tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ! pip install --upgrade emcee corner pytorch-lightning tqdm nflows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/Caskroom/miniforge/base/envs/torch-mps/lib/python3.9/site-packages/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension: dlopen(/opt/homebrew/Caskroom/miniforge/base/envs/torch-mps/lib/python3.9/site-packages/torchvision/image.so, 0x0006): Symbol not found: (__ZN3c106detail19maybe_wrap_dim_slowIxEET_S2_S2_b)\n",
      "  Referenced from: '/opt/homebrew/Caskroom/miniforge/base/envs/torch-mps/lib/python3.9/site-packages/torchvision/image.so'\n",
      "  Expected in: '/opt/homebrew/Caskroom/miniforge/base/envs/torch-mps/lib/python3.9/site-packages/torch/lib/libc10.dylib'\n",
      "  warn(f\"Failed to load image Python extension: {e}\")\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torch.utils.data import TensorDataset, DataLoader, random_split\n",
    "import numpy as np\n",
    "import emcee\n",
    "from scipy.stats import poisson\n",
    "from scipy.stats import chi2\n",
    "from scipy.optimize import basinhopping\n",
    "from tqdm import tqdm\n",
    "import matplotlib.pyplot as plt\n",
    "import pytorch_lightning as pl\n",
    "import corner"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simulation-based (likelihood-free) inference\n",
    "**Siddharth Mishra-Sharma** ([smsharma@mit.edu](mailto:smsharma@mit.edu))\n",
    "\n",
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/smsharma/sbi-lecture-mit/blob/main/tutorial.ipynb)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Simulation-based inference (SBI) is a powerful class of methods for performing inference in settings where the likelihood is computationally intractable, but simulations can be realized via forward modeling. \n",
    "\n",
    "In this lecture we will\n",
    "- Introduce the notion of an implicit likelihood, and how to leverage it to perform inference;\n",
    "- Look at a \"traditional\" method for likelihood-free inference, Approximate Bayesian Computation (ABC);\n",
    "- Build up two common modern _neural_ SBI techniques: neural likelihood-ratio estimation (NRE) and neural posterior estimation (NPE);\n",
    "- Introduce the concept of statistical coverage testing and calibration.\n",
    "\n",
    "As examples, we will look at a simple Gaussian-signal-on-power-law-background (\"bump hunt\"), where the likelihood is tractable, and a more complicated example of inferring a distribution of point sources, where the likelihood is computationally intractable. We will emphasize what it means for a likelihood to be computationally intractable/challenging and where the advantages of SBI come in."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=./assets/likelihood.png alt= “” width=800>\n",
    "<img src=./assets/sbi.png alt= “” width=800>\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simple bump-on-power-law example"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As an initial example, consider a Gaussian signal parameterized by {amplitude, mean location, std} on top of a power law background parameterize by {amplitude, power-law exponent}.\n",
    "$$ x_b = A_b\\,y^{n_b}$$\n",
    "$$x_s = A_s\\,\\exp^{-(y - \\mu_s)^2 / 2\\sigma_s^2}$$\n",
    "$$x \\sim \\mathrm{Pois}(x_b + x_s)$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bump_forward_model(y, amp_s, mu_s, std_s, amp_b, exp_b):\n",
    "    \"\"\" Forward model for a Gaussian bump (amp_s, mu_s, std_s) on top of a power-law background (amp_b, exp_b).\n",
    "    \"\"\"\n",
    "    x_b = amp_b * (y ** exp_b)  # Power-law background\n",
    "    x_s = amp_s * np.exp(-((y - mu_s) ** 2) / (2 * std_s ** 2))  # Gaussian signal\n",
    "\n",
    "    x = x_b + x_s  # Total mean signal\n",
    "\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x29dfcad30>"
      ]
     },
     "execution_count": 4,
     "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": [
    "def poisson_interval(k, alpha=0.32): \n",
    "    \"\"\" Uses chi2 to get the poisson interval.\n",
    "    \"\"\"\n",
    "    a = alpha\n",
    "    low, high = (chi2.ppf(a/2, 2*k) / 2, chi2.ppf(1-a/2, 2*k + 2) / 2)\n",
    "    if k == 0: \n",
    "        low = 0.0\n",
    "    return k - low, high - k\n",
    "\n",
    "y = np.linspace(0.1, 1, 50)  # Dependent variable\n",
    "\n",
    "# Mean expected counts\n",
    "x_mu = bump_forward_model(y, \n",
    "                    amp_s=50, mu_s=0.8, std_s=0.05,  # Signal params\n",
    "                    amp_b=50, exp_b=-0.5)  # Background params\n",
    "\n",
    "# Realized counts\n",
    "x = np.random.poisson(x_mu)\n",
    "x_err = np.array([poisson_interval(k) for k in x.T]).T\n",
    "\n",
    "# Plot\n",
    "plt.plot(y, x_mu, color='k', ls='--', label=\"Mean expected counts\")\n",
    "plt.errorbar(y, x, yerr=x_err, fmt='o', color='k', label=\"Realized counts\")\n",
    "\n",
    "plt.xlabel(\"$y$\")\n",
    "plt.ylabel(\"Counts\")\n",
    "\n",
    "plt.legend()\n",
    "\n",
    "# plt.savefig(\"assets/x1.pdf\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The explicit likelihood"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case, we can write down a log-likelihood as a Poisson over the mean returned by the forward model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def log_like(theta, y, x):\n",
    "    \"\"\" Log-likehood function for a Gaussian bump (amp_s, mu_s, std_s) on top of a power-law background (amp_b, exp_b).\n",
    "    \"\"\"\n",
    "    amp_s, mu_s, std_s, amp_b, exp_b = theta\n",
    "    mu = bump_forward_model(y, amp_s, mu_s, std_s, amp_b, exp_b)\n",
    "    return poisson.logpmf(x, mu).sum()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's focus on just 2 parameters for simplicity, the signal amplitude and mean location. The likelihood in this case is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-180.34215350064508"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def log_like_sig(params, y, x):\n",
    "    \"\"\" Log-likehood function for a Gaussian bump (amp_s, mu_s) on top of a fixed PL background.\n",
    "    \"\"\"\n",
    "    amp_s, mu_s = params\n",
    "    std_s, amp_b, exp_b = 0.05, 50, -0.5\n",
    "    mu = bump_forward_model(y, amp_s, mu_s, std_s, amp_b, exp_b)\n",
    "    return poisson.logpmf(x, mu).sum()\n",
    "\n",
    "log_like_sig([50, 0.8], y, x)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get a maximum-liklelihood estimate:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLE parameters: [50.73460467  0.79370059]; true parameters: (50, 0.8)\n"
     ]
    }
