Structured prediction

In this example\ :math:\newcommand{\reals}{\mathbf{R}}\ :math:\newcommand{\ones}{\mathbf{1}}, we fit a regression model to structured data, using an LLCP. The training dataset :math:\mathcal D contains :math:N input-output pairs :math:(x, y), where :math:x \in \reals^{n}_{++} is an input and :math:y \in \reals^{m}_{++} is an outputs. The entries of each output :math:y are sorted in ascending order, meaning :math:y_1 \leq y_2 \leq \cdots \leq y_m.

Our regression model :math:\phi : \reals^{n}_{++} \to \reals^{m}_{++} takes as input a vector :math:x \in \reals^{n}_{++}, and solves an LLCP to produce a prediction :math:\hat y \in \reals^{m}_{++}. In particular, the solution of the LLCP is model's prediction. The model is of the form

.. math::

\begin{equation} \begin{array}{lll} \phi(x) = & \mbox{argmin} & \ones^T (z/y + y / z) \ & \mbox{subject to} & y_i \leq y_{i+1}, \quad i=1, \ldots, m-1 \ && z_i = c_i x_1^{A_{i1}}x_2^{A_{i2}}\cdots x_n^{A_{in}}, \quad i = 1, \ldots, m. \end{array}\label{e-model} \end{equation}

Here, the minimization is over :math:y \in \reals^{m}_{++} and an auxiliary variable :math:z \in \reals^{m}_{++}, :math:\phi(x) is the optimal value of :math:y, and the parameters are :math:c \in \reals^{m}_{++} and :math:A \in \reals^{m \times n}. The ratios in the objective are meant elementwise, as is the inequality :math:y \leq z, and :math:\ones denotes the vector of all ones. Given a vector :math:x, this model finds a sorted vector :math:\hat y whose entries are close to monomial functions of :math:x (which are the entries of :math:z), as measured by the fractional error.

The training loss :math:\mathcal{L}(\phi) of the model on the training set is the mean squared loss

.. math::

\mathcal{L}(\phi) = \frac{1}{N}\sum_{(x, y) \in \mathcal D} |y - \phi(x)|_2^2.

We emphasize that :math:\mathcal{L}(\phi) depends on :math:c and :math:A. In this example, we fit the parameters :math:c and :math:A in the LLCP to minimize the training loss :math:\mathcal{L}(\phi).

Fitting. We fit the parameters by an iterative projected gradient descent method on :math:\mathcal L(\phi). In each iteration, we first compute predictions :math:\phi(x) for each input in the training set; this requires solving :math:N LLCPs. Next, we evaluate the training loss :math:\mathcal L(\phi). To update the parameters, we compute the gradient :math:\nabla \mathcal L(\phi) of the training loss with respect to the parameters :math:c and :math:A. This requires differentiating through the solution map of the LLCP. We can compute this gradient efficiently, using the backward method in CVXPY (or CVXPY Layers). Finally, we subtract a small multiple of the gradient from the parameters. Care must be taken to ensure that :math:c is strictly positive; this can be done by clamping the entries of :math:c at some small threshold slightly above zero. We run this method for a fixed number of iterations.

This example is described in the paper Differentiating through Log-Log Convex Programs <http://web.stanford.edu/~boyd/papers/pdf/diff_llcvx.pdf>__.

Shane Barratt formulated the idea of using an optimization layer to regress on sorted vectors.

Requirements. This example requires PyTorch and CvxpyLayers >= v0.1.3.

.. code:: ipython3

from cvxpylayers.torch import CvxpyLayer


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

torch.set_default_tensor_type(torch.DoubleTensor)
%matplotlib inline

Data generation


.. code:: ipython3

    n = 20
    m = 10
    
    # Number of training input-output pairs
    N = 100
    
    # Number of validation pairs
    N_val = 50

.. code:: ipython3

    torch.random.manual_seed(243)
    np.random.seed(243)
    
    normal = torch.distributions.multivariate_normal.MultivariateNormal(torch.zeros(n), torch.eye(n))
    lognormal = lambda batch: torch.exp(normal.sample(torch.tensor([batch])))
    
    A_true = torch.randn((m, n)) / 10
    c_true = np.abs(torch.randn(m))

