examples/variational_inference/variational_api_quickstart.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(variational_api_quickstart)=\n",
    "\n",
    "# Introduction to Variational Inference with PyMC\n",
    "\n",
    "The most common strategy for computing posterior quantities of Bayesian models is via sampling,  particularly Markov chain Monte Carlo (MCMC) algorithms. While sampling algorithms and associated computing have continually improved in performance and efficiency, MCMC methods still scale poorly with data size, and become prohibitive for more than a few thousand observations. A more scalable alternative to sampling is variational inference (VI), which re-frames the problem of computing the posterior distribution as an optimization problem. \n",
    "\n",
    "In PyMC, the variational inference API is focused on approximating posterior distributions through a suite of modern algorithms. Common use cases to which this module can be applied include:\n",
    "\n",
    "* Sampling from model posterior and computing arbitrary expressions\n",
    "* Conducting Monte Carlo approximation of expectation, variance, and other statistics\n",
    "* Removing symbolic dependence on PyMC random nodes and evaluate expressions (using `eval`)\n",
    "* Providing a bridge to arbitrary PyTensor code\n",
    "\n",
    ":::{post} Jan 13, 2023 \n",
    ":tags: variational inference\n",
    ":category: intermediate, how-to\n",
    ":author: Maxim Kochurov, Chris Fonnesbeck\n",
    ":::"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import arviz as az\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pymc as pm\n",
    "import pytensor\n",
    "import seaborn as sns\n",
    "\n",
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Distributional Approximations\n",
    "\n",
    "There are severa methods in statistics that use a simpler distribution to approximate a more complex distribution. Perhaps the best-known example is the **Laplace (normal) approximation**. This involves constructing a Taylor series of the target posterior, but retaining only the terms of quadratic order and using those to construct a multivariate normal approximation.\n",
    "\n",
    "Similarly, variational inference is another distributional approximation method where, rather than leveraging a Taylor series, some class of approximating distribution is chosen and its parameters are optimized such that the resulting distribution is as close as possible to the posterior. In essence, VI is a deterministic approximation that places bounds on the density of interest, then uses opimization to choose from that bounded set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gamma_data = np.random.gamma(2, 0.5, size=200)\n",
    "sns.histplot(gamma_data);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "with pm.Model() as gamma_model:\n",
    "    alpha = pm.Exponential(\"alpha\", 0.1)\n",
    "    beta = pm.Exponential(\"beta\", 0.1)\n",
    "\n",
    "    y = pm.Gamma(\"y\", alpha, beta, observed=gamma_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      <progress value='10000' class='' max='10000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      100.00% [10000/10000 00:00&lt;00:00 Average Loss = 169.86]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finished [100%]: Average Loss = 169.87\n"
     ]
    }
   ],
   "source": [
    "with gamma_model:\n",
    "    # mean_field = pm.fit()\n",
    "    mean_field = pm.fit(obj_optimizer=pm.adagrad_window(learning_rate=1e-2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [alpha, beta]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      100.00% [8000/8000 00:02&lt;00:00 Sampling 4 chains, 0 divergences]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 3 seconds.\n"
     ]
    }
   ],
   "source": [
    "with gamma_model:\n",
    "    trace = pm.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pymc.variational.approximations.MeanField at 0x7fca20419e50>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_field"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(mean_field.hist);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "approx_sample = mean_field.sample(1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(trace.posterior[\"alpha\"].values.flatten(), label=\"NUTS\")\n",
    "sns.kdeplot(approx_sample.posterior[\"alpha\"].values.flatten(), label=\"ADVI\")\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic setup\n",
    "\n",
    "We do not need complex models to play with the VI API; let's begin with a simple mixture model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "w = np.array([0.2, 0.8])\n",
    "mu = np.array([-0.3, 0.5])\n",
    "sd = np.array([0.1, 0.1])\n",
    "\n",
    "with pm.Model() as model:\n",
    "    x = pm.NormalMixture(\"x\", w=w, mu=mu, sigma=sd)\n",
    "    x2 = x**2\n",
    "    sin_x = pm.math.sin(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can't compute analytical expectations for this model. However, we can obtain an approximation using Markov chain Monte Carlo methods; let's use NUTS first. \n",
    "\n",
    "To allow samples of the expressions to be saved, we need to wrap them in `Deterministic` objects:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "with model:\n",
    "    pm.Deterministic(\"x2\", x2)\n",
    "    pm.Deterministic(\"sin_x\", sin_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [x]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      <progress value='24000' class='' max='24000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      100.00% [24000/24000 00:04&lt;00:00 Sampling 4 chains, 0 divergences]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 4 chains for 1_000 tune and 5_000 draw iterations (4_000 + 20_000 draws total) took 5 seconds.\n"
     ]
    }
   ],
   "source": [
    "with model:\n",
    "    trace = pm.sample(5000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "az.plot_trace(trace);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above are traces for $x^2$ and $sin(x)$. We can see there is clear multi-modality in this model. One drawback, is that you need to know in advance what exactly you want to see in trace and wrap it with `Deterministic`.\n",
    "\n",
    "The VI API takes an alternate approach: You obtain inference from model, then calculate expressions based on this model afterwards. \n",
    "\n",
    "Let's use the same model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "with pm.Model() as model:\n",
    "    x = pm.NormalMixture(\"x\", w=w, mu=mu, sigma=sd)\n",
    "    x2 = x**2\n",
    "    sin_x = pm.math.sin(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we will use automatic differentiation variational inference (ADVI)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      <progress value='10000' class='' max='10000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      100.00% [10000/10000 00:00&lt;00:00 Average Loss = 2.2066]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finished [100%]: Average Loss = 2.216\n"
     ]
    }
   ],
   "source": [
    "with model:\n",
    "    mean_field = pm.fit(method=\"advi\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AC08cRm0b7/FimkLb+++/3+7nEpEFCCIHc+7cOQFAABBjx44VxcXFpsc++eQTAUBEREcLhbubCH7leZFaWSGEECIrK0sEBAQIAOLMmTOm51x99dUCgJg/f77Q6XSm42fOnBExMTFCqVSKQ4cONath1apVorKystmx8vJyce211woA4tNPP232WFNdAMSDDz4o6uvrTY8tXrxYABCjR49u8V4jIyNNzzP359y5c6bnHzp0SAAQ3t7ebd7PxMREAUAcPny4zXOaVNfXi+HrfxZRq78V/zt1XAghxJgxYwQAsWPHjos+9+effxYAxPPPP286lpSUJACIFStWXPK1iajjOHuCHJZSqcRHH30Eb29v07FZs2Zh/vz5SD97Fp43z8CMiZMQ6eoGAAgJCcHMmTPx1ltvYcuWLYiOjsbBgwfx22+/YcSIEVi4cCGk8zawio6OxhtvvIFp06Zh6dKlzfr9p02b1qIed3d3vPXWW1izZg1++uknzJo1q8U5TddUqf7+n+7999+P559/Hjt37oROp4P6vLEXM2bMQGFhYbvui5ubm+mfKysrAQAuF1mbwtXVtdm5F6NVqfBYzwQ8fGAXlqScwI3hUWbVVFlZif/85z+Ii4vDY489ZtZziMjyGBrIYUVFRbUYiKdQKBAcHo6CggJoExNMzelNYmJiAAA5OTkAgA0bNgAApk6d2iwwNBk1ahQAYM+ePS0eS0lJwa+//orTp0+jqqoKRqPRNH2wrdkIY8eOhZOTU7NjKpUK0dHR2LdvH4qKihAcHGx67PXXX2/7BpihqZ7W3tuF55jr+tBwLD+XgoOlxXijcWnuS3niiSeQkZGBjRs3QqPRtOv1iMhyGBrIYYWGhrZ6vFTR8AtyRPc49PL0avZY01/VTYMVU1NTAQCPPfbYRf8CPv+vfSEE5s6di7feeqvNX7gVFRWtHg8LC2v1eFPrQGuDKDvC3b1hj42qqqo2z6murm5Ww6VIkoRn+iRi+rY/sDIjFb56/UXP3717N9577z3cfvvtGD9+vJmVE1FnYGggh9XaX8/l9fUorKsFANwcGX3JazQNWBw9ejSio9s+38/Pz/TP33zzDd58802EhYVh8eLFGD58OPz9/eHk5ASdTgeNRtNmmLjYX/ytmTt3bru7J15//XVTvREREQCAkpISVFVVmULT+TIzM5uda47+3r64PjQcq7MykFZ98W6NX3/9FUajEUeOHMHYsWObPXbyZMOU2AULFuCDDz7AjBkz8MADD5hdBxG1D0MD0Xl+yEyFofEX9iAfv0uc/fdf/jNmzLjoqojnW7VqFQDg/fffx7XXXtvssbNnz7an3Ev67rvvkJaW1q7nPPfcc6bQ4OXlhYiICKSnp+PAgQOm7pYmmZmZKCwsREREBDw9Pdv1OvN7JGBdThZy6uvNOv/gwYNtPnbixAmcOHECiYmJ7aqBiNqHUy6JGhmFwGfnTpv+3Zy/6q+88koAaHM9htaUlJQAAMLDw1s89u2335p9HXOkpqZCCNGun6ioqGbXuOaaawA0BJALrVy5EgBahB9zhLq44J7zVtnUtTEF87nnnmuz1qSkJADAihUrIITA4sWL210HEZmPoYGo0fbCfJyrqoSyHV0Aw4YNw4QJE/Dnn39izpw5LWYQGI1GrF+/Htu2bTMdi4uLAwD83//9X7NuiK1bt2LRokUdfBeW99BDD0GpVOKDDz5otqlUSkoKXnrpJSiVyhatLFlZWejRowd69Ohx4eWauS+2B9SKhq+hdblZli+eiCyKoYGoUVMrQ3s3pfryyy+RkJCAxYsXIzIyEhMmTMDNN9+M0aNHIygoCJMnT8bevXtN58+ePRuurq5YsmQJ+vTpg1tuuQVXXHEFxowZg/vuu8+i78kS4uPjsWjRItTV1WH06NGYMmUKpk2bhn79+qGoqAiLFi1CfHzzfTnq6+uRnJyM5OTkFtfLycnBsGHDGgLXqFHQn00FALz56DwMHjoUw4YNwwsvvNAVb42I2oljGogAZFZXYWNeNoCGlQvbMwogMDAQO3fuxAcffIBvvvkGe/bsgU6nQ3BwMPr374+pU6fipptuMp0fFxeHPXv24LHHHsOuXbuwevVqxMfH48MPP8Q999zT4WmSnWHOnDmIjY3FokWLsHXrVgDAwIEDMW/evHZvGlVXV4ddu3a1OF6blo69aQ2rT16qhYKI5CGJ9k6yJrJDC48fxodnkjHSLwBfDG/frovUcb9mZ+L+fTvgolRi04QpDrsFOZG1Y/cEObw6gwHfpJ8DAMzq1v5dF6njrg4ORYKXN6oNBryXckLucoioDQwN5PDW5mShtF6HEGctJgSGyF2OQ5IkCY/1TAAAfJV6BulVl16Smoi6HkMDObxvMxpaGW6M6NaumRNkWSP8AjDKLxD1QuCt5GNyl0NErWBoIIeWXlWJvwrzIQGYYebmSdR55vfsCwBYnZWOMxXlMldDRBdiaCCHtjIjFQAw0i8QYS4tl0imrtXXyxtXBobACOBdjm0gsjoMDeSwDELg+8bQ8P8iuslbDJk8HN8LAFsbiKwRQwM5rK35uciprYGXkxoTgzgA0lr09vTGpKAQCADvpByXuxwiOg9DAzmsbxtbGaaFRUCjVMpbDDXzUFxvAMDPWRlIYWsDkdVgaCCHVF6vM60AeWM4uyasTS9PL0wOCoUA8O4ptjYQWQuGBnJIv+VkQWc0Is7dAz092relM3WN2XENYxvWZLO1gchaMDSQQ/oxs2F3iWmhkWZtgU1d7/zWhg9On5S7HCICQwM5oJyaauwqKgAAXBcaLnM1dDH/7t6wcdXqrHRkVlfJXA0RMTSQw1mdlQEBYIiPH9dmsHL9vHww0i8AeiGw9OwpucshcngMDeRwfsxq6JqYGhYpcyVkjn/HNrQ2fJN2DkV1dTJXQ+TYGBrIoZwsL8PJ8jI4SRKmBIfJXQ6ZYYRfABI8vVFrNOCTcylyl0Pk0BgayKH81NjKMC4wGF5qtczVkDkkSTKNbfg89TSq9HqZKyJyXAwN5DCMQmB1ZjoAYGoouyZsyaSgUES5uqG8vh4/ZKbKXQ6Rw2JoIIexp7gQ2bU1cFepMCEwWO5yqB0UkoSkbrEAgGVnU2AUQuaKiBwTQwM5jN9yMgE0/NXKZaNtz43h3eCuckJqVSU25efKXQ6RQ2JoIIdgFALrcrIAAFeHcACkLXJVqUy7kS7j9EsiWTA0kEM4WFKM3NoauKlUGOUXKHc5dJmSusVCAWB7YT6Sy8vkLofI4TA0kENo6poYHxjMrgkbFubiismNU2WXcfolUZdjaCC7J4TA2sbQcFUQuyZs3V3R3QE07B/CxZ6IuhZDA9m9Y2WlyKyphlapxNiAILnLoQ4a4O2LBC9v6IxGrEg7K3c5RA6FoYHsXlPXxJiAIGhVKpmroY6SJAlJUQ2tDV+nn4WB0y+JugxDA9k1IYQpNFzNZaPtxpSQMHg5qZFVU40tnH5J1GUYGsiunaoox7mqSqgVCowL4IJO9sJZqcQN4VEAgC/TzshbDJEDYWggu9Y0AHKUfyDcnZxkroYs6ZbIaADAn3k5yKqulrkaIsfA0EB2bW1uw4JO3NHS/sS4uWOEXwCMAL5J54BIoq7A0EB2K7O6CifLy6BAw/oMZH9ubWxt+Cb9HOqNRpmrIbJ/DA1kt37PywYADPLxg7daI3M11BkmBoXCT6NBfl0tNjb+9yaizsPQQHZrY24OAGBCUIjMlVBnUSsUuCm8YT+KL1PZRUHU2RgayC5V1NdjV1E+AODKQIYGe3ZLZDQkANsK85BRXSV3OUR2jaGB7NKWglzUC4Furm6IdnOXuxzqRGEurhjhFwAAWJWZJnM1RPaNoYHs0sa8hq6JK9k14RCa1mz4LiMVRq4QSdRpGBrI7uiNRvzZGBomsGvCIUwOCoWbSoWM6irsKS6Uuxwiu8XQQHZnf0kRSut18HJSY6C3r9zlUBdwUalMa3H8kJEqbzFEdoyhgexO01TLsQFBUCn4EXcUTV0Uv2Rnolqvl7cYIjvFb1SyO01TLTmewbEM9vFDhIsrqgx6rM3JkrscIrvE0EB25WxlBc5WVcBJknCFf5Dc5VAXkiTJ1NrwQ2aqrLUQ2SuGBrIrTQMgh/j6c4MqBzQ9LBIA8FdhPjK5ZgORxTE0kF3ZWpAHABjLbbAdUpiLK4b7+kMA+DEzXe5yiOwOQwPZjTqDAbuKCgAAo/0DZa6G5PKPxtaG1VnpEFyzgciiGBrIbuwuLkSt0YAgZy3i3D3kLodkMjk4FGqFAimV5ThZUSZ3OUR2haGB7MaW/FwADa0MkiTJXA3JxcNJjbEBDYNgf87KkLkaIvvC0EB2Y2tBQ2jgrAm6LjQCAPAzuyiILIqhgexCbk0NkivKIQEY6R8gdzkkswkBwXBVqpBZU42DpcVyl0NkNxgayC5sK2yYNdHXyxveao3M1ZDctCqVaXGv1VmcRUFkKQwNZBeaxjOwa4KaXN/YRfFLdiYM7KIgsgiGBrJ5BiGwrXF9BoYGajLKPxBeTmoU1NViZ2G+3OUQ2QWGBrJ5R8tKUFKvg7tKhURvH7nLISuhVihwdePOl6uzOYuCyBIYGsjmNXVNjPALhBN3taTzXB8aDgBYm5MJndEoczVEto/fsGTztpq6JrgKJDU32NcfARpnlNfX46/GzwkRXT6GBrJpFfX1OFBSBAAYHcDxDNScUpIwOTgUAPBbTqbM1RDZPoYGsmk7CvOhFwJRrm4Id3GVuxyyQk3jGtbnZqOeXRREHcLQQDZtC1eBpEsY7OMHX7UGpfU604ZmRHR5GBrIZgkhsKVpPEMAxzNQ61QKBSY2LvTELgqijmFoIJuVWlWJjOoqOEkShvly6WhqW1MXxbqcLC70RNQBDA1ks5pmTQz08YOrSiVzNWTNhvsFwNPJCUW6OuwpKpS7HCKbxdBANovjGchcTgoFJgY1zKJYm8suCqLLxdBANklnNGJnYcOgNo5nIHNc1Tj1cm1OJozsoiC6LAwNZJP2FxeiyqCHr1qDnh5ecpdDNmCUXyDcVCrk1dbiYAm3yya6HAwNZJOaZk2M9g+EQpJkroZsgUapxITAhlkUv3IWBdFlYWggm9S038RojmegdmiaRbE2JxOCXRRE7cbQQDansK4Wx8pLAQCjOZ6B2uEK/0BolUpk1VTjSFmJ3OUQ2RyGBrI52xq7Jnp5eMFf4yxzNWRLtCoVxgUEAwDW5mTJXA2R7WFoIJvDVSCpI5o2sFqfy9BA1F4MDWRTjEJgK9dnoA4YGxAMJ0nCmcoKnKkol7scIpvC0EA25WR5GQrr6uCiVGKAt6/c5ZAN8nBywnC/hmXHN+Rly1wNkW1haCCb0rQK5DDfAGiUSpmrIVs1KYhdFESXg6GBbErTfhOcNUEdcWXjrpcHSoqRX1sjczVEtoOhgWxGtV6PvcUNmw1xPAN1RKCzFolePgCA33PZRUFkLoYGshk7iwqgMxoRpnVBN1c3ucshG9fURbGOXRREZmNoIJth2tUyIAgSl46mDprU2EWxozAf5fX1MldDZBsYGshm/L10NMczUMfFuHsgxs0d9UJgc36O3OUQ2QSGBrIJmdVVOFdVCaUkYYQfQwNZxt+zKDiugcgcDA1kE5pWgezv5QMPJyeZqyF7MbGxi2JTfg7qDAaZqyGyfgwNZBO2NnVNBHDWBFlOPy8fBGicUanXY2dRgdzlEFk9hgayenqjEdsL8wFwqiVZlkKSTK0N67iBFdElMTSQ1TtYWowKfT28nNTo6+UtdzlkZyY2jmvYkJcFoxAyV0Nk3RgayOo1zZoY6R8AJadakoUN9wuAu0qFwro6HCwplrscIqvG0EBWr2npaHZNUGdQKxQYFxgMgHtREF0KQwNZtVKdDodLG/764/oM1Fkmnrc6pGAXBVGbGBrIqm0rzIMRQJy7B4K1LnKXQ3ZqjH8Q1AoFUqsqcaayQu5yiKwWQwNZNdNUS3ZNUCdyd3LCCL8AAOyiILoYhgayWkII06JO7JqgzjaRG1gRXRJDA1mtlMpy5NbWQKNQYKivv9zlkJ2bGBgCCcDh0hLk1FTLXQ6RVWJoIKu1Nb+hlWGIrz+clUqZqyF75+/sjAHevgCA3/O4FwVRaxgayGqZtsLmeAbqIk1dFOtzGBqIWsPQQFap1mDArsa9AK4I4HgG6hqTGpeU3lmUj/J6nczVEFkfhgaySruLClBnNCLIWYvubh5yl0MOopubO7q7eUAvBP7My5W7HCKrw9BAVun8WRMSl46mLmTawIqzKIhaYGggq7SV4xlIJpOCG8Y1bM7PQZ3BIHM1RNaFoYGsTm5NDU5VlENCwyZVRF2pr6c3gpy1qDYYTFuyE1EDhgayOk2tDAlePvBWa2SuhhyNQpJMXRRcHZKoOYYGsjpcBZLkNqlx6uXvudkwcAMrIhOGBrIqBiGwjVthk8yG+vrDXeWEIl0dDpQUyV0OkdVgaCCrcqS0BKX1OrirVEj09pG7HHJQTgoFxgcGAwDW5bCLgqgJQwNZlaZVIEf4BcJJwY8nyaepi2JDbhYEuyiIADA0kJXZlJ8DABgTwK4JktcVAYFQKxRIq67CqYpyucshsgoMDWQ1SnR1OFRSDAAYy9BAMnNTOWGUX8NgXM6iIGrA0EBWY1tBHowA4t09EKx1kbscItNCTxtyuYEVEcDQQFZkU37jKpBsZSArMSEwGBKAI2UlyK6plrscItkxNJBVMAphGgQ5NiBY5mqIGvhpnDHQxw8AuyiIAIYGshLHy0pRWFcHF6USgxq/pImsQdN22eyiIGJoICvR1DUxwq9hxDqRtZjYOPVyV1EBSnU6mashkhe/nckqbDZ1TXA8A1mXKFc3xLt7wCAE/sjLkbscIlkxNJDsyut1pqV6uT4DWaOJ5y30ROTIGBpIdlsL8mAQAjFu7ghzcZW7HKIWJjeGhs0Fuag1GGSuhkg+DA0kuy357Jog69bb0wshzlrUGAymDdWIHBFDA8lKCGEazzCGUy3JSkmShImNCz1x6iU5MoYGktXJijLk1dZCq1RiCKdakhVr2sDq99xs6I1GmashkgdDA8lqc2PXxHDfAGiUSpmrIWrbYB8/eDo5oaReh32NA3eJHA1DA8mqaQobZ02QtXNSKDA+sGmhJ3ZRkGNiaCDZlOjqsK+4EAAwPpDjGcj6Nc2iWJ+bDSGEzNUQdT2GBpLNpvzcxl0tPTnVkmzCaP9AaBQKZFRX4WRFmdzlEHU5hgaSzca8hrX8r2xc25/I2rmoVBjt39CVtj6HXRTkeBgaSBY6o9E0CHICuybIhjRtYLWeG1iRA2JoIFnsLipApV4PP40G/bx85C6HyGzjA0OgAHC8vBSZ1VVyl0PUpRgaSBZNXRPjA4KhkCSZqyEyn69Gg8G+/gCA33IyZa6GqGsxNFCXE0JgY+NUywmBHM9AtueakDAAwC/ZDA3kWBgaqMulVJYjo7oKaoUCo/wD5S6HqN2uCgqDAsCh0mJksIuCHAhDA3W53xsHkI30C4CLSiVzNUTt5+/sjKGNXRS/ZGfIXA1R12FooC7HrgmyB9eGhAMAfmUXBTkQhgbqUoV1tTjQuG4/V4EkWzY5uKGL4khZCdKqKuUuh6hLMDRQl9qQmw0BoI+nF4K1LnKXQ3TZfDUaDPcLAMDWBnIcDA3UpdY2TlG7KjhM5kqIOu6axi4KjmsgR8HQQF2mTKfDX4X5AICrGRrIDkwOCoVSknCsvBTnKivkLoeo0zE0UJf5PS8beiEQ7+6BaDd3ucsh6jAfjQYjm7oouNATOQCGBuoyv7FrguxQUxfFz1np3C6b7B5DA3WJSn09thbkAWBoIPsyOSgUaoUCyRXlOFHO7bLJvjE0UJf4My8HOqMRUa5uiHf3kLscIovxVKtNa478kJkmczVEnYuhgbrEbzlZAIApwWGQuEEV2Zl/hEUCAFZnpUNvNMpcDVHnYWigTlej12NTfsMqkOyaIHs0JiAI3k5qFNTVYnvjDCEie8TQQJ1uc0EuagwGhGld0MfTS+5yiCxOrVDg2tCGAZGr2EVBdoyhgTrd2sauicnsmiA71tRFsT43C1V6vczVEHUOhgbqVNV6PTbkNoSGa0LYNUH2K9HLB1GubqgxGEwrnxLZG4YG6lQbcrNRbTAg0sUViV4+cpdD1GkkSTK1NrCLguwVQwN1qp+yGr48rw+NYNcE2b2m0PBXYT5yaqplrobI8hgaqNMU1dVhS+OCTlNDI2Suhqjzhbu4YpCPHwSAn7LS5S6HyOIYGqjT/JqTAYMQ6OPphRgu6EQOoqm14cfMNC4rTXaHoYE6zU+ZDX9pTQ2NlLkSoq5zTXCYaVnpo2WlcpdDZFEMDdQpMqqrsK+kCBKAaxs39CFyBJ5qNSYHhQIAVqSdlbkaIstiaKBO0TR6fLhfAIK0WpmrIepat0bGAGgYCFxRXy9zNUSWw9BAFmcUAivTzwEAbgiLkrcYIhkM9fVDjJs7qg0GDogku8LQQBa3s6gAmTXVcFepcHVwqNzlEHU5SZIws7G14au0MxwQSXaDoYEs7tvGVobrQiOgValkroZIHtPDIqFRKHCivAwHS4vlLofIIhgayKLKdDr81riE7k0R3WSuhkg+nmq1aRDwl6lnZK6GyDIYGsiifs7OgM5oRLy7BxI8veUuh0hWM6MauijWZGegTKeTuRqijmNoIItq6pq4Mbwbl40mh5fo5YOeHp6oMxrxA/ejIDvA0EAWc6S0BEfKSuAkSZgWxgWdiM4fEPlZ6mkYOSCSbBxDA1nMF2kN/bZTQsLhq9HIXA2RdZgWFgl3lRNSqyqxKT9X7nKIOoShgSyiTKfD6sZlo29r7MclIsBVpcLNkQ2Dgj85myJzNUQdw9BAFvF9ZipqjQb08PDEQG9fucshsiqzomKhALCtMA+nKsrkLofosjE0UIcZhcDnjVPKbo+M4QBIoguEubhiUuNCZ2xtIFvG0EAd9ldhPlKrKuGmUmEqB0ASterObnEAGvZlKa6rk7kaosvD0EAd9tm50wCA6WFRcOUKkEStGuTji76e3qgzGrEinbtfkm1iaKAOOVNZgd/zsgEAszgAkqhNkiThn9HdAQCfp56GzmiUuSKi9mNooA75+MwpCABXBoYgxt1D7nKIrNo1IeHw1zgjr7YWP3P3S7JBDA102QrravF9ZioA4J6YOHmLkcnOnTsxdepU+Pn5wdnZGXFxcXjqqadQXV1t9jWuvPJKSJIESZKQm9tyHn9tbS3uv/9++Pn5wdXVFddffz3S0lpfXbCsrAxBQUG45ZZb2v1eUlNTIUkSoqKiLnreHXfcAUmSsHz58laPN/0oFAp4enoiKioK1113HV577TXk5eW1+7r2RK1Q4M7G1ob3T5/kYk9kcxga6LJ9dq6hiTXRyweDffzkLqfLffnllxg1ahRWr16NqKgoTJkyBbW1tXjppZcwYsQIVFRUXPIay5cvx8aNGy864+Shhx7CkiVLEBkZidGjR2PNmjWYMmUKDAZDi3OfeeYZVFVV4fXXX+/Qe+uIkSNHIikpCbNmzcKkSZMQFhaGjRs34rHHHkNERAReffVVh94qemZkDDycnHCmsgLrcrLkLoeoXRga6LLU6PWmaZb3xMQ73DTLzMxM3H333TAYDFi2bBn27t2LH374ASkpKbjxxhtx6NAhzJ8//6LXKCgowNy5czFp0iRERES0ek5OTg6WLVuGq6++Gnv37sXatWvxwgsv4Pjx41i1alWzc48ePYolS5bg6aefRmhoqMXea3vdfffdWL58OZYvX46VK1di27ZtKCoqwjvvvAOVSoXHH38cTz75pGz1yc3dyQlJUbEAgCWnTzh0gCLbw9BAl2VlRipK63WIcHHF5GD5fkHJZfny5aitrcXEiRPxz3/+03Rco9Hgvffeg4uLCz7++GMUFRW1eY2HH34YVVVVWLJkSZvnHD16FHq9HrNmzTIFszvvvBMAcPDgwWbnPvDAA4iJicGcOXM68M46h1arxYMPPohffvkFSqUSr7zyCg4dOiR3WbK5o1t3aJVKHC0rxZaCtrtsiKwNQwO1m0EIfHz2FADgrug4KB2slQEA9u3bBwAYO3Zsi8f8/f3Rq1cv1NfX49dff231+evWrcNXX32FJ598EjExbc86KSkpAQB4e/+9zXjTPxcXF5uOffXVV9i8eTPeffddODk5tfv9dJWxY8eaxlu8++67MlcjHx+NBrdGRgMAlqSckLkaIvMxNFC7rcnKQHp1Fbyc1JgRHiV3ObKoqqoC0PyX+fl8fHwAoNW/pqurq3HfffehR48el+zCaOq2SEn5exXBU6caAltkZMNCWpWVlZg3bx5uuOEGTJw4sZ3vpOvdfPPNAIA///xT5krkdXd0PNQKBXYXF2JPUaHc5RCZhaGB2sUgBN45dRxAQyuDi4Mu5uTv7w8Abc5iaDqempra4rGnn34aqampeP/996FWqy/6OomJiQgODsabb76Jo0ePIi8vD/Pnz4ckSbj66qsBAAsWLEBpaSnefPPNDryjrpOYmAgAOHv2LHQ6nbzFyChIq8UNYVEAgP+lHJe3GCIzMTRQu6zOSsfZqgp4OamR1C1W7nJkM2bMGADAihUrWvzi27lzJ5KTkwGgxQyK/fv34+2330ZSUlKrXRsXcnZ2xqJFi5Camoq+ffsiKCgI69atw3333YeEhAQkJydj8eLFeOKJJ5oNpqypqbnsAXZpaWnNpk5e+PPpp59e1nWb+Pn9PdOmqfvFUd0XGw+VJGFLQR5bG8gmOOafiXRZ9EajqZXhXzHxcLfivvPONnPmTLz00ktIT0/H1KlT8frrryMiIgLbt2/HPffcA5VKBb1eD4Xi71xuMBhwzz33wMvLq11TImfOnIno6GisXLkStbW1GD9+PG644QYAwIMPPoiIiAjMnTsXAPD111/j8ccfR1paGjw9PfHAAw9gwYIFzeq4FFdXV8yYMaPNx7dt24YzZ86Yfb0LnR9mHG3WzYUiXN1wY3g3rEg/i9dPHsHXI8Y6/D0h68bQQGb7KSsdqVWV8FGrMcuBWxmAhl+sa9aswbXXXou1a9di7dq1psciIiLwyCOP4LXXXms25mHx4sXYv38/Pv7442Z/bZtj+PDhGD58eLNj33//PTZs2IA1a9ZAo9Fg3759uPXWWzF58mS8/fbb2Lx5M1566SUEBARg9uzZZr+Wn5/fRRdYuuOOOzoUGgoL//6Luq0xIY7kwbie+D4zFbuLC7GtMB+j/QPlLomoTQwNZBbdea0M98TEc2MqAH379sXJkyexcuVK7N27F3q9Hv369cOtt96KF198EQDQu3dv0/k///yzqXn/s88+a3atppUgp0+fDrVajRdffBGjRo1q87Vramrw6KOP4rrrrsM111wDAHjjjTfg5uaGb7/9Fu7u7pg6dSr279+PRYsWtSs0dLamqaLdu3e36pkeXSVY64LbImOw7FwKXj95BKP8AtjaQFaL3/xklhVpZ5FeXQV/jTNmRTl2K8P5tFotZs2ahVmzZjU7/vvvvwNoOSVTCIEtW7a0eb0dO3YAaP7XeGtefvll5OXlYfHixaZjJ0+eRI8ePeDu7m46NmTIEGzevBnl5eXw8LCOvUG+/vprAMC4ceNkrsR6/Lt7D6xIP4vDpSXYkJeNSUGOt/YJ2QYOhKRLqtTX493GVoaH43o57IwJc23evBn79+9H7969MXLkSNPxTZs2QQjR6k/T9MmcnBwIITBt2rQ2r3/mzBksWrQI8+fPR3R0dLPHLtzzomlqqLX85bpp0yZ8/fXXkCQJDz74oNzlWA0/jTP+2a1hT4o3Tx7jnhRktRga6JKWnjmFIl0durm64caIbnKXYzUOHjwIvV7f7Nj+/ftx6623QpKkTlu86KGHHkJwcDAef/zxZsd79+6N48eP48CBAwAaZm78/PPPiIiIaNb6IIfa2lr873//wzXXXAODwYCnn34affr0kbUma/OvmHi4q5yQXFGGn7gDJlkp/slIF1VQW4uPzjRMH5zXoy+c2jEK3949/PDDOH78OBITE+Hn54fU1FTs2rULCoUCH374Yac0v//yyy/45ZdfsGrVKmi12maPzZs3D1999RXGjRuH8ePH48CBA8jIyMAHH3xg8TouZunSpdi0aROAhpaP3Nxc7Nu3D9XV1dBoNHjttddMsz3ob55qNe6Ljceik0fx+smjuDo4DM5KpdxlETXD0EAX9U7KcVQbDOjn5YOrHHCPiYu57bbb8MUXX+DgwYMoLS2Fv78/br75ZsybN8+0gJEl1dXV4aGHHsLkyZNb7b5ISEjAjz/+iKeeegpr1qxBUFAQFi5ciHvvvdfitVzM9u3bsX37dkiSBDc3N/j4+GDcuHEYM2YMkpKSEBAQ0KX12JI7o+PwReoZZNdUY/m5FNwX20PukoiakQS3WKM2nKuswKRN66AXAiuGj8UwP3+5SyKyez9kpOHRg7vhrlLhz/FT4KvRyF0SkQnbmqlVQggsOHYQeiEwLiCIgYGoi0wLi0BvDy9U6PWmac5E1oKhgVq1Pjcbm/JzoVYo8FTvRLnLIXIYCknCE737AQC+SjuDs5UVl3gGUddhaKAWqvV6LDh2EEDDQk7RbvKOvCdyNCP8AjA+IBh6IfDaiSNyl0NkwtBALbyXcgLZNdUI1brgfg7EIpLF470SoACwLjcLu4sK5C6HCABDA13gTGWFaYrlM30SoeVCTkSy6O7ugZsjGxbveun4IS74RFaBoYFMhBB49sh+1DcOfpwYGCJ3SUQO7eG43nBVqnC4tARrsjPkLoeIoYH+9mtOJrYX5kOtUODZPv2tZulhIkfl7+yMe2PjAQCvnjiCWoNB5orI0TE0EACgSq/Hi8cOAQD+HdsDka5uMldERABwd3QcQpy1yK6pNnUdEsmFoYEAAIuTjyG3tgYRLq5chY7IimhVKjzWKwEA8P7pk8itqZG5InJkDA2Ew6XFWHb2FADguT79ud49kZW5LiQcA719UWMw4LWTnIJJ8mFocHA6oxHzD+6FEcDU0AiMCwyWuyQiuoAkSXi6TyIAYFVmGg6WFMtbEDkshgYH98Hpk0iuKIOPWo2nufIjkdXq5+WD6WGRAIAXjh0Etw0iOTA0OLCUinL8r3Ft+2f79OfGOERWbn7PvnBRKrG/pAirszgFk7oeQ4ODMgiBxw7tQb0QGB8YjOtCwuUuiYguIdBZi3/H9gQAvHriMKr1epkrIkfD0OCgPjt3GgdKiuGuUuHFvgO4JgORjbg7Jg6hWhfk1NZwCiZ1OYYGB5RRXYVFjSOwH+/VD8FaF5krIiJzOSuVeLxxCuYHp5ORXVMtc0XkSBgaHIwQAv89tBc1BgOG+frj5ohucpdERO10TXAYBvn4odZo4C6Y1KUYGhzMdxmp2F6YD41CgVf6DYKC3RJENkeSJDzTOxESgJ+y0rG/uEjukshBMDQ4kPzaGrx4vGGp6Ed69EEUl4omsll9vbwxIzwKALDg2EHugkldgqHBQQgh8MyRAyivr0eCpzfu7NZd7pKIqIPm9egLV6UKh0qL8WNmutzlkANgaHAQv+VkYV1uFlSShIWJg6BS8D89ka3zd3bG/d0bpmC+fpK7YFLn428OB1Cq0+GZI/sBAP/p3hM9PbzkLYiILOaf0d0R0jgF8+PGPWSIOgtDgwN45cRhFOnq0N3NA//hDpZEdsVZqcS8Hn0AAO+nnERBXa3MFZE9Y2iwc3uLC/Ft+jkAwCv9BkLDHSyJ7M71oRFI8PRGlUGPd5KPy10O2TGGBjtWbzTiqcP7AAD/L6IbBvr4yVwREXUGhSThv736AQBWpJ/F6YpymSsie8XQYMc+OZeC5Ipy+KjVeKxnX7nLIaJONMzPHxMDQ2AQAq+cOCx3OWSnGBrsVFZ1NRYnHwMA/LdXP3iruYMlkb17vFcCVJKEP/Jy8FdhvtzlkB1iaLBTC44dQI3BgME+frghLFLucoioC0S7uePWyBgAwMvHDnHBJ7I4hgY79HtuNtbnZkMlSXgxgTtYEjmSh+J6wV2lwrHyUqzO4oJPZFkMDXamWq/Hc0cPAGjYQjfO3VPmioioK/loNPh3bNOCT0dRxwWfyIIYGuzMuynHkVVTjVCtCx7s3kvucohIBv+M7o4gZy2yaqrxWeppucshO8LQYEdOVZRh6ZmGFeGe69MfLiqVzBURkRyclUo8Et8bAPBeygmU6XQyV0T2gqHBTggh8NTh/dALgYmBIbgyKETukohIRtPDoxDv7oGy+nosOX1S7nLITjA02InvM9Owp7gQWqUSz/bpL3c5RCQzpSTh8Z4JAIDl51KQWV0lc0VkDxga7ECJrg6vHD8EAHgorjdCXVxkroiIrMGYgCAM9/WHzmjEm43rthB1BEODHXj1xBEU63SId/fAndHd5S6HiKyEJEl4vFdDa8OPmWk4XlYqb0Fk8xgabNy+4kJ807gh1Qt9B8JJwf+kRPS3BC8fXB8aDgFgIZeXpg7ibxgbVm804snD+wEAN4VHYbAvN6Qiopbm9ugLJ0nC1oI8bC3Ik7scsmEMDTZs+bkUJFeUwdtJjccaBzwREV0o3MUVt0fFAgAWHj/M5aXpsjE02Kjsmr83pHq8VwJ8NNyQioja9kBcT7irnHC8vBQ/cXlpukwMDTZqwdGDqDYYMMjbFzPCo+Quh4isnLdag/907wGAy0vT5WNosEEb87KxLjcLKknCCwkDoeCGVERkhju6dUewsxbZXF6aLhNDg42p0evx3JGGDanujI5DDw9uSEVE5nFWKvFIjz4AgP+lnEApl5emdmJosDH/SzmBzJpqhDhrMTuOG1IRUfv8IywS8e6eKK+vx3spJ+Quh2wMQ4MNSakox/+dSQYAPNu3P1y5IRURtZNSkvDfxgWfPks9jQwuL03twNBgIxo2pNoHvRCYEBiMSUGhcpdERDbqCv9AjPILhM5oxKtc8InagaHBRvyQmYbdjRtSPccNqYioAyRJwpO9+0EB4JfsTOwrLpS7JLIRDA02oFSnw8uNG1LNjuuFMBdXmSsiIlvXw8MTN0Z0AwC8eOwQBBd8IjMwNNiAV08cRrFOh+5uHrgzOk7ucojITjwa3wcuSiUOlhZjTXaG3OWQDWBosHL7i4vwddOGVAkDoOaGVERkIf7OzrgvtmHBp4UnjqCWCz7RJfA3kBXTG4148sg+AMCM8CgM9fWXuSIisjd3R8eZFnz65GyK3OWQlWNosGLLz53GyfIyeDmp8Tg3pCKiTqBVqTCvZ18AwJLTJ1BYVytzRWTNGBqsVHZNNd5KPgoAeKxnX/hyQyoi6iRTQyOQ4OmNSr3etBEeUWsYGqzU80cPoNpgwEBvX9zUOMKZiKgzKCQJT/TuBwBYkXYWx8pKZK6IrBVDgxXakJuN9bnZUEkSXuKGVETUBYb6+uPakHAYATxxeB8MnIJJrWBosDJVej2eO7IfAHB3TBziuSEVEXWRp3v3g7tKhcOlJfgq7Yzc5ZAVYmiwMouTjyG7tgZhWhfM7s4NqYio6wQ4azG3R8OgyEUnjiC/tkbmisjaMDRYkeNlpfjkXMOUpwV9B0DLDamIqIvNjIpBgpc3KvR6vHDskNzlkJVhaLASBiHwZGM/4tXBYRgXGCx3SUTkgJSShJcTBkIBYE12Bjbn58pdElkRhgYr8VXaGRwsLYabSoVn+yTKXQ4RObDent64o1t3AMAzR/ZzpUgyYWiwAlnV1Xj1+BEAwNwefRHorJW5IiJydHN69Eawsxbp1VV4i2s3UCOGBpkJIfDUkX2oMugxyNsXt0fFyF0SERHcVE5Y0HcAAOCjM8nYXVQgc0VkDRgaZPZjVjo25edCrVBgYb9BXJOBiKzGlUEhuDE8CgLA3IN7UKmvl7skkhlDg4wK62qx4OhBAMDsuF6IcfeQtyAiogs83TsRoVoXZFRX4UXOpnB4DA0X8ddff2HKlCnw8fGBm5sbhgwZgk8//bTd11m+fDkkSWrx4++sxcGrp6Hurffwr5j4Np+/ceNGTJs2DYGBgdBoNAgNDcWUKVOwevXqjrw9IqI2nThxAjfeeCOiQ0Kw57oZyHrwUfzfu+/i58y0dl+rtrYWL7/8Mvr16wdXV1c4Ozuje/fumD17NnJzW5+d0dp35vk/tbXcWEsOXAigDatWrcKNN94Io9GIK664An5+fti4cSPuuOMOHDp0CG+++Wa7r9mvXz8kJiYCaNiQ6q/CfCgA3DXlOjgpWs9vjz/+OF599VWo1WqMHDkSgYGByMrKwpYtWxASEoLrr7++A++SiKilnTt3YsKECaiursaQIUMQFRWF3/74AyUff4bbTp7C/t/Wmd0yWltbizFjxmD37t3w8fHBuHHjoFarsXv3brz77rtYuXIlduzYgaioqBbPdXV1xYwZM1q9rlKp7MhbpMslqIXi4mLh6ekpAIjvv//edDw3N1fExsYKAOKPP/4w+3qffPKJACCeffZZIYQQRbW1YvC61SJq9bdi4fFDbT5vyZIlAoAYPHiwSE9Pb/ZYVVWVOHLkSPveGBHRJdTX14uYmBgBQLz55pum4yVlZcK3Ty8BQPSZ/4iorq8363pvv/22ACCGDh0qysrKTMdra2vFjTfeKACIWbNmtXgeABEZGdnh90OWxe6JVixduhRlZWWYOnUqpk+fbjoeGBiI1157DQAuq6UBaJgt8fihvSioq0Wsmzseiuvd6nmlpaV47LHH4O7ujp9++gnh4eHNHndxcUGfPn0uqwYiorasWrUKZ86cQb9+/TBnzhzTcS8PD3zz0VIAQPI3K/HE4X0QZmxqtWXLFgDAnDlz4OHxd+uERqPBE088AQDYs2ePJd8CdSKGhlasWbMGAFptFrvmmmvg7OyM33///bL61L5OP4cNedlwkiS8PWAYnNtoYluxYgUqKipwyy23IDiYq0MSUde42PffhGHDERIVifq0DKzcvxfvppy45PU0Gs0lz/Hx8Wl/oSQLjmloxeHDhwEAAwYMaPGYWq1Gnz59sHfvXiQnJ6Nfv35mX3frrl1498gB6KqqMaZ7HArc/YExY1o9d+PGjQCAiRMnIi8vD19++SVOnToFd3d3jBw5Etdddx379IjI4g4dapgh0dr3HwCMGDQY36Wmof5cGt4KPIYIF1dMC4ts83oTJ07EV199hcWLF+Pqq682tTbodDq8/PLLAICkpKRWn1tVVYWXXnoJ6enpcHFxQf/+/TF9+nS4ubl15C1SR8jdP2JtysrKBAABoFn/2/mmTZsmAIjVq1ebdc2mMQ2t/YwZM0bk5ua2eE6PHj0EAPH666+bxlec/9O/f3+RmZnZofdKRHQhb29vAUAcOtT6eKuHH35YABDXPvGYiFr9rYhb853YXpDX5vX0er1p7IKPj4+49tprxT/+8Q8RGhoqPDw8xEsvvdTq89r6zvT19RVr1qyxyHul9mP3xAUqKytN/+zi4tLqOa6uri3OvZjg4GBMvP/fCF78Gvp89yUOnTuL1atXo0ePHti8eTOuueYaGC5Y272kpARAw+yJ2NhY7Ny5E+Xl5dixYwf69++PAwcOYMaMGWb1KRIRmavpe+1S33/DXT1wdXAYdEYj7t69DbvaWDFSqVRixYoVmDt3LoqLi7FmzRqsWrUKWVlZSExMxKhRo1p93qxZs7B27VpkZWWhsrISBw4cwO23346ioiJMnz4du3fvtsC7pfZiaLiAOb+E2/uL2m1AIk5PHgdNdBReHX4FEqK64brrrsOePXsQFxeHffv24Ztvvmn2nKYQodVqsXbtWgwdOhTu7u4YNmwY1q5dC1dXV+zcudPUjUFEZElSG6vTNn3/SZKEt/oPwdiAINQYDLhz11bsLS5scX5JSQkmTJiAJUuW4O2330ZmZiaKi4vx448/4ty5c5gwYQJ+/PHHFs/79NNPMXnyZISEhMDV1RWJiYn47LPP8N///hc6nQ5PPfWURd8vmcchQ8Mdd9zR4qfpQ+vu7m46r7q6utXnNx03p18tu6Yas/fvhABwU3gUpoSEmR5zc3PD7NmzAQDr1q1r9rymOq6//nr4+fk1eywgIADXXHMNAGDTpk2XrIGIyFxN32tVVVWtPn7+959GqcQHg0ZglF8gqg0G3LFza4uttOfMmYPNmzfj5ZdfxuzZsxEaGgpvb29MnToVP/zwA4xGIx566CHo9Xqz6nvsscegVCqxadMm6HS6DrxTuhwOGRo+/fTTFj8HDx4EAHh4eMDT0xMAkJmZ2erzm45HRERc9HXqDAb8Z+8OFOt06O3hhef7thxY1L17w/azOTk5zY43LXQSGdn6AKOmx/Pz8y9aAxFRezR9r5n7/adRKvF/g0dgpF8Aqgx63LV7G1amnwPQ0GK6YsUKAK3Pxhg0aBC6deuG9PR0nD171qz6PD09ERAQgPr6ehQWtmzZoM7lkKFBCNHi57nnnjM93jQjYv/+/S2eW19fj6NHj0Kj0SA+vu2ln4UQeP7oQRwqLYankxPeHzyi1emVTWMXLmy16N+/PwCguLi41esXFRW1+jwioo642Pff+ccTEhJMx7QqFZYNHY1poREwCIH5h/bi1ROHkZ2ba2oNOH+NhvM1HW/ru+5CRqMR5eXlAPj9JweHDA2X0tT0/91337V4bM2aNaitrcWECRPg7Ozc5jWWnzuNFelnIQFY3H8owl1cWz3v+++/BwAMHDiw2fGm5aE3b94Mo9HY7DGDwYCtW7cCaHtaFBHR5bjY99+BAwdw9uxZ9OrVC926dWv2mFqhwJv9h+A/sT0AAB+cTsbs5CNQq9UAgL1797a4Xnl5OZKTkwG03ap6obVr16KqqgqxsbFtBhHqRHJN27BmRUVFwsPDo8Uy0nl5eaZlpH///fcWz4uPjxfx8fHi6wP7RPTqb0XU6m/Fh6dPirfffltUVFQ0O1en04nnnntOABBarbbV6ZPDhw8XAMTzzz/f7PjTTz8tAIiAgABRWVlpoXdNRNTw3dStW7cWy0hXVlaavpOWLl3a4nnjx48X8fHxYteuXWJNVrro++sPImr1t8Jj2GABQCQmJors7GzT+TU1NWLmzJkCgBg5cmSza61cuVIkJye3eI1NmzaJkJCQFrVR12FoaMN3330nFAqFkCRJjB07VsyYMUN4eXkJAGL27NmtPgeN84i7L/tARK3+Vjx+cI8wGo2mYDBw4EAxffp0MWXKFNMH39nZuVkwOd/p06dFQECAACB69uwpbrjhBtP6DVqtVmzYsKEzbwEROajt27cLrVZr2jPipptuEsHBwQKAmDZtmjAYDC2eExkZKQCIP//8UwghRHpVpZi25XcR+uE7QuHVsNaMq7u7mDRpkpg6darpO9DHx6fFPjpJSUkN36Xdu4tJkyaJG2+8USQmJpq+Y2+++eZWa6DOx9BwEdu2bRNXXXWV8PLyEi4uLmLgwIFi2bJlbZ7f9IEO/eh/YuZfm4Su8UP9zDPPiIkTJ4rw8HCh1WqFs7OziI2NFffee684efLkRWvIyckR9957rwgLCxNOTk4iMDBQ3HLLLeLo0aMWfa9EROc7evSouOGGG4Svr6/QaDSiV69e4vXXXxd6vb7V8y8MDUIIoTMYxAcpJ0Xs5x8Lj6nXCKewUKHSaIRaoxGxsbHi/vvvFxkZGS2u9euvv4qZM2eKHj16CC8vL6FSqURAQIC4+uqrxcqVKzvrLZMZJCG4OpAlZFVX46btfyC7tga9PLywYsQYeDip5S6LiEh2GdVVeO7oAfyR1zBLTClJuDYkHP+M7o5+Xtx3wpYwNFhAbk0NbtmxCalVlYh2dcc3I8fCT9P2IEkiIkd0oKQI75w6jk3nreUw0NsXM8KjMCUkHB5OTjJWR+ZgaOigzOoqzNyxGenVVQjVumDlyHEI1ra+/CoREQFHS0uw7FwK1mSlo77xV5BaocCVgSG4NiQco/wD4c4AYZUYGjrgbGUFbtuxGTm1NYhwccWXw8cgrI2plURE1Fx+bQ1+yEzDDxlpSKksNx1XSRIG+/hhXGAwxgcEI9rNvc1lralrMTRcpp2FBfjP3r9QUq9DrJs7vhg+BoHOWrnLIiKyOUIIHCsrxY9ZadiYl4PUquabAYZqXTDU1x9DfP0x1NcfkS6uDBEyYWi4DF+lncWzR/ZDLwQSPL3x8dBRHMNARGQh5yorsCk/F3/k5WB3cQF0FyxwF6BxxhBffwzx9cNQX3/EunlAwRDRJRga2qFGr8fzxw7im8Z11a8LCcdriYNbXR6aiIg6rkqvx77iQuwqKsDu4kIcLi1uESK8ndQY3Bgghvj4o6enF5QMEZ2CocFMyeVleGDfDpyurIAE4JH4Pri/ew82kRERdaFagwEHS4qxu7gAu4sKsK+4CLVGQ7Nz3FUq9PXyQYKXNxI8fZDg5YMQrZbf1xbA0HAJNXo9lpw+if87kwyd0YgAjTPeGjAUI/wC5C6NiMjh6YxGHCsraWiJKCrA3uJCVLSyzbafRoMELx/08/JBgqc3Erx84KPRyFCxbWNoaINRCPyak4mFxw8jq6Zh//jxgcF4rd9g+PKDRkRklQxCILm8DIdLi3G4tASHSouRXFEGQyu/6sJdXBtaI7x8kODpg75e3nBVqWSo2nYwNFzAIAQ25GZhcfJxJFeUAQBCtC54pnciJgWFsHmLiMjG1BoMOF5WikOlxThUWozDpcU4d8EMDaBh2+dYd4+GENEYJnq4e0LDcWsmDA2NsmuqsTL9HL7NSEV2Y8uCu0qFu6LjcE9MPFyYPomI7EZ5vQ5HGlsiDpeW4HBpMXJqa1qcpwAQ4eqG7m4eiHX3QIybO7q7eyDGzcMhWyUcNjQYhcDxslJsLcjD1oJc7CwqQNON8HRywu1Rsbg7Og6eau4fQUTkCPJra3CoMUA0dW+U1uvaPD9A44xgrQuCtVqEOLsgSKtFsNYFIc4Nx/w0znBSKLrwHXQ+hwgNNXo9Uqsrca6yEifKS3G0rOFDUaxr/mEY5uuPmyOicVVwKJujiIgcnBAC+XW1OF1RjpTKcpypqMDpynKcrixHYV2dWddwUSrh4aSGh5MTPFROcHNygpOkgJOi4Ufd+P9VkgLjAoIwNjC4k99Vx8gaGnYVFWBXUYHp34UQMO0v3VjWhf/+9zEBIWBqHdAbjag06FGlr0eVXo/K+noU63Qo0tWirL6+1dd3VaowzM8fo/2DMDYgCJGubp3xNmVVUFBw6ZOIyGH4+/vLXYJdKNXpkFFdhZyaamTXViOnpga5tdXIrqlBTk018mproG/nr9eH43rhofjenVSxZcjaIbOjMB9vnzreJa/l6eSEbq4NfVF9PL3R18sbvT29obazpqMLBQRwaigR/c0BGpe7hJdaDS+1Gn29vFt93CAEKurrUV6vQ3l9Pcr19SjT6VCpr0e9EKg3GlFvNEIvjNA1/vMQX+sPdLK2NGzMy8bG3Bw0TUiQAEiQ0Ph/Df8MQDrv35vmLjQ8p+HfJalhf3ZXpRNcVSq4qVRwVTnBR62Gr8YZ/hpneDno2ATO9iCi8zE0UEc4xJgGR8bQQETn41c+dYR9t80TERGRxTjeJFMHk5+fL3cJRERkJ9g9QURERGZh9wQRERGZhaGBiIiIzMLQQERERGZhaCAiIiKzMDQQERGRWRgaiIiIyCwMDURERGQWhgYiIiIyC0MDERERmYWhgYiIiMzC0EBERERmYWggIiIiszA0EBERkVkYGoiIiMgsDA1ERERkFoYGIiIiMgtDAxEREZmFoYGIiIjMwtBAREREZlGZc5IQAjqdrrNrISIiok6gVqshSVKHr2NWaNDpdFi4cGGHX4yIiIi63uOPPw6NRtPh60hCCHGpk7q6pSE3NxfLly/HHXfcgaCgoC57XUfF+931eM+7Fu931+M971qXut9d2tIgSZJFEoq51Gq16f935es6Kt7vrsd73rV4v7se73nX6qr7zYGQREREZBarDA1ubm4YM2YM3Nzc5C7FIfB+dz3e867F+931eM+7Vlfdb7PGNBARERFZZUsDERERWR+GBiIiIjILQwMRERGZhaGBiIiIzMLQQERERGaRNTRs2bIFc+fOxbhx4+Dp6QlJknDHHXdc9vXWrVuHsWPHwsPDA+7u7hg7dizWrVtnuYLtRG5uLu6++24EBwfD2dkZcXFxWLBgQbtX/ZQkqc0fR1x2fM+ePZgyZQq8vb3h6uqKIUOG4KuvvmrXNYxGI/73v/8hISEBWq0W/v7+uOmmm5CSktJJVduujt7vTZs2XfQzvHPnzk6s3vZ88cUXuPfeezFo0CBoNBpIkoTly5e3+zr8jJvHEve7Mz7jZq0I2VmWLVuGTz/9FC4uLoiIiEB5efllX+vLL7/EbbfdBj8/PyQlJUGSJHz77be46qqr8MUXX2DmzJkWrNx25ebmYujQocjIyMC0adMQFxeHbdu24dlnn8WOHTvwyy+/QKEwP0tGRka2GvRGjRplwaqt36ZNmzB58mSo1WrcfPPN8PT0xA8//ICZM2ciNTUVTzzxhFnXue+++/DRRx+hV69eePDBB5GXl4dvvvkG69evx19//YVevXp18juxDZa63wAwZswYjB07tsXxsLAwC1Zs+5566imkpaXBz88PwcHBSEtLu6zr8DNuHkvdb8DCn3Ehoz179oijR48KvV4vduzYIQCIpKSkdl+nuLhYeHl5CT8/P5Genm46np2dLYKCgoSXl5coLi62YOW2a9asWQKAWLJkiemY0WgUSUlJAoBYtmyZ2dcCIMaMGdMJVdqW+vp6ERMTIzQajdi/f7/peHl5uejdu7dQqVTi1KlTl7zOH3/8IQCI0aNHi9raWtPx33//XUiSJK644opOqd/WWOp+//nnnwKAePbZZzuxWvuxYcMGkZqaKoQQ4pVXXhEAxCeffNKua/Azbj5L3O/O+IzL2j0xaNAg9O7dG0qlskPXWblyJUpLS/Hggw8iPDzcdDw4OBgPP/wwSktLsXLlyo6Wa/MqKirwzTffIDo6Gvfdd5/puCRJeOWVV6BQKPDRRx/JWKFt+uOPP3DmzBnceuut6N+/v+m4u7s7nn76aej1enzyySeXvE7TvX/xxRebrR0/YcIETJ48GVu2bMGpU6cs/wZsjKXuN7XPlVdeicjIyA5dg59x81nifncGuxgIuWnTJgDApEmTWjw2efJkAMDmzZu7siSrtGPHDtTV1WHixIktdjsLDg5G3759sWvXLtTW1pp9zdLSUixduhQvv/wyPvroI4fsl7zY56/pmDmfv02bNsHV1RUjR45s8Rg/x3+z1P1ukpKSgnfeeQcLFy7EihUrUFhYaJE6qSV+xuVhyc+4rGMaLKXpF1X37t1bPNZ0zBF/mV3oYvep6fihQ4dw9uxZs/sVDx06hHvuucf075IkYebMmfjwww/h4uLS8aJtwMXuq7e3N/z8/C75+auqqkJOTg769OnTassbP8d/s8T9Pt9XX33VbAClVqvF888/j3nz5nW8WDLhZ1w+lvyM20VLQ1lZGQDA09OzxWOurq5QKpWmcxzZxe4TAHh4eDQ771Lmzp2LXbt2obi4GCUlJfjjjz8wdOhQfPHFF7jrrrssU7QNMOe+XuqeWvq/jT2zxP0GAH9/fyxatAgnTpxAVVUVsrKy8MUXX8DHxwfz58/Hhx9+aNG6HR0/412vMz7jHQ4Nfn5+F53SceFPU9MiXT5rueeLFi3CkCFD4O3tDS8vL4wbNw4bN25EbGwsvv76axw7dqxTXpfIEnr37o25c+eiR48ecHFxQUhICGbOnIm1a9dCrVbj2WefhdFolLtMosvWGZ/xDndP3HLLLaioqDD7/KCgoI6+ZAtNybWsrAy+vr7NHquqqoLBYGgz3dqiy73n59+n1jRNee3IvXJxccEtt9yCF154Adu3b0fv3r0v+1q2wpz7eql72hX/beyFJe73xfTp0wdDhw7F1q1bcfr0acTFxV32tehv/Ixbj458xjscGt59992OXqLDunfvjr179yIlJaVFaLhUP74tutx7fqk+w5SUFCgUCkRHR192bUBDSwgAVFdXd+g6tuL8+zpw4MBmj5WUlKCwsBAjRoy46DVcXV0RHByMc+fOwWAwtOjztcfP8eWyxP2+FEf7DHcFfsaty+V+xu1iTMOYMWMAAOvXr2/xWNOKkE3nOLJhw4ZBo9Fgw4YNEEI0eywnJwdHjhzB0KFD4ezs3KHX2bVrFwAgKiqqQ9exFRf7/DUdM+fzN2bMGFRVVWH79u0tHuPn+G+Wut9t0ev12L9/PyRJQkRExGVfh1riZ9w6dOgzbrEVHzrInMWdqqqqxIkTJ0RaWlqz48XFxcLT05OLO5mhvYs7tXXP9+/fL6qqqlpc/9tvvxWSJAk/Pz9RUVHROW/CytTX14vo6Gih0WjEgQMHTMfPX2woOTnZdLygoECcOHFCFBQUNLvO+Qvf1NXVmY5z4ZvmLHW///rrL2E0Gltc++GHHxYAxFVXXdWp78OWXWqxIX7GLety73dnfMZlDQ1bt24VSUlJIikpSUyZMkUAEDExMaZjr7zySrPzm1a3am0Vws8//1wAEH5+fuKBBx4Qs2fPFoGBgQKA+Pzzz7voHVm/7OxsER4eLiRJEtOnTxePP/64GDlypAAgJk+eLAwGQ7Pz27rnSUlJwtPTU0yfPl08/PDD4qGHHhKjR48WAISzs7P45ZdfuvBdye+PP/4QTk5Ows3NTdxzzz3i0UcfFd26dRMAxIsvvtjs3GeffbbNVdruvvtuAUD06tVLzJs3T8yaNUtoNBrh6ekpjh071kXvxvpZ4n5HRkaKqKgoceutt4p58+aJe+65R8THxwsAIiIiwrQaHzX46KOPTN/NAwYMEADEyJEjTcdWrVplOpef8Y6zxP3ujM+4rKHhk08+EQDa/LnwF9XFQoMQQvz222/iiiuuEG5ubsLNzU1cccUVYu3atZ3/RmxMdna2uPPOO0VgYKBQq9UiNjZWPP/8882WdW3S1j3/4YcfxNSpU0VUVJRwcXERarVadOvWTdx1113ixIkTXfROrMuuXbvEVVddJTw9PYVWqxWDBg0SX3zxRYvzLvaFajAYxDvvvCN69+4tNBqN8PX1FTNmzGj2lzM16Oj9XrhwoRg7dqwICQkRarVauLi4iISEBPHkk0+yZbIVTa2Rbf2cf3/5Ge84S9zvzviMS0Jc0LlNRERE1Aq7GAhJREREnY+hgYiIiMzC0EBERERmYWggIiIiszA0EBERkVkYGoiIiMgsDA1ERERkFoYGIiIiMgtDAxEREZmFoYGIiIjMwtBAREREZmFoICIiIrP8fydMg1sSWMg+AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "az.plot_posterior(mean_field.sample(1000), color=\"LightSeaGreen\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that ADVI has failed to approximate the multimodal distribution, since it uses a Gaussian distribution that has a single mode.\n",
    "\n",
    "## Checking convergence"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use the default arguments for `CheckParametersConvergence` as they seem to be reasonable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      <progress value='10000' class='' max='10000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      100.00% [10000/10000 00:00&lt;00:00 Average Loss = 2.2449]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finished [100%]: Average Loss = 2.239\n"
