MLAS2024_GPS_Session_1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GEjqkyP1MkcV"
   },
   "source": [
    "# **GPytorch Regression and Classification Tutorial**\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "T-HicF0lMuxf"
   },
   "source": [
    "The first step is to install gpytorch in google colab.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "xsumMLROLnTn",
    "outputId": "7f08a9ee-1f02-4980-fd00-2bc396ac1c1b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
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     ]
    }
   ],
   "source": [
    "!pip install gpytorch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Zijd5FF4Myn6"
   },
   "source": [
    "Now we are ready to use gpytorch's Gaussian processes for regression and classification. We can import the required python packages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "mN6GZdJXL4sE"
   },
   "outputs": [],
   "source": [
    "import math\n",
    "import torch\n",
    "import gpytorch\n",
    "import numpy as np\n",
    "from torch.nn import Parameter\n",
    "from matplotlib import pyplot as plt\n",
    "import torch\n",
    "\n",
    "torch.set_default_dtype(torch.float64) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BPWWYwuvsYq0"
   },
   "source": [
    "## **Regression**\n",
    "\n",
    "First, we consider the case in which the targets take values in the real line. Later on, we will explain how to use GPytorch for classification using the Laplace approximation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "A4azTF_HNfX5"
   },
   "source": [
    "### **Setting Up the Model**\n",
    "\n",
    "The next cell demonstrates the most critical features of a Gaussian process model in GPyTorch. It provides the tools necessary to quickly construct a GP.\n",
    "\n",
    "For most GP regression models, you will need to construct and use the following GPyTorch objects:\n",
    "\n",
    "1. A GP Model (**gpytorch.models.ExactGP**) - This handles most of the inference.\n",
    "\n",
    "2. A Likelihood (**gpytorch.likelihoods.GaussianLikelihood**) - This is the most common likelihood used for GP regression.\n",
    "\n",
    "3. A Mean - This defines the prior mean of the GP. (If you don’t know which mean to use, a **gpytorch.means.ConstantMean\\(\\)** is a good place to start.)\n",
    "\n",
    "4. A Kernel - This defines the prior covariance of the GP.(If you don’t know which kernel to use, a **gpytorch.kernels.ScaleKernel(gpytorch.kernels.RBFKernel)** is a good place to start).\n",
    "\n",
    "5. A MultivariateNormal Distribution (**gpytorch.distributions.MultivariateNormal**) - This is the object used to represent multivariate normal distributions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GDKxGQIoQj4y"
   },
   "source": [
    "### **The Model**\n",
    "\n",
    "The components of a user built GP model in GPyTorch are, broadly speaking:\n",
    "\n",
    "1. An **\\_\\_init\\_\\_** method that takes the training data and a likelihood, and constructs whatever objects are necessary for the model’s **forward** method. This will most commonly include things like a mean module and a kernel module.\n",
    "\n",
    "2. A **forward** method that takes in some  data x and returns a **MultivariateNormal** with the prior mean and covariance evaluated at x. In other words, we return the vector  and the  matrix  representing the prior mean and covariance matrix of the GP."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "-5TsoOgER6vf"
   },
   "outputs": [],
   "source": [
    "# We create a class called ExactGPModel that inheritates from ExactGP and sets custom\n",
    "# mean and covariance functions.\n",
    "\n",
    "class ExactGPModel(gpytorch.models.ExactGP):\n",
    "    def __init__(self, train_x, train_y, likelihood):\n",
    "\n",
    "        # We call the superclass with the training data and the associated likelihood.\n",
    "\n",
    "        super(ExactGPModel, self).__init__(train_x, train_y, likelihood)\n",
    "\n",
    "        # We consdier a constant mean equal to zero, which the typical case.\n",
    "\n",
    "        self.mean_module = gpytorch.means.ZeroMean()\n",
    "\n",
    "        # We consider a squared exponential (RBF) kernel with an amplitude (scale) parameter.\n",
    "\n",
    "        self.covar_module = gpytorch.kernels.ScaleKernel(gpytorch.kernels.RBFKernel())\n",
    "\n",
    "    def forward(self, x):\n",
    "\n",
    "        # The forwward method simply computes the prior means and covariances of the\n",
    "        # process values associated to the input data.\n",
    "\n",
    "        mean_x = self.mean_module(x)\n",
    "        covar_x = self.covar_module(x)\n",
    "\n",
    "        # We return a multivariate Gaussian distribution with the corresponding mean\n",
    "        # and covariances.\n",
    "\n",
    "        return gpytorch.distributions.MultivariateNormal(mean_x, covar_x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NhPjHWCuUhdq"
   },
   "source": [
    "### **The Likelihood**\n",
    "\n",
    "The simplest likelihood for regression is the **gpytorch.likelihoods.GaussianLikelihood**. This assumes a homoskedastic noise model (i.e. all inputs have the same observational noise)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "oA9x8bydU617"
   },
   "outputs": [],
   "source": [
    "# initialize likelihood to be Gaussian.\n",
    "\n",
    "likelihood = gpytorch.likelihoods.GaussianLikelihood()\n",
    "likelihood.noise = 0.1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rBFfgYd5WI35"
   },
   "source": [
    "### **Set Up the Training Data**\n",
    "\n",
    "We set up the training data for this example. 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.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "LHVzuDcMWaIO"
   },
   "outputs": [],
   "source": [
    "# Training data are 100 points regularly spaced in the interval [0,1].\n",
    "\n",
    "train_x = torch.linspace(0, 1, 100)\n",
    "\n",
    "# True function is sin(2*pi*x) with additive Gaussian noise with variance 0.04.\n",
    "\n",
    "train_y = torch.sin(train_x * (2 * math.pi)) + torch.randn(train_x.size()) * math.sqrt(0.04)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3ktFB8k6Wxv5"
   },
   "source": [
    "\n",
    "\n",
    "We plot the generated data  for visualization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 545
    },
    "id": "-aPkJO64W1pw",
    "outputId": "30eadc3d-e158-40d1-fe60-bca389d1dd4d"
   },
   "outputs": [
    {
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     "execution_count": 6,
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     "output_type": "execute_result"
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     "data": {
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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize plot\n",
    "\n",
    "f, ax = plt.subplots(1, 1, figsize=(8, 6))\n",
    "\n",
    "# Plot training data as black stars\n",
    "\n",
    "ax.plot(train_x.numpy(), train_y.numpy(), 'k*')\n",
    "ax.set_ylim([-3, 3])\n",
    "ax.legend(['Observed Data'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "C_LZwxorXOSG"
