.tensor(K_setminus_M), torch.tensor(M)\n", - "S, c, c_test = create_problem_data_params(K_setminus_M, num_train_pts)\n", - "S_tch = torch.tensor(S.todense(out=np.zeros((num_train_pts, N))))\n", - "c_tch, c_test_tch = torch.tensor(c), torch.tensor(c_test)" + "num_train_pts, train_mask, tune_mask, test_mask = split_data() # will still use this after CV\n", + "S, c, c_tune, c_test = create_problem_vectorized_data(train_mask, tune_mask, test_mask, num_train_pts)\n", + "c_test_tch = torch.tensor(c_test) \n", + "c_tune_tch = torch.tensor(c_tune)" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ - "eta_tch = torch.tensor(0.5, requires_grad=True)\n", - "rho_tch = torch.tensor(2.0, requires_grad=True)\n", - "params = [eta_tch, rho_tch] \n", "robust_layer = create_robust_kalman_smoother(S, c)\n", - "x_hat_orig, w_hat_orig, v_hat_orig = robust_layer(*params)\n", - "train_loss = lambda parameters : learning_error_fn(layer=robust_layer, y=c_tch,\n", - " observable_measurements=K_setminus_M_tch,\n", - " params=parameters)\n", - "test_loss = lambda parameters : kalman_recovery_mse(y=c_test_tch, observable_measurements=M_tch,\n", - " layer=robust_layer, params=parameters)" + "tau_tch = torch.tensor(2.0, requires_grad=True)\n", + "rho_tch = torch.tensor(2.0, requires_grad=True)\n", + "robust_params = [tau_tch, rho_tch]\n", + "roubst_x_hat_orig, roubst_y_hat_orig, roubst_w_hat_orig = robust_layer(*robust_params)" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ - "batch_robust_results = robust_layer(torch.tensor([0.5, 1.0], requires_grad=True), torch.tensor([2.0, 3.0], requires_grad=True))" + "Fn = partial(F, y=c_tune_tch, measurement_mask=tune_mask)\n", + "Ln = lambda y_hat : L(y=c_test_tch, measurement_mask=test_mask, y_hat=y_hat)" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor(53.8178, grad_fn=)" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Ln(roubst_y_hat_orig) # DEBUG\n", + "Ln(roubst_y_hat_orig)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor(90.4367, grad_fn=)" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Fn(y_hat=roubst_y_hat_orig, params=robust_params) # DEBUG\n", + "Fn(y_hat=roubst_y_hat_orig, params=robust_params)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "test loss 001 | nan\n" - ] - }, - { - "ename": "AttributeError", - "evalue": "'csr_matrix' object has no attribute 'A'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[18], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m train_losses, test_losses \u001b[38;5;241m=\u001b[39m tune_kalman_smoother(train_loss, test_loss, params, verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, print_every\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5\u001b[39m)\n", - "Cell \u001b[0;32mIn[14], line 38\u001b[0m, in \u001b[0;36mtune_kalman_smoother\u001b[0;34m(train_loss, test_loss, params, epochs, lr, verbose, print_every)\u001b[0m\n\u001b[1;32m 36\u001b[0m opt\u001b[38;5;241m.\u001b[39mzero_grad()\n\u001b[1;32m 37\u001b[0m loss \u001b[38;5;241m=\u001b[39m train_loss(params)\n\u001b[0;32m---> 38\u001b[0m loss\u001b[38;5;241m.\u001b[39mbackward()\n\u001b[1;32m 39\u001b[0m opt\u001b[38;5;241m.\u001b[39mstep()\n\u001b[1;32m 41\u001b[0m neg_theta \u001b[38;5;241m=\u001b[39m \u001b[38;5;28many\u001b[39m([p\u001b[38;5;241m.