     ]
    }
   ],
   "source": [
    "from pymc.variational.callbacks import CheckParametersConvergence\n",
    "\n",
    "with model:\n",
    "    mean_field = pm.fit(method=\"advi\", callbacks=[CheckParametersConvergence()])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can access inference history via `.hist` attribute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(mean_field.hist);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is not a good convergence plot, despite the fact that we ran many iterations. The reason is that the mean of the ADVI approximation is close to zero, and therefore taking the relative difference (the default method) is unstable for checking convergence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      <progress value='4613' class='' max='10000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      46.13% [4613/10000 00:00&lt;00:00 Average Loss = 3.3199]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Convergence achieved at 6200\n",
      "Interrupted at 6,199 [61%]: Average Loss = 4.3808\n"
     ]
    }
   ],
   "source": [
    "with model:\n",
    "    mean_field = pm.fit(\n",
    "        method=\"advi\", callbacks=[pm.callbacks.CheckParametersConvergence(diff=\"absolute\")]\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(mean_field.hist);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's much better! We've reached convergence after less than 5000 iterations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tracking parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another useful callback allows users to track parameters. It allows for the tracking of arbitrary statistics during inference, though it can be memory-hungry. Using the `fit` function, we do not have direct access to the approximation before inference. However, tracking parameters requires access to the approximation. We can get around this constraint by using the object-oriented (OO) API for inference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "with model:\n",
    "    advi = pm.ADVI()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pymc.variational.approximations.MeanField at 0x7fca1daee6a0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "advi.approx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Different approximations have different hyperparameters. In mean-field ADVI, we have $\\rho$ and $\\mu$ (inspired by [Bayes by BackProp](https://arxiv.org/abs/1505.05424))."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mu': mu, 'rho': rho}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "advi.approx.shared_params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are convenient shortcuts to relevant statistics associated with the approximation. This can be useful, for example, when specifying a mass matrix for NUTS sampling:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0.34]), array([0.69314718]))"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "advi.approx.mean.eval(), advi.approx.std.eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can roll these statistics into the `Tracker` callback."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "tracker = pm.callbacks.Tracker(\n",
    "    mean=advi.approx.mean.eval,  # callable that returns mean\n",
    "    std=advi.approx.std.eval,  # callable that returns std\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, calling `advi.fit` will record the mean and standard deviation of the approximation as it runs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      <progress value='20000' class='' max='20000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      100.00% [20000/20000 00:02&lt;00:00 Average Loss = 2.3202]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finished [100%]: Average Loss = 2.2862\n"
     ]
    }
   ],
   "source": [
    "approx = advi.fit(20000, callbacks=[tracker])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now plot both the evidence lower bound and parameter traces:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x900 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(16, 9))\n",
    "mu_ax = fig.add_subplot(221)\n",
    "std_ax = fig.add_subplot(222)\n",
    "hist_ax = fig.add_subplot(212)\n",
    "mu_ax.plot(tracker[\"mean\"])\n",
    "mu_ax.set_title(\"Mean track\")\n",
    "std_ax.plot(tracker[\"std\"])\n",
    "std_ax.set_title(\"Std track\")\n",
    "hist_ax.plot(advi.hist)\n",
    "hist_ax.set_title(\"Negative ELBO track\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that there are convergence issues with the mean, and that lack of convergence does not seem to change the ELBO trajectory significantly. As we are using the OO API, we can run the approximation longer until convergence is achieved."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      <progress value='100000' class='' max='100000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      100.00% [100000/100000 00:12&lt;00:00 Average Loss = 2.1328]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finished [100%]: Average Loss = 2.1363\n"
     ]
    }
   ],
   "source": [
    "advi.refine(100_000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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      "text/plain": [
       "<Figure size 1600x900 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(16, 9))\n",
    "mu_ax = fig.add_subplot(221)\n",
    "std_ax = fig.add_subplot(222)\n",
    "hist_ax = fig.add_subplot(212)\n",
    "mu_ax.plot(tracker[\"mean\"])\n",
    "mu_ax.set_title(\"Mean track\")\n",
    "std_ax.plot(tracker[\"std\"])\n",
    "std_ax.set_title(\"Std track\")\n",
    "hist_ax.plot(advi.hist)\n",
    "hist_ax.set_title(\"Negative ELBO track\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We still see evidence for lack of convergence, as the mean has devolved into a random walk. This could be the result of choosing a poor algorithm for inference. At any rate, it is unstable and can produce very different results even using different random seeds.\n",
    "\n",
    "Let's compare results with the NUTS output:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(trace.posterior[\"x\"].values.flatten(), label=\"NUTS\")\n",
    "sns.kdeplot(approx.sample(20000).posterior[\"x\"].values.flatten(), label=\"ADVI\")\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, we see that ADVI is not able to cope with multimodality; we can instead use SVGD, which generates an approximation based on a large number of particles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      <progress value='300' class='' max='300' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      100.00% [300/300 00:44&lt;00:00]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with model:\n",
    "    svgd_approx = pm.fit(\n",
    "        300,\n",
    "        method=\"svgd\",\n",
    "        inf_kwargs=dict(n_particles=1000),\n",
    "        obj_optimizer=pm.sgd(learning_rate=0.01),\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(trace.posterior[\"x\"].values.flatten(), label=\"NUTS\")\n",
    "sns.kdeplot(approx.sample(10000).posterior[\"x\"].values.flatten(), label=\"ADVI\")\n",
    "sns.kdeplot(svgd_approx.sample(2000).posterior[\"x\"].values.flatten(), label=\"SVGD\")\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That did the trick, as we now have a multimodal approximation using SVGD. \n",
    "\n",
    "With this, it is possible to calculate arbitrary functions of the parameters with this variational approximation. For example we can calculate $x^2$ and $sin(x)$, as with the NUTS model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# recall x ~ NormalMixture\n",
    "a = x**2\n",
    "b = pm.math.sin(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To evaluate these expressions with the approximation, we need `approx.sample_node`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(0.06251754)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_sample = svgd_approx.sample_node(a)\n",
    "a_sample.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(0.06251754)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_sample.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(0.06251754)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_sample.eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Every call yields a different value from the same node. This is because it is **stochastic**. \n",
    "\n",
    "By applying replacements, we are now free of the dependence on the PyMC model; instead, we now depend on the approximation. Changing it will change the distribution for stochastic nodes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(np.array([a_sample.eval() for _ in range(2000)]))\n",
    "plt.title(\"$x^2$ distribution\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a more convenient way to get lots of samples at once: `sample_node`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "a_samples = svgd_approx.sample_node(a, size=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(a_samples.eval())\n",
    "plt.title(\"$x^2$ distribution\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `sample_node` function includes an additional dimension, so taking expectations or calculating variance is specified by `axis=0`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(0.13313996)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_samples.var(0).eval()  # variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(0.24540344)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_samples.mean(0).eval()  # mean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A symbolic sample size can also be specified:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pytensor.tensor as pt\n",
    "\n",
    "i = pt.iscalar(\"i\")\n",
    "i.tag.test_value = 1\n",
    "a_samples_i = svgd_approx.sample_node(a, size=i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100,)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_samples_i.eval({i: 100}).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000,)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_samples_i.eval({i: 10000}).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unfortunately the size must be a scalar value."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multilabel logistic regression\n",
    "\n",
    "Let's illustrate the use of `Tracker` with the famous Iris dataset. We'll attempy multi-label classification and compute the expected accuracy score as a diagnostic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X, y = load_iris(return_X_y=True)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](http://5047-presscdn.pagely.netdna-cdn.com/wp-content/uploads/2015/04/iris_petal_sepal.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A relatively simple model will be sufficient here because the classes are roughly linearly separable; we are going to fit multinomial logistic regression."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xt = pytensor.shared(X_train)\n",