   },
   "source": [
    "### **Creating the Model and Model Modes**\n",
    "\n",
    "To create the model we only have to instantiate the previously defined class.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "QyqHJBoYX_3L"
   },
   "outputs": [],
   "source": [
    "model = ExactGPModel(train_x, train_y, likelihood)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "i2BItDeTX8Wp"
   },
   "source": [
    "Like most PyTorch modules, the ExactGP has a **.train()** and **.eval()** mode. - **.train()** mode is for optimizing model hyperameters. - **.eval()** mode is for computing predictions through the model posterior. If the model is in **training mode**, its forward method will compute prior Gaussian parameters at the training data. If the model is in **eval mode**, its forward method will compute posterior Gaussian parameters at the input data. By default, the model is in training mode."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gud1h8MgY5Qo"
   },
   "source": [
    "### **Sampling from the GP Prior**\n",
    "\n",
    "We can sample from the GP prior simply by letting data go through the GP model in train mode."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 580
    },
    "id": "aCV-DzWpZFsf",
    "outputId": "e154bfa9-6458-480d-9fcf-2188533d79e5"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/linear_operator/utils/cholesky.py:40: NumericalWarning: A not p.d., added jitter of 1.0e-08 to the diagonal\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x76e7fc89c6a0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We sample the prior process values at the training points simply by letting the data go through the GP model.\n",
    "# This returns a Multivariate Gaussian distribution from which we can sample. We generate 5 samples and we plot\n",
    "# them.\n",
    "\n",
    "f, ax = plt.subplots(1, 1, figsize=(8, 6))\n",
    "\n",
    "n_samples = 5\n",
    "\n",
    "ax.plot(train_x.numpy(), train_y.numpy(), 'k*')\n",
    "\n",
    "for i in range(n_samples):\n",
    "\n",
    "  # We sample from the prior distribution of the process values at the training points.\n",
    "\n",
    "  prior_sample = model(train_x).sample()\n",
    "\n",
    "  # Plot training data as black stars\n",
    "\n",
    "  ax.plot(train_x.numpy(), prior_sample.numpy(), color='blue',\n",
    "     linewidth=1, markersize=1)\n",
    "\n",
    "ax.set_ylim([-3, 3])\n",
    "ax.legend(['Observed Data', 'Prior GP Samples'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_CbqWZFcdLz1"
   },
   "source": [
    "### **Task 1: Asssess the Influence of the Kernel Hyper-parameters**\n",
    "\n",
    "The samples from the prior look too smooth to have generated the observed data. Let us change the covariance function and the lengthscale parameter simply by doing the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-BPlNLKseQT1",
    "outputId": "1209493a-408a-4e2e-c4ec-50ec17fbd650"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RBFKernel(\n",
       "  (raw_lengthscale_constraint): Positive()\n",
       ")"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.covar_module.base_kernel.initialize(lengthscale = 0.01)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uDpSXN-ce88R"
   },
   "source": [
    "Try different values for the length scales, i.e., \\{0.01, 0.2, 0.5, 1.0, 10.0\\}. Simply run the previous cell for each value, followed by the cell above that generates and plots the samples from the prior.\n",
    "\n",
    "1. Indicate which lengthscale value is more likely to have generated the\n",
    "observed data.\n",
    "2. What values for the lengthscale lead to smoother functions?\n",
    "3. What values for the lengthscale lead to more wiggling functions?\n",
    "4. Run the code below to plot the corresponding predictive distribution."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XMgbwPQdgdX0"
   },
   "source": [
    "### **Plotting the Posterior Predictive Distribution**\n",
    "\n",
    "We obtain the parameters of the GP posterior predictive distribution simply by letting the data go through the model once it is in **eval** mode. Of course, we have to add the likelihood contribution to plot the predictive distribution for y. Otherwise, we will be computing the predictive distribution for the noiseless target value. The confidence region considered is the mean plus/minus 2 times the standard deviation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 615
    },
    "id": "FuSFHBdGg2Jd",
    "outputId": "f2951a7f-f5d3-4d13-fc21-65e44051dc39"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianLikelihood(\n",
       "  (noise_covar): HomoskedasticNoise(\n",
       "    (raw_noise_constraint): GreaterThan(1.000E-04)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get into evaluation (predictive posterior) mode.\n",
    "\n",
    "model.eval()\n",
    "likelihood.eval()\n",
    "\n",
    "# Test points are regularly spaced along [-0.5,1.5].\n",
    "# Make predictions for y by feeding model through likelihood.\n",
    "\n",
    "with torch.no_grad():\n",
    "    test_x = torch.linspace(-0.5, 1.5, 51)\n",
    "    predictions = likelihood(model(test_x))\n",
    "\n",
    "# We plot the predictive distribution (mean +- standard deviation).\n",
    "\n",
    "f, ax = plt.subplots(1, 1, figsize=(8, 6))\n",
    "\n",
    "# Get upper and lower confidence bounds\n",
    "\n",
    "lower, upper = predictions.confidence_region()\n",
    "\n",
    "# Plot training data as black stars\n",
    "\n",
    "ax.plot(train_x.numpy(), train_y.numpy(), 'k*')\n",
    "\n",
    "# Plot predictive means as blue line\n",
    "\n",
    "ax.plot(test_x.numpy(), predictions.mean.numpy(), 'b')\n",
    "\n",
    "# Shade between the lower and upper confidence bounds\n",
    "\n",
    "ax.fill_between(test_x.numpy(), lower.numpy(), upper.numpy(), alpha=0.5)\n",
    "ax.set_ylim([-3, 3])\n",
    "ax.legend(['Observed Data', 'Mean', 'Confidence'])\n",
    "\n",
    "model.train()\n",
    "likelihood.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "S77j09RtoBpy"
   },
   "source": [
    "### **Finding Good Model Hyper-parameters**\n",
    "\n",
    "To find good model hyper-parameters one can simply maximize the marginal likelihood. Gpytorch provides a framework for this task that can be easily carried out. With this goal, we consider the zero mean prior and the RBF kernel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "_EQS5wU7onKj"
   },
   "outputs": [],
   "source": [
    "class ExactGPModel(gpytorch.models.ExactGP):\n",
    "    def __init__(self, train_x, train_y, likelihood):\n",
    "\n",
    "        # We call the superclass with the training data and the associated likelihood.\n",
    "\n",
    "        super(ExactGPModel, self).__init__(train_x, train_y, likelihood)\n",
    "\n",
    "        # We consdier a constant mean equal to zero, which the typical case.\n",
    "\n",
    "        self.mean_module = gpytorch.means.ZeroMean()\n",
    "\n",
    "        # We consider a squared exponential (RBF) kernel with an amplitude (scale) parameter.\n",
    "\n",
