sheet11_solution.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch import nn\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2) Event Generation with Diffusion Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(c)\n",
    "\n",
    "The following code provides a minimal implementation of a Denoising Diffusion Probabilistic Model (DDPM). It uses the same notation as in the lecture notes and https://arxiv.org/pdf/2305.10475.pdf. We recommend reading it alongside Figures 1, 2 of the paper."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DDPM(nn.Module):\n",
    "    \n",
    "    def __init__(self, \n",
    "                data_dim, T=100, \n",
    "                 n_layers=3, hidden_dim=64):\n",
    "        super().__init__()\n",
    "        # initialize diffusion parameters\n",
    "        self.T = T\n",
    "        self.data_dim = data_dim\n",
    "        self.beta = torch.linspace(1e-4, 2e-2, self.T)\n",
    "        self.beta_bar = 1-torch.cumprod(1-self.beta, dim=0)\n",
    "        self.sigma2 = torch.ones_like(self.beta)\n",
    "        self.sigma2[1:] = self.beta_bar[:-1]/self.beta_bar[1:] * self.beta[1:]\n",
    "        \n",
    "        # construct simple MLP\n",
    "        layers = []\n",
    "        # network gets xt (data_dim-dimensional) and T (1-dimensional) as inputs\n",
    "        layers.append(nn.Linear(self.data_dim+1, hidden_dim))\n",
    "        for _ in range(n_layers-1):\n",
    "            layers.append(nn.Linear(hidden_dim, hidden_dim))\n",
    "            layers.append(nn.ReLU())\n",
    "        layers.append(nn.Linear(hidden_dim, self.data_dim))\n",
    "        self.net = nn.Sequential(*layers)\n",
    "        \n",
    "    def batch_loss(self, x0):\n",
    "        t = torch.randint(low=1, high=self.T, size=(x0.shape[0], 1))\n",
    "        epsilon = torch.randn_like(x0)\n",
    "        \n",
    "        # TODO: Compute x_t(x_0, epsilon)\n",
    "        xt = torch.sqrt(1-self.beta_bar[t])*x0 + torch.sqrt(self.beta_bar[t])*epsilon\n",
    "        \n",
    "        epsilon_theta = self.net(torch.cat((xt, t), dim=1))\n",
    "        \n",
    "        # loss function (eqn 17 in the paper)\n",
    "        prefactor = 1/(2*self.sigma2[t]) * self.beta[t]**2 / (1-self.beta[t]) / self.beta_bar[t]\n",
    "        loss = prefactor.unsqueeze(-1) * (epsilon - epsilon_theta)**2 \n",
    "        return loss.mean()\n",
    "    \n",
    "    def sample(self, n_samples):\n",
    "        # x_t starts at x_T and is diffused over x_{T-1}, x_{T-2} etc to x_0\n",
    "        xt = torch.randn(n_samples, self.data_dim)\n",
    "        for t in reversed(range(self.T)):\n",
    "            z = torch.randn(n_samples, self.data_dim) if t>0 else 0.\n",
    "            epsilon_theta = self.net(torch.cat((xt, torch.full((xt.shape[0], 1),\n",
    "                                                             t)), dim=1))\n",
