{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Softmax demo, with histosys!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](assets/softmax_pyhf_animation.gif)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "\n",
    "import jax\n",
    "import jax.experimental.optimizers as optimizers\n",
    "import jax.experimental.stax as stax\n",
    "import jax.random\n",
    "from jax.random import PRNGKey\n",
    "import numpy as np\n",
    "from functools import partial\n",
    "\n",
    "import pyhf\n",
    "pyhf.set_backend('jax')\n",
    "pyhf.default_backend = pyhf.tensor.jax_backend(precision='64b')\n",
    "\n",
    "from neos import data, infer, makers\n",
    "\n",
    "rng = PRNGKey(22)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# regression net\n",
    "final_layer = 3\n",
    "init_random_params, predict = stax.serial(\n",
    "    stax.Dense(1024),\n",
    "    stax.Relu,\n",
    "    stax.Dense(1024),\n",
    "    stax.Relu,\n",
    "    stax.Dense(final_layer),\n",
    "    stax.Softmax,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compose differentiable workflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dgen = data.generate_blobs(rng, blobs=4) \n",
    "hmaker = makers.hists_from_nn(dgen, predict, method='softmax')\n",
    "nnm = makers.histosys_model_from_hists(hmaker)\n",
    "get_cls = infer.expected_CLs(nnm, solver_kwargs=dict(pdf_transform=True))\n",
    "\n",
    "# get_cls returns a list of metrics -- let's just index into the first one (CLs)\n",
    "def loss(params, test_mu):\n",
    "    return get_cls(params, test_mu)[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Randomly initialise nn weights and check that we can get the gradient of the loss wrt nn params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(DeviceArray(0.05995673, dtype=float64),\n",
       " [(DeviceArray([[-2.3217741e-04, -1.7894249e-04, -6.0847378e-05, ...,\n",
       "                  6.3718908e-05,  7.7229757e-05,  2.2041100e-05],\n",
       "                [ 2.8023310e-04,  1.4383352e-04,  7.4426265e-05, ...,\n",
       "                 -5.3240507e-05, -1.0534972e-04,  1.1296924e-05]],            dtype=float32),\n",
       "   DeviceArray([-7.3751187e-05,  6.5396616e-06, -4.4865723e-05, ...,\n",
       "                -1.6381196e-05,  7.2083632e-05,  1.0425624e-05],            dtype=float32)),\n",
       "  (),\n",
       "  (DeviceArray([[ 1.2862045e-06, -1.3878989e-06,  1.5523256e-06, ...,\n",
       "                 -1.2467436e-07,  2.8923242e-07,  2.0936200e-07],\n",
       "                [ 1.5300036e-06,  1.8212451e-07,  2.9150870e-06, ...,\n",
       "                  1.1441897e-07,  9.0753144e-07, -5.0317476e-07],\n",
       "                [-1.1893795e-07, -1.0953570e-05,  6.7028125e-08, ...,\n",
       "                 -1.6470675e-06, -2.2597978e-06,  1.5723747e-06],\n",
       "                ...,\n",
       "                [ 2.5895874e-06,  5.2436440e-07,  2.9629405e-06, ...,\n",
       "                  2.1880231e-07,  1.0292639e-06, -1.5711187e-08],\n",
       "                [-3.2612098e-07, -1.5878672e-05,  2.3735276e-07, ...,\n",
       "                 -2.4954293e-06, -3.3734680e-06,  2.2382624e-06],\n",
       "                [-1.0781149e-06, -3.3740948e-06, -1.3529205e-07, ...,\n",
       "                 -8.3125019e-07, -1.3781491e-06,  5.0933130e-07]],            dtype=float32),\n",
       "   DeviceArray([ 8.21395006e-05, -6.79768636e-05,  1.04689920e-04, ...,\n",
       "                -6.16907482e-06,  1.12354055e-05, -3.18819411e-06],            dtype=float32)),\n",
       "  (),\n",