\u001b[39mitem() \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m0\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m p \u001b[38;5;129;01min\u001b[39;00m params])\n", - "File \u001b[0;32m~/opt/anaconda3/envs/deep-learning-env/lib/python3.12/site-packages/torch/_tensor.py:522\u001b[0m, in \u001b[0;36mTensor.backward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m 512\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m has_torch_function_unary(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 513\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m handle_torch_function(\n\u001b[1;32m 514\u001b[0m Tensor\u001b[38;5;241m.\u001b[39mbackward,\n\u001b[1;32m 515\u001b[0m (\u001b[38;5;28mself\u001b[39m,),\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 520\u001b[0m inputs\u001b[38;5;241m=\u001b[39minputs,\n\u001b[1;32m 521\u001b[0m )\n\u001b[0;32m--> 522\u001b[0m torch\u001b[38;5;241m.\u001b[39mautograd\u001b[38;5;241m.\u001b[39mbackward(\n\u001b[1;32m 523\u001b[0m \u001b[38;5;28mself\u001b[39m, gradient, retain_graph, create_graph, inputs\u001b[38;5;241m=\u001b[39minputs\n\u001b[1;32m 524\u001b[0m )\n", - "File \u001b[0;32m~/opt/anaconda3/envs/deep-learning-env/lib/python3.12/site-packages/torch/autograd/__init__.py:266\u001b[0m, in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[1;32m 261\u001b[0m retain_graph \u001b[38;5;241m=\u001b[39m create_graph\n\u001b[1;32m 263\u001b[0m \u001b[38;5;66;03m# The reason we repeat the same comment below is that\u001b[39;00m\n\u001b[1;32m 264\u001b[0m \u001b[38;5;66;03m# some Python versions print out the first line of a multi-line function\u001b[39;00m\n\u001b[1;32m 265\u001b[0m \u001b[38;5;66;03m# calls in the traceback and some print out the last line\u001b[39;00m\n\u001b[0;32m--> 266\u001b[0m Variable\u001b[38;5;241m.\u001b[39m_execution_engine\u001b[38;5;241m.\u001b[39mrun_backward( \u001b[38;5;66;03m# Calls into the C++ engine to run the backward pass\u001b[39;00m\n\u001b[1;32m 267\u001b[0m tensors,\n\u001b[1;32m 268\u001b[0m grad_tensors_,\n\u001b[1;32m 269\u001b[0m retain_graph,\n\u001b[1;32m 270\u001b[0m create_graph,\n\u001b[1;32m 271\u001b[0m inputs,\n\u001b[1;32m 272\u001b[0m allow_unreachable\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m 273\u001b[0m accumulate_grad\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m 274\u001b[0m )\n", - "File \u001b[0;32m~/opt/anaconda3/envs/deep-learning-env/lib/python3.12/site-packages/torch/autograd/function.py:289\u001b[0m, in \u001b[0;36mBackwardCFunction.apply\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m 283\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[1;32m 284\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mImplementing both \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbackward\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m and \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mvjp\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m for a custom \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 285\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFunction is not allowed. You should only implement one \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 286\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mof them.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 287\u001b[0m )\n\u001b[1;32m 288\u001b[0m user_fn \u001b[38;5;241m=\u001b[39m vjp_fn \u001b[38;5;28;01mif\u001b[39;00m vjp_fn \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m Function\u001b[38;5;241m.