    "            \n",
    "            # TODO: Compute x_t (x_{t+1}, epsilon_theta, z)\n",
    "            xt = (xt - epsilon_theta * self.beta[t] / torch.sqrt(self.beta_bar[t])) / torch.sqrt(1-self.beta[t]) + torch.sqrt(self.sigma2[t]) * z\n",
    "        return xt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [],
   "source": [
    "# standard imports\n",
    "from sklearn.datasets import make_moons\n",
    "\n",
    "BATCHSIZE = 100\n",
    "N_DIM = 2\n",
    "    \n",
    "model = DDPM(data_dim=N_DIM)\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=0.001)\n",
    "\n",
    "# a very basic training loop\n",
    "losses = []\n",
    "for i in range(10000):\n",
    "    optimizer.zero_grad()\n",
    "    # sample data from the moons distribution\n",
    "    data, label = make_moons(n_samples=BATCHSIZE, noise=0.05)\n",
    "    x0 = torch.Tensor(data)\n",
    "    # compute loss of DDPM\n",
    "    loss = model.batch_loss(x0)\n",
    "    # backpropagate and update the weights\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "    losses.append(loss.item())\n",
    "\n",
    "# sample from the DDPM\n",
    "with torch.no_grad():\n",
    "    samples = model.sample(BATCHSIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(losses)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "with torch.no_grad():\n",
    "    samples = model.sample(10000)\n",
    "truth, _ = make_moons(n_samples=10000, noise=0.05)\n",
    "plt.plot(truth[:,0], truth[:,1], \"k,\")\n",
    "plt.plot(samples[:,0], samples[:,1], \"r,\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# download data to folder data09\n",
    "# this might take some time (50MB)\n",
    "# you can also do this manually (download + unpack zip)\n",
    "import os, sys\n",
    "import wget\n",
    "from zipfile import ZipFile\n",
    "from tqdm import tqdm\n",
    "\n",
    "DESTINATION = \"data09\"\n",
    "url = \"https://www.thphys.uni-heidelberg.de/~plehn/pics/\"\n",
    "filename = \"tutorial-11-data.zip\"\n",
    "url = url + filename\n",
    "\n",
    "os.makedirs(DESTINATION, exist_ok=True)\n",
    "os.chdir(DESTINATION)\n",
    "wget.download(url, filename)\n",
    "with ZipFile(filename, \"r\") as zip_ref:\n",
    "    for file in tqdm(iterable=zip_ref.namelist(), total=len(zip_ref.namelist())):\n",
    "        zip_ref.extract(member=file)\n",
    "os.chdir(\"..\")\n",
    "%ls data09"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1384081, 8) (296588, 8) (296588, 8)\n"
     ]
    }
   ],
   "source": [
    "data_trn = np.load(\"data09/tutorial-11-data/dy_trn_data.npy\")\n",
    "data_tst = np.load(\"data09/tutorial-11-data/dy_tst_data.npy\")\n",
    "data_val = np.load(\"data09/tutorial-11-data/dy_val_data.npy\")\n",
    "print(data_trn.shape, data_tst.shape, data_val.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_mass(particle):\n",