    "        self.covar_module = gpytorch.kernels.ScaleKernel(gpytorch.kernels.RBFKernel())\n",
    "\n",
    "        # We set the initial value for the length-scale parameter\n",
    "\n",
    "    def forward(self, x):\n",
    "\n",
    "        # The forwward method simply computes the prior means and covariances of the\n",
    "        # process values associated to the input data.\n",
    "\n",
    "        mean_x = self.mean_module(x)\n",
    "        covar_x = self.covar_module(x)\n",
    "\n",
    "        # We return a multivariate Gaussian distribution with teh corresponding mean\n",
    "        # and covariances.\n",
    "\n",
    "        return gpytorch.distributions.MultivariateNormal(mean_x, covar_x)\n",
    "\n",
    "# We create the model again with the new prior mean and covariance function.\n",
    "\n",
    "model = ExactGPModel(train_x, train_y, likelihood)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7mSrGXkLoxvB"
   },
   "source": [
    "To maximize the marginal likelhood we define that loss function and use the ADAM optimizer. Any other optimizer may be used for this purpose."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "YDaotGNToy5K",
    "outputId": "e28c3854-8a0f-4611-c070-b15eb7e7b7df"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/torch/autograd/__init__.py:266: UserWarning: CUDA initialization: CUDA unknown error - this may be due to an incorrectly set up environment, e.g. changing env variable CUDA_VISIBLE_DEVICES after program start. Setting the available devices to be zero. (Triggered internally at ../c10/cuda/CUDAFunctions.cpp:108.)\n",
      "  Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iter 1/200 - Loss: 0.742   lengthscale: 0.693   noise: 0.100\n",
      "Iter 2/200 - Loss: 0.637   lengthscale: 0.644   noise: 0.110\n",
      "Iter 3/200 - Loss: 0.545   lengthscale: 0.598   noise: 0.120\n",
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     ]
    }
   ],
   "source": [
    "# We set the model mode to training.\n",
    "\n",
    "model.train()\n",
    "likelihood.train()\n",
    "\n",
    "# Use instantiate the ADAM optimizer, which will optimize all model's parameters\n",
    "# including the likelihood paramters, i.e., the noise variance.\n",
    "\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=0.1)\n",
    "\n",
    "# \"Loss\" for GPs - the marginal log likelihood\n",
    "\n",
    "mll = gpytorch.mlls.ExactMarginalLogLikelihood(likelihood, model)\n",
    "\n",
    "training_iter = 200\n",
    "\n",
    "for i in range(training_iter):\n",
    "\n",
    "    # Zero gradients from previous iteration\n",
    "\n",
    "    optimizer.zero_grad()\n",
    "\n",
    "    # Compute model predictions prior predictions.\n",
    "\n",
    "    output = model(train_x)\n",
    "\n",
    "    # Calc loss and backprop gradients.\n",
    "\n",
    "    loss = -mll(output, train_y)\n",
    "    loss.backward()\n",
    "\n",
    "    print('Iter %d/%d - Loss: %.3f   lengthscale: %.3f   noise: %.3f' % (\n",
    "        i + 1, training_iter, loss.item(),\n",
    "        model.covar_module.base_kernel.lengthscale.item(),\n",
    "        model.likelihood.noise.item()\n",
    "    ))\n",
    "\n",
    "    optimizer.step()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TQRzxmKsp1sP"
   },
   "source": [
    "After training, we can compute predictions for the newly found hyper-parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 615
    },
    "id": "Po1lozj6p6is",
    "outputId": "2e69d14f-7c4f-4225-a176-cad5f06e2750"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianLikelihood(\n",
       "  (noise_covar): HomoskedasticNoise(\n",
       "    (raw_noise_constraint): GreaterThan(1.000E-04)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get into evaluation (predictive posterior) mode.\n",
    "\n",
    "model.eval()\n",
    "likelihood.eval()\n",
    "\n",
    "# Test points are regularly spaced along [-0.5,1.5].\n",
    "# Make predictions for y by feeding model through likelihood.\n",
    "\n",
    "with torch.no_grad():\n",
    "    test_x = torch.linspace(-0.5, 1.5, 51)\n",
    "    predictions = likelihood(model(test_x))\n",
    "\n",
    "# We plot the predictive distribution (mean +- standard deviation).\n",
    "\n",
    "f, ax = plt.subplots(1, 1, figsize=(8, 6))\n",
    "\n",
    "# Get upper and lower confidence bounds\n",
    "\n",
    "lower, upper = predictions.confidence_region()\n",
    "\n",
    "# Plot training data as black stars\n",
    "\n",
    "ax.plot(train_x.numpy(), train_y.numpy(), 'k*')\n",
    "\n",
    "# Plot predictive means as blue line\n",
    "\n",
    "ax.plot(test_x.numpy(), predictions.mean.numpy(), 'b')\n",
    "\n",
    "# Shade between the lower and upper confidence bounds\n",
    "\n",
    "ax.fill_between(test_x.numpy(), lower.numpy(), upper.numpy(), alpha=0.5)\n",
    "ax.set_ylim([-3, 3])\n",
    "ax.legend(['Observed Data', 'Mean', 'Confidence'])\n",
    "\n",
    "model.train()\n",
    "likelihood.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ojxclo__iooK"
   },
   "source": [
    "### **Task 2: Influence of the covariance and mean function**\n",
    "\n",
    "Changing the covariance and mean function of the GP has an effect on the prior samples and the posterior predictive distribution. To assess this, we will consider a linear mean (LinearMean\\(\\)) function and a different covariance function, e.g., the Matérn Covariance function (MaternKernel). For this, simply replace the class object defined before by:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "pp4pUtpFjnRv"
   },
   "outputs": [],
   "source": [
    "class ExactGPModel(gpytorch.models.ExactGP):\n",
    "    def __init__(self, train_x, train_y, likelihood):\n",
    "\n",
    "        # We call the superclass with the training data and the associated likelihood.\n",
    "\n",
    "        super(ExactGPModel, self).__init__(train_x, train_y, likelihood)\n",
    "\n",
    "        # We consdier a linear mean in one dimension with slope 2.0 and bias -1.0.\n",
    "\n",
    "        self.mean_module = gpytorch.means.LinearMean(input_size = 1)\n",
    "        self.mean_module.initialize(weights = 2.0, bias = -1.0)\n",
    "\n",
    "        # We consider a squared exponential (RBF) kernel with an amplitude (scale) parameter.\n",
    "\n",
    "        self.covar_module = gpytorch.kernels.ScaleKernel(gpytorch.kernels.MaternKernel(nu = 0.5))\n",
    "\n",
    "    def forward(self, x):\n",
    "\n",
    "        # The forward method simply computes the prior means and covariances of the\n",
    "        # process values associated to the input data.\n",
    "\n",
    "        mean_x = self.mean_module(x)\n",
    "        covar_x = self.covar_module(x)\n",
    "\n",
    "        # We return a multivariate Gaussian distribution with teh corresponding mean\n",
    "        # and covariances.\n",
    "\n",
    "        return gpytorch.distributions.MultivariateNormal(mean_x, covar_x)\n",
    "\n",
    "# We create the likelihood and the model again with the new prior mean and covariance function.\n",
    "\n",
    "likelihood = gpytorch.likelihoods.GaussianLikelihood()\n",
    "likelihood.noise = 0.1\n",