   ],
   "source": [
    "# Initial guess for the parameters\n",
    "initial_guess = [100., 0.1]\n",
    "\n",
    "# Set up the minimizer_kwargs for the basinhopping algorithm\n",
    "minimizer_kwargs = {\"method\": \"L-BFGS-B\", \"bounds\": ((0, 200), (0, 1))}\n",
    "\n",
    "# Perform the optimization using basinhopping\n",
    "opt = basinhopping(lambda thetas: -log_like_sig(thetas, y, x), initial_guess, minimizer_kwargs=minimizer_kwargs)\n",
    "\n",
    "print(\"MLE parameters: {}; true parameters: {}\".format(opt.x, (50, 0.8)))\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And approximate posterior using `emcee`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 5000/5000 [00:05<00:00, 928.28it/s]\n"
     ]
    }
   ],
   "source": [
    "def log_prior(thetas):\n",
    "    \"\"\" Log-prior function for a Gaussian bump (amp_s, mu_s) on top of a fixed PL background.\n",
    "    \"\"\"\n",
    "    amp_s, mu_s = thetas\n",
    "    if 0 < amp_s < 200 and 0 < mu_s < 2:\n",
    "        return 0\n",
    "    else:\n",
    "        return -np.inf\n",
    "    \n",
    "def log_post(thetas, y, x):\n",
    "    \"\"\" Log-posterior function for a Gaussian bump (amp_s, mu_s) on top of a fixed PL background.\n",
    "    \"\"\"\n",
    "    lp = log_prior(thetas)\n",
    "    if not np.isfinite(lp):\n",
    "        return -np.inf\n",
    "    else:\n",
    "        return lp + log_like_sig(thetas, y, x)\n",
    "    \n",
    "# Sampling with `emcee`\n",
    "ndim, nwalkers = 2, 32\n",
    "sampler = emcee.EnsembleSampler(nwalkers, ndim, log_post, args=(y, x))\n",
    "\n",
    "pos = opt.x + 1e-3 * np.random.randn(nwalkers, ndim)\n",
    "sampler.run_mcmc(pos, 5000, progress=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 550x550 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot posterior samples\n",
    "flat_samples = sampler.get_chain(discard=1000, flat=True)\n",
    "corner.corner(flat_samples, labels=[\"amp_s\", \"mu_s\"], truths=[50, 0.8], smooth=1.);"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The implicit likelihood"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will do inference without relying on the explicit likelihood evaluation. The key realization is that samples from the forward model implicitly encode the likelihood; when we are simulating data points $x$ for different parameter points $\\theta$, we are drawing samples from the likelihood:\n",
    "$$x\\sim p(x\\mid\\theta)$$\n",
    "which is where the _implicit_ aspect comes from. Let's write down a bump simulator:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([141, 144, 144, 126,  98, 103, 110, 111, 103, 109, 113, 109,  90,\n",
       "        90,  93,  73,  80,  93,  80,  67,  80,  83,  76,  42,  75,  71,\n",
       "        69,  67,  66,  51,  72,  63,  51,  62,  71,  69,  82,  92, 103,\n",
       "       120,  86,  83,  74,  60,  65,  59,  37,  51,  58,  56])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def bump_simulator(thetas, y):\n",
    "    \"\"\" Simulate samples from the bump forward model given theta = (amp_s, mu_s) and abscissa points y.\n",
    "    \"\"\"\n",
    "    amp_s, mu_s = thetas\n",
    "    std_s, amp_b, exp_b = 0.05, 50, -0.5\n",
    "    x_mu = bump_forward_model(y, amp_s, mu_s, std_s, amp_b, exp_b)\n",
    "    x = np.random.poisson(x_mu)\n",
    "    return x\n",
    "\n",
    "# Test it out\n",
    "bump_simulator([50, 0.8], y)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Approximate Bayesian Computation (ABC)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=./assets/abc.png alt= “” width=800>\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The idea behind ABC is to realize samples from the forward model (with the parameters $\\theta$ drawn from a prior) and compare it to the dataset of interest $x$. If the data and realized samples are close enough to each other according to some criterion, we keep the parameter points."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The comparison criterion here is a simple MSE on the data points. Play around with the parameters of the forward model to see how the criterion `eps` changes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "232.7"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_fwd = bump_simulator([50, 0.8], y)\n",
    "eps = np.sum(np.abs(x - x_fwd) ** 2) / len(x)\n",
    "eps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Accepted Samples: 100%|██████████| 5000/5000 [00:18<00:00, 276.69samples/s, acceptance_ratio=0.009]\n"
     ]
    }
   ],
   "source": [
    "def abc(y, x, eps_thresh=500., n_samples=1000):\n",
    "    \"\"\"ABC algorithm for Gaussian bump model.\n",
    "\n",
    "    Args:\n",
    "        y (np.ndarray): Abscissa points.\n",
    "        x (np.ndarray): Data counts.\n",
    "        eps_thresh (float, optional): Acceptance threshold. Defaults to 500.0.\n",
    "        n_samples (int, optional): Number of samples after which to stop. Defaults to 1000.\n",
    "\n",
    "    Returns:\n",
    "        np.ndarray: Accepted samples approximating the posterior p(theta|x).\n",
    "    \"\"\"\n",
    "    samples = []\n",
    "    total_attempts = 0\n",
    "    progress_bar = tqdm(total=n_samples, desc=\"Accepted Samples\", unit=\"samples\")\n",
    "\n",
    "    # Keep simulating until we have enough accepted samples\n",
    "    while len(samples) < n_samples:\n",
    "        params = np.random.uniform(low=[0, 0], high=[200, 1])  # Priors; theta ~ p(theta)\n",
    "        x_fwd = bump_simulator(params, y)  # x ~ p(x|theta)\n",
    "        eps = np.sum(np.abs(x - x_fwd) ** 2) / len(x)  # Distance metric; d(x, x_fwd)\n",
    "        total_attempts += 1\n",
    "\n",
    "        # If accepted, add to samples\n",
    "        if eps < eps_thresh:\n",
    "            samples.append(params)\n",
    "            progress_bar.update(1)\n",
    "            acceptance_ratio = len(samples) / total_attempts\n",
    "            progress_bar.set_postfix(acceptance_ratio=f\"{acceptance_ratio:.3f}\")\n",
    "\n",
    "    progress_bar.close()\n",
    "    return np.array(samples)\n",
    "\n",
    "n_samples = 5_000\n",
    "post_samples = abc(y, x, eps_thresh=200, n_samples=n_samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Too few points to create valid contours\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 550x550 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = corner.corner(post_samples, labels=[\"amp_s\", \"mu_s\"], truths=[50, 0.8], range=[(0, 200), (0.3, 1)], bins=50);\n",