    "    return np.sqrt(np.clip(particle[:,0]**2 - np.sum(particle[:,1:]**2, axis=-1), 0, None))\n",
    "\n",
    "def get_pt(particle):\n",
    "    return np.sqrt(particle[:,1]**2 + particle[:,2]**2)\n",
    "\n",
    "def get_eta(particle):\n",
    "    p_absolute = np.sqrt(np.sum(particle[:,1:]**2, axis=-1))\n",
    "    return np.arctanh(particle[:,3] / p_absolute)\n",
    "\n",
    "def get_phi(particle):\n",
    "    return np.arctan2(particle[:,2], particle[:,1])\n",
    "\n",
    "def get_pt_phi_eta_mass(particle):\n",
    "    pt = get_pt(particle)\n",
    "    phi = get_phi(particle)\n",
    "    eta = get_eta(particle)\n",
    "    mass = get_mass(particle)\n",
    "    return np.stack((pt, phi, eta, mass), axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cuts in the dataset\n",
    "get_pt(data_tst[:,:4]).min(), get_pt(data_tst[:,4:]).min() # pt cuts\n",
    "get_eta(data_tst[:,:4]).min(), get_eta(data_tst[:,:4]).max()\n",
    "\n",
    "eta_cut = 2.5 + 1e-5 # should be 2.5 but one event is weird\n",
    "pt_cut = 10."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess(events, mean=None, std=None):\n",
    "    particle1, particle2 = events[:,:4], events[:,4:]\n",
    "    events_jetcoordinates = np.stack((get_pt(particle1), get_phi(particle1), get_eta(particle1), get_mass(particle1), get_pt(particle2), get_phi(particle2), get_eta(particle2), get_mass(particle2)), axis=-1)\n",
    "    events_reduced = events_jetcoordinates[:,[0,2,6]]\n",
    "    \n",
    "    events_reduced[:,0] = np.log(events_reduced[:,0] - pt_cut)\n",
    "    events_reduced[:,1:] = np.arctanh(events_reduced[:,1:] / eta_cut)\n",
    "    \n",
    "    if mean is None or std is None:\n",
    "        mean = events_reduced.mean(axis=0)\n",
    "        std = events_reduced.std(axis=0)\n",
    "    events_reduced = (events_reduced - mean) / std\n",
    "    \n",
    "    assert np.isfinite(events_reduced).all()\n",
    "    events_reduced = torch.tensor(events_reduced).float()\n",
    "    return events_reduced, mean, std\n",
    "    \n",
    "def undo_preprocess(events_reduced, mean, std):\n",
    "    events_reduced = events_reduced * std + mean\n",
    "    \n",
    "    events_reduced[:,0] = np.exp(events_reduced[:,0]) + pt_cut\n",
    "    events_reduced[:,[1,2]] = np.tanh(events_reduced[:,[1,2]]) * eta_cut\n",
    "    \n",
    "    pt1, eta1, eta2 = events_reduced.T\n",
    "    phi1 = np.random.uniform(0, 2*np.pi, events_reduced.shape[0])\n",
    "    mass1, mass2 = np.ones((2, events_reduced.shape[0])) * 0.105\n",
    "    px1 = pt1 * np.cos(phi1)\n",
    "    py1 = pt1 * np.sin(phi1)\n",
    "    pz1 = pt1 * np.sinh(eta1)\n",
    "    e1 = np.sqrt(mass1**2 + px1**2 + py1**2 + pz1**2)\n",
    "    px2 = -px1\n",
    "    py2 = -py1\n",
    "    pt2 = pt1\n",
    "    pz2 = pt2 * np.sinh(eta2)\n",