    "\n",
    "model = ExactGPModel(train_x, train_y, likelihood)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Zz6QuU1ll4m2"
   },
   "source": [
    "You are asked to re-generate the prior samples and the posterior predictive distribution for the new prior GP mean and prior covariance function. Simply, re-run the previous cells for task 1. Do not optimize the model hyper-paramters!\n",
    "\n",
    "1. What is the effect of the prior mean on the prior samples and the posteiror predictive distribution?\n",
    "\n",
    "2. What is the effect of the Matern covariance function on the prior samples and the predictive distribuiton? Try changing the nu parameter to the values \\{0.5, 1.5, 2.5\\} to see the impact of the paramter. Recall that it is the number of times that the GP samples can be differentiated."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "r2h93DBbxiet"
   },
   "source": [
    "### **Extrapolation and Interpretation**\n",
    "\n",
    "This example is based on Section 5.4.3 of the book “Gaussian Processes for Machine Learning”. It illustrates an example of complex kernel engineering and hyperparameter optimization using gradient ascent on the log-marginal-likelihood. The data consists of the monthly average atmospheric CO2 concentrations (in parts per million by volume (ppm)) collected at the Mauna Loa Observatory in Hawaii, between 1958 and 2001. The objective is to model the CO2 concentration as a function of the time  and extrapolate for years after 2001.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 241
    },
    "id": "BEmsmmGMyBbK",
    "outputId": "19f637f8-2ebb-4867-9dd0-e71a57bb8db5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (1.4.0)\n",
      "Requirement already satisfied: numpy<2.0,>=1.19.5 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.26.3)\n",
      "Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.11.4)\n",
      "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.3.2)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (3.2.0)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>weight</th>\n",
       "      <th>flag</th>\n",
       "      <th>station</th>\n",
       "      <th>co2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1958</td>\n",
       "      <td>3</td>\n",
       "      <td>29</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>MLO</td>\n",
       "      <td>316.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1958</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>MLO</td>\n",
       "      <td>317.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1958</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>MLO</td>\n",
       "      <td>317.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1958</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>MLO</td>\n",
       "      <td>317.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1958</td>\n",
       "      <td>4</td>\n",
       "      <td>26</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>MLO</td>\n",
       "      <td>316.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  month  day  weight  flag station    co2\n",
       "0  1958      3   29       4     0     MLO  316.1\n",
       "1  1958      4    5       6     0     MLO  317.3\n",
       "2  1958      4   12       4     0     MLO  317.6\n",
       "3  1958      4   19       6     0     MLO  317.5\n",
       "4  1958      4   26       2     0     MLO  316.4"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "!pip install scikit-learn\n",
    "from sklearn.datasets import fetch_openml\n",
    "\n",
    "co2 = fetch_openml(data_id=41187, as_frame=True, parser=\"pandas\")\n",
    "co2.frame.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hK9vYgOUx_pr"
   },
   "source": [
    "\n",
    "First, we process the original dataframe to create a date index and select only the CO2 column.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 238
    },
    "id": "eTJO-vBmyZnm",
    "outputId": "8fc97894-5834-4fbb-a9b5-e92fdf439e95"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>co2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1958-03-29</th>\n",
       "      <td>316.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-04-05</th>\n",
       "      <td>317.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-04-12</th>\n",
       "      <td>317.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-04-19</th>\n",
       "      <td>317.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-04-26</th>\n",
       "      <td>316.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              co2\n",
       "date             \n",
       "1958-03-29  316.1\n",
       "1958-04-05  317.3\n",
       "1958-04-12  317.6\n",
       "1958-04-19  317.5\n",
       "1958-04-26  316.4"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "co2_data = co2.frame\n",
    "co2_data[\"date\"] = pd.to_datetime(co2_data[[\"year\", \"month\", \"day\"]])\n",
    "co2_data = co2_data[[\"date\", \"co2\"]].set_index(\"date\")\n",
    "co2_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7ekDMBwqykG9",
    "outputId": "117f445d-9c3d-4806-b3f6-7e5e96a7bad3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Timestamp('1958-03-29 00:00:00'), Timestamp('2001-12-29 00:00:00'))"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "co2_data.index.min(), co2_data.index.max()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DtBLbHQHyWv2"
   },
   "source": [
    "We see that we get CO2 concentration for some days from March, 1958 to December, 2001. We can plot these raw information to have a better understanding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 444
    },
    "id": "1YHtdE4My_CH",
    "outputId": "00c7ed03-e520-47fc-b70a-6fa264296608"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "co2_data.plot()\n",
    "plt.ylabel(\"CO$_2$ concentration (ppm)\")\n",
    "_ = plt.title(\"Raw air samples measurements from the Mauna Loa Observatory\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2EsdhOZuzHXs"
   },
   "source": [
    "We will preprocess the dataset by taking a monthly average and drop month for which no measurements were collected. Such a processing will have an smoothing effect on the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 465
    },
    "id": "ULs6dFObzO4u",
    "outputId": "04ea2135-c8d1-4f70-d685-ef2d79023e33"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_438696/4051184181.py:1: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  co2_data = co2_data.resample(\"M\").mean().dropna(axis=\"index\", how=\"any\")\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "co2_data = co2_data.resample(\"M\").mean().dropna(axis=\"index\", how=\"any\")\n",
    "co2_data.plot()\n",
    "plt.ylabel(\"Monthly average of CO$_2$ concentration (ppm)\")\n",
    "_ = plt.title(\n",
    "    \"Monthly average of air samples measurements\\nfrom the Mauna Loa Observatory\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "97xFc39Uza_w"
   },
   "source": [
    "The idea in this example will be to predict the CO2 concentration in function of the date. We are as well interested in extrapolating for upcoming year after 2001.\n",