\u001b[39mvjp \u001b[38;5;28;01melse\u001b[39;00m backward_fn\n\u001b[0;32m--> 289\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m user_fn(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs)\n", - "File \u001b[0;32m~/opt/anaconda3/envs/deep-learning-env/lib/python3.12/site-packages/cvxpylayers/torch/cvxpylayer.py:345\u001b[0m, in \u001b[0;36m_CvxpyLayerFn.._CvxpyLayerFnFn.backward\u001b[0;34m(ctx, *dvars)\u001b[0m\n\u001b[1;32m 343\u001b[0m grad \u001b[38;5;241m=\u001b[39m [[] \u001b[38;5;28;01mfor\u001b[39;00m _ \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(param_ids))]\n\u001b[1;32m 344\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(ctx\u001b[38;5;241m.\u001b[39mbatch_size):\n\u001b[0;32m--> 345\u001b[0m del_param_dict \u001b[38;5;241m=\u001b[39m compiler\u001b[38;5;241m.\u001b[39mapply_param_jac(\n\u001b[1;32m 346\u001b[0m dcs[i], \u001b[38;5;241m-\u001b[39mdAs[i], dbs[i])\n\u001b[1;32m 347\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m j, pid \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(param_ids):\n\u001b[1;32m 348\u001b[0m grad[j] \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m [to_torch(del_param_dict[pid],\n\u001b[1;32m 349\u001b[0m ctx\u001b[38;5;241m.\u001b[39mdtype, ctx\u001b[38;5;241m.\u001b[39mdevice)\u001b[38;5;241m.\u001b[39munsqueeze(\u001b[38;5;241m0\u001b[39m)]\n", - "File \u001b[0;32m~/opt/anaconda3/envs/deep-learning-env/lib/python3.12/site-packages/cvxpy/reductions/dcp2cone/cone_matrix_stuffing.py:252\u001b[0m, in \u001b[0;36mParamConeProg.apply_param_jac\u001b[0;34m(self, delc, delA, delb, active_params)\u001b[0m\n\u001b[1;32m 247\u001b[0m del_param_vec \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39msqueeze(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mA\u001b[38;5;241m.\u001b[39mT\u001b[38;5;241m.\u001b[39mdot(delAb\u001b[38;5;241m.\u001b[39mtoarray()))\n\u001b[1;32m 248\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 249\u001b[0m \u001b[38;5;66;03m# slow path.\u001b[39;00m\n\u001b[1;32m 250\u001b[0m \u001b[38;5;66;03m# TODO: make this faster by intelligently operating on the\u001b[39;00m\n\u001b[1;32m 251\u001b[0m \u001b[38;5;66;03m# sparse matrix data / making use of reduced_A\u001b[39;00m\n\u001b[0;32m--> 252\u001b[0m del_param_vec \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39msqueeze((delAb\u001b[38;5;241m.\u001b[39mT \u001b[38;5;241m@\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mA)\u001b[38;5;241m.\u001b[39mA)\n\u001b[1;32m 253\u001b[0m del_param_vec \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39msqueeze(del_param_vec)\n\u001b[1;32m 255\u001b[0m param_id_to_delta_param \u001b[38;5;241m=\u001b[39m {}\n", - "\u001b[0;31mAttributeError\u001b[0m: 'csr_matrix' object has no attribute 'A'" + "test loss 001 | 53.81776\n", + "train loss 001 | 90.43666\n", + "test loss 002 | 53.81879\n", + "train loss 002 | 90.41793\n", + "test loss 003 | 53.81986\n", + "train loss 003 | 90.40053\n", + "test loss 004 | 53.82071\n", + "train loss 004 | 90.38434\n", + "test loss 005 | 53.82154\n", + "train loss 005 | 90.36844\n", + "test loss 006 | 53.82253\n", + "train loss 006 | 90.35218\n", + "test loss 007 | 53.82326\n", + "train loss 007 | 90.33639\n", + "test loss 008 | 53.82419\n", + "train loss 008 | 90.32039\n", + "test loss 009 | 53.82505\n", + "train loss 009 | 90.30495\n", + "test loss 010 | 53.82577\n", + "train loss 010 | 90.28987\n", + "test loss 011 | 53.82650\n", + "train loss 011 | 90.27464\n", + "test loss 012 | 53.82714\n", + "train loss 012 | 90.25910\n", + "test loss 013 | 53.82776\n", + "train loss 013 | 90.24343\n", + "test loss 014 | 53.82843\n", + "train loss 014 | 90.22831\n", + "test loss 015 | 53.82891\n", + "train loss 015 | 90.21360\n" ] } ], "source": [ - "train_losses, test_losses = tune_kalman_smoother(train_loss, test_loss, params, verbose=True, print_every=5)" + "robust_result = tune_kalman_smoother(robust_layer, Fn, Ln, robust_params, num_iters=15, verbose=True, print_every=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "last_theta = robust_result[2][14]\n", + "last_theta_tch = [torch.tensor(p) for p in last_theta]" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "related_x = robust_layer(*last_theta_tch)[0]" ] }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 37, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] }, "metadata": {}, @@ -1603,8 +1892,11 @@ } ], "source": [ - "plot_positions([x_true,x_hat_orig.detach().numpy()], ['True', 'KF orig. recovery'], [-4,14,-5,20])\n", - "# plot_positions([x_true,x_hat.detach().numpy()], ['True', 'KF tuned recovery'], [-4,14,-5,20])" + "plot_positions(traj=[[y, x_true, roubst_x_hat_orig.detach().numpy(), related_x.numpy()]],\n", + " titles=['Observations vs. True vs. Recovery vs. Tuned Recovery'],\n", + " legends=[['Measurements', 'True', 'Recovery', 'Tuned Recovery']],\n", + " plot_args=[['ro', 'g', 'k', 'b']],\n", + " axis=[-4,14,-5,20])" ] }, { @@ -1627,7 +1919,7 @@ "\n", "$$\n", "\\begin{equation}\n", - "\\text{minimize} \\; \\sum_{t = 1}^{T} \\phi_M(Ax_t - b_t),\n", + "\\text{minimize} \\quad \\sum_{t = 1}^{T} \\phi_M(Ax_t - b_t),\n", "\\tag{A1}\n", "\\end{equation}\n", "$$\n", @@ -1648,7 +1940,7 @@ "$$\n", "\\begin{equation}\n", "\\begin{array}{lll}\n", - "\\text{minimize} \\; & \\sum_{i = 1}^{n} \\left(u_t^2 + 2Mv_t \\right) & \\\\\n", + "\\text{minimize} \\; & \\sum_{t = 1}^{T} \\left(u_t^2 + 2Mv_t \\right) & \\\\\n", "\\text{subject to} & \\left\\lVert Ax_t - b_t \\right\\rVert_{2} \\le u_t + v_t, \\; & t = 1, \\ldots, T \\\\\n", "& 0 \\preceq u \\preceq M \\mathbf{1} \\; & \\\\\n", "& v \\succeq 0,\n", @@ -1685,12 +1977,12 @@ "\n", "Considering solely the objective function, a sum of (convex) quadratic functions, calculus tells us that $u_t = M$ minimizes each summand of $f_0$. However, if $\\left\\lVert Ax_t - b_t \\right\\rVert_{2} < M$ for some $t \\in \\left\\{1, \\ldots, T \\right\\}$, then $u_t = \\left\\lVert Ax_t - b_t \\right\\rVert_{2}$ becomes the minimizer of that particular summand. \n", "\n", - "Therefore, when $\\left\\lVert Ax_t - b_t \\right\\rVert_{2} \\le M$, we choose $u_t = \\left\\lVert Ax_t - b_t \\right\\rVert_{2}$ and the $t$ th summand simplifies to\n", + "Therefore, when $\\left\\lVert Ax_t - b_t \\right\\rVert_{2} \\le M$, we choose $u_t = \\left\\lVert Ax_t - b_t \\right\\rVert_{2}$ and the $t\\text{th}$ summand simplifies to\n", "$$\n", "\\left\\lVert Ax_t - b_t \\right\\rVert_{2}^{2} - 2M\\left\\lVert Ax_t - b_t \\right\\rVert_{2}^{2} + 2M \\left\\lVert Ax_t - b_t \\right\\rVert_{2}^{2} = \\left\\lVert Ax_t - b_t \\right\\rVert_{2}^{2}.\n", "$$\n", "\n", - "Otherwise, if $\\left\\lVert Ax_t - b_t \\right\\rVert_{2} > M$, we choose $u_t = M$ and the $t$ th summand becomes\n", + "Otherwise, if $\\left\\lVert Ax_t - b_t \\right\\rVert_{2} > M$, we choose $u_t = M$ and the $t\\text{th}$ summand becomes\n", "\n", "$$\n", "M^2 - 2M^2 + 2M \\left\\lVert Ax_t - b_t \\right\\rVert_{2} = M(2\\left\\lVert Ax_t - b_t \\right\\rVert_{2} - M).\n", @@ -1702,101 +1994,216 @@ "∑_{t=1}^T ϕ_M(Ax_t - b_t).\n", "$$\n", "\n", - "## Selection matrix construction\n", + "## Indexing and vectorization\n", + "\n", + "Consider the matrix containing observations, $y \\in \\mathbf{R}^{p \\times T}$, found in the smoother implementation section where $p = 2$ and $T = 1000$, and expressed as\n", + "\n", + "$$\n", + "y = \\begin{bmatrix}\n", + "y_{11} & y_{12} & \\cdots y_{1,1000} \\\\\n", + "y_{21} & \\hat{y}_{22} & \\cdots y_{2, 1000} \\\\\n", + "\\end{bmatrix}.