    "    e2 = np.sqrt(mass2**2 + px2**2 + py2**2 + pz2**2)\n",
    "    return np.stack((e1, px1, py1, pz1, e2, px2, py2, pz2), axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import TensorDataset, DataLoader\n",
    "\n",
    "data_trn_prepd, mean, std = preprocess(data_trn)\n",
    "data_tst_prepd, _, _ = preprocess(data_tst, mean, std)\n",
    "data_val_prepd, _, _ = preprocess(data_val, mean, std)\n",
    "\n",
    "dataset_trn = TensorDataset((data_trn_prepd))\n",
    "dataset_tst = TensorDataset((data_tst_prepd))\n",
    "dataset_val = TensorDataset((data_val_prepd))\n",
    "\n",
    "batchsize = 4096\n",
    "dataloader_trn = DataLoader(dataset_trn, batch_size=batchsize, shuffle=True)\n",
    "dataloader_tst = DataLoader(dataset_tst, batch_size=batchsize, shuffle=False)\n",
    "dataloader_val = DataLoader(dataset_val, batch_size=batchsize, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [],
   "source": [
    "def batch_loss(model, x):\n",
    "    loss = model.batch_loss(x)\n",
    "    return loss\n",
    "\n",
    "def train_epoch(model, loader, losses):\n",
    "    model.train()\n",
    "    for (x,) in loader:\n",
    "        loss = batch_loss(model, x)\n",
    "        \n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        losses.append(loss.item())\n",
    "    return losses\n",
    "\n",
    "def val_epoch(model, loader):\n",
    "    losses = []\n",
    "    model.eval()\n",
    "    with torch.no_grad():\n",
    "        for (x,) in loader:\n",
    "            loss = batch_loss(model, x)\n",
    "            losses.append(loss.item())\n",
    "    return np.mean(losses)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model has 8835 parameters\n",
      "0/5\t 0.025137206182292062\n",
      "1/5\t 0.024092435300962566\n",
      "2/5\t 0.02388990713819249\n",
      "3/5\t 0.023796810296504465\n",
      "4/5\t 0.02352490194448053\n"
     ]
    }
   ],
   "source": [
    "# construct DDPM\n",
    "ndim = 3\n",
    "model = DDPM(ndim)\n",
    "total_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad)\n",
    "print(f\"Model has {total_parameters} parameters\")\n",
    "    \n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)\n",
    "\n",
    "# train DDPM\n",
    "nepochs = 5\n",
    "losses = []\n",
    "for epoch in range(nepochs):\n",
    "    losses = train_epoch(model, dataloader_trn, losses)\n",
    "    val_loss = val_epoch(model, dataloader_val)\n",