    "\n",
    "As a first step, we will divide the data and the target to estimate. The data being a date, we will convert it into a numeric."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "TOaoSX7yzuY2"
   },
   "outputs": [],
   "source": [
    "X = torch.from_numpy((co2_data.index.year + co2_data.index.month / 12).to_numpy().reshape(-1, 1))\n",
    "y = torch.from_numpy(co2_data[\"co2\"].to_numpy())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tk0GY7P00h5u"
   },
   "source": [
    "### **Design the proper covariance function**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-Rwu3GI70l1h"
   },
   "source": [
    "To design the covariance function to use with our Gaussian process, we can make some assumption regarding the data at hand. We observe that they have several characteristics: we see a long term rising trend, a pronounced seasonal variation and some smaller irregularities. We can use different appropriate covariance functions that would capture these features.\n",
    "\n",
    "First, the long term rising trend could be fitted using a radial basis function (RBF) covariance function with a large length-scale parameter. The RBF kernel with a large length-scale enforces this component to be smooth. A trending increase is not enforced as to give a degree of freedom to our model. The specific length-scale and the amplitude are free hyperparameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-_n7L-KQ1TwI",
    "outputId": "ce44e776-6c45-4190-92ed-93abacf7e05d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ScaleKernel(\n",
       "  (base_kernel): RBFKernel(\n",
       "    (raw_lengthscale_constraint): Positive()\n",
       "  )\n",
       "  (raw_outputscale_constraint): Positive()\n",
       ")"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "long_term_trend_cov = gpytorch.kernels.ScaleKernel(gpytorch.kernels.RBFKernel())\n",
    "long_term_trend_cov.base_kernel.initialize(lengthscale = 50.0)\n",
    "long_term_trend_cov.initialize(outputscale = 50**2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Q2LGSizD2Xr9"
   },
   "source": [
    "The seasonal variation is explained by the periodic exponential sine squared covariace function with a fixed periodicity of 1 year. The length-scale of this periodic component, controlling its smoothness, is a free parameter. In order to allow decaying away from exact periodicity, the product with an RBF covariance function is taken. The length-scale of this RBF component controls the decay time and is a further free parameter. This type of covariance function is also known as locally periodic covariance function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "Lk9IwqNn2tNe"
   },
   "outputs": [],
   "source": [
    "rbf_covariance = gpytorch.kernels.ScaleKernel(gpytorch.kernels.RBFKernel())\n",
    "rbf_covariance.base_kernel.initialize(lengthscale = 100.0)\n",
    "rbf_covariance.initialize(outputscale = 2.0**2)\n",
    "exp_sine_covariance = gpytorch.kernels.PeriodicKernel()\n",
    "exp_sine_covariance.initialize(lengthscale = 1.0)\n",
    "exp_sine_covariance.initialize(period_length = 1.0)\n",
    "\n",
    "seasonal_covariance = rbf_covariance * exp_sine_covariance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "diG6zywd8oIh"
   },
   "source": [
    "The small irregularities are to be explained by a rational quadratic kernel component, whose length-scale and alpha parameter, which quantifies the diffuseness of the length-scales, are to be determined. A rational quadratic kernel is equivalent to an RBF kernel with several length-scales and will better accommodate the different irregularities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "OsPUgFMc8q60",
    "outputId": "c7a41ad6-5c7a-4ad5-ba2f-6f29336b8ec8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ScaleKernel(\n",
       "  (base_kernel): RQKernel(\n",
       "    (raw_lengthscale_constraint): Positive()\n",
       "    (raw_alpha_constraint): Positive()\n",
       "  )\n",
       "  (raw_outputscale_constraint): Positive()\n",
       ")"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "irregularities_covariance = gpytorch.kernels.ScaleKernel(gpytorch.kernels.RQKernel(alpha_consraint=1.0))\n",
    "irregularities_covariance.base_kernel.initialize(lengthscale = 1.0)\n",
    "irregularities_covariance.initialize(outputscale = 0.5**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hrbfZN3_9pQH"
   },
   "source": [
    "Finally, the noise in the dataset can be accounted with a kernel consisting of an RBF kernel contribution, which shall explain the correlated noise components such as local weather phenomena. The relative amplitudes and the RBF’s length scale are further free parameters.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Gd1dHqYT9x7H",
    "outputId": "6bdd2a8a-988d-43d6-b9d2-0a0a6e557df1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ScaleKernel(\n",
       "  (base_kernel): RBFKernel(\n",
       "    (raw_lengthscale_constraint): Positive()\n",
       "  )\n",
       "  (raw_outputscale_constraint): Positive()\n",
       ")"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extra_noise_covariance =  gpytorch.kernels.ScaleKernel(gpytorch.kernels.RBFKernel())\n",
    "extra_noise_covariance.base_kernel.initialize(lengthscale = 0.1)\n",
    "extra_noise_covariance.initialize(outputscale = 0.1**2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "u7o6Odcj-GS2"
   },
   "source": [
    "Thus, our final kernel is an addition of all previous kernels.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "SgmTaggo-RGO"
   },
   "outputs": [],
   "source": [
    "co2_cov_function = long_term_trend_cov + seasonal_covariance + irregularities_covariance + extra_noise_covariance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "njtrTYGt-elX"
   },
   "source": [
    "### **Fitting the GP Model**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QnnjHB23-oWt"
   },
   "source": [
    "We fit the model and plot predictions. We use L-BFGS to find the best model hyper-parameters by optimizing the marginal likelihood."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "pMi9sKmm-yAM",
    "outputId": "738b5bab-60c6-4028-8ad4-c073fbf08f8d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iter 1/50 - Loss: 9.639\n",
      "Iter 2/50 - Loss: 9.449\n",
      "Iter 3/50 - Loss: 0.416\n",
      "Iter 4/50 - Loss: 0.323\n",
      "Iter 5/50 - Loss: 0.323\n",
      "Iter 6/50 - Loss: 0.313\n",
      "Iter 7/50 - Loss: 0.248\n",
      "Iter 8/50 - Loss: 0.247\n",
      "Iter 9/50 - Loss: 0.247\n",
      "Iter 10/50 - Loss: 0.247\n",
      "Iter 11/50 - Loss: 0.241\n",
      "Iter 12/50 - Loss: 0.235\n",
      "Iter 13/50 - Loss: 0.235\n",
      "Iter 14/50 - Loss: 0.235\n",
      "Iter 15/50 - Loss: 0.234\n",
      "Iter 16/50 - Loss: 0.223\n",
      "Iter 17/50 - Loss: 0.223\n",
      "Iter 18/50 - Loss: 0.223\n",
      "Iter 19/50 - Loss: 0.223\n",
      "Iter 20/50 - Loss: 0.222\n",
      "Iter 21/50 - Loss: 0.221\n",
      "Iter 22/50 - Loss: 0.221\n",
      "Iter 23/50 - Loss: 0.221\n",
      "Iter 24/50 - Loss: 0.221\n",
      "Iter 25/50 - Loss: 0.221\n",