    "yt = pytensor.shared(y_train)\n",
    "\n",
    "with pm.Model() as iris_model:\n",
    "    # Coefficients for features\n",
    "    β = pm.Normal(\"β\", 0, sigma=1e2, shape=(4, 3))\n",
    "    # Transoform to unit interval\n",
    "    a = pm.Normal(\"a\", sigma=1e4, shape=(3,))\n",
    "    p = pt.special.softmax(Xt.dot(β) + a, axis=-1)\n",
    "\n",
    "    observed = pm.Categorical(\"obs\", p=p, observed=yt)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Applying replacements in practice\n",
    "PyMC models have symbolic inputs for latent variables. To evaluate an expression that requires knowledge of latent variables, one needs to provide fixed values. We can use values approximated by VI for this purpose. The function `sample_node` removes the symbolic dependencies. \n",
    "\n",
    "`sample_node` will use the whole distribution at each step, so we will use it here. We can apply more replacements in single function call using the `more_replacements` keyword argument in both replacement functions.\n",
    "\n",
    "> **HINT:** You can use `more_replacements` argument when calling `fit` too:\n",
    ">   * `pm.fit(more_replacements={full_data: minibatch_data})`\n",
    ">   * `inference.fit(more_replacements={full_data: minibatch_data})`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "with iris_model:\n",
    "    # We'll use SVGD\n",
    "    inference = pm.SVGD(n_particles=500, jitter=1)\n",
    "\n",
    "    # Local reference to approximation\n",
    "    approx = inference.approx\n",
    "\n",
    "    # Here we need `more_replacements` to change train_set to test_set\n",
    "    test_probs = approx.sample_node(p, more_replacements={Xt: X_test}, size=100)\n",
    "\n",
    "    # For train set no more replacements needed\n",
    "    train_probs = approx.sample_node(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By applying the code above, we now have 100 sampled probabilities (default number for `sample_node` is `None`) for each observation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we create symbolic expressions for sampled accuracy scores:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_ok = pt.eq(test_probs.argmax(-1), y_test)\n",
    "train_ok = pt.eq(train_probs.argmax(-1), y_train)\n",
    "test_accuracy = test_ok.mean(-1)\n",
    "train_accuracy = train_ok.mean(-1)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tracker expects callables so we can pass `.eval` method of PyTensor node that is function itself. \n",
    "\n",
    "Calls to this function are cached so they can be reused."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "eval_tracker = pm.callbacks.Tracker(\n",
    "    test_accuracy=test_accuracy.eval, train_accuracy=train_accuracy.eval\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      <progress value='100' class='' max='100' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      100.00% [100/100 00:07&lt;00:00]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "inference.fit(100, callbacks=[eval_tracker]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, ax = plt.subplots(1, 1)\n",
    "df = pd.DataFrame(eval_tracker[\"test_accuracy\"]).T.melt()\n",
    "sns.lineplot(x=\"variable\", y=\"value\", data=df, color=\"red\", ax=ax)\n",
    "ax.plot(eval_tracker[\"train_accuracy\"], color=\"blue\")\n",
    "ax.set_xlabel(\"epoch\")\n",
    "plt.legend([\"test_accuracy\", \"train_accuracy\"])\n",
    "plt.title(\"Training Progress\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Training does not seem to be working here. Let's use a different optimizer and boost the learning rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      <progress value='91' class='' max='400' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      22.75% [91/400 00:06&lt;00:20]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "inference.fit(400, obj_optimizer=pm.adamax(learning_rate=0.1), callbacks=[eval_tracker]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, ax = plt.subplots(1, 1)\n",
    "df = pd.DataFrame(np.asarray(eval_tracker[\"test_accuracy\"])).T.melt()\n",
    "sns.lineplot(x=\"variable\", y=\"value\", data=df, color=\"red\", ax=ax)\n",
    "ax.plot(eval_tracker[\"train_accuracy\"], color=\"blue\")\n",
    "ax.set_xlabel(\"epoch\")\n",
    "plt.legend([\"test_accuracy\", \"train_accuracy\"])\n",
    "plt.title(\"Training Progress\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is much better! \n",
    "\n",
    "So, `Tracker` allows us to monitor our approximation and choose good training schedule."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Minibatches\n",
    "When dealing with large datasets, using minibatch training can drastically speed up and improve approximation performance. Large datasets impose a hefty cost on the computation of gradients. \n",
    "\n",
    "There is a nice API in PyMC to handle these cases, which is available through the `pm.Minibatch` class. The minibatch is just a highly specialized PyTensor tensor."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To demonstrate, let's simulate a large quantity of data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Raw values\n",
    "data = np.random.rand(40000, 100)\n",
    "# Scaled values\n",
    "data *= np.random.randint(1, 10, size=(100,))\n",
    "# Shifted values\n",
    "data += np.random.rand(100) * 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For comparison, let's fit a model without minibatch processing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with pm.Model() as model:\n",
    "    mu = pm.Flat(\"mu\", shape=(100,))\n",
    "    sd = pm.HalfNormal(\"sd\", shape=(100,))\n",
    "    lik = pm.Normal(\"lik\", mu, sigma=sd, observed=data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just for fun, let's create a custom special purpose callback to halt slow optimization. Here we define a callback that causes a hard stop when approximation runs too slowly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def stop_after_10(approx, loss_history, i):\n",
    "    if (i > 0) and (i % 10) == 0:\n",
    "        raise StopIteration(\"I was slow, sorry\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[66], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[39mwith\u001b[39;00m model:\n\u001b[0;32m----> 2\u001b[0m     advifit \u001b[39m=\u001b[39m pm\u001b[39m.\u001b[39;49mfit(callbacks\u001b[39m=\u001b[39;49m[stop_after_10])\n",
      "File \u001b[0;32m~/mambaforge/envs/pie/lib/python3.9/site-packages/pymc/variational/inference.py:747\u001b[0m, in \u001b[0;36mfit\u001b[0;34m(n, method, model, random_seed, start, start_sigma, inf_kwargs, **kwargs)\u001b[0m\n\u001b[1;32m    745\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    746\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mTypeError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mmethod should be one of \u001b[39m\u001b[39m{\u001b[39;00m\u001b[39mset\u001b[39m(_select\u001b[39m.\u001b[39mkeys())\u001b[39m}\u001b[39;00m\u001b[39m or Inference instance\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m--> 747\u001b[0m \u001b[39mreturn\u001b[39;00m inference\u001b[39m.\u001b[39;49mfit(n, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
      "File \u001b[0;32m~/mambaforge/envs/pie/lib/python3.9/site-packages/pymc/variational/inference.py:138\u001b[0m, in \u001b[0;36mInference.fit\u001b[0;34m(self, n, score, callbacks, progressbar, **kwargs)\u001b[0m\n\u001b[1;32m    136\u001b[0m     callbacks \u001b[39m=\u001b[39m []\n\u001b[1;32m    137\u001b[0m score \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_maybe_score(score)\n\u001b[0;32m--> 138\u001b[0m step_func \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mobjective\u001b[39m.\u001b[39;49mstep_function(score\u001b[39m=\u001b[39;49mscore, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m    139\u001b[0m \u001b[39mif\u001b[39;00m progressbar:\n\u001b[1;32m    140\u001b[0m     progress \u001b[39m=\u001b[39m progress_bar(\u001b[39mrange\u001b[39m(n), display\u001b[39m=\u001b[39mprogressbar)\n",
      "File \u001b[0;32m~/mambaforge/envs/pie/lib/python3.9/site-packages/pytensor/configparser.py:47\u001b[0m, in \u001b[0;36m_ChangeFlagsDecorator.__call__.<locals>.res\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     44\u001b[0m \u001b[39m@wraps\u001b[39m(f)\n\u001b[1;32m     45\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mres\u001b[39m(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[1;32m     46\u001b[0m     \u001b[39mwith\u001b[39;00m \u001b[39mself\u001b[39m:\n\u001b[0;32m---> 47\u001b[0m         \u001b[39mreturn\u001b[39;00m f(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
      "File \u001b[0;32m~/mambaforge/envs/pie/lib/python3.9/site-packages/pymc/variational/opvi.py:387\u001b[0m, in \u001b[0;36mObjectiveFunction.step_function\u001b[0;34m(self, obj_n_mc, tf_n_mc, obj_optimizer, test_optimizer, more_obj_params, more_tf_params, more_updates, more_replacements, total_grad_norm_constraint, score, fn_kwargs)\u001b[0m\n\u001b[1;32m    385\u001b[0m seed \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mapprox\u001b[39m.\u001b[39mrng\u001b[39m.\u001b[39mrandint(\u001b[39m2\u001b[39m\u001b[39m*\u001b[39m\u001b[39m*\u001b[39m\u001b[39m30\u001b[39m, dtype\u001b[39m=\u001b[39mnp\u001b[39m.\u001b[39mint64)\n\u001b[1;32m    386\u001b[0m \u001b[39mif\u001b[39;00m score:\n\u001b[0;32m--> 387\u001b[0m     step_fn \u001b[39m=\u001b[39m compile_pymc([], updates\u001b[39m.\u001b[39;49mloss, updates\u001b[39m=\u001b[39;49mupdates, random_seed\u001b[39m=\u001b[39;49mseed, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mfn_kwargs)\n\u001b[1;32m    388\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    389\u001b[0m     step_fn \u001b[39m=\u001b[39m compile_pymc([], [], updates\u001b[39m=\u001b[39mupdates, random_seed\u001b[39m=\u001b[39mseed, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mfn_kwargs)\n",
      "File \u001b[0;32m~/mambaforge/envs/pie/lib/python3.9/site-packages/pymc/pytensorf.py:1121\u001b[0m, in \u001b[0;36mcompile_pymc\u001b[0;34m(inputs, outputs, random_seed, mode, **kwargs)\u001b[0m\n\u001b[1;32m   1119\u001b[0m opt_qry \u001b[39m=\u001b[39m mode\u001b[39m.\u001b[39mprovided_optimizer\u001b[39m.\u001b[39mincluding(\u001b[39m\"\u001b[39m\u001b[39mrandom_make_inplace\u001b[39m\u001b[39m\"\u001b[39m, check_parameter_opt)\n\u001b[1;32m   1120\u001b[0m mode \u001b[39m=\u001b[39m Mode(linker\u001b[39m=\u001b[39mmode\u001b[39m.\u001b[39mlinker, optimizer\u001b[39m=\u001b[39mopt_qry)\n\u001b[0;32m-> 1121\u001b[0m pytensor_function \u001b[39m=\u001b[39m pytensor\u001b[39m.\u001b[39;49mfunction(\n\u001b[1;32m   1122\u001b[0m     inputs,\n\u001b[1;32m   1123\u001b[0m     outputs,\n\u001b[1;32m   1124\u001b[0m     updates\u001b[39m=\u001b[39;49m{\u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mrng_updates, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs\u001b[39m.\u001b[39;49mpop(\u001b[39m\"\u001b[39;49m\u001b[39mupdates\u001b[39;49m\u001b[39m\"\u001b[39;49m, {})},\n\u001b[1;32m   1125\u001b[0m     mode\u001b[39m=\u001b[39;49mmode,\n\u001b[1;32m   1126\u001b[0m     \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs,\n\u001b[1;32m   1127\u001b[0m )\n\u001b[1;32m   1128\u001b[0m \u001b[39mreturn\u001b[39;00m pytensor_function\n",