    "    print(f\"{epoch}/{nepochs}\\t {val_loss}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZKCxXlyWor6/3xsbsj7f/28uHue+xFwF44bO3M6OsKOvrEBHJFTN7wd3rozw3lt+0fTt3LJ41MP3U9qM5rEREZHyZcIFfmJ/H03/+bgD+V8M2+vp0YTUREZiAgQ9wcXUZ//2Wi+lK9vGMztgREQEmaOAD3H/HpQD86KVDOa5ERGR8mLCBn59IvbQfv3SInt4xu+KDiMi4NWEDH2BmeeoMnT/WefkiIhM78DcFF1f7+W5dX0dEZEIH/rTSQlYtTV0V4rmdOngrIpPbhA58gD+7PXXw9t5/0LCOiExuEz7wZ04tHphe+88v57ASEZHcmvCBD/DT+1Nj+Ru27B+hp4jIxDUpAv+SmeUsqioF4Dub38xxNSIiuTEpAh/g4Q9dA8Dn/uVVHvjBy+TqonEiIrkyaQL/xtrprP+jGwB4/Nf7WfjARv56007+6O+fZ9/x9iGfs/9EO72ha/Gc6ug5bx7QtXpEJDYm3OWRR/LY8/v4yx++8pb2d9VVve35+lVlhRw70w1AUX4e77lyNpfNLueRTTv55G11vHH8LD/aeogl8yt579VzyMszth9qY9fR03zitjrau5M8vaOZq2sq+MLGHQDMqShmRlkh9Qumc2PtdK6YU86v9hxnTkUxOw638ddP7uLuq2dz2+WzmDm1iMMnO5k3bQpFBXmc7erl2nmVfO25JlbfvIi/fW4Pf/eL1/nLuy9n6cIZLJheQlFBHh3dvQCc7kzy6qFTXFNTSWVpAYdPdlJbVcJPXjlCVVkR3b293LRoBq3tPZQWJshP5PHim6187bkm7vtPl9Cd7OPWy2fS1pnkkU2vcemscj5wXQ2lhfnk5RmNb5zg4MkOrq6pYGFVKWapew0fbevkQGsH186roKOnl/LiAnr7nDOdScqL82k508WOw200n+5iae10Zk0tpjvZR587G7bs5/iZLv701jqmTsln07YjvPvSmRQX5PHr109wxdypTClI0NHTy77j7dTNKqMoPzHwb3bibDfJ3j5OdyW5uLoMSP2B7nXnxNluqsuKON2ZpM+d9p5eaiqn0NfnA/P7jrcztbiAi2aUAPD6sbOc7uyhuCDBJdVl5OWdu58ygLtzqqOHypLCgbajbZ1MLS5gSmGqrmRvH8+/foKbFs0gMej5g/X/3zRLvZeOneliyUWVFOcnKMxP/due6uhhdkUxXcne81572NmuJIk840BrO1OnFDCjtIg8Sy33tSNtzCgtojr4kmJnTy+ng3+bM11JqsqK6OzppbW9mzkVU4at88TZ1P+NGWVF9PY5bR09VEwp4NCpDvYdb6eqvIhLZ5UPPKf1bDdnupLMn14y0HayvXvgPVIabC8z48TZbiqmFJDIM9q7kxQk8niicT8JMz5843y6g2/Sh19/srePZJ9TXJCgp7ePo22dzJtWMvAaiwtSfX/VdIzrF0yjuCAxsL37HBJ5hrsPvI/DTnX0sPPIaa6qmUpJYf5A7bubz3B1TcXAsod6frK3j0MnOwfeU1FcyOWRJ13gA7R19nDN55/MybpFRtOiqlL2nWgnOU4/eS6sKuX1Y2cH5msqp3C6s4e2zuSIz61fMI3GN1tHs7wRlRWl/hBeqK98eAnvv64m0nN1PfwMTS0u4NlP38Inbr2EPf/7bv79U+/iv91yMXkGlSUFLLtyNtNKCvjIby3g9itmcv1FlSyqLuVz71tM+A92fmgP7bcvnsFHfmsBf/GeywbaivJTm7cw+H1j7bQRaytIpJZ5w4JpVEwpyMbLHTDSHuVw6heMXLeMD3uPnR23YQ+cF/YAB092pBX2QM7DHshK2APnfbIZS5NyD3+8CX/EHCz8sT7TZRYm8jh+tjv4Q1Y4EPiHTnbQG3zcrSorHFh2+COou+POecMWfX1OW2cPBYk8EnnGma4kL77Zyo2108kzo7gwD/fU8FF4uQAd3b0Dwxr9H38PtnZw2exyrpgzle5kH13JXkoL82k+3cXBkx2c7uyhdkYpC2aUDCyrp7eP7mQfpUX59PU5eXnGG8fOUlKYYEphgp1HTnPl3ArMYF9wDGZhVSnFBQkOtLbzH3uOU1lSyJVzp9Lb53zjZ3uYUzGFy2eXc+xMF7977VzyzOju7WNqccFbPpb39vlArcfPdjOnopim5jP85NUjXDqrjPddM5dDJzuYUVZEcX4e+Yk8upN9fGfzmyyYXsI18ytobku9vhtrp1M5pYDNrx+ntDCfy2aXU1yQ4NiZLvLzjGdea+bOK2fj7hw/001lSQF5eTZQF0BbR5L1P9/DdfOnUVacT2lhfjBE1sl7r5nLrqOnqSorpPVsD1XlRcytLKan1+nq6eXVQ23cccUsftl0jKtqKmhqPkNZcT41lVPY3XyaksJ8igvy+MpTu/nwjfO5+dJqDrZ28IumY2w7dIqraipYsWQue1vOsv1QG3Wzyujo6eWZHc1UlhRw2eypvHLwFCuWzGXXkdMcO9vNvGlTeOclVRjQ3tPLS/tPsqf5DPOmlfDk9iPcuXg2cypTw3rzp5fw9I6jvGPhDKrLi/ibJ3cxa2oRre093H7FTEoK89l5tI3fubiKipICfrPvJAdbO3jHoul87bk9fPS3a6kuL+LHLx9mT/MZLp5Zxs11VTzRuJ9bL5/J9NIiChLGkVOdTJ1SwKGTHTz2/D4+977F7G4+zb++dJj7br2EI6c66e1z9rSc4bmdLXzy9jquv2gax850sfPIab781C4+fON83n9dDdsOtTG3MvWe6OjuZf70Ej77w1dZvmQudTPL6Ez2ccOCaZQV5Wf0/zlMQzoiIpOEhnRERGREaQW+mS0zs51m1mRma4d4vMjMvhs8/ryZ1Wa9UhERuSAjBr6ZJYB1wF3AYmCVmS0e1O1jQKu7XwJ8GXg424WKiMiFSWcPfynQ5O573b0b2ACsGNRnBfCtYPr7wG2W6VFGEREZVekEfg0QvurYgaBtyD7ungROATMGL8jMVptZo5k1trS0RKtYREQiGdODtu6+3t3r3b2+urp6LFctIjLppRP4B4H5ofl5QduQfcwsH6gAjmejQBERyY50An8LUGdmC82sEFgJNAzq0wDcE0x/CHjGdTlKEZFxJa0vXpnZ3cBXgATwqLt/wcweAhrdvcHMioHvANcBJ4CV7r53hGW2AFEvTl8FxO3O5Kp5bKjmsRPHuidCzQvcPdKYeM6+aXshzKwx6jfNckU1jw3