      "Iter 26/50 - Loss: 0.221\n",
      "Iter 27/50 - Loss: 0.221\n",
      "Iter 28/50 - Loss: 0.220\n",
      "Iter 29/50 - Loss: 0.220\n",
      "Iter 30/50 - Loss: 0.220\n",
      "Iter 31/50 - Loss: 0.220\n",
      "Iter 32/50 - Loss: 0.220\n",
      "Iter 33/50 - Loss: 0.220\n",
      "Iter 34/50 - Loss: 0.220\n",
      "Iter 35/50 - Loss: 0.220\n",
      "Iter 36/50 - Loss: 0.220\n",
      "Iter 37/50 - Loss: 0.220\n",
      "Iter 38/50 - Loss: 0.220\n",
      "Iter 39/50 - Loss: 0.220\n",
      "Iter 40/50 - Loss: 0.220\n",
      "Iter 41/50 - Loss: 0.220\n",
      "Iter 42/50 - Loss: 0.220\n",
      "Iter 43/50 - Loss: 0.220\n",
      "Iter 44/50 - Loss: 0.220\n",
      "Iter 45/50 - Loss: 0.220\n",
      "Iter 46/50 - Loss: 0.220\n",
      "Iter 47/50 - Loss: 0.220\n",
      "Iter 48/50 - Loss: 0.220\n",
      "Iter 49/50 - Loss: 0.220\n",
      "Iter 50/50 - Loss: 0.220\n"
     ]
    }
   ],
   "source": [
    "likelihood = gpytorch.likelihoods.GaussianLikelihood()\n",
    "likelihood.noise = 0.1\n",
    "mean_y = y.mean()\n",
    "model = ExactGPModel(X, y - mean_y, likelihood)\n",
    "model.covar_module = co2_cov_function # We use the specific covariance function\n",
    "mll = gpytorch.mlls.ExactMarginalLogLikelihood(likelihood, model)\n",
    "\n",
    "def train_model(model):\n",
    "\n",
    "  model.train()\n",
    "\n",
    "  # Instantiate the LBFGS optimizer, which will optimize the hyper-parameters.\n",
    "\n",
    "  optimizer = torch.optim.LBFGS(model.parameters(), \\\n",
    "      history_size=10, max_iter=4, line_search_fn=\"strong_wolfe\")\n",
    "\n",
    "  def closure():\n",
    "      optimizer.zero_grad()\n",
    "      output = model(X)\n",
    "      loss = -mll(output, y-mean_y)\n",
    "      loss.backward()\n",
    "      return loss\n",
    "\n",
    "  training_iter = 50\n",
    "\n",
    "  for i in range(training_iter):\n",
    "\n",
    "    loss = -mll(model(X), y - mean_y)\n",
    "    print('Iter %d/%d - Loss: %.3f' % (i + 1, training_iter, loss.item()))\n",
    "\n",
    "    optimizer.step(closure)\n",
    "\n",
    "train_model(model)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HfEHj--2C_yP"
   },
   "source": [
    "We compute predictions for new data points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "9h5dASQKDX0T"
   },
   "outputs": [],
   "source": [
    "model.eval()\n",
    "\n",
    "import datetime\n",
    "import numpy as np\n",
    "\n",
    "today = datetime.datetime.now()\n",
    "current_month = today.year + today.month / 12\n",
    "X_test = torch.from_numpy(np.linspace(start=1958, stop=current_month, num=1_000).reshape(-1, 1))\n",
    "with torch.no_grad():\n",
    "  result = model(X_test)\n",
    "mean_y_pred = (result.mean + mean_y).numpy()\n",
    "std_y_pred = torch.sqrt(result.variance).numpy()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 493
    },
    "id": "9tOV4VJ9D-6D",
    "outputId": "2ae36b4d-80d8-4be2-9d5d-962f7098a6be"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(X, y, color=\"black\", linestyle=\"dashed\", label=\"Measurements\")\n",
    "plt.plot(X_test, mean_y_pred, color=\"tab:blue\", alpha=0.4, label=\"Gaussian process\")\n",
    "plt.fill_between(\n",
    "    X_test.ravel(),\n",
    "    mean_y_pred - std_y_pred,\n",
    "    mean_y_pred + std_y_pred,\n",
    "    color=\"tab:blue\",\n",
    "    alpha=0.2,\n",
    ")\n",
    "plt.legend()\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Monthly average of CO$_2$ concentration (ppm)\")\n",
    "_ = plt.title(\n",
    "    \"Monthly average of air samples measurements\\nfrom the Mauna Loa Observatory\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MQCUc0psCiiw"
   },
   "source": [
    "We inspect the resulting covariance function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Y2UmFoxYClhS",
    "outputId": "3b819df1-701e-4216-ae84-63be10c10024"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Long Term Cov. RBF, outputscale: tensor(2500.0000, grad_fn=<SoftplusBackward0>)\n",
      "Long Term Cov. RBF, lengthscale: tensor([[50.1337]], grad_fn=<SoftplusBackward0>) \n",
      "\n",
      "Seasonal Cov. RBF, outputscale: tensor(4.6201, grad_fn=<SoftplusBackward0>)\n",
      "Seasonal Cov. RBF, lengthscale: tensor([[99.9886]], grad_fn=<SoftplusBackward0>)\n",
      "Seasonal Cov. PeriodicKernel, lengthscale: tensor([[1.7324]], grad_fn=<SoftplusBackward0>)\n",
      "Seasonal Cov. PeriodicKernel, period: tensor([[0.9996]], grad_fn=<SoftplusBackward0>) \n",
      "\n",
      "Irregularities, outputscale: tensor(0.3315, grad_fn=<SoftplusBackward0>)\n",
      "Irregularities, lengthscale: tensor([[1.0401]], grad_fn=<SoftplusBackward0>) \n",
      "\n",
      "Extra Noise, outputscale: tensor(0.0349, grad_fn=<SoftplusBackward0>)\n",
      "Extra Noise, lengthscale: tensor([[0.1246]], grad_fn=<SoftplusBackward0>) \n",
      "\n",
      "Likelihood noise: tensor([0.0370], grad_fn=<AddBackward0>)\n"
     ]
    }
   ],
   "source": [
    "print(\"Long Term Cov. RBF, outputscale:\", model.covar_module.kernels[0].outputscale)\n",
    "print(\"Long Term Cov. RBF, lengthscale:\", model.covar_module.kernels[0].base_kernel.lengthscale, \"\\n\")\n",
    "\n",
    "print(\"Seasonal Cov. RBF, outputscale:\", model.covar_module.kernels[1].kernels[0].outputscale)\n",
    "print(\"Seasonal Cov. RBF, lengthscale:\", model.covar_module.kernels[1].kernels[0].base_kernel.lengthscale)\n",
    "print(\"Seasonal Cov. PeriodicKernel, lengthscale:\", model.covar_module.kernels[1].kernels[1].lengthscale)\n",
    "print(\"Seasonal Cov. PeriodicKernel, period:\", model.covar_module.kernels[1].kernels[1].period_length, \"\\n\")\n",
    "\n",
    "print(\"Irregularities, outputscale:\", model.covar_module.kernels[2].outputscale)\n",
    "print(\"Irregularities, lengthscale:\", model.covar_module.kernels[2].base_kernel.lengthscale, \"\\n\")\n",
    "\n",
    "print(\"Extra Noise, outputscale:\", model.covar_module.kernels[3].outputscale)\n",
    "print(\"Extra Noise, lengthscale:\", model.covar_module.kernels[3].base_kernel.lengthscale, \"\\n\")\n",
    "\n",
    "print(\"Likelihood noise:\", model.likelihood.noise)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VDqWFz2hKtoV"
   },
   "source": [
    "Thus, most of the target signal, with the mean subtracted, is explained by a long-term rising trend for ~sqrt(2500)ppm and a length-scale of ~50.1337 years. The periodic component has an amplitude of ~sqrt(4.6201)ppm, a decay time of ~sqrt(99.9) years and a length-scale of ~1.73. The long decay time indicates that we have a component very close to a seasonal periodicity. The correlated noise has an amplitude of ~sqrt(0.0349) ppm with a length scale of ~0.12 years and a white-noise contribution of ~0.036 ppm. Thus, the overall noise level is very small, indicating that the data can be very well explained by the model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "P1u1YT7bOmFZ"
   },
   "source": [
    "## **Task 3**\n",
    "\n",