\n", + "$$\n", + "\n", + "More intuitively, the columns of $y$ are the measurement vectors $y_t$ over the $T$ time periods.\n", + "\n", + "Let $N = Tp$ ($= 2000$, in this instance). Flattening $y$ in row-major order yields the vector $\\textbf{vec}_{\\text{R}} \\, y \\in \\mathbf{R}^{N}$, defined as\n", + "\n", + "$$\n", + "\\textbf{vec}_{\\text{R}} \\, y = (y_{11}, y_{12}, \\ldots, y_{1,1000}, y_{21}, y_{22}, \\ldots, y_{2, 1000}).\n", + "$$\n", + "\n", + "In words, $\\textbf{vec}_{\\text{R}} \\, y$ is obtained by taking the first row of $y$ (every first element of a measurement) and concatenating to that first row the second row of $y$.\n", + "\n", + "Flattening $y$ in a column-major order yields the vector $\\textbf{vec}_{\\text{F}} \\, y \\in \\mathbf{R}^{N}$, defined as\n", + "\n", + "$$\n", + "\\textbf{vec}_{\\text{F}} \\, y = (y_{11}, y_{21}, y_{12}, y_{22}, \\ldots, y_{1, 1000}, y_{2, 1000}).\n", + "$$\n", + "\n", + "In words, $\\textbf{vec}_{\\text{F}} \\, y$ is obtained by concatenating all measurement vectors, starting with $y_1$ and ending with $y_T$.\n", + "\n", + "### Related NumPy (and PyTorch, loosely)\n", + "\n", + "Let `y` be a `np.ndarray` with shape `(p, T)` representing $y$.\n", + "\n", + "- `y.flat` produces a 1-D iterator over the array. The iterator moves through `y` in a $\\textbf{vec}_{\\text{R}} \\, y$ fashion.\n", + "- Let `F` be a `np.ndarray` with shape `(T, p)` and `dtype==bool` (a $T \\times p$ mask matrix) such that only `F[0, 0]`, `F[0, 1]`, `F[999, 0]`, and `F[999, 1]` are `True`. Taking `c = y.T[M]`, `c` is the implementation version of\n", + "$$\n", + "c = (y_{11}, y_{21}, y_{1, 1000}, y_{2, 1000}).\n", + "$$ \n", + "\n", + "Resources\n", + "- [Reshape documentation](https://numpy.org/doc/stable/reference/generated/numpy.reshape.html#numpy.reshape)\n", + "- [Flat documentation](https://numpy.org/doc/stable/reference/generated/numpy.reshape.html#numpy.reshape)\n", + "\n", + "### Related CVXPY\n", + "\n", + "Let `y_hat` be a `cp.Variable` representing $\\hat{y}$. Taking `z = cp.vec(y_hat, order='F')`, `z` is an `Expression` object that can be understood (and used) as $\\textbf{vec}_{\\text{F}} \\, \\hat{y}$.\n", + "\n", + "Resources\n", + "- [vec atom](https://www.cvxpy.org/api_reference/cvxpy.atoms.affine.html#vec)\n", + "- [Clarification on vec (see bottom of page)](https://www.cvxpy.org/tutorial/functions/index.html)\n", + "\n", + "### Creating the selector matrix\n", "\n", - "rows, cols = matrix.nonzero() are constructed by looping row wise through the matrix, recording the row and column value, respectively, whenever the matrix has a nonzero value" + "Consider the two smoother formulations (and related code). When `c` is passed to either smoother layer creation function, it is some subset of the output measurements flattened in column-major order (*e.g*, the `c` created above). The vector of output optimization variables, `z`, is likewise flattened in column-major order. Furthermore, to implement the equality constraint $Sz = c$, we just need to implement the (sparse) selector matrix $S$ exactly as it exists mathematically. To do this, consider the following code chunk:\n", + "\n", + "```python\n", + "time_indices, measurement_indices = F.nonzero()\n", + "S = sparse.csc_matrix(\n", + " (\n", + " np.ones(num_measurements), # data filled into the sparse array\n", + " (\n", + " np.arange(num_measurements),\n", + " time_indices*p + measurement_indices\n", + " )\n", + " ),\n", + " shape=(num_measurements, N)\n", + ")\n", + "```\n", + "\n", + "The