       "  (DeviceArray([[ 1.03998200e-05,  1.63612594e-06, -1.20359455e-05],\n",
       "                [-6.80154481e-05, -7.90334161e-06,  7.59187824e-05],\n",
       "                [ 9.48391680e-05,  1.46424081e-05, -1.09481574e-04],\n",
       "                ...,\n",
       "                [-7.93024446e-06,  3.01809592e-07,  7.62843229e-06],\n",
       "                [ 6.66008200e-05,  1.11586596e-05, -7.77594832e-05],\n",
       "                [ 2.66423867e-05,  4.79195842e-06, -3.14343451e-05]],            dtype=float32),\n",
       "   DeviceArray([-1.2324574e-05,  3.4578399e-05, -2.2253709e-05], dtype=float32)),\n",
       "  ()])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_, network = init_random_params(jax.random.PRNGKey(2), (-1, 2))\n",
    "\n",
    "# gradient wrt nn weights\n",
    "jax.value_and_grad(loss)(network, test_mu=1.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define training loop!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "opt_init, opt_update, opt_params = optimizers.adam(1e-3)\n",
    "\n",
    "def train_network(N):\n",
    "    cls_vals = []\n",
    "    _, network = init_random_params(jax.random.PRNGKey(1), (-1, 2))\n",
    "    state = opt_init(network)\n",
    "    losses = []\n",
    "\n",
    "    # parameter update function\n",
    "    # @jax.jit\n",
    "    def update_and_value(i, opt_state, mu):\n",
    "        net = opt_params(opt_state)\n",
    "        value, grad = jax.value_and_grad(loss)(net, mu)\n",
    "        return opt_update(i, grad, state), value, net\n",
    "\n",
    "    for i in range(N):\n",
    "        start_time = time.time()\n",
    "        state, value, network = update_and_value(i, state, 1.0)\n",
    "        epoch_time = time.time() - start_time\n",
    "        losses.append(value)\n",
    "        metrics = {\"loss\": losses}\n",
    "        yield network, metrics, epoch_time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting helper function for awesome animations :)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Choose colormap\n",
    "import matplotlib.pylab as pl\n",
    "from matplotlib.colors import ListedColormap\n",
    "def to_transp(cmap):\n",
    "    #cmap = pl.cm.Reds_r\n",
    "    my_cmap = cmap(np.arange(cmap.N))\n",
    "    #my_cmap[:,-1] = np.geomspace(0.001, 1, cmap.N)\n",
    "    my_cmap[:,-1] = np.linspace(0, 0.7, cmap.N)\n",
    "    #my_cmap[:,-1] = np.ones(cmap.N)\n",
    "    return ListedColormap(my_cmap)\n",
    "\n",
    "def plot(axarr, network, metrics, maxN):\n",
    "    xlim = (-5, 5)\n",
    "    ylim = (-5, 5)\n",
    "    g = np.mgrid[xlim[0]:xlim[1]:101j, ylim[0]:ylim[1]:101j]\n",
    "    levels = np.linspace(0, 1, 20)\n",
    "        \n",
    "    ax = axarr[0]\n",
    "    ax.contourf(\n",
    "        g[0],\n",
    "        g[1],\n",
    "        predict(network, np.moveaxis(g, 0, -1)).reshape(101, 101, final_layer)[:, :, 0],\n",
    "        levels=levels,\n",
    "        cmap = to_transp(pl.cm.Reds),\n",
    "    )\n",
    "    ax.contourf(\n",
    "        g[0],\n",
    "        g[1],\n",
    "        predict(network, np.moveaxis(g, 0, -1)).reshape(101, 101, final_layer)[:, :, 1],\n",
    "        levels=levels,\n",
    "        cmap = to_transp(pl.cm.Blues),\n",
    "    )\n",
    "  \n",
    "    ax.contourf(\n",
    "        g[0],\n",
    "        g[1],\n",
    "        predict(network, np.moveaxis(g, 0, -1)).reshape(101, 101, final_layer)[:, :, 2],\n",
    "        levels=levels,\n",
    "        cmap = to_transp(pl.cm.Greens),\n",
    "    )\n",
    "\n",
    "    sig, bkg_nom, bkg_up, bkg_down = dgen()\n",