    "corner.corner(flat_samples, labels=[\"amp_s\", \"mu_s\"], truths=[50, 0.8], fig=fig, color=\"red\", weights=np.ones(len(flat_samples)) * n_samples / len(flat_samples), range=[(0, 200), (0.3, 1)], bins=50);"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Downsides of vanilla ABC:\n",
    "- How to summarize the data? Curse of dimensionality / loss of information.\n",
    "- How to compare with data? Likelihood may not be available.\n",
    "- Choice of acceptance threshold: Precision/efficiency tradeoff.\n",
    "- Need to re-run pipeline for new data or new prior."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Neural likelihood-ratio estimation (NRE)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=./assets/nre.png alt= “” width=800>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For numerical stability, the alternate hypothesis $\\theta_0$ can be assumed to be one where $x$ and $\\theta$ are not correlated, i.e., drawn from the individual marginal distributions $\\{x, \\theta\\} \\sim p(x)\\,p(\\theta)$. Then the alternate has support over the entire parameter space, instead of being a single hypothesis $\\theta_0$.\n",
    "\n",
    "In this case, we get the likelihood-to-evidence ratio,\n",
    "\n",
    "$$\\hat r(x, \\theta) = \\frac{s(x, \\theta)}{1 - s(x, \\theta)} = \\frac{p(x,\\theta)}{p(x)p(\\theta)} = \\frac{p(x\\mid\\theta)}{p(x)}$$\n",
    "\n",
    "$\\hat r(x, \\theta)$ can be shown to be the classifier logit, i.e. the output before softmaxxing into the decision function with range between 0 and 1."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Start by creating some training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 50000/50000 [00:00<00:00, 66862.19it/s]\n"
     ]
    }
   ],
   "source": [
    "n_train = 50_000\n",
    "\n",
    "# Simulate training data\n",
    "theta_samples = np.random.uniform(low=[0, 0], high=[200, 1], size=(n_train, 2))  # Parameter proposal\n",
    "x_samples = np.array([bump_simulator(theta, y) for theta in tqdm(theta_samples)])\n",
    "\n",
    "# Convert to torch tensors\n",
    "theta_samples = torch.tensor(theta_samples, dtype=torch.float32)\n",
    "x_samples = torch.tensor(x_samples, dtype=torch.float32)\n",
    "\n",
    "# Normalize the data\n",
    "x_mean = x_samples.mean(dim=0)\n",
    "x_std = x_samples.std(dim=0)\n",
    "x_samples = (x_samples - x_mean) / x_std\n",
    "\n",
    "theta_mean = theta_samples.mean(dim=0)\n",
    "theta_std = theta_samples.std(dim=0)\n",
    "theta_samples = (theta_samples - theta_mean) / theta_std"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As our parameterized classifier, we will use a simple MLP."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_mlp(input_dim, hidden_dim, output_dim, layers, activation=nn.GELU()):\n",
    "    \"\"\"Create an MLP from the configuration.\"\"\"\n",
    "    seq = [nn.Linear(input_dim, hidden_dim), activation]\n",
    "    for _ in range(layers):\n",
    "        seq += [nn.Linear(hidden_dim, hidden_dim), activation]\n",
    "    seq += [nn.Linear(hidden_dim, output_dim)]\n",
    "    return nn.Sequential(*seq)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a neural ratio estimator class, with a corresponding loss function. The loss is a simple binary cross-entropy loss that discriminates between samples from the joint distribution $\\{x, \\theta\\} \\sim p(x\\mid\\theta)$ and those from a product of marginals $\\{x, \\theta\\} \\sim p(x)\\,p(\\theta)$. Samples from the latter are obtained by shuffling joint samples from within a batch.\n",
    "\n",
    "The binary cross-entropy loss is used as the classifier loss to distinguish samples from the joint and marginals,\n",
    "$$\\mathcal L = - \\sum_i y_i \\log(p_i)$$\n",
    "where $y_i$ are the true labels and $p_i$ the softmaxxed probabilities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "class NeuralRatioEstimator(pl.LightningModule):\n",
    "    \"\"\" Simple neural likelihood-to-evidence ratio estimator, using an MLP as a parameterized classifier.\n",
    "    \"\"\"\n",
    "    def __init__(self, x_dim, theta_dim):\n",
    "        super().__init__()\n",
    "        self.classifier = build_mlp(input_dim=x_dim + theta_dim, hidden_dim=128, output_dim=1, layers=4)\n",
    "\n",
    "    def forward(self, x):\n",
    "        return self.classifier(x)\n",
    "    \n",
    "    def loss(self, x, theta):\n",
    "\n",
    "        # Repeat x in groups of 2 along batch axis\n",
    "        x = x.repeat_interleave(2, dim=0)\n",
    "\n",
    "        # Get a shuffled version of theta\n",
    "        theta_shuffled = theta[torch.randperm(theta.shape[0])]\n",
    "\n",
    "        # Interleave theta and shuffled theta\n",
    "        theta = torch.stack([theta, theta_shuffled], dim=1).reshape(-1, theta.shape[1])\n",
    "\n",
    "        # Get labels; ones for pairs from joint, zeros for pairs from marginals\n",
    "        labels = torch.ones(x.shape[0], device=x.device) \n",
    "        labels[1::2] = 0.0\n",
    "\n",
    "        # Pass through parameterized classifier to get logits\n",
    "        logits = self(torch.cat([x, theta], dim=1))\n",
    "        probs = torch.sigmoid(logits).squeeze()\n",
    "\n",
    "        return nn.BCELoss(reduction='none')(probs, labels)\n",
    "\n",
    "\n",
    "    def training_step(self, batch, batch_idx):\n",
    "        x, theta = batch\n",
    "        loss = self.loss(x, theta).mean()\n",
    "        self.log(\"train_loss\", loss)\n",
    "        return loss\n",
    "\n",
    "    def validation_step(self, batch, batch_idx):\n",
    "        x, theta = batch\n",
    "        loss = self.loss(x, theta).mean()\n",
    "        self.log(\"val_loss\", loss)\n",
    "        return loss\n",
    "\n",
    "    def configure_optimizers(self):\n",
    "        return torch.optim.Adam(self.parameters(), lr=3e-4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0.6805, 0.7058, 0.6810, 0.7051, 0.6813, 0.7051, 0.6813, 0.7052, 0.6813,\n",
       "        0.7051, 0.6813, 0.7051, 0.6803, 0.7061, 0.6812, 0.7051, 0.6820, 0.7044,\n",
       "        0.6822, 0.7044, 0.6805, 0.7057, 0.6808, 0.7055, 0.6815, 0.7051, 0.6812,\n",
       "        0.7053, 0.6815, 0.7049, 0.6805, 0.7058, 0.6804, 0.7060, 0.6807, 0.7058,\n",
       "        0.6819, 0.7047, 0.6821, 0.7044, 0.6812, 0.7051, 0.6813, 0.7051, 0.6816,\n",
       "        0.7048, 0.6806, 0.7058, 0.6810, 0.7052, 0.6798, 0.7066, 0.6811, 0.7051,\n",
       "        0.6798, 0.7066, 0.6819, 0.7047, 0.6819, 0.7047, 0.6816, 0.7048, 0.6814,\n",
       "        0.7048, 0.6817, 0.7049, 0.6808, 0.7058, 0.6812, 0.7051, 0.6812, 0.7052,\n",