      "File \u001b[0;32m~/mambaforge/envs/pie/lib/python3.9/site-packages/pytensor/compile/function/__init__.py:315\u001b[0m, in \u001b[0;36mfunction\u001b[0;34m(inputs, outputs, mode, updates, givens, no_default_updates, accept_inplace, name, rebuild_strict, allow_input_downcast, profile, on_unused_input)\u001b[0m\n\u001b[1;32m    309\u001b[0m     fn \u001b[39m=\u001b[39m orig_function(\n\u001b[1;32m    310\u001b[0m         inputs, outputs, mode\u001b[39m=\u001b[39mmode, accept_inplace\u001b[39m=\u001b[39maccept_inplace, name\u001b[39m=\u001b[39mname\n\u001b[1;32m    311\u001b[0m     )\n\u001b[1;32m    312\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    313\u001b[0m     \u001b[39m# note: pfunc will also call orig_function -- orig_function is\u001b[39;00m\n\u001b[1;32m    314\u001b[0m     \u001b[39m#      a choke point that all compilation must pass through\u001b[39;00m\n\u001b[0;32m--> 315\u001b[0m     fn \u001b[39m=\u001b[39m pfunc(\n\u001b[1;32m    316\u001b[0m         params\u001b[39m=\u001b[39;49minputs,\n\u001b[1;32m    317\u001b[0m         outputs\u001b[39m=\u001b[39;49moutputs,\n\u001b[1;32m    318\u001b[0m         mode\u001b[39m=\u001b[39;49mmode,\n\u001b[1;32m    319\u001b[0m         updates\u001b[39m=\u001b[39;49mupdates,\n\u001b[1;32m    320\u001b[0m         givens\u001b[39m=\u001b[39;49mgivens,\n\u001b[1;32m    321\u001b[0m         no_default_updates\u001b[39m=\u001b[39;49mno_default_updates,\n\u001b[1;32m    322\u001b[0m         accept_inplace\u001b[39m=\u001b[39;49maccept_inplace,\n\u001b[1;32m    323\u001b[0m         name\u001b[39m=\u001b[39;49mname,\n\u001b[1;32m    324\u001b[0m         rebuild_strict\u001b[39m=\u001b[39;49mrebuild_strict,\n\u001b[1;32m    325\u001b[0m         allow_input_downcast\u001b[39m=\u001b[39;49mallow_input_downcast,\n\u001b[1;32m    326\u001b[0m         on_unused_input\u001b[39m=\u001b[39;49mon_unused_input,\n\u001b[1;32m    327\u001b[0m         profile\u001b[39m=\u001b[39;49mprofile,\n\u001b[1;32m    328\u001b[0m         output_keys\u001b[39m=\u001b[39;49moutput_keys,\n\u001b[1;32m    329\u001b[0m     )\n\u001b[1;32m    330\u001b[0m \u001b[39mreturn\u001b[39;00m fn\n",
      "File \u001b[0;32m~/mambaforge/envs/pie/lib/python3.9/site-packages/pytensor/compile/function/pfunc.py:367\u001b[0m, in \u001b[0;36mpfunc\u001b[0;34m(params, outputs, mode, updates, givens, no_default_updates, accept_inplace, name, rebuild_strict, allow_input_downcast, profile, on_unused_input, output_keys, fgraph)\u001b[0m\n\u001b[1;32m    353\u001b[0m     profile \u001b[39m=\u001b[39m ProfileStats(message\u001b[39m=\u001b[39mprofile)\n\u001b[1;32m    355\u001b[0m inputs, cloned_outputs \u001b[39m=\u001b[39m construct_pfunc_ins_and_outs(\n\u001b[1;32m    356\u001b[0m     params,\n\u001b[1;32m    357\u001b[0m     outputs,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    364\u001b[0m     fgraph\u001b[39m=\u001b[39mfgraph,\n\u001b[1;32m    365\u001b[0m )\n\u001b[0;32m--> 367\u001b[0m \u001b[39mreturn\u001b[39;00m orig_function(\n\u001b[1;32m    368\u001b[0m     inputs,\n\u001b[1;32m    369\u001b[0m     cloned_outputs,\n\u001b[1;32m    370\u001b[0m     mode,\n\u001b[1;32m    371\u001b[0m     accept_inplace\u001b[39m=\u001b[39;49maccept_inplace,\n\u001b[1;32m    372\u001b[0m     name\u001b[39m=\u001b[39;49mname,\n\u001b[1;32m    373\u001b[0m     profile\u001b[39m=\u001b[39;49mprofile,\n\u001b[1;32m    374\u001b[0m     on_unused_input\u001b[39m=\u001b[39;49mon_unused_input,\n\u001b[1;32m    375\u001b[0m     output_keys\u001b[39m=\u001b[39;49moutput_keys,\n\u001b[1;32m    376\u001b[0m     fgraph\u001b[39m=\u001b[39;49mfgraph,\n\u001b[1;32m    377\u001b[0m )\n",
      "File \u001b[0;32m~/mambaforge/envs/pie/lib/python3.9/site-packages/pytensor/compile/function/types.py:1766\u001b[0m, in \u001b[0;36morig_function\u001b[0;34m(inputs, outputs, mode, accept_inplace, name, profile, on_unused_input, output_keys, fgraph)\u001b[0m\n\u001b[1;32m   1754\u001b[0m     m \u001b[39m=\u001b[39m Maker(\n\u001b[1;32m   1755\u001b[0m         inputs,\n\u001b[1;32m   1756\u001b[0m         outputs,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1763\u001b[0m         fgraph\u001b[39m=\u001b[39mfgraph,\n\u001b[1;32m   1764\u001b[0m     )\n\u001b[1;32m   1765\u001b[0m     \u001b[39mwith\u001b[39;00m config\u001b[39m.\u001b[39mchange_flags(compute_test_value\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39moff\u001b[39m\u001b[39m\"\u001b[39m):\n\u001b[0;32m-> 1766\u001b[0m         fn \u001b[39m=\u001b[39m m\u001b[39m.\u001b[39;49mcreate(defaults)\n\u001b[1;32m   1767\u001b[0m \u001b[39mfinally\u001b[39;00m:\n\u001b[1;32m   1768\u001b[0m     t2 \u001b[39m=\u001b[39m time\u001b[39m.\u001b[39mperf_counter()\n",
      "File \u001b[0;32m~/mambaforge/envs/pie/lib/python3.9/site-packages/pytensor/compile/function/types.py:1659\u001b[0m, in \u001b[0;36mFunctionMaker.create\u001b[0;34m(self, input_storage, trustme, storage_map)\u001b[0m\n\u001b[1;32m   1656\u001b[0m start_import_time \u001b[39m=\u001b[39m pytensor\u001b[39m.\u001b[39mlink\u001b[39m.\u001b[39mc\u001b[39m.\u001b[39mcmodule\u001b[39m.\u001b[39mimport_time\n\u001b[1;32m   1658\u001b[0m \u001b[39mwith\u001b[39;00m config\u001b[39m.\u001b[39mchange_flags(traceback__limit\u001b[39m=\u001b[39mconfig\u001b[39m.\u001b[39mtraceback__compile_limit):\n\u001b[0;32m-> 1659\u001b[0m     _fn, _i, _o \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mlinker\u001b[39m.\u001b[39;49mmake_thunk(\n\u001b[1;32m   1660\u001b[0m         input_storage\u001b[39m=\u001b[39;49minput_storage_lists, storage_map\u001b[39m=\u001b[39;49mstorage_map\n\u001b[1;32m   1661\u001b[0m     )\n\u001b[1;32m   1663\u001b[0m end_linker \u001b[39m=\u001b[39m time\u001b[39m.\u001b[39mperf_counter()\n\u001b[1;32m   1665\u001b[0m linker_time \u001b[39m=\u001b[39m end_linker \u001b[39m-\u001b[39m start_linker\n",
      "File \u001b[0;32m~/mambaforge/envs/pie/lib/python3.9/site-packages/pytensor/link/basic.py:254\u001b[0m, in \u001b[0;36mLocalLinker.make_thunk\u001b[0;34m(self, input_storage, output_storage, storage_map, **kwargs)\u001b[0m\n\u001b[1;32m    247\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mmake_thunk\u001b[39m(\n\u001b[1;32m    248\u001b[0m     \u001b[39mself\u001b[39m,\n\u001b[1;32m    249\u001b[0m     input_storage: Optional[\u001b[39m\"\u001b[39m\u001b[39mInputStorageType\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    252\u001b[0m     \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs,\n\u001b[1;32m    253\u001b[0m ) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m Tuple[\u001b[39m\"\u001b[39m\u001b[39mBasicThunkType\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mInputStorageType\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mOutputStorageType\u001b[39m\u001b[39m\"\u001b[39m]:\n\u001b[0;32m--> 254\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mmake_all(\n\u001b[1;32m    255\u001b[0m         input_storage\u001b[39m=\u001b[39;49minput_storage,\n\u001b[1;32m    256\u001b[0m         output_storage\u001b[39m=\u001b[39;49moutput_storage,\n\u001b[1;32m    257\u001b[0m         storage_map\u001b[39m=\u001b[39;49mstorage_map,\n\u001b[1;32m    258\u001b[0m     )[:\u001b[39m3\u001b[39m]\n",
      "File \u001b[0;32m~/mambaforge/envs/pie/lib/python3.9/site-packages/pytensor/link/vm.py:1246\u001b[0m, in \u001b[0;36mVMLinker.make_all\u001b[0;34m(self, profiler, input_storage, output_storage, storage_map)\u001b[0m\n\u001b[1;32m   1241\u001b[0m thunk_start \u001b[39m=\u001b[39m time\u001b[39m.\u001b[39mperf_counter()\n\u001b[1;32m   1242\u001b[0m \u001b[39m# no-recycling is done at each VM.__call__ So there is\u001b[39;00m\n\u001b[1;32m   1243\u001b[0m \u001b[39m# no need to cause duplicate c code by passing\u001b[39;00m\n\u001b[1;32m   1244\u001b[0m \u001b[39m# no_recycling here.\u001b[39;00m\n\u001b[1;32m   1245\u001b[0m thunks\u001b[39m.\u001b[39mappend(\n\u001b[0;32m-> 1246\u001b[0m     node\u001b[39m.\u001b[39;49mop\u001b[39m.\u001b[39;49mmake_thunk(node, storage_map, compute_map, [], impl\u001b[39m=\u001b[39;49mimpl)\n\u001b[1;32m   1247\u001b[0m )\n\u001b[1;32m   1248\u001b[0m linker_make_thunk_time[node] \u001b[39m=\u001b[39m time\u001b[39m.\u001b[39mperf_counter() \u001b[39m-\u001b[39m thunk_start\n\u001b[1;32m   1249\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mhasattr\u001b[39m(thunks[\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m], \u001b[39m\"\u001b[39m\u001b[39mlazy\u001b[39m\u001b[39m\"\u001b[39m):\n\u001b[1;32m   1250\u001b[0m     \u001b[39m# We don't want all ops maker to think about lazy Ops.\u001b[39;00m\n\u001b[1;32m   1251\u001b[0m     \u001b[39m# So if they didn't specify that its lazy or not, it isn't.\u001b[39;00m\n\u001b[1;32m   1252\u001b[0m     \u001b[39m# If this member isn't present, it will crash later.\u001b[39;00m\n",
      "File \u001b[0;32m~/mambaforge/envs/pie/lib/python3.9/site-packages/pytensor/link/c/op.py:131\u001b[0m, in \u001b[0;36mCOp.make_thunk\u001b[0;34m(self, node, storage_map, compute_map, no_recycling, impl)\u001b[0m\n\u001b[1;32m    127\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprepare_node(\n\u001b[1;32m    128\u001b[0m     node, storage_map\u001b[39m=\u001b[39mstorage_map, compute_map\u001b[39m=\u001b[39mcompute_map, impl\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mc\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    129\u001b[0m )\n\u001b[1;32m    130\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 131\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mmake_c_thunk(node, storage_map, compute_map, no_recycling)\n\u001b[1;32m    132\u001b[0m \u001b[39mexcept\u001b[39;00m (\u001b[39mNotImplementedError\u001b[39;00m, MethodNotDefined):\n\u001b[1;32m    133\u001b[0m     \u001b[39m# We requested the c code, so don't catch the error.\u001b[39;00m\n\u001b[1;32m    134\u001b[0m     \u001b[39mif\u001b[39;00m impl \u001b[39m==\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mc\u001b[39m\u001b[39m\"\u001b[39m:\n",
      "File \u001b[0;32m~/mambaforge/envs/pie/lib/python3.9/site-packages/pytensor/link/c/op.py:96\u001b[0m, in \u001b[0;36mCOp.make_c_thunk\u001b[0;34m(self, node, storage_map, compute_map, no_recycling)\u001b[0m\n\u001b[1;32m     94\u001b[0m         \u001b[39mprint\u001b[39m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mDisabling C code for \u001b[39m\u001b[39m{\u001b[39;00m\u001b[39mself\u001b[39m\u001b[39m}\u001b[39;00m\u001b[39m due to unsupported float16\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m     95\u001b[0m         \u001b[39mraise\u001b[39;00m \u001b[39mNotImplementedError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mfloat16\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m---> 96\u001b[0m outputs \u001b[39m=\u001b[39m cl\u001b[39m.\u001b[39;49mmake_thunk(\n\u001b[1;32m     97\u001b[0m     input_storage\u001b[39m=\u001b[39;49mnode_input_storage, output_storage\u001b[39m=\u001b[39;49mnode_output_storage\n\u001b[1;32m     98\u001b[0m )\n\u001b[1;32m     99\u001b[0m thunk, node_input_filters, node_output_filters \u001b[39m=\u001b[39m outputs\n\u001b[1;32m    101\u001b[0m \u001b[39m@is_cthunk_wrapper_type\u001b[39m\n\u001b[1;32m    102\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mrval\u001b[39m():\n",