    "To check the importance of choosing an adequate covariance function to explain the data, try to change the covariance function used in the previous problem (e.g., simply by using a squared exponential covariance function) and recompute the predictive distribution. Compare also the estimates of the marginal likelihood."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6BhwZG5EsRNJ"
   },
   "source": [
    "## **Classification**\n",
    "\n",
    "In this section, we explain how to use GPytorch for classification using the Laplace approximation. Importantly, the Laplace approximation is not implemented in GPytorch. However, GPytorch is flexible enough so that it can be easily extended to consider it. We will consider the sigmoid function to obtain class probabilities. Therefore, we will restrict to binary classification problems. We assume binary class labels in \\{0,1\\}."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RFWHZLoMOjKl"
   },
   "source": [
    "### **Setting Up the Training Data**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "600HRrNiP_Lu"
   },
   "source": [
    "We generate first some synthetic data in a two dimensional space. The problem considered is the XOR problem, which is non-linear."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "id": "6q39xeznOiPd"
   },
   "outputs": [],
   "source": [
    "# We generate random data uniformly in the unit box with a fixed random seed.\n",
    "\n",
    "n_train = 200\n",
    "\n",
    "torch.random.manual_seed(0)\n",
    "\n",
    "x1 = torch.rand(n_train, 1) - 0.5\n",
    "x2 = torch.rand(n_train,1) - 0.5\n",
    "\n",
    "# We define the target function which generates the class label.\n",
    "\n",
    "data_fn = lambda x1, x2: (torch.sign(torch.sign(x1) * torch.sign(x2)) + 1.0) * 0.5\n",
    "\n",
    "# We generate the observed data.\n",
    "\n",
    "latent_fn = data_fn(x1, x2)\n",
    "train_y_c = torch.round(latent_fn).long().squeeze()\n",
    "train_x_c = torch.cat((x1, x2),dim=1)\n",
    "color = np.array([\"red\", \"blue\"])[ train_y_c.numpy() ]\n",
    "train_y_c = train_y_c.float()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Uwx5ymCXW5N5"
   },
   "source": [
    "We now plot the data for visualization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 447
    },
    "id": "o8BJU2zOW8pu",
    "outputId": "d8d4a9ad-7543-44a5-9d8c-fc4ac4205aa1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x76e7fc7ea920>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(train_x_c[:,0].numpy(), train_x_c[:,1].numpy(), c = color)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pB2WiWuebiW8"
   },
   "source": [
    "### **Defining the Custom GP Model**\n",
    "\n",
    "We describe a custom GP model called LaplaceGPModel that implements classifications tasks using the Laplace approximation.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "id": "DnXEK24Rta2x"
   },
   "outputs": [],
   "source": [
    "# We create a class called LaplaceGPModel that inherits from ExactGP, sets custom\n",
    "# mean and covariance functions, and which will be used for classification.\n",
    "\n",
    "class LaplaceGPModel(gpytorch.models.ExactGP):\n",
    "    def __init__(self, train_x, train_y, lengthscale = 1.0):\n",
    "\n",
    "        noise = 1e-4 * torch.ones(train_x.shape[ 0 ])\n",
    "        likelihood = gpytorch.likelihoods.FixedNoiseGaussianLikelihood(noise)\n",
    "\n",
    "        # We call the superclass with the training data and the associated\n",
    "        # likelihood which has almost no-noise.\n",
    "\n",
    "        super(LaplaceGPModel, self).__init__(train_x, train_y, likelihood)\n",
    "        self.likelihood = likelihood\n",
    "\n",
    "        # We consider a constant zero mean and an RBF kernel\n",
    "\n",
    "        self.mean_module = gpytorch.means.ZeroMean()\n",
    "        self.covar_module = gpytorch.kernels.ScaleKernel(gpytorch.kernels.RBFKernel())\n",
    "        self.covar_module.base_kernel.initialize(lengthscale = lengthscale)\n",
    "        self.covar_module.initialize(outputscale = 1.0)\n",
    "\n",
    "        # We store training data\n",
    "\n",
    "        self.train_x = train_x\n",
    "        self.train_y = train_y\n",
    "\n",
    "        # We define new parameters which are the latent process values at the\n",
    "        # training points to be optimized to find the posterior's mode.\n",
    "\n",
    "        self.register_parameter(name=\"latent_process_values\", \\\n",
    "            parameter=Parameter(torch.ones(train_x.shape[ 0 ]) * 0.1 * (train_y * 2.0 - 1.0)))\n",
    "\n",
    "        # We store the sigmoid function\n",
    "\n",
    "        self.sigmoid = torch.nn.Sigmoid()\n",
    "\n",
    "    def evaluate_neg_log_posterior(self):\n",
    "\n",
    "      mean_x = self.mean_module(self.train_x)\n",
    "      covar_x = self.covar_module(self.train_x)\n",
    "\n",
    "      self.likelihood.noise = torch.ones(self.train_x.shape[ 0 ]) * 1e-6\n",
    "\n",
    "      # We return the negative log posterior probability when considering a sigmoid\n",
    "      # activation function. The reason is that ADAM minimizes instead of maximizing.\n",
    "\n",
    "      return -1.0 * (self.likelihood(gpytorch.distributions.MultivariateNormal(mean_x, \\\n",
    "          covar_x)).log_prob(self.latent_process_values) + \\\n",
    "          torch.sum(torch.log(self.sigmoid(self.latent_process_values * (self.train_y * 2.0 - 1.0)))))\n",
    "\n",
    "    def forward(self, x):\n",
    "\n",
    "        # The forward method simply computes the prior means and covariances of the\n",
    "        # process values associated to the input data.\n",
    "\n",
    "        mean_x = self.mean_module(x)\n",
    "        covar_x = self.covar_module(x)\n",
    "        pi = self.sigmoid(self.latent_process_values)\n",
    "\n",
    "        # The Laplace approximation can be understood as a Gaussian\n",
    "        # approximation of the sigmoid factors at the mode. The second\n",
    "        # derivative of the sigmoid is - pi(1 - pi), with pi the vector\n",
    "        # of probabilities.\n",
    "\n",
    "        self.likelihood.noise = 1.0 / (pi * (1 - pi))\n",
    "\n",
    "        # The targets are the found process values that maximize the posterior\n",
    "\n",
    "        self.train_targets = self.latent_process_values.detach()\n",
    "\n",
    "        # We return a multivariate Gaussian distribution with the corresponding\n",
    "        # prior mean and covariances.\n",
    "\n",
    "        return gpytorch.distributions.MultivariateNormal(mean_x, covar_x)\n",
    "\n",
    "    def evaluate_log_marginal_likelihood(self):\n",
    "\n",
    "        covar_x = self.covar_module(self.train_x)\n",
    "        pi = self.sigmoid(self.latent_process_values * (self.train_y * 2.0 - 1.0))\n",
    "        A = torch.diag(pi * (1 - pi)) + \\\n",
    "          torch.linalg.inv(torch.eye(self.train_x.shape[ 0 ]) * 1e-6 + \\\n",
    "          covar_x.representation()[ 0 ])\n",
    "\n",
    "        # We return the estimate of the marginal likelihood by the Laplace\n",
    "        # approximation.\n",
    "\n",
    "        return -1.0 * self.evaluate_neg_log_posterior() + 0.5 * \\\n",
    "          self.train_x.shape[ 0 ] * torch.log(torch.ones(1) * 2 * 3.1415926) \\\n",