    "\n",
    "    ax.scatter(sig[:, 0], sig[:, 1], alpha=0.3, c=\"C9\")\n",
    "    ax.scatter(bkg_up[:, 0], bkg_up[:, 1], alpha=0.1, c=\"C1\", marker=6)\n",
    "    ax.scatter(bkg_down[:, 0], bkg_down[:, 1], alpha=0.1, c=\"C1\", marker=7)\n",
    "    ax.scatter(bkg_nom[:, 0], bkg_nom[:, 1], alpha=0.3, c=\"C1\")\n",
    "\n",
    "\n",
    "    ax.set_xlim(-5, 5)\n",
    "    ax.set_ylim(-5, 5)\n",
    "    ax.set_xlabel(\"x\")\n",
    "    ax.set_ylabel(\"y\")\n",
    "\n",
    "    ax = axarr[1]\n",
    "    ax.axhline(0.05, c=\"slategray\", linestyle=\"--\")\n",
    "    ax.plot(metrics[\"loss\"], c=\"steelblue\", linewidth=2.0)\n",
    "\n",
    "    ax.set_ylim(0, 0.15)\n",
    "    ax.set_xlim(0, maxN)\n",
    "    ax.set_xlabel(\"epoch\")\n",
    "    ax.set_ylabel(r\"$CL_s$\")\n",
    "\n",
    "    ax = axarr[2]\n",
    "    s, b, bup, bdown = hmaker([network,None])\n",
    "\n",
    "    bins = np.linspace(0,1,final_layer+1)\n",
    "    bin_width = 1. / final_layer\n",
    "    centers = bins[:-1] + np.diff(bins) / 2.0\n",
    "    ax.bar(centers, b, color=\"C1\", width=bin_width)\n",
    "    ax.bar(centers, s, bottom=b, color=\"C9\", width=bin_width)\n",
    "\n",
    "    bunc = np.asarray([[x, y] if x > y else [y, x] for x, y in zip(bup, bdown)])\n",
    "    plot_unc = []\n",
    "    for unc, be in zip(bunc, b):\n",
    "        if all(unc > be):\n",
    "            plot_unc.append([max(unc), be])\n",
    "        elif all(unc < be):\n",
    "            plot_unc.append([be, min(unc)])\n",
    "        else:\n",
    "            plot_unc.append(unc)\n",
    "\n",
    "    plot_unc = np.asarray(plot_unc)\n",
    "    b_up, b_down = plot_unc[:, 0], plot_unc[:, 1]\n",
    "\n",
    "    ax.bar(centers, b_up - b, bottom=b, alpha=0.4, color=\"black\", width=bin_width)\n",
    "    ax.bar(\n",
    "        centers, b - b_down, bottom=b_down, alpha=0.4, color=\"black\", width=bin_width\n",
    "    )\n",
    "\n",
    "    ax.set_ylim(0, 100)\n",
    "    ax.set_ylabel(\"frequency\")\n",
    "    ax.set_xlabel(\"nn output\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Install celluloid to create animations if you haven't already by running this next cell:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: celluloid in /Users/phinate/me/neos/env/lib/python3.8/site-packages (0.2.0)\n",
      "Requirement already satisfied: matplotlib in /Users/phinate/me/neos/env/lib/python3.8/site-packages (from celluloid) (3.3.0)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in /Users/phinate/me/neos/env/lib/python3.8/site-packages (from matplotlib->celluloid) (2.8.1)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /Users/phinate/me/neos/env/lib/python3.8/site-packages (from matplotlib->celluloid) (1.2.0)\n",
      "Requirement already satisfied: numpy>=1.15 in /Users/phinate/me/neos/env/lib/python3.8/site-packages (from matplotlib->celluloid) (1.19.1)\n",
      "Requirement already satisfied: cycler>=0.10 in /Users/phinate/me/neos/env/lib/python3.8/site-packages (from matplotlib->celluloid) (0.10.0)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /Users/phinate/me/neos/env/lib/python3.8/site-packages (from matplotlib->celluloid) (7.2.0)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in /Users/phinate/me/neos/env/lib/python3.8/site-packages (from matplotlib->celluloid) (2.4.7)\n",
      "Requirement already satisfied: six>=1.5 in /Users/phinate/me/neos/env/lib/python3.8/site-packages (from python-dateutil>=2.1->matplotlib->celluloid) (1.15.0)\n"
     ]
    }
   ],
   "source": [