.. code:: ipython3

    def generate_data(num_points, seed):
        torch.random.manual_seed(seed)
        np.random.seed(seed)
        
        latent = lognormal(num_points)
        noise = lognormal(num_points)
        inputs = noise + latent
    
        input_cp = cp.Parameter(pos=True, shape=(n,))
        prediction = cp.multiply(c_true.numpy(), cp.gmatmul(A_true.numpy(), input_cp))
        y = cp.Variable(pos=True, shape=(m,))
        objective_fn = cp.sum(prediction / y + y/prediction)
        constraints = []
        for i in range(m-1):
            constraints += [y[i] <= y[i+1]]
        problem = cp.Problem(cp.Minimize(objective_fn), constraints)
        
        outputs = []
        for i in range(num_points):
            input_cp.value = inputs[i, :].numpy()
            problem.solve(cp.SCS, gp=True)
            outputs.append(y.value)
        return inputs, torch.stack([torch.tensor(t) for t in outputs])

.. code:: ipython3

    train_inputs, train_outputs = generate_data(N, 243)
    plt.plot(train_outputs[0, :].numpy())




.. parsed-literal::

    [<matplotlib.lines.Line2D at 0x12b367cd0>]




.. image:: structured_prediction_files/structured_prediction_6_1.png


.. code:: ipython3

    val_inputs, val_outputs = generate_data(N_val, 0)
    plt.plot(val_outputs[0, :].numpy())




.. parsed-literal::

    [<matplotlib.lines.Line2D at 0x12da7e410>]




.. image:: structured_prediction_files/structured_prediction_7_1.png


Monomial fit to each component
------------------------------

We will initialize the parameters in our LLCP model by fitting monomials
to the training data, without enforcing the monotonicity constraint.

.. code:: ipython3

    log_c = cp.Variable(shape=(m,1))
    theta = cp.Variable(shape=(n, m))
    inputs_np = train_inputs.numpy()
    log_outputs_np = np.log(train_outputs.numpy()).T
    log_inputs_np = np.log(inputs_np).T
    offsets = cp.hstack([log_c]*N)

.. code:: ipython3

    cp_preds = theta.T @ log_inputs_np + offsets
    objective_fn = (1/N) * cp.sum_squares(cp_preds - log_outputs_np)
    lstq_problem = cp.Problem(cp.Minimize(objective_fn))

.. code:: ipython3

    lstq_problem.is_dcp()




.. parsed-literal::

    True



.. code:: ipython3

    lstq_problem.solve(verbose=True)


.. parsed-literal::

    -----------------------------------------------------------------
               OSQP v0.6.0  -  Operator Splitting QP Solver
                  (c) Bartolomeo Stellato,  Goran Banjac
            University of Oxford  -  Stanford University 2019
    -----------------------------------------------------------------
    problem:  variables n = 1210, constraints m = 1000
              nnz(P) + nnz(A) = 23000
    settings: linear system solver = qdldl,
              eps_abs = 1.0e-05, eps_rel = 1.0e-05,
              eps_prim_inf = 1.0e-04, eps_dual_inf = 1.0e-04,
              rho = 1.00e-01 (adaptive),
              sigma = 1.00e-06, alpha = 1.60, max_iter = 10000
              check_termination: on (interval 25),
              scaling: on, scaled_termination: off
              warm start: on, polish: on, time_limit: off
    
    iter   objective    pri res    dua res    rho        time
       1   0.0000e+00   3.30e+00   1.22e+04   1.00e-01   3.06e-03s
      50   1.0014e-02   1.72e-07   1.64e-07   1.75e-03   7.37e-03s
    plsh   1.0014e-02   1.56e-15   1.17e-14   --------   9.68e-03s
    
    status:               solved
    solution polish:      successful
    number of iterations: 50
    optimal objective:    0.0100
    run time:             9.68e-03s
    optimal rho estimate: 8.77e-05
    




.. parsed-literal::

    0.010014212812318733



.. code:: ipython3

    c = torch.exp(torch.tensor(log_c.value)).squeeze()
    lstsq_val_preds = []
    for i in range(N_val):
        inp = val_inputs[i, :].numpy()
        pred = cp.multiply(c,cp.gmatmul(theta.T.value, inp))
        lstsq_val_preds.append(pred.value)