VPHbiWPdkr1nftBURmSQU+CIik0RcA399rguIQDWPDdU8duJY96SuOZZj+CIikrm47uGLiEiGFPgiIpNE7AJ/pEs154qZzTezZ81su5ltM7NPBu2fN7ODZrY1+Lk79JwHgtex08zek6O63zCzV4LaGoO26Wb2lJntDn5PC9rNzL4a1PyymV2fg3ovC23LrWbWZmafGm/b2cweNbNmM3s11JbxdjWze4L+u83snqHWNco1P2JmrwV1/dDMKoP2WjPrCG3vr4eec0PwnmoKXteoXUhxmJozfi+MZa4MU/N3Q/W+YWZbg/bsbufUrezi8UPqi197gEVAIfASsDjXdQW1zQGuD6bLgV2kLif9eeDTQ/RfHNRfBCwMXlciB3W/AVQNavsisDaYXgs8HEzfDfwEMOAm4Plx8H44AiwYb9sZuBm4Hng16nYFpgN7g9/TgulpY1zznUB+MP1wqObacL9By/l18DoseF13jXHNGb0XxjpXhqp50ON/Azw4Gts5bnv46VyqOSfc/bC7vxhMnwZ28NarioatADa4e5e7vw40kXp940H4ctffAt4fav+2p2wGKs1sTg7q63cbsMfd3+4b2znZzu7+M1LfOh9cSybb9T3AU+5+wt1bgaeAZWNZs7s/6akr4AJsJnUtrWEFdU91982eSqVvc+51Zt0w23k4w70XxjRX3q7mYC/994HH324ZUbdz3AI/nUs155yl7vh1HfB80LQm+Ej8aP/HeMbPa3HgSTN7wcxWB22z3P1wMH0EmBVMj5ea+63k/P8Y43k7Q+bbdTzVDvDHpPYk+y00s9+Y2f8zs3cFbTWk6uyXq5ozeS+Mp+38LuCou+8OtWVtO8ct8Mc9MysD/hn4lLu3AX8LXAwsAQ6T+rg2nrzT3a8ndUez+8zs5vCDwd7DuDt311IX8lsOfC9oGu/b+TzjdbsOx8w+AySBfwqaDgMXuft1wP3AY2Y2NVf1DRKr98Igqzh/Jyar2zlugZ/OpZpzxswKSIX9P7n7DwDc/ai797p7H/BNzg0njIvX4u4Hg9/NwA9J1Xe0f6gm+N0cdB8XNQfuAl5096Mw/rdzINPtOi5qN7N7gfcBfxD8oSIYFjkeTL9Aagz80qC+8LDPmNcc4b0wXrZzPvB7wHf727K9neMW+OlcqjkngrG3vwd2uPuXQu3hMe4PAP1H5huAlZa6AfxCoI7UQZgxY2alZlbeP03qAN2rnH+563uAH4Vq/khwVslNwKnQEMVYO29PaDxv55BMt+sm4E4zmxYMS9wZtI0ZM1sG/A9gubu3h9qrLXW/a8xsEantujeou83Mbgr+T3yEc69zrGrO9L0wXnLlduA1dx8Yqsn6dh6tI9Gj9UPqjIZdpP7SfSbX9YTqeiepj+gvA1uDn7tJXTb6laC9AZgTes5ngtexk1E8k+Ftal5E6oyEl4Bt/duT1O0pnwZ2Az8FpgftRuqG9nuC11Sfo21dSuoGOxWhtnG1nUn9MToM9JAaX/1YlO1Katy8Kfj5aA5qbiI1vt3/nv560PeDwXtmK/Ai8Luh5dSTCtk9wP8l+Eb/GNac8XthLHNlqJqD9n8E/mRQ36xuZ11aQURkkojbkI6IiESkwBcRmSQU+CIik4QCX0RkklDgi4hMEgp8EZFJQoEvIjJJ/H9GpgKaACG63QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(losses)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([50000, 3]) (50000, 8)\n"
     ]
    }
   ],
   "source": [
    "with torch.no_grad():\n",
    "    samples_prepd = model.sample(50000)\n",
    "samples = undo_preprocess(samples_prepd.detach().numpy(), mean, std)\n",
    "print(samples_prepd.shape, samples.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "components_Eppp = [\"E [GeV]\", \"px [GeV]\", \"py [GeV]\", \"pz [GeV]\"]\n",
    "components_jetcoordinates = [\"pt [GeV]\", \"phi\", \"eta\", \"mass [GeV]\"]\n",
    "def plot(truth, generated, bins=50):\n",
    "    fig, axs = plt.subplots(4,4, figsize=(15,15))\n",
    "    \n",
    "    # plot (E, px, py, pz) for both particles\n",
    "    for iparticle in range(2):\n",
    "        for icomponent in range(4):\n",
    "            ax = axs[iparticle, icomponent]\n",
    "            xlabel = f\"{components_Eppp[icomponent]} of particle {iparticle+1}\"\n",
    "            i = iparticle*4 + icomponent\n",
    "            \n",
    "            bins_local = bins\n",
    "            _, bins_local, _ = ax.hist(truth[:,i], bins=bins_local, alpha=.5, label=\"truth\", density=True)\n",