       "        0.6800, 0.7063, 0.6809, 0.7055, 0.6800, 0.7063, 0.6813, 0.7051, 0.6812,\n",
       "        0.7051, 0.6815, 0.7051, 0.6814, 0.7051, 0.6815, 0.7050, 0.6804, 0.7063,\n",
       "        0.6804, 0.7062, 0.6817, 0.7048, 0.6816, 0.7049, 0.6800, 0.7064, 0.6826,\n",
       "        0.7041, 0.6806, 0.7062, 0.6817, 0.7049, 0.6809, 0.7056, 0.6813, 0.7053,\n",
       "        0.6813, 0.7052, 0.6814, 0.7050, 0.6820, 0.7047, 0.6813, 0.7052, 0.6814,\n",
       "        0.7052, 0.6784, 0.7080, 0.6811, 0.7053, 0.6810, 0.7057, 0.6826, 0.7041,\n",
       "        0.6807, 0.7056], grad_fn=<BinaryCrossEntropyBackward0>)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate loss; initially it should be around -log(0.5) = 0.693\n",
    "nre = NeuralRatioEstimator(x_dim=50, theta_dim=2)\n",
    "nre.loss(x_samples[:64], theta_samples[:64])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instantiate dataloader and train."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "val_fraction = 0.1\n",
    "batch_size = 128\n",
    "n_samples_val = int(val_fraction * len(x_samples))\n",
    "\n",
    "dataset = TensorDataset(x_samples, theta_samples)\n",
    "\n",
    "dataset_train, dataset_val = random_split(dataset, [len(x_samples) - n_samples_val, n_samples_val])\n",
    "train_loader = DataLoader(dataset_train, batch_size=batch_size, num_workers=8, pin_memory=True, shuffle=True)\n",
    "val_loader = DataLoader(dataset_val, batch_size=batch_size, num_workers=8, pin_memory=True, shuffle=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: True (mps), used: False\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/torch-mps/lib/python3.9/site-packages/pytorch_lightning/trainer/setup.py:200: UserWarning: MPS available but not used. Set `accelerator` and `devices` using `Trainer(accelerator='mps', devices=1)`.\n",
      "  rank_zero_warn(\n",
      "\n",
      "  | Name       | Type       | Params\n",
      "------------------------------------------\n",
      "0 | classifier | Sequential | 73.0 K\n",
      "------------------------------------------\n",
      "73.0 K    Trainable params\n",
      "0         Non-trainable params\n",
      "73.0 K    Total params\n",
      "0.292     Total estimated model params size (MB)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a9a1dc5831b34530a072fc1e2b419284",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Sanity Checking: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bdd02a40a37c467a96386bd7ca5a9a10",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e589993d874c4994b5575f7f91b581f9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8ec3f96da20b49ab911dc10a122c6051",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cb2b5e6833d140f3a6090fba7b921601",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d5254def37664bdb8a48ad6fdaaa1f15",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b71958bbbfb6403fb214dc5d2dd1a185",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dd10a805744c47028de83cf6fe337298",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "113806c2a79b4455b2e9ae48bc0d2361",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fc3d8d4fe9014d77817279089734f387",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7aa322b9cc4346f79e0eef519514f2f0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "278475a8fe484bb3bda5adefaafb1dcc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3b206656d7ee4a2797782d4049200021",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ff021d0b588241099942a0eeac193484",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fb2b845ea8c04e82bfdbef9e795f23b0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "02c96b19240a4f2ab9c7b38dd7af204f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1a09f666a65444f086c07ad7ad4fd520",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b724be7c264d4c53b876d3492c883a08",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ad183f77f3864f46b3596a265b438476",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "372d8b5f64964017b974b0d35d8b572b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d86748e47f9b424eb143ae69d27c64d2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e1f5d24c35a34b5e92700f177d3d5577",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`Trainer.fit` stopped: `max_epochs=20` reached.\n"
     ]
    }
   ],
   "source": [
    "trainer = pl.Trainer(max_epochs=20)\n",
    "trainer.fit(model=nre, train_dataloaders=train_loader, val_dataloaders=val_loader);"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The classifier logits are now an estimator for the likelihood ratio. We can write down a log-likelihood function and use it to sample from the corresponding posterior distribution, just like before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(5.0554, grad_fn=<SqueezeBackward0>)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def log_like(theta, x):\n",
    "    \"\"\" Log-likelihood ratio estimator using trained classifier logits.\n",
    "    \"\"\"\n",
    "        \n",
    "    x = torch.Tensor(x)\n",
    "    theta = torch.Tensor(theta)\n",
    "\n",
    "    # Normalize\n",
    "    x = (x - x_mean) / x_std\n",
    "    theta = (theta - theta_mean) / theta_std\n",
    "\n",
    "    x = torch.atleast_1d(x)\n",
    "    theta = torch.atleast_1d(theta)\n",
    "\n",
    "    return nre.classifier(torch.cat([x, theta], dim=-1)).squeeze()\n",
    "\n",
    "theta_test = np.array([90, 0.8])\n",
    "x_test = bump_simulator(theta_test, y)\n",
    "\n",
    "log_like(theta_test, x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def log_post(theta, x):\n",
    "    \"\"\" Log-posterior distribution, for sampling.\n",
    "    \"\"\"\n",
    "    lp = log_prior(theta)\n",
    "    if not np.isfinite(lp):\n",
    "        return -np.inf\n",
    "    else:\n",
    "        return lp + log_like(theta, x)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sample with `emcee`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 5000/5000 [00:21<00:00, 231.63it/s]\n"
     ]
    }
   ],
   "source": [
    "ndim, nwalkers = 2, 32\n",
    "sampler = emcee.EnsembleSampler(nwalkers, ndim, log_post, args=(x_test,))\n",
    "\n",
    "pos = opt.x + 1e-3 * np.random.randn(nwalkers, ndim)\n",
    "sampler.run_mcmc(pos, 5000, progress=True);"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot approximate posterior:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 550x550 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "flat_samples = sampler.get_chain(discard=1000, flat=True)\n",
