{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Batch QEP Regression\n", "\n", "## Introduction\n", "\n", "In this notebook, we demonstrate how to train Gaussian processes in the batch setting -- that is, given `b` training sets and `b` separate test sets, QPyTorch is capable of training independent QEPs on each training set and then testing each QEP separately on each test set in parallel. This can be extremely useful if, for example, you would like to do k-fold cross validation.\n", "\n", "**Note:** When operating in batch mode, we do **NOT** account for any correlations between the different functions being modeled. If you wish to do this, see the multitask examples instead." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import math\n", "import torch\n", "import qpytorch\n", "from matplotlib import pyplot as plt\n", "\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Set up training data\n", "\n", "In the next cell, we set up the training data for this example. For the x values, we'll be using 100 regularly spaced points on [0,1] which we evaluate the function on and add Gaussian noise to get the training labels. For the training labels, we'll be modeling four functions independently in batch mode: two sine functions with different periods and two cosine functions with different periods. \n", "\n", "In total, `train_x` will be `4 x 100 x 1` (`b x n x 1`) and `train_y` will be `4 x 100` (`b x n`)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# Training data is 100 points in [0,1] inclusive regularly spaced\n", "train_x = torch.linspace(0, 1, 100).view(1, -1, 1).repeat(4, 1, 1)\n", "# True functions are sin(2pi x), cos(2pi x), sin(pi x), cos(pi x)\n", "sin_y = torch.sin(train_x[0] * (2 * math.pi)) + 0.5 * torch.rand(1, 100, 1)\n", "sin_y_short = torch.sin(train_x[0] * (math.pi)) + 0.5 * torch.rand(1, 100, 1)\n", "cos_y = torch.cos(train_x[0] * (2 * math.pi)) + 0.5 * torch.rand(1, 100, 1)\n", "cos_y_short = torch.cos(train_x[0] * (math.pi)) + 0.5 * torch.rand(1, 100, 1)\n", "train_y = torch.cat((sin_y, sin_y_short, cos_y, cos_y_short)).squeeze(-1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setting up the model\n", "\n", "The next cell adapts the model from the Simple QEP regression tutorial to the batch setting. Not much changes: the only modification is that we add a `batch_shape` to the mean and covariance modules. What this does internally is replicates the mean constant and lengthscales `b` times so that we learn a different value for each function in the batch." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# We will use the simplest form of QEP model, exact inference\n", "POWER = 1.0\n", "class ExactQEPModel(qpytorch.models.ExactQEP):\n", " def __init__(self, train_x, train_y, likelihood):\n", " super(ExactQEPModel, self).