      "File \u001b[0;32m~/mambaforge/envs/pie/lib/python3.9/site-packages/pytensor/link/c/basic.py:1202\u001b[0m, in \u001b[0;36mCLinker.make_thunk\u001b[0;34m(self, input_storage, output_storage, storage_map, cache, **kwargs)\u001b[0m\n\u001b[1;32m   1167\u001b[0m \u001b[39m\"\"\"Compile this linker's `self.fgraph` and return a function that performs the computations.\u001b[39;00m\n\u001b[1;32m   1168\u001b[0m \n\u001b[1;32m   1169\u001b[0m \u001b[39mThe return values can be used as follows:\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1199\u001b[0m \n\u001b[1;32m   1200\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m   1201\u001b[0m init_tasks, tasks \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mget_init_tasks()\n\u001b[0;32m-> 1202\u001b[0m cthunk, module, in_storage, out_storage, error_storage \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m__compile__(\n\u001b[1;32m   1203\u001b[0m     input_storage, output_storage, storage_map, cache\n\u001b[1;32m   1204\u001b[0m )\n\u001b[1;32m   1206\u001b[0m res \u001b[39m=\u001b[39m _CThunk(cthunk, init_tasks, tasks, error_storage, module)\n\u001b[1;32m   1207\u001b[0m res\u001b[39m.\u001b[39mnodes \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mnode_order\n",
      "File \u001b[0;32m~/mambaforge/envs/pie/lib/python3.9/site-packages/pytensor/link/c/basic.py:1122\u001b[0m, in \u001b[0;36mCLinker.__compile__\u001b[0;34m(self, input_storage, output_storage, storage_map, cache)\u001b[0m\n\u001b[1;32m   1120\u001b[0m input_storage \u001b[39m=\u001b[39m \u001b[39mtuple\u001b[39m(input_storage)\n\u001b[1;32m   1121\u001b[0m output_storage \u001b[39m=\u001b[39m \u001b[39mtuple\u001b[39m(output_storage)\n\u001b[0;32m-> 1122\u001b[0m thunk, module \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcthunk_factory(\n\u001b[1;32m   1123\u001b[0m     error_storage,\n\u001b[1;32m   1124\u001b[0m     input_storage,\n\u001b[1;32m   1125\u001b[0m     output_storage,\n\u001b[1;32m   1126\u001b[0m     storage_map,\n\u001b[1;32m   1127\u001b[0m     cache,\n\u001b[1;32m   1128\u001b[0m )\n\u001b[1;32m   1129\u001b[0m \u001b[39mreturn\u001b[39;00m (\n\u001b[1;32m   1130\u001b[0m     thunk,\n\u001b[1;32m   1131\u001b[0m     module,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1140\u001b[0m     error_storage,\n\u001b[1;32m   1141\u001b[0m )\n",
      "File \u001b[0;32m~/mambaforge/envs/pie/lib/python3.9/site-packages/pytensor/link/c/basic.py:1647\u001b[0m, in \u001b[0;36mCLinker.cthunk_factory\u001b[0;34m(self, error_storage, in_storage, out_storage, storage_map, cache)\u001b[0m\n\u001b[1;32m   1645\u001b[0m     \u001b[39mif\u001b[39;00m cache \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m   1646\u001b[0m         cache \u001b[39m=\u001b[39m get_module_cache()\n\u001b[0;32m-> 1647\u001b[0m     module \u001b[39m=\u001b[39m cache\u001b[39m.\u001b[39;49mmodule_from_key(key\u001b[39m=\u001b[39;49mkey, lnk\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m)\n\u001b[1;32m   1649\u001b[0m \u001b[39mvars\u001b[39m \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39minputs \u001b[39m+\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39moutputs \u001b[39m+\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39morphans\n\u001b[1;32m   1650\u001b[0m \u001b[39m# List of indices that should be ignored when passing the arguments\u001b[39;00m\n\u001b[1;32m   1651\u001b[0m \u001b[39m# (basically, everything that the previous call to uniq eliminated)\u001b[39;00m\n",
      "File \u001b[0;32m~/mambaforge/envs/pie/lib/python3.9/site-packages/pytensor/link/c/cmodule.py:1231\u001b[0m, in \u001b[0;36mModuleCache.module_from_key\u001b[0;34m(self, key, lnk)\u001b[0m\n\u001b[1;32m   1229\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m   1230\u001b[0m     location \u001b[39m=\u001b[39m dlimport_workdir(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdirname)\n\u001b[0;32m-> 1231\u001b[0m     module \u001b[39m=\u001b[39m lnk\u001b[39m.\u001b[39;49mcompile_cmodule(location)\n\u001b[1;32m   1232\u001b[0m     name \u001b[39m=\u001b[39m module\u001b[39m.\u001b[39m\u001b[39m__file__\u001b[39m\n\u001b[1;32m   1233\u001b[0m     \u001b[39massert\u001b[39;00m name\u001b[39m.\u001b[39mstartswith(location)\n",
      "File \u001b[0;32m~/mambaforge/envs/pie/lib/python3.9/site-packages/pytensor/link/c/basic.py:1546\u001b[0m, in \u001b[0;36mCLinker.compile_cmodule\u001b[0;34m(self, location)\u001b[0m\n\u001b[1;32m   1544\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m   1545\u001b[0m     _logger\u001b[39m.\u001b[39mdebug(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mLOCATION \u001b[39m\u001b[39m{\u001b[39;00mlocation\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\n\u001b[0;32m-> 1546\u001b[0m     module \u001b[39m=\u001b[39m c_compiler\u001b[39m.\u001b[39;49mcompile_str(\n\u001b[1;32m   1547\u001b[0m         module_name\u001b[39m=\u001b[39;49mmod\u001b[39m.\u001b[39;49mcode_hash,\n\u001b[1;32m   1548\u001b[0m         src_code\u001b[39m=\u001b[39;49msrc_code,\n\u001b[1;32m   1549\u001b[0m         location\u001b[39m=\u001b[39;49mlocation,\n\u001b[1;32m   1550\u001b[0m         include_dirs\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mheader_dirs(),\n\u001b[1;32m   1551\u001b[0m         lib_dirs\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mlib_dirs(),\n\u001b[1;32m   1552\u001b[0m         libs\u001b[39m=\u001b[39;49mlibs,\n\u001b[1;32m   1553\u001b[0m         preargs\u001b[39m=\u001b[39;49mpreargs,\n\u001b[1;32m   1554\u001b[0m     )\n\u001b[1;32m   1555\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m   1556\u001b[0m     e\u001b[39m.\u001b[39margs \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m (\u001b[39mstr\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfgraph),)\n",
      "File \u001b[0;32m~/mambaforge/envs/pie/lib/python3.9/site-packages/pytensor/link/c/cmodule.py:2591\u001b[0m, in \u001b[0;36mGCC_compiler.compile_str\u001b[0;34m(module_name, src_code, location, include_dirs, lib_dirs, libs, preargs, py_module, hide_symbols)\u001b[0m\n\u001b[1;32m   2588\u001b[0m     \u001b[39mprint\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39m \u001b[39m\u001b[39m\"\u001b[39m\u001b[39m.\u001b[39mjoin(cmd), file\u001b[39m=\u001b[39msys\u001b[39m.\u001b[39mstderr)\n\u001b[1;32m   2590\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m-> 2591\u001b[0m     p_out \u001b[39m=\u001b[39m output_subprocess_Popen(cmd)\n\u001b[1;32m   2592\u001b[0m     compile_stderr \u001b[39m=\u001b[39m p_out[\u001b[39m1\u001b[39m]\u001b[39m.\u001b[39mdecode()\n\u001b[1;32m   2593\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m:\n\u001b[1;32m   2594\u001b[0m     \u001b[39m# An exception can occur e.g. if `g++` is not found.\u001b[39;00m\n",
      "File \u001b[0;32m~/mambaforge/envs/pie/lib/python3.9/site-packages/pytensor/utils.py:261\u001b[0m, in \u001b[0;36moutput_subprocess_Popen\u001b[0;34m(command, **params)\u001b[0m\n\u001b[1;32m    258\u001b[0m p \u001b[39m=\u001b[39m subprocess_Popen(command, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mparams)\n\u001b[1;32m    259\u001b[0m \u001b[39m# we need to use communicate to make sure we don't deadlock around\u001b[39;00m\n\u001b[1;32m    260\u001b[0m \u001b[39m# the stdout/stderr pipe.\u001b[39;00m\n\u001b[0;32m--> 261\u001b[0m out \u001b[39m=\u001b[39m p\u001b[39m.\u001b[39;49mcommunicate()\n\u001b[1;32m    262\u001b[0m \u001b[39mreturn\u001b[39;00m out \u001b[39m+\u001b[39m (p\u001b[39m.\u001b[39mreturncode,)\n",
      "File \u001b[0;32m~/mambaforge/envs/pie/lib/python3.9/subprocess.py:1130\u001b[0m, in \u001b[0;36mPopen.communicate\u001b[0;34m(self, input, timeout)\u001b[0m\n\u001b[1;32m   1127\u001b[0m     endtime \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m   1129\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m-> 1130\u001b[0m     stdout, stderr \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_communicate(\u001b[39minput\u001b[39;49m, endtime, timeout)\n\u001b[1;32m   1131\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyboardInterrupt\u001b[39;00m:\n\u001b[1;32m   1132\u001b[0m     \u001b[39m# https://bugs.python.org/issue25942\u001b[39;00m\n\u001b[1;32m   1133\u001b[0m     \u001b[39m# See the detailed comment in .wait().\u001b[39;00m\n\u001b[1;32m   1134\u001b[0m     \u001b[39mif\u001b[39;00m timeout \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n",
      "File \u001b[0;32m~/mambaforge/envs/pie/lib/python3.9/subprocess.py:1977\u001b[0m, in \u001b[0;36mPopen._communicate\u001b[0;34m(self, input, endtime, orig_timeout)\u001b[0m\n\u001b[1;32m   1970\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_check_timeout(endtime, orig_timeout,\n\u001b[1;32m   1971\u001b[0m                         stdout, stderr,\n\u001b[1;32m   1972\u001b[0m                         skip_check_and_raise\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\n\u001b[1;32m   1973\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mRuntimeError\u001b[39;00m(  \u001b[39m# Impossible :)\u001b[39;00m\n\u001b[1;32m   1974\u001b[0m         \u001b[39m'\u001b[39m\u001b[39m_check_timeout(..., skip_check_and_raise=True) \u001b[39m\u001b[39m'\u001b[39m\n\u001b[1;32m   1975\u001b[0m         \u001b[39m'\u001b[39m\u001b[39mfailed to raise TimeoutExpired.\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[0;32m-> 1977\u001b[0m ready \u001b[39m=\u001b[39m selector\u001b[39m.\u001b[39;49mselect(timeout)\n\u001b[1;32m   1978\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_check_timeout(endtime, orig_timeout, stdout, stderr)\n\u001b[1;32m   1980\u001b[0m \u001b[39m# XXX Rewrite these to use non-blocking I/O on the file\u001b[39;00m\n\u001b[1;32m   1981\u001b[0m \u001b[39m# objects; they are no longer using C stdio!\u001b[39;00m\n",
      "File \u001b[0;32m~/mambaforge/envs/pie/lib/python3.9/selectors.py:416\u001b[0m, in \u001b[0;36m_PollLikeSelector.select\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    414\u001b[0m ready \u001b[39m=\u001b[39m []\n\u001b[1;32m    415\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 416\u001b[0m     fd_event_list \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_selector\u001b[39m.\u001b[39;49mpoll(timeout)\n\u001b[1;32m    417\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mInterruptedError\u001b[39;00m:\n\u001b[1;32m    418\u001b[0m     \u001b[39mreturn\u001b[39;00m ready\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "with model:\n",