    "corner.corner(flat_samples, labels=[\"amp_s\", \"mu_s\"], truths=[90, 0.8]);"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Neural posterior estimation (NPE)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=./assets/npe.png alt= “” width=800>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nflows.flows.base import Flow\n",
    "from nflows.distributions.normal import StandardNormal\n",
    "from nflows.transforms.base import CompositeTransform\n",
    "from nflows.transforms.autoregressive import MaskedAffineAutoregressiveTransform\n",
    "from nflows.transforms.permutations import ReversePermutation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([2, 100, 2]) torch.Size([2, 100])\n"
     ]
    }
   ],
   "source": [
    "def get_flow(d_in=2, d_hidden=32, d_context=16, n_layers=4):\n",
    "    \"\"\" Instantiate a simple (Masked Autoregressive) normalizing flow.\n",
    "    \"\"\"\n",
    "\n",
    "    base_dist = StandardNormal(shape=[d_in])\n",
    "\n",
    "    transforms = []\n",
    "    for _ in range(n_layers):\n",
    "        transforms.append(ReversePermutation(features=d_in))\n",
    "        transforms.append(MaskedAffineAutoregressiveTransform(features=d_in, hidden_features=d_hidden, context_features=d_context))\n",
    "    transform = CompositeTransform(transforms)\n",
    "\n",
    "    flow = Flow(transform, base_dist)\n",
    "    return flow\n",
    "\n",
    "# Instantiate flow\n",
    "flow = get_flow()\n",
    "\n",
    "# Make sure sampling and log-prob calculation makes sense\n",
    "samples, log_prob = flow.sample_and_log_prob(num_samples=100, context=torch.randn(2, 16))\n",
    "print(samples.shape, log_prob.shape)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Construct a neural posterior estimator. It uses a normalizing flow as a (conditional) posterior density estimator, and a feature-extraction network that aligns the directions of variations in parameters $\\theta$ and data $x$.\n",
    "$$  \\mathcal L = -\\log p_\\phi(\\theta\\mid s_\\varphi(x))$$\n",
    "where $\\{\\phi, \\varphi\\}$ are the parameters of the normalizing flow and featurizer MLP, respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "class NeuralPosteriorEstimator(pl.LightningModule):\n",
    "    \"\"\" Simple neural posterior estimator class using a normalizing flow as the posterior density estimator.\n",
    "    \"\"\"\n",
    "    def __init__(self, featurizer, d_context=16):\n",
    "        super().__init__()\n",
    "        self.featurizer = featurizer\n",
    "        self.flow = get_flow(d_in=2, d_hidden=32, d_context=d_context, n_layers=4)\n",
    "\n",
    "    def forward(self, x):\n",
    "        return self.featurizer(x)\n",
    "    \n",
    "    def loss(self, x, theta):\n",
    "        context = self(x)\n",
    "        return -self.flow.log_prob(inputs=theta, context=context)\n",
    "\n",
    "    def training_step(self, batch, batch_idx):\n",
    "        x, theta = batch\n",
    "        loss = self.loss(x, theta).mean()\n",
    "        self.log(\"train_loss\", loss)\n",
    "        return loss\n",
    "\n",
    "    def validation_step(self, batch, batch_idx):\n",
    "        x, theta = batch\n",
    "        loss = self.loss(x, theta).mean()\n",
    "        self.log(\"val_loss\", loss)\n",
    "        return loss\n",
    "\n",
    "    def configure_optimizers(self):\n",
    "        return torch.optim.Adam(self.parameters(), lr=3e-4)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instantiate the NPE class and look at the loss:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([3.7843, 3.8738, 3.9518, 4.0794, 3.6791, 3.9817, 4.3857, 4.4798, 4.5228,\n",
       "        4.7221, 3.6710, 4.4517, 4.2133, 3.9601, 4.4137, 4.6108, 3.9406, 4.7328,\n",
       "        4.3728, 4.4590, 3.7482, 4.2472, 4.3484, 4.0043, 4.0342, 3.9290, 3.7566,\n",
       "        4.2563, 4.4851, 4.3799, 4.2393, 3.7989, 4.3761, 4.1313, 3.9437, 4.2510,\n",
       "        4.7588, 4.3307, 4.2605, 3.9174, 3.6827, 4.3489, 4.4073, 3.8204, 4.1285,\n",
       "        4.5042, 4.6367, 4.4233, 3.8328, 4.8085, 4.4506, 4.7571, 3.7327, 4.0766,\n",
       "        4.4962, 4.2689, 4.4157, 4.5896, 4.2844, 4.4578, 3.8447, 4.2823, 4.8359,\n",
       "        4.1344], grad_fn=<NegBackward0>)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "npe = NeuralPosteriorEstimator(featurizer=build_mlp(input_dim=50, hidden_dim=128, output_dim=16, layers=4))\n",
    "npe.loss(x_samples[:64], theta_samples[:64])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train using the same data as before:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: True (mps), used: False\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "\n",
      "  | Name       | Type       | Params\n",
      "------------------------------------------\n",
      "0 | featurizer | Sequential | 74.6 K\n",
      "1 | flow       | Flow       | 24.3 K\n",
      "------------------------------------------\n",
      "99.0 K    Trainable params\n",
      "0         Non-trainable params\n",
      "99.0 K    Total params\n",
      "0.396     Total estimated model params size (MB)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6328b91b5ccc40a3a1823c25307a1fbd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Sanity Checking: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "93c09022688a4f41b090a112dd5595b3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0d6cfa246056495f8efa98a526c80a40",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "69efb14e24904ee4b05220002afa858c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3c1d4736cda9443c96aeed07c789c688",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "34f4fe3862474c72a49d20b514c0ffb8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "52b9aee8e2904d47ba257b399f952fdd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bffdf82413b045efb5f32cc5ac1f648f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f9c2a69d8aac4454b51209ba23119927",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d1dbc2a2ce8142e4b99b0d641d214ff8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d079bcecf8aa4bcab263bdf901fae278",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dd179d69172340aab793befe69564d1d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2025c19dfa484e928891307d268c1f12",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "93c7ff0c453a467ca89d18fb74c283f6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "859bb349fd224c1da60cc3e5a4c5a6b6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "52df11be5b8f4825b2b97888d1f53c59",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ERROR: Unexpected segmentation fault encountered in worker.\n",