variables `time_indices` and `measurement_indices` are 1-D arrays containing the row and column indices of `F` that are nonzero (or rather, that are `True`). The indices are obtained by iterating over `F` in a row-major fashion. (Note that for our vehicle tracking example, `time_indices` will contain values ranging from $0$ to $999$ and `measurement indices` can only contain the values $0$ or $1$.) Furthermore, the $j\\text{th}$ entry of `time_indices` and `measurement_indices` are the\n", + "\n", + "yields the $(t, i)$ of the $j\\text{th}$ entry in the vector `c`.\n", + "\n", + "\n", + "However, because $\\mathcal{F}$ is implemented as the mask matrix `F`, constructing `S` requires different manipulation than turning $\\mathcal{F}$ into $S$.\n", + "\n", + "Specifically, consider the following code chunk taken from `create_problem_vectorized_data`:\n", + "\n", + "\n", + "\n", + "To see $S$, think about the **picture** of matrix-vector multiplication. \n", + "\n", + "$S$ is then constructed by placing a $1$ in every $(i, j)$ in $(1, + ),$\n", + "\n", + "\n", + "Resources\n", + "- [SciPy csc_matrix documentation](https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.csc_matrix.html)" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "cp.sum(cp.vec(y).value == c).value # check if c is ordered correctly as column majored" + "## (informal) Testing" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[8 0 1 2]\n", + "[-0.60331112 -0.74586404 0.61854093 -0.74426384]\n", + "[-0.60331112 -0.74586404 0.61854093 1.18430381 1.96604923 0.98671515\n", + " 0.28121689 -0.95360892 -0.74426384 -1.18353915 -0.94735021 -0.22503192]\n" + ] + } + ], "source": [ - "c_test = y[K_setminus_M.T]" + "indices = np.arange(12)\n", + "np.random.shuffle(indices)\n", + "splits = np.array_split(indices, 3)\n", + "print(splits[1])\n", + "\n", + "mask_matrix = np.zeros((4, 3))\n", + "mask_matrix.flat[splits[1]] = True\n", + "mask_matrix = mask_matrix.astype(bool)\n", + "\n", + "y_random = np.random.randn(4, 3)\n", + "print(y_random[mask_matrix])\n", + "print(y_random.flatten())" ] }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ - "nm, KM = split_data(1.0)" + "test_mask = np.zeros(N, dtype=bool)\n", + "test_mask = test_mask.reshape((T, p))" ] }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([-1.26099346, -1.02476223, -0.18265832, ..., 18.51687772,\n", - " 19.28085045, 19.33229155])" + "array([[False, False],\n", + " [False, False],\n", + " [False, False],\n", + " ...,\n", + " [False, False],\n", + " [False, False],\n", + " [False, False]])" ] }, - "execution_count": 38, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "y[KM.T]" + "test_mask" ] }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[-1.26099346, -1.02476223, -0.18265832, ..., 4.69332517,\n", - " 2.68689846, 2.3267197 ],\n", - " [ 0.13240848, -1.65302845, -1.50460709, ..., 18.51687772,\n", - " 19.28085045, 19.33229155]])" + "" ] }, - "execution_count": 39, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "y" + "test_mask.flat" ] }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "arr1 = np.array([1, 2, 3])" + ] + }, + { + "cell_type": "code", + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([-1.26099346, 0.13240848, -1.02476223, ..., 19.28085045,\n", - " 2.3267197 , 19.33229155])" + "1" ] }, - "execution_count": 40, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "y.T[KM]" + "arr1.ndim" ] }, { @@ -1823,7 +2230,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.2" + "version": "3.11.7" } }, "nbformat": 4,