__init__(train_x, train_y, likelihood)\n", " self.power = torch.tensor(POWER)\n", " self.mean_module = qpytorch.means.ConstantMean(batch_shape=torch.Size([4]))\n", " self.covar_module = qpytorch.kernels.ScaleKernel(\n", " qpytorch.kernels.MaternKernel(batch_shape=torch.Size([4])),\n", " batch_shape=torch.Size([4])\n", " )\n", " \n", " def forward(self, x):\n", " mean_x = self.mean_module(x)\n", " covar_x = self.covar_module(x)\n", " return qpytorch.distributions.MultivariateQExponential(mean_x, covar_x, power=self.power)\n", "\n", "# initialize likelihood and model\n", "likelihood = qpytorch.likelihoods.QExponentialLikelihood(batch_shape=torch.Size([4]), power=torch.tensor(POWER))\n", "model = ExactQEPModel(train_x, train_y, likelihood)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Training the model\n", "\n", "In the next cell, we handle using Type-II MLE to train the hyperparameters of the q-exponential process. This loop is nearly identical to the simple QEP regression setting with one key difference. Now, the call through the mariginal log likelihood returns `b` losses, one for each QEP. Since we have different parameters for each QEP, we can simply sum these losses before calling `backward`." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Iter 1/50 - Loss: 5.821\n", "Iter 2/50 - Loss: 5.602\n", "Iter 3/50 - Loss: 5.384\n", "Iter 4/50 - Loss: 5.170\n", "Iter 5/50 - Loss: 4.963\n", "Iter 6/50 - Loss: 4.766\n", "Iter 7/50 - Loss: 4.582\n", "Iter 8/50 - Loss: 4.413\n", "Iter 9/50 - Loss: 4.263\n", "Iter 10/50 - Loss: 4.131\n", "Iter 11/50 - Loss: 4.014\n", "Iter 12/50 - Loss: 3.909\n", "Iter 13/50 - Loss: 3.814\n", "Iter 14/50 - Loss: 3.725\n", "Iter 15/50 - Loss: 3.641\n", "Iter 16/50 - Loss: 3.559\n", "Iter 17/50 - Loss: 3.479\n", "Iter 18/50 - Loss: 3.400\n", "Iter 19/50 - Loss: 3.321\n", "Iter 20/50 - Loss: 3.242\n", "Iter 21/50 - Loss: 3.163\n", "Iter 22/50 - Loss: 3.083\n", "Iter 23/50 - Loss: 3.002\n", "Iter 24/50 - Loss: 2.921\n", "Iter 25/50 - Loss: 2.838\n", "Iter 26/50 - Loss: 2.755\n", "Iter 27/50 - Loss: 2.670\n", "Iter 28/50 - Loss: 2.585\n", "Iter 29/50 - Loss: 2.499\n", "Iter 30/50 - Loss: 2.412\n", "Iter 31/50 - Loss: 2.325\n", "Iter 32/50 - Loss: 2.237\n", "Iter 33/50 - Loss: 2.149\n", "Iter 34/50 - Loss: 2.061\n", "Iter 35/50 - Loss: 1.972\n", "Iter 36/50 - Loss: 1.884\n", "Iter 37/50 - Loss: 1.796\n", "Iter 38/50 - Loss: 1.709\n", "Iter 39/50 - Loss: 1.621\n", "Iter 40/50 - Loss: 1.535\n", "Iter 41/50 - Loss: 1.449\n", "Iter 42/50 - Loss: 1.363\n", "Iter 43/50 - Loss: 1.279\n", "Iter 44/50 - Loss: 1.195\n", "Iter 45/50 - Loss: 1.112\n", "Iter 46/50 - Loss: 1.030\n", "Iter 47/50 - Loss: 0.949\n", "Iter 48/50 - Loss: 0.870\n", "Iter 49/50 - Loss: 0.791\n", "Iter 50/50 - Loss: 0.713\n" ] } ], "source": [ "# this is for running the notebook in our testing framework\n", "import os\n", "smoke_test = ('CI' in os.environ)\n", "training_iter = 2 if smoke_test else 50\n", "\n", "\n", "# Find optimal model hyperparameters\n", "model.train()\n", "likelihood.train()\n", "\n", "# Use the adam optimizer\n", "optimizer = torch.optim.Adam(model.parameters(), lr=0.1) # Includes GaussianLikelihood parameters\n", "\n", "# \"Loss\" for QEPs - the marginal log likelihood\n", "mll = qpytorch.mlls.ExactMarginalLogLikelihood(likelihood, model)\n", "\n", "for i in range(training_iter):\n", " # Zero gradients from previous iteration\n", " optimizer.zero_grad()\n", " # Output from model\n", " output = model(train_x)\n", " # Calc loss and backprop gradients\n", " loss = -mll(output, train_y).sum()\n", " loss.backward()\n", " print('Iter %d/%d - Loss: %.3f' % (i + 1, training_iter, loss.item()))\n", " optimizer.step()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Make predictions with the model\n", "\n", "Making predictions with batched QEPs is straight forward: we simply call the model (and optionally the likelihood) on batch `b x t x 1` test data. The resulting `MultivariateQExponential` will have a `b x n` mean and a `b x n x n` covariance matrix. Standard calls like `preds.confidence_region()` still function -- for example `preds.var()` returns a `b x n` set of predictive variances.