Fitting
-------

.. code:: ipython3

    A_param = cp.Parameter(shape=(m, n))
    c_param = cp.Parameter(pos=True, shape=(m,))
    x_slack = cp.Variable(pos=True, shape=(n,))
    x_param = cp.Parameter(pos=True, shape=(n,))
    y = cp.Variable(pos=True, shape=(m,))
    
    prediction = cp.multiply(c_param, cp.gmatmul(A_param, x_slack))
    objective_fn = cp.sum(prediction / y + y / prediction)
    constraints = [x_slack == x_param]
    for i in range(m-1):
        constraints += [y[i] <= y[i+1]]
    problem = cp.Problem(cp.Minimize(objective_fn), constraints)
    problem.is_dgp(dpp=True)




.. parsed-literal::

    True



.. code:: ipython3

    A_param.value = np.random.randn(m, n)
    x_param.value = np.abs(np.random.randn(n))
    c_param.value = np.abs(np.random.randn(m))
    
    layer = CvxpyLayer(problem, parameters=[A_param, c_param, x_param], variables=[y], gp=True)

.. code:: ipython3

    torch.random.manual_seed(1)
    A_tch = torch.tensor(theta.T.value)
    A_tch.requires_grad_(True)
    c_tch = torch.tensor(np.squeeze(np.exp(log_c.value)))
    c_tch.requires_grad_(True)
    train_losses = []
    val_losses = []
    
    lam1 = torch.tensor(1e-1)
    lam2 = torch.tensor(1e-1)
    
    opt = torch.optim.SGD([A_tch, c_tch], lr=5e-2)
    for epoch in range(10):
        preds = layer(A_tch, c_tch, train_inputs, solver_args={'acceleration_lookback': 0})[0]
        loss = (preds - train_outputs).pow(2).sum(axis=1).mean(axis=0)
    
        with torch.no_grad():
            val_preds = layer(A_tch, c_tch, val_inputs, solver_args={'acceleration_lookback': 0})[0]
            val_loss = (val_preds - val_outputs).pow(2).sum(axis=1).mean(axis=0)
    
        print('(epoch {0}) train / val ({1:.4f} / {2:.4f}) '.format(epoch, loss, val_loss))
        train_losses.append(loss.item())
        val_losses.append(val_loss.item())
        
        opt.zero_grad()
        loss.backward()
        opt.step()
        with torch.no_grad():
            c_tch = torch.max(c_tch, torch.tensor(1e-8))


.. parsed-literal::

    (epoch 0) train / val (0.0018 / 0.0014) 
    (epoch 1) train / val (0.0017 / 0.0014) 
    (epoch 2) train / val (0.0017 / 0.0014) 
    (epoch 3) train / val (0.0017 / 0.0014) 
    (epoch 4) train / val (0.0017 / 0.0014) 
    (epoch 5) train / val (0.0017 / 0.0014) 
    (epoch 6) train / val (0.0016 / 0.0014) 
    (epoch 7) train / val (0.0016 / 0.0014) 
    (epoch 8) train / val (0.0016 / 0.0014) 
    (epoch 9) train / val (0.0016 / 0.0014) 


.. code:: ipython3

    with torch.no_grad():
        train_preds_tch = layer(A_tch, c_tch, train_inputs)[0]
        train_preds = [t.detach().numpy() for t in train_preds_tch]

.. code:: ipython3

    with torch.no_grad():
        val_preds_tch = layer(A_tch, c_tch, val_inputs)[0]
        val_preds = [t.detach().numpy() for t in val_preds_tch]

.. code:: ipython3

    fig = plt.figure()
    
    
    i = 0
    plt.plot(val_preds[i], label='LLCP', color='teal')
    plt.plot(lstsq_val_preds[i], label='least squares', linestyle='--', color='gray')
    plt.plot(val_outputs[i], label='true', linestyle='-.', color='orange')
    w, h = 8, 3.5
    plt.xlabel(r'$i$')
    plt.ylabel(r'$y_i$')
    plt.legend()
    plt.show()



.. image:: structured_prediction_files/structured_prediction_20_0.png