    "!python -m pip install celluloid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's run it!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 0: CLs = 0.05996983692115765, took 1.512545108795166s\n",
      "epoch 1: CLs = 0.030596786927458375, took 2.022122859954834s\n",
      "epoch 2: CLs = 0.008953316468476746, took 2.060853958129883s\n",
      "epoch 3: CLs = 0.0019975995692056436, took 1.9914181232452393s\n",
      "epoch 4: CLs = 0.0005502178847760497, took 2.0072109699249268s\n",
      "epoch 5: CLs = 0.00020702991665033643, took 2.014936923980713s\n",
      "epoch 6: CLs = 0.00010119025847199481, took 2.006727933883667s\n",
      "epoch 7: CLs = 6.000147548346213e-05, took 1.9773640632629395s\n",
      "epoch 8: CLs = 4.079480919605416e-05, took 2.002415180206299s\n",
      "epoch 9: CLs = 3.054388948453557e-05, took 2.017674207687378s\n",
      "epoch 10: CLs = 2.4492523543750977e-05, took 2.083250045776367s\n",
      "epoch 11: CLs = 2.0634761843663085e-05, took 2.018139123916626s\n",
      "epoch 12: CLs = 1.802757826063761e-05, took 2.0502779483795166s\n",
      "epoch 13: CLs = 1.618189460184105e-05, took 2.0046041011810303s\n",
      "epoch 14: CLs = 1.4823435986466293e-05, took 2.002837896347046s\n",
      "epoch 15: CLs = 1.379036056659011e-05, took 2.0102739334106445s\n",
      "epoch 16: CLs = 1.2985120553032914e-05, took 2.008023977279663s\n",
      "epoch 17: CLs = 1.2342630452355507e-05, took 1.983170986175537s\n",
      "epoch 18: CLs = 1.1818754673154075e-05, took 2.117082118988037s\n",
      "epoch 19: CLs = 1.1384625839827578e-05, took 2.0161149501800537s\n"
     ]
    },
    {
     "data": {
      "image/png": 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       "<Figure size 936x288 with 3 Axes>"
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     "metadata": {
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   ],
   "source": [
    "# slow\n",
    "import numpy as np\n",
    "from IPython.display import HTML\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "plt.rcParams.update(\n",
    "    {\n",
    "        \"axes.labelsize\": 13,\n",
    "        \"axes.linewidth\": 1.2,\n",
    "        \"xtick.labelsize\": 13,\n",
    "        \"ytick.labelsize\": 13,\n",
    "        \"figure.figsize\": [13.0, 4.0],\n",
    "        \"font.size\": 13,\n",
    "        \"xtick.major.size\": 3,\n",
    "        \"ytick.major.size\": 3,\n",
    "        \"legend.fontsize\": 11,\n",
    "    }\n",
    ")\n",
    "\n",
    "\n",
    "fig, axarr = plt.subplots(1, 3, dpi=120)\n",
    "\n",
    "maxN = 20  # make me bigger for better results!\n",
    "\n",
    "animate = True  # animations fail tests...\n",
    "\n",
    "if animate:\n",
    "    from celluloid import Camera\n",
    "\n",
    "    camera = Camera(fig)\n",
    "\n",
    "# Training\n",
    "for i, (network, metrics, epoch_time) in enumerate(train_network(maxN)):\n",
    "    print(f\"epoch {i}:\", f'CLs = {metrics[\"loss\"][-1]}, took {epoch_time}s')\n",
    "    if animate:\n",
    "        plot(axarr, network, metrics, maxN=maxN)\n",
    "        plt.tight_layout()\n",
    "        camera.snap()\n",
    "        if i % 10 == 0:\n",
    "            camera.animate().save(\"animation.gif\", writer=\"imagemagick\", fps=8)\n",
    "            # HTML(camera.animate().to_html5_video())\n",
    "if animate:\n",
    "    camera.animate().save(\"animation.gif\", writer=\"imagemagick\", fps=8)"
   ]
  },
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   "source": []
  },
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   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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