    "            ax.hist(generated[:,i], bins=bins_local, alpha=.5, label=\"model\", density=True)\n",
    "            ax.legend()\n",
    "            ax.set_xlabel(xlabel)\n",
    "    \n",
    "    # plot (pt phi eta mass) for both particles\n",
    "    truth = np.concatenate((get_pt_phi_eta_mass(truth[:,:4]), \n",
    "                            get_pt_phi_eta_mass(truth[:,4:])), axis=-1)\n",
    "    generated = np.concatenate((get_pt_phi_eta_mass(generated[:,:4]), \n",
    "                                get_pt_phi_eta_mass(generated[:,4:])), axis=-1)\n",
    "    for iparticle in range(2):\n",
    "        for icomponent in range(4):\n",
    "            ax = axs[2+iparticle, icomponent]\n",
    "            xlabel = f\"{components_jetcoordinates[icomponent]} of particle {iparticle+1}\"\n",
    "            i = iparticle*4 + icomponent\n",
    "            \n",
    "            bins_local = bins\n",
    "            _, bins_local, _ = ax.hist(truth[:,i], bins=bins_local, alpha=.5, label=\"truth\", density=True)\n",
    "            ax.hist(generated[:,i], bins=bins_local, alpha=.5, label=\"model\", density=True)\n",
    "            ax.legend()\n",
    "            ax.set_xlabel(xlabel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(data_tst, samples)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(e)\n",
    "\n",
    "This is a minimal implementation of Conditional Flow Matching (CFM). Again, it uses the same notation as in the lecture notes and https://arxiv.org/pdf/2305.10475.pdf. We recommend reading it alongside Figures 3 of the paper."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [],
   "source": [
    "# use ode solver that can operate on gpu\n",
    "from torchdiffeq import odeint\n",
    "\n",
    "class CFM(nn.Module):\n",
    "    \n",
    "    def __init__(self, data_dim, n_layers=3, hidden_dim=64):\n",
    "        super().__init__()\n",
    "        self.data_dim = data_dim\n",
    "        \n",
    "        # construct simple MLP\n",
    "        layers = []\n",
    "        # network gets x_t (data_dim-dimensional) and t (1-dimensional) as inputs\n",
    "        layers.append(nn.Linear(self.data_dim+1, hidden_dim))\n",
    "        for _ in range(n_layers-1):\n",
    "            layers.append(nn.Linear(hidden_dim, hidden_dim))\n",
    "            layers.append(nn.ReLU())\n",
    "        layers.append(nn.Linear(hidden_dim, self.data_dim))\n",
    "        self.net = nn.Sequential(*layers)\n",
    "        \n",
    "    def batch_loss(self, x0):\n",
    "        t = torch.rand(x0.shape[0], 1)\n",
    "        epsilon = torch.randn_like(x0)\n",
    "        \n",
    "        # TODO: Define linear trajectory\n",
    "        x_t = (1-t)*x0 + t*epsilon\n",
    "        v_t = -x0 + epsilon\n",
    "        \n",
    "        # Predict v_theta with neural network\n",