      "\u0000"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e2cf6ec486ec4681b0ef1dcbfb5fbc37",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "81fbb86df91842fb9ff83df549a7b352",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8a5d6047e7924575a352f329be539f96",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c473437718c7467f80d91a316bd88eda",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "71e67251d66946a4b001d6e3ed79077d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d28a7deb70924535a0905a809e0b408a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`Trainer.fit` stopped: `max_epochs=20` reached.\n"
     ]
    }
   ],
   "source": [
    "trainer = pl.Trainer(max_epochs=20)\n",
    "trainer.fit(model=npe, train_dataloaders=train_loader, val_dataloaders=val_loader);"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get a test data sample, pass it through the feature extractor, and condition the flow density estimator on it. We get posterior samples by drawing from \n",
    "$$\\theta \\sim p_\\phi(\\theta\\mid s_\\varphi(x)).$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "theta_test = np.array([90, 0.8])\n",
    "x_test = bump_simulator(theta_test, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test_norm = (torch.Tensor(x_test) - x_mean) / x_std\n",
    "context = npe.featurizer(x_test_norm).unsqueeze(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 550x550 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "samples_test = npe.flow.sample(num_samples=10000, context=context) * theta_std + theta_mean\n",
    "samples_test = samples_test.detach().numpy()\n",
    "corner.corner(samples_test, labels=[\"amp_s\", \"mu_s\"], truths=[90, 0.8]);"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A more complicated example: distribution of point sources in a 2D image"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, let's look at a more complicated example, one that is closer to a typical application of SBI and where the likelihood is formally intractable.\n",
    "\n",
    "The forward model simulates a map of point sources with mean counts drawn from a power law (Pareto) distribution. The distribution of the mean counts is given by the following equation:\n",
    "\n",
    "$$\n",
    "\\frac{\\mathrm dn}{\\mathrm  ds} = A s^{\\beta}\n",
    "$$\n",
    "\n",
    "where $A$ is the amplitude (amp_b), $s$ is the flux, and $\\beta$ is the exponent (exp_b). The fluxes are drawn from a truncated power law with minimum and maximum bounds, $s_\\text{min}$ and $s_\\text{max}$, respectively.\n",
    "\n",
    "The number of sources is determined by integrating the power law distribution within the flux limits and taking a Poisson realization:\n",
    "\n",
    "$$\n",
    "N_\\text{sources} \\sim \\text{Pois}\\left(\\int_{s_\\text{min}}^{s_\\text{max}} \\, \\mathrm ds \\frac{\\mathrm dn}{\\mathrm ds}\\right)\n",
    "$$\n",
    "\n",
    "For each source, a position is randomly assigned within the box of size `box_size`. The fluxes are then binned into a grid with `resolution` number of bins in both x and y directions. The resulting map is convolved with a Gaussian point spread function (PSF) with a standard deviation of `sigma_psf` to account for the spatial resolution of the instrument.\n",
    "\n",
    "The output is a 2D map of counts, representing the simulated observation of the point sources in the sky."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Pixels')"
      ]
     },
     "execution_count": 32,
     "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": [
    "from scipy.stats import binned_statistic_2d\n",
    "from astropy.convolution import convolve, Gaussian2DKernel\n",
    "\n",
    "\n",
    "def simulate_sources(amp_b, exp_b, s_min=0.5, s_max=50.0, box_size=1., resolution=64, sigma_psf=0.01):\n",
    "    \"\"\" Simulate a map of point sources with mean counts drawn from a power law (Pareto) distribution dn/ds = amp_b * s ** exp_b\n",
    "    \"\"\"\n",
    "    # Get number of sources by analytically integrating dn/ds and taking Poisson realization\n",
    "    n_sources = np.random.poisson(-amp_b * (s_min ** (exp_b - 1)) / (exp_b - 1))\n",
    "\n",
    "    # Draw fluxes from truncated power law amp_b * s ** (exp_b - 1), with s_min and s_max as the bounds\n",
    "    fluxes = draw_powerlaw_flux(n_sources, s_min, s_max, exp_b)\n",
    "\n",
    "    positions = np.random.uniform(0., box_size, size=(n_sources, 2))\n",
    "    bins = np.linspace(0, box_size, resolution + 1)\n",
    "\n",
    "    pixel_size = box_size / resolution\n",
    "    kernel = Gaussian2DKernel(x_stddev=1.0 * sigma_psf / pixel_size)\n",
    "\n",
    "    mu_signal = binned_statistic_2d(x=positions[:, 0], y=positions[:, 1], values=fluxes, statistic='sum', bins=bins).statistic\n",
    "    counts = np.random.poisson(convolve(mu_signal, kernel))\n",
    "                \n",
    "    return fluxes, counts\n",
    "\n",
    "def draw_powerlaw_flux(n_sources, s_min, s_max, exp_b):\n",
    "    \"\"\"\n",
    "    Draw from a powerlaw with slope `exp_b` and min/max mean counts `s_min` and `s_max`. From:\n",
    "    https://stackoverflow.com/questions/31114330/python-generating-random-numbers-from-a-power-law-distribution\n",
    "    \"\"\"\n",
    "    u = np.random.uniform(0, 1, size=n_sources)\n",
    "    s_low_u, s_high_u = s_min ** (exp_b + 1), s_max ** (exp_b + 1)\n",
    "    return (s_low_u + (s_high_u - s_low_u) * u) ** (1.0 / (exp_b + 1.0))\n",
    "\n",
    "fluxes, counts = simulate_sources(amp_b=200., exp_b=-1.2)\n",
    "plt.imshow(counts, cmap='viridis', vmax=20)\n",
    "plt.xlabel(\"Pixels\")\n",
    "plt.ylabel(\"Pixels\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x1200 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Draw parameters from the prior\n",
    "n_params = 16\n",
    "\n",
    "amp_b_prior = (100., 300.)\n",
    "exp_b_prior = (-2.0, -0.5)\n",
    "\n",
    "amp_bs = np.random.uniform(amp_b_prior[0], amp_b_prior[1], n_params)\n",
    "exp_bs = np.random.uniform(exp_b_prior[0], exp_b_prior[1], n_params)\n",
    "\n",
    "# Plot the data samples on a grid\n",
    "fig, axes = plt.subplots(4, 4, figsize=(12, 12))\n",
    "\n",
    "for i, ax in enumerate(axes.flatten()):\n",
    "    fluxes, counts = simulate_sources(amp_b=amp_bs[i], exp_b=exp_bs[i])\n",
    "    im = ax.imshow(counts, cmap='viridis', vmax=20)\n",
    "    ax.set_title(f'$A_b={amp_bs[i]:.2f}, n_b={exp_bs[i]:.2f}$')\n",
    "    ax.set_xticks([])\n",