    "    advifit = pm.fit(callbacks=[stop_after_10])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inference is too slow, taking several seconds per iteration; fitting the approximation would have taken hours!\n",
    "\n",
    "Now let's use minibatches. At every iteration, we will draw 500 random values:\n",
    "\n",
    "> Remember to set `total_size` in observed\n",
    "\n",
    "**total_size** is an important parameter that allows PyMC to infer the right way of rescaling densities. If it is not set, you are likely to get completely wrong results. For more information please refer to the comprehensive documentation of `pm.Minibatch`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pm.Minibatch(data, batch_size=500)\n",
    "\n",
    "with pm.Model() as model:\n",
    "    mu = pm.Normal(\"mu\", 0, sigma=1e5, shape=(100,))\n",
    "    sd = pm.HalfNormal(\"sd\", shape=(100,))\n",
    "    likelihood = pm.Normal(\"likelihood\", mu, sigma=sd, observed=X, total_size=data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      <progress value='10000' class='' max='10000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      100.00% [10000/10000 00:09<00:00 Average Loss = 1.5106e+05]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finished [100%]: Average Loss = 1.5101e+05\n"
     ]
    }
   ],
   "source": [
    "with model:\n",
    "    advifit = pm.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(advifit.hist);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Minibatch inference is dramatically faster. Multidimensional minibatches may be needed for some corner cases where you do matrix factorization or model is very wide.\n",
    "\n",
    "Here is the docstring for `Minibatch` to illustrate how it can be customized."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Multidimensional minibatch that is pure TensorVariable\n",
      "\n",
      "    Parameters\n",
      "    ----------\n",
      "    data: np.ndarray\n",
      "        initial data\n",
      "    batch_size: ``int`` or ``List[int|tuple(size, random_seed)]``\n",
      "        batch size for inference, random seed is needed\n",
      "        for child random generators\n",
      "    dtype: ``str``\n",
      "        cast data to specific type\n",
      "    broadcastable: tuple[bool]\n",
      "        change broadcastable pattern that defaults to ``(False, ) * ndim``\n",
      "    name: ``str``\n",
      "        name for tensor, defaults to \"Minibatch\"\n",
      "    random_seed: ``int``\n",
      "        random seed that is used by default\n",
      "    update_shared_f: ``callable``\n",
      "        returns :class:`ndarray` that will be carefully\n",
      "        stored to underlying shared variable\n",
      "        you can use it to change source of\n",
      "        minibatches programmatically\n",
      "    in_memory_size: ``int`` or ``List[int|slice|Ellipsis]``\n",
      "        data size for storing in ``aesara.shared``\n",
      "\n",
      "    Attributes\n",
      "    ----------\n",
      "    shared: shared tensor\n",
      "        Used for storing data\n",
      "    minibatch: minibatch tensor\n",
      "        Used for training\n",
      "\n",
      "    Notes\n",
      "    -----\n",
      "    Below is a common use case of Minibatch with variational inference.\n",
      "    Importantly, we need to make PyMC \"aware\" that a minibatch is being used in inference.\n",
      "    Otherwise, we will get the wrong :math:`logp` for the model.\n",
      "    the density of the model ``logp`` that is affected by Minibatch. See more in the examples below.\n",
      "    To do so, we need to pass the ``total_size`` parameter to the observed node, which correctly scales\n",
      "    the density of the model ``logp`` that is affected by Minibatch. See more in the examples below.\n",
      "\n",
      "    Examples\n",
      "    --------\n",
      "    Consider we have `data` as follows:\n",
      "\n",
      "    >>> data = np.random.rand(100, 100)\n",
      "\n",
      "    if we want a 1d slice of size 10 we do\n",
      "\n",
      "    >>> x = Minibatch(data, batch_size=10)\n",
      "\n",
      "    Note that your data is cast to ``floatX`` if it is not integer type\n",
      "    But you still can add the ``dtype`` kwarg for :class:`Minibatch`\n",
      "    if you need more control.\n",
      "\n",
      "    If we want 10 sampled rows and columns\n",
      "    ``[(size, seed), (size, seed)]`` we can use\n",
      "\n",
      "    >>> x = Minibatch(data, batch_size=[(10, 42), (10, 42)], dtype='int32')\n",
      "    >>> assert str(x.dtype) == 'int32'\n",
      "\n",
      "\n",
      "    Or, more simply, we can use the default random seed = 42\n",
      "    ``[size, size]``\n",
      "\n",
      "    >>> x = Minibatch(data, batch_size=[10, 10])\n",
      "\n",
      "\n",
      "    In the above, `x` is a regular :class:`TensorVariable` that supports any math operations:\n",
      "\n",
      "\n",
      "    >>> assert x.eval().shape == (10, 10)\n",
      "\n",
      "\n",
      "    You can pass the Minibatch `x` to your desired model:\n",
      "\n",
      "    >>> with pm.Model() as model:\n",
      "    ...     mu = pm.Flat('mu')\n",
      "    ...     sigma = pm.HalfNormal('sigma')\n",
      "    ...     lik = pm.Normal('lik', mu, sigma, observed=x, total_size=(100, 100))\n",
      "\n",
      "\n",
      "    Then you can perform regular Variational Inference out of the box\n",
      "\n",
      "\n",
      "    >>> with model:\n",
      "    ...     approx = pm.fit()\n",
      "\n",
      "\n",
      "    Important note: :class:``Minibatch`` has ``shared``, and ``minibatch`` attributes\n",
      "    you can call later:\n",
      "\n",
      "    >>> x.set_value(np.random.laplace(size=(100, 100)))\n",
      "\n",
      "    and minibatches will be then from new storage\n",
      "    it directly affects ``x.shared``.\n",
      "    A less convenient convenient, but more explicit, way to achieve the same\n",
      "    thing:\n",
      "\n",
      "    >>> x.shared.set_value(pm.floatX(np.random.laplace(size=(100, 100))))\n",
      "\n",
      "    The programmatic way to change storage is as follows\n",
      "    I import ``partial`` for simplicity\n",
      "    >>> from functools import partial\n",
      "    >>> datagen = partial(np.random.laplace, size=(100, 100))\n",
      "    >>> x = Minibatch(datagen(), batch_size=10, update_shared_f=datagen)\n",
      "    >>> x.update_shared()\n",
      "\n",
      "    To be more concrete about how we create a minibatch, here is a demo:\n",
      "    1. create a shared variable\n",
      "\n",
      "        >>> shared = aesara.shared(data)\n",
      "\n",
      "    2. take a random slice of size 10:\n",
      "\n",
      "        >>> ridx = pm.at_rng().uniform(size=(10,), low=0, high=data.shape[0]-1e-10).astype('int64')\n",
      "\n",
      "    3) take the resulting slice:\n",
      "\n",
      "        >>> minibatch = shared[ridx]\n",
      "\n",
      "    That's done. Now you can use this minibatch somewhere else.\n",
      "    You can see that the implementation does not require a fixed shape\n",
      "    for the shared variable. Feel free to use that if needed.\n",
      "    *FIXME: What is \"that\" which we can use here?  A fixed shape?  Should this say\n",
      "    \"but feel free to put a fixed shape on the shared variable, if appropriate?\"*\n",
      "\n",
      "    Suppose you need to make some replacements in the graph, e.g. change the minibatch to testdata\n",
      "\n",
      "    >>> node = x ** 2  # arbitrary expressions on minibatch `x`\n",
      "    >>> testdata = pm.floatX(np.random.laplace(size=(1000, 10)))\n",
      "\n",
      "    Then you should create a `dict` with replacements:\n",
      "\n",
      "    >>> replacements = {x: testdata}\n",
      "    >>> rnode = aesara.clone_replace(node, replacements)\n",
      "    >>> assert (testdata ** 2 == rnode.eval()).all()\n",
      "\n",
      "    *FIXME: In the following, what is the **reason** to replace the Minibatch variable with\n",
      "    its shared variable?  And in the following, the `rnode` is a **new** node, not a modification\n",
      "    of a previously existing node, correct?*\n",
      "    To replace a minibatch with its shared variable you should do\n",
      "    the same things. The Minibatch variable is accessible through the `minibatch` attribute.\n",
      "    For example\n",
      "\n",
      "    >>> replacements = {x.minibatch: x.shared}\n",
      "    >>> rnode = aesara.clone_replace(node, replacements)\n",
      "\n",
      "    For more complex slices some more code is needed that can seem not so clear\n",
      "\n",
      "    >>> moredata = np.random.rand(10, 20, 30, 40, 50)\n",
      "\n",
      "    The default ``total_size`` that can be passed to PyMC random node\n",
      "    is then ``(10, 20, 30, 40, 50)`` but can be less verbose in some cases\n",
      "\n",
      "    1. Advanced indexing, ``total_size = (10, Ellipsis, 50)``\n",
      "\n",
      "        >>> x = Minibatch(moredata, [2, Ellipsis, 10])\n",
      "\n",
      "        We take the slice only for the first and last dimension\n",
      "\n",
      "        >>> assert x.eval().shape == (2, 20, 30, 40, 10)\n",
      "\n",
      "    2. Skipping a particular dimension, ``total_size = (10, None, 30)``:\n",
      "\n",
      "        >>> x = Minibatch(moredata, [2, None, 20])\n",
      "        >>> assert x.eval().shape == (2, 20, 20, 40, 50)\n",
      "\n",
      "    3. Mixing both of these together, ``total_size = (10, None, 30, Ellipsis, 50)``:\n",
      "\n",
      "        >>> x = Minibatch(moredata, [2, None, 20, Ellipsis, 10])\n",
      "        >>> assert x.eval().shape == (2, 20, 20, 40, 10)\n",
      "    \n"
     ]
    }
   ],
   "source": [
    "print(pm.Minibatch.__doc__)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Authors\n",
    "\n",
    "* Authored by Maxim Kochurov\n",
    "* Updated by Chris Fonnesbeck ([pymc-examples#429](https://github.com/pymc-devs/pymc-examples/pull/497))\n",
    "\n",
    "## Watermark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last updated: Sun Nov 20 2022\n",
      "\n",
      "Python implementation: CPython\n",
      "Python version       : 3.10.4\n",
      "IPython version      : 8.4.0\n",
      "\n",
      "sys       : 3.10.4 | packaged by conda-forge | (main, Mar 24 2022, 17:39:37) [Clang 12.0.1 ]\n",
      "pymc      : 4.3.0\n",
      "seaborn   : 0.11.2\n",
      "arviz     : 0.13.0\n",
      "numpy     : 1.22.4\n",
      "matplotlib: 3.5.2\n",
      "aesara    : 2.8.9+11.ge8eed6c18\n",
      "pandas    : 1.4.2\n",
      "\n",
      "Watermark: 2.3.1\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -n -u -v -iv -w"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ":::{include} ../page_footer.md\n",
    ":::"
   ]
  }
 ],
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