    "          - 0.5 * torch.logdet(A)\n",
    "\n",
    "# We create the model again with the new prior mean and covariance function.\n",
    "\n",
    "model = LaplaceGPModel(train_x_c, train_y_c, lengthscale = 0.25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UimvUsLLOYpB"
   },
   "source": [
    "### **Finding the Posterior Mode**\n",
    "\n",
    "The next step is finding the mode of the log posterior. For that, we use LBFGS, a quasi-Newton optimization algorithm, and the model just created."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "BxF-KTkvcKcr",
    "outputId": "f8bbec2d-8844-4633-9e28-9312e3988b73"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iter 1/1000 - Loss: 72526.548\n",
      "Iter 2/1000 - Loss: -370.660\n",
      "Iter 3/1000 - Loss: -634.744\n",
      "Iter 4/1000 - Loss: -687.256\n",
      "Iter 5/1000 - Loss: -701.602\n",
      "Iter 6/1000 - Loss: -706.045\n",
      "Iter 7/1000 - Loss: -711.209\n",
      "Iter 8/1000 - Loss: -716.367\n",
      "Iter 9/1000 - Loss: -718.972\n",
      "Iter 10/1000 - Loss: -720.813\n",
      "Iter 11/1000 - Loss: -722.516\n",
      "Iter 12/1000 - Loss: -724.078\n",
      "Iter 13/1000 - Loss: -724.864\n",
      "Iter 14/1000 - Loss: -725.783\n",
      "Iter 15/1000 - Loss: -726.389\n",
      "Iter 16/1000 - Loss: -726.891\n",
      "Iter 17/1000 - Loss: -727.379\n",
      "Iter 18/1000 - Loss: -727.823\n",
      "Iter 19/1000 - Loss: -728.111\n",
      "Iter 20/1000 - Loss: -728.495\n",
      "Iter 21/1000 - Loss: -728.704\n",
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     ]
    }
   ],
   "source": [
    "def train_model(model):\n",
    "\n",
    "  model.train()\n",
    "\n",
    "  # Use instantiate the LBFGS optimizer, which will optimize the latent process values\n",
    "  # to find the posterior mode.\n",
    "\n",
    "  optimizer = torch.optim.LBFGS([ model.latent_process_values ], \\\n",
    "      history_size=10, max_iter=4, line_search_fn=\"strong_wolfe\")\n",
    "\n",
    "  def closure():\n",
    "      optimizer.zero_grad()\n",
    "      loss = model.evaluate_neg_log_posterior()\n",
    "      loss.backward()\n",
    "      return loss\n",
    "\n",
    "  training_iter = 1000\n",
    "\n",
    "  for i in range(training_iter):\n",
    "\n",
    "    loss = model.evaluate_neg_log_posterior()\n",
    "    print('Iter %d/%d - Loss: %.3f' % (i + 1, training_iter, loss.item()))\n",
    "\n",
    "    optimizer.step(closure)\n",
    "\n",
    "train_model(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cpsjBYwgtM6c"
   },
   "source": [
    "### **Obtaining the Estimate of the Log Marginal Likelihood**\n",
    "\n",
    "We evaluate the estimate of the log marginal likelihood given by the Laplace approximation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "W1WG13OUsQ-N",
    "outputId": "fdcb65e6-f5a5-4029-fd03-2908dec35342"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([-75.4406], grad_fn=<SubBackward0>)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate_log_marginal_likelihood()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iDh3r87eve56"
   },
   "source": [
    "### **Computing the Predictive Distribution**\n",
    "\n",
    "After training the model and computing the Laplace approximation we can compute an approximate predictive distribution. For this, we only have to sample from the GP posterior approximation at the test point and marginalize the class probabilities given by the sigmoid fucntion. This marginalization is intractable, but it can be approximated via Monte Carlo, by sampling."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Z56uMW0YzU2X"
   },
   "source": [
    "First, we generate the test data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "id": "GGyIvE_uwmz3"
   },
   "outputs": [],
   "source": [
    "x1_test = torch.linspace(-1, 1, 20)\n",
    "x2_test = torch.linspace(-1, 1, 20)\n",
    "test_x_c = torch.cartesian_prod(x1_test, x2_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "po8eGZ6Nzkj-"
   },
   "source": [
    "We set the model in evaluation mode and compute latent predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "id": "XYwn2hN0z_ot"
   },
   "outputs": [],
   "source": [
    "model.eval()\n",
    "\n",
    "# For each number of samples we generate posterior class probabilities\n",
    "\n",
    "with torch.no_grad():\n",
    "  latent_process_values = model(test_x_c)\n",
    "\n",
    "# We approximate the sigmoid function by a re-escaled probit function.\n",
    "# See (Bishop, 2006) page 219.\n",
    "\n",
    "class_probs = 0.5 * (1 + torch.erf((latent_process_values.mean / \\\n",
    "  torch.sqrt(latent_process_values.variance + 3.1415926 / 8)) / math.sqrt(2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "j6BarYS4kO9e"
   },
   "source": [
    "We compute now the test error to see what is the generalization performance of the previous classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9TxSJ4E1pv7_",
    "outputId": "9a28fa9b-c341-4002-9ea7-a7ca8fa420ab"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test error: 0.003\n"
     ]
    }
   ],
   "source": [
    "test_labels = data_fn(test_x_c[:,0:1], test_x_c[:,1:2])\n",
    "test_error = torch.mean(((class_probs > 0.5).type(torch.DoubleTensor) \\\n",
    "    != test_labels[:,0]).type(torch.DoubleTensor))\n",
    "print('Test error: %.3f' % test_error)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Wo8isANO9ugr"
   },
   "source": [
    "We plot the predictive distribution. That is the estimated class posterior probability for each potential label.\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 545
    },
    "id": "DTZ245s-9ya2",
    "outputId": "0b773b6e-80c8-47e2-a419-5c5a39269ec1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x76e7fc6bd570>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (8, 6))\n",
    "CS = ax.contour(x1_test.numpy(), x2_test.numpy(), class_probs.numpy().reshape((20,20)))\n",
    "ax.clabel(CS, inline=True, fontsize=10)\n",
    "CS = ax.contour(x1_test.numpy(), x2_test.numpy(), class_probs.numpy().reshape((20,20)), levels = [0.5])\n",
    "ax.clabel(CS, inline=True, fontsize=10)\n",
    "plt.scatter(train_x_c[:,0].numpy(), train_x_c[:,1].numpy(), c = color)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "P75L705KO5JP"
   },
   "source": [
    "We observe that the class posterior probability is high in regions where there are data instances of each class label and it becomes closer to 0.5 near the decision boundary and as we move away from the data, as expected."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oawMNFxysjmq"
   },
   "source": [
    "## **Task 4**\n",
    "\n",
    "We will explore again the utility of the marginal likelihood for model selection. In this task, you are asked to re-fit the previous model for different values of the lengthscale parameter. Try the values \\{0.1, 0.25, 1.0\\} and observe the estimate of the marginal likelihood and the test error computed above. Do you observe a correlation between the test error and the estimate of the log-marginal likelhood?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}