    "    ax.set_yticks([])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explicit likelihood"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The (marginal) likelihood, which we would need to plug into something like MCMC, is computationally intractable! This is because it involves an integral over a cumbersome latent space, which consists of all possible number $n$ of sources and their positions $\\{z\\}=\\{x, y\\}_{i=1}^{n}$. Let's write this out formally:\n",
    "$$p(x \\mid \\theta)=\\sum_{n} \\int \\mathrm{d}^{n} \\{z\\}\\, p\\left(n \\mid \\theta\\right) \\prod_i^{n} p\\left(z_{i} \\mid \\theta\\right) \\, p\\left(x \\mid \\theta,\\left\\{z_{i}\\right\\}\\right)$$"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implicit inference: Neural posterior estimation"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use neural posterior estimation with a normalizing flow again. Get a training sample:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 30000/30000 [00:19<00:00, 1506.35it/s]\n"
     ]
    }
   ],
   "source": [
    "n_train = 30_000\n",
    "\n",
    "# Sample from prior, then simulate\n",
    "theta_samples = np.random.uniform(low=[10., -3.], high=[200., -0.99], size=(n_train, 2))\n",
    "x_samples = np.array([simulate_sources(theta[0], theta[1])[1] for theta in tqdm(theta_samples)])\n",
    "\n",
    "# Convert to torch tensors\n",
    "theta_samples = torch.Tensor(theta_samples)\n",
    "x_samples = torch.Tensor(x_samples)\n",
    "\n",
    "# Normalize the data\n",
    "x_mean = x_samples.mean(dim=0)\n",
    "x_std = x_samples.std(dim=0)\n",
    "x_samples = (x_samples - x_mean) / x_std\n",
    "\n",
    "theta_mean = theta_samples.mean(dim=0)\n",
    "theta_std = theta_samples.std(dim=0)\n",
    "theta_samples = (theta_samples - theta_mean) / theta_std\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "val_fraction = 0.1\n",
    "batch_size = 128\n",
    "n_samples_val = int(val_fraction * len(x_samples))\n",
    "\n",
    "dataset = TensorDataset(x_samples, theta_samples)\n",
    "\n",
    "dataset_train, dataset_val = random_split(dataset, [len(x_samples) - n_samples_val, n_samples_val])\n",
    "train_loader = DataLoader(dataset_train, batch_size=batch_size, num_workers=8, pin_memory=True, shuffle=True)\n",
    "val_loader = DataLoader(dataset_val, batch_size=batch_size, num_workers=8, pin_memory=True, shuffle=False)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we're working with images, use a simple convolutional neural network (CNN) as the feature extractor. The normalizing flow will be conditioned on the output of the CNN."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CNN(nn.Module):\n",
    "    \"\"\" Simple CNN feature extractor.\n",
    "    \"\"\"\n",
    "    def __init__(self, output_dim):\n",
    "        super(CNN, self).__init__()\n",
    "\n",
    "        self.conv1 = nn.Conv2d(1, 8, kernel_size=3, padding=1)\n",
    "        self.pool1 = nn.MaxPool2d(2, 2)\n",
    "\n",
    "        self.conv2 = nn.Conv2d(8, 16, kernel_size=3, padding=1)\n",
    "        self.pool2 = nn.MaxPool2d(2, 2)\n",
    "\n",
    "        self.fc1 = nn.Linear(16 * 16 * 16, 64)\n",
    "        self.fc2 = nn.Linear(64, output_dim)\n",
    "\n",
    "    def forward(self, x):\n",
    "        \n",
    "        x = x.unsqueeze(1)  # Add channel dim\n",
    "        \n",
    "        x = self.pool1(F.leaky_relu(self.conv1(x), negative_slope=0.02))\n",
    "        x = self.pool2(F.leaky_relu(self.conv2(x), negative_slope=0.02))\n",
    "\n",
    "        x = x.view(x.size(0), -1)\n",
    "\n",
    "        x = F.leaky_relu(self.fc1(x), negative_slope=0.01)\n",
    "        x = self.fc2(x)\n",
    "\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([4.6032, 4.6815, 4.6393, 4.7667, 4.8952, 4.6751, 4.5814, 4.5703, 4.7069,\n",
       "        4.6073, 4.6009, 4.5933, 4.5588, 4.7268, 4.6565, 4.6085, 4.6249, 4.5696,\n",
       "        4.5533, 4.6116, 4.5951, 4.5671, 4.5492, 4.6952, 4.5614, 4.7098, 4.6358,\n",
       "        4.6032, 4.6983, 4.5966, 4.5579, 4.8535, 4.6801, 4.6082, 4.5699, 4.7806,\n",
       "        4.5955, 4.6414, 4.6073, 4.6327, 4.6385, 4.5838, 4.6477, 4.7587, 4.6138,\n",
       "        4.5785, 4.5634, 4.6014, 4.5467, 4.6470, 4.5991, 4.5953, 4.5526, 4.5932,\n",
       "        4.6120, 4.5714, 4.6183, 4.6632, 4.5355, 4.6438, 4.6128, 4.7842, 4.6496,\n",
       "        4.6381], grad_fn=<NegBackward0>)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "npe = NeuralPosteriorEstimator(featurizer=CNN(output_dim=32), d_context=32)\n",
    "npe.loss(x_samples[:64], theta_samples[:64])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: True (mps), used: False\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/torch-mps/lib/python3.9/site-packages/pytorch_lightning/trainer/setup.py:200: UserWarning: MPS available but not used. Set `accelerator` and `devices` using `Trainer(accelerator='mps', devices=1)`.\n",
      "  rank_zero_warn(\n",
      "\n",
      "  | Name       | Type | Params\n",
      "------------------------------------\n",
      "0 | featurizer | CNN  | 265 K \n",
      "1 | flow       | Flow | 30.5 K\n",
      "------------------------------------\n",
      "296 K     Trainable params\n",
      "0         Non-trainable params\n",
      "296 K     Total params\n",
      "1.184     Total estimated model params size (MB)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "18ddae41f27f489eb0f9642535914d22",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Sanity Checking: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "01e561009117405ca1e0bd761160b59f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "00d7dce22b87406c86ca28a941edc0ee",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6a741232ed7f40b3a687d3fdd8697cb8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "150c30e21e34446d9cffdac60070ee75",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8df75445a9714a0784969549da024762",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7ddf6d2a063245c984ea28c5c5d67cb6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "100ef1e32f124ff18d411c1d3848f257",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b1845eac1417473c9e87179cd5c3b4a7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5c01fa0471464dd49245b14d80a1d933",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "afb3c7a9ec4b41cc9ee69642d75b0b18",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6f326ceab73549e0a2af195a0d7d9718",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8985257d364745eab8fe375068c61003",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1d7dc5252dad42cf8a104e84936b32f5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dd9ca822ed22445eb52ff7e19c9418a0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3c458587d9ad417ab5e946962f141d2b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a0ba057e6a95486780e88c77e19260ca",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`Trainer.fit` stopped: `max_epochs=15` reached.\n"