\n", "\n", "In the cell below, we make predictions in batch mode for the four functions and plot the QEP fit for each one." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Set into eval mode\n", "model.eval()\n", "likelihood.eval()\n", "\n", "# Initialize plots\n", "f, ((y1_ax, y2_ax), (y3_ax, y4_ax)) = plt.subplots(2, 2, figsize=(8, 8))\n", "# Test points every 0.02 in [0,1]\n", "\n", "# Make predictions\n", "with torch.no_grad():\n", " test_x = torch.linspace(0, 1, 51).view(1, -1, 1).repeat(4, 1, 1)\n", " observed_pred = likelihood(model(test_x))\n", " # Get mean\n", " mean = observed_pred.mean\n", " # Get lower and upper confidence bounds\n", " lower, upper = observed_pred.confidence_region()\n", "\n", "\n", "# Plot training data as black stars\n", "y1_ax.plot(train_x[0].detach().numpy(), train_y[0].detach().numpy(), 'k*')\n", "# Predictive mean as blue line\n", "y1_ax.plot(test_x[0].squeeze().numpy(), mean[0, :].numpy(), 'b')\n", "# Shade in confidence \n", "y1_ax.fill_between(test_x[0].squeeze().numpy(), lower[0, :].numpy(), upper[0, :].numpy(), alpha=0.5)\n", "y1_ax.set_ylim([-3, 3])\n", "y1_ax.legend(['Observed Data', 'Mean', 'Confidence'])\n", "y1_ax.set_title('Observed Values (Likelihood)')\n", "\n", "y2_ax.plot(train_x[1].detach().numpy(), train_y[1].detach().numpy(), 'k*')\n", "y2_ax.plot(test_x[1].squeeze().numpy(), mean[1, :].numpy(), 'b')\n", "y2_ax.fill_between(test_x[1].squeeze().numpy(), lower[1, :].numpy(), upper[1, :].numpy(), alpha=0.5)\n", "y2_ax.set_ylim([-3, 3])\n", "y2_ax.legend(['Observed Data', 'Mean', 'Confidence'])\n", "y2_ax.set_title('Observed Values (Likelihood)')\n", "\n", "y3_ax.plot(train_x[2].detach().numpy(), train_y[2].detach().numpy(), 'k*')\n", "y3_ax.plot(test_x[2].squeeze().numpy(), mean[2, :].numpy(), 'b')\n", "y3_ax.fill_between(test_x[2].squeeze().numpy(), lower[2, :].numpy(), upper[2, :].numpy(), alpha=0.5)\n", "y3_ax.set_ylim([-3, 3])\n", "y3_ax.legend(['Observed Data', 'Mean', 'Confidence'])\n", "y3_ax.set_title('Observed Values (Likelihood)')\n", "\n", "y4_ax.plot(train_x[3].detach().numpy(), train_y[3].detach().numpy(), 'k*')\n", "y4_ax.plot(test_x[3].squeeze().numpy(), mean[3, :].numpy(), 'b')\n", "y4_ax.fill_between(test_x[3].squeeze().numpy(), lower[3, :].numpy(), upper[3, :].numpy(), alpha=0.5)\n", "y4_ax.set_ylim([-3, 3])\n", "y4_ax.legend(['Observed Data', 'Mean', 'Confidence'])\n", "y4_ax.set_title('Observed Values (Likelihood)')\n", "\n", "None" ] } ], "metadata": { "anaconda-cloud": {}, "bento_stylesheets": { "bento/extensions/flow/main.css": true, "bento/extensions/kernel_selector/main.css": true, "bento/extensions/kernel_ui/main.css": true, "bento/extensions/system_usage/main.css": true }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.12.12" } }, "nbformat": 4, "nbformat_minor": 4 }