     ]
    }
   ],
   "source": [
    "trainer = pl.Trainer(max_epochs=15)\n",
    "trainer.fit(model=npe, train_dataloaders=train_loader, val_dataloaders=val_loader);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "npe = npe.eval()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get a test map, extract features, condition normalizing flow, extract samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "params_test = np.array([15., -1.4])\n",
    "x_test = simulate_sources(params_test[0], params_test[1])[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 550x550 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_test_norm = (torch.Tensor(x_test) - x_mean) / x_std\n",
    "context = npe.featurizer(x_test_norm.unsqueeze(0))\n",
    "\n",
    "samples_test = npe.flow.sample(num_samples=10000, context=context) * theta_std + theta_mean\n",
    "samples_test = samples_test.detach().numpy()\n",
    "\n",
    "corner.corner(samples_test, labels=[\"amp\", \"exp\"], truths=params_test);"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test of statistical coverage"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=./assets/coverage.png alt= “” width=800>\n",
    "\n",
    "Figure from 2209.01845."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can do some checks to make sure that our posterior has the correct statistical interpretation. In particular, let's test the posterior statistical coverage by evaluating how well the Highest Posterior Density (HPD) intervals capture the true parameter values.\n",
    "\n",
    "The **Highest Posterior Density (HPD)** interval is a region in the parameter space that contains the most probable values for a given credible mass (e.g., 95% or 99%). In other words, it is the shortest interval that contains the specified credible mass of the posterior distribution. This is one of summarizing a posterior distribution.\n",
    "\n",
    "**Nominal coverage** is the probability, or the proportion of the parameter space, that the HPD interval is intended to contain. For example, if the nominal coverage is 0.95, the HPD interval should theoretically contain the true parameter value 95% of the time.\n",
    "\n",
    "**Empirical coverage** is the proportion of true parameter values that actually fall within the HPD interval, based on a set of test cases or simulations.\n",
    "\n",
    "For a perfectly calibrated posterior estimator, empirical coverage = nominal coverage for all credibility levels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/nx/bx2847k56j3dddp761x637pc0000gn/T/ipykernel_9601/4001163492.py:4: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/utils/tensor_new.cpp:233.)\n",
      "  x_test = torch.Tensor([simulate_sources(params_test[0], params_test[1])[1] for _ in range(n_test)])\n"
     ]
    }
   ],
   "source": [
    "n_test = 200  # How many test samples to draw for coverage test\n",
    "\n",
    "# Get samples \n",
    "x_test = torch.Tensor([simulate_sources(params_test[0], params_test[1])[1] for _ in range(n_test)])\n",
    "x_test_norm = (torch.Tensor(x_test) - x_mean) / x_std\n",
    "\n",
    "# and featurize\n",
    "context = npe.featurizer(x_test_norm)\n",
    "\n",
    "# Get posterior for all samples together in a batch\n",
    "samples_test = npe.flow.sample(num_samples=1000, context=context) * theta_std + theta_mean\n",
    "samples_test = samples_test.detach().numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18.008186, 19.924797)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def hpd(samples, credible_mass=0.95):\n",
    "    \"\"\"Compute highest posterior density (HPD) of array for given credible mass.\"\"\"\n",
    "    sorted_samples = np.sort(samples)\n",
    "    interval_idx_inc = int(np.floor(credible_mass * sorted_samples.shape[0]))\n",
    "    n_intervals = sorted_samples.shape[0] - interval_idx_inc\n",
    "    interval_width = np.zeros(n_intervals)\n",
    "    for i in range(n_intervals):\n",
    "        interval_width[i] = sorted_samples[i + interval_idx_inc] - sorted_samples[i]\n",
    "    hdi_min = sorted_samples[np.argmin(interval_width)]\n",
    "    hdi_max = sorted_samples[np.argmin(interval_width) + interval_idx_inc]\n",
    "    return hdi_min, hdi_max\n",
    "\n",
    "hpd(samples_test[0, :, 0], credible_mass=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "p_nominals = np.linspace(0.01, 0.99, 50)\n",
    "contains_true = np.zeros((2, n_test, len(p_nominals)))\n",
    "\n",
    "for i_param in range(2):\n",
    "    for i, sample in enumerate(samples_test[:, :, i_param]):\n",
    "        for j, p_nominal in enumerate(p_nominals):\n",
    "            hdi_min, hdi_max = hpd(sample, credible_mass=p_nominal)\n",
    "            if hdi_min < params_test[i_param] < hdi_max:\n",
    "                contains_true[i_param, i, j] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Coverage for exponent')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make two plots, one for each parameter\n",
    "\n",
    "fig, ax = plt.subplots(1, 2, figsize=(12, 4))\n",
    "\n",
    "ax[0].plot(p_nominals, contains_true[0].sum(0) / n_test)\n",
    "ax[0].plot([0, 1], [0, 1], color=\"black\", linestyle=\"--\")\n",
    "ax[0].set_xlabel(\"Nominal coverage\")\n",
    "ax[0].set_ylabel(\"Expected coverage\")\n",
    "ax[0].set_title(\"Coverage for amplitude\")\n",
    "\n",
    "\n",
    "\n",
    "ax[1].plot(p_nominals, contains_true[1].sum(0) / n_test)\n",
    "ax[1].plot([0, 1], [0, 1], color=\"black\", linestyle=\"--\")\n",
    "ax[1].set_xlabel(\"Nominal coverage\")\n",
    "ax[1].set_ylabel(\"Expected coverage\")\n",
    "ax[1].set_title(\"Coverage for exponent\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Further reading"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- [The frontier of simulation-based inference](https://arxiv.org/abs/1911.01429) (Cranmer, Brehmer, Louppe): Review paper\n",
    "- [simulation-based-inference.org](http://simulation-based-inference.org/): List of papers and resources\n",
    "- [awesome-neural-sbi](https://github.com/smsharma/awesome-neural-sbi): List of papers and resources"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=./assets/header.png alt= “” width=800>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "torch-mps",
   "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": "3.9.13"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}