    "        v_theta = self.net(torch.cat((x_t, t), dim=1))\n",
    "        \n",
    "        # MSE loss\n",
    "        loss = torch.mean( (v_theta - v_t)**2)\n",
    "        return loss\n",
    "    \n",
    "    def sample(self, n_samples):\n",
    "        # velocity function to be called by the ODE solver\n",
    "        def velocity(t, x_t):\n",
    "            t = t * torch.ones(x_t.shape[0], 1).float()\n",
    "            v_t = self.net(torch.cat((x_t, t), dim=1))\n",
    "            return v_t\n",
    "\n",
    "        # apply ODE solver starting from N(0,1) noise\n",
    "        epsilon = torch.randn(n_samples, self.data_dim)\n",
    "        x_t = odeint(velocity, epsilon, torch.tensor([1.,0.]))[-1]\n",
    "        return x_t         "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [],
   "source": [
    "# standard imports\n",
    "from sklearn.datasets import make_moons\n",
    "\n",
    "BATCHSIZE = 1000\n",
    "N_DIM = 2\n",
    "    \n",
    "model = CFM(data_dim=N_DIM)\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=0.001)\n",
    "\n",
    "# a very basic training loop\n",
    "losses = []\n",
    "for i in range(1000):\n",
    "    optimizer.zero_grad()\n",
    "    # sample data from the moons distribution\n",
    "    data, label = make_moons(n_samples=BATCHSIZE, noise=0.05)\n",
    "    x0 = torch.Tensor(data)\n",
    "    # compute loss of DDPM\n",
    "    loss = model.batch_loss(x0)\n",
    "    # backpropagate and update the weights\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "    losses.append(loss.item())\n",
    "\n",
    "# sample from the CFM\n",
    "with torch.no_grad():\n",
    "    samples = model.sample(BATCHSIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(losses)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "with torch.no_grad():\n",
    "    samples = model.sample(10000)\n",
    "truth, _ = make_moons(n_samples=10000, noise=0.05)\n",
    "plt.plot(truth[:,0], truth[:,1], \"k,\")\n",
    "plt.plot(samples[:,0], samples[:,1], \"r,\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(f) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model has 8835 parameters\n",
      "0/5\t 1.3899259256989989\n",
      "1/5\t 1.3707882459849527\n",
      "2/5\t 1.3607870209706974\n",
      "3/5\t 1.3546251336189166\n",
      "4/5\t 1.3552507537685028\n"
     ]
    }
   ],
   "source": [
    "# construct CFM\n",
    "ndim = 3\n",
    "model = CFM(ndim)\n",
    "total_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad)\n",
    "print(f\"Model has {total_parameters} parameters\")\n",
    "    \n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)\n",
    "\n",
    "# train CFM\n",
    "nepochs = 5\n",
    "losses = []\n",
    "for epoch in range(nepochs):\n",
    "    losses = train_epoch(model, dataloader_trn, losses)\n",
    "    val_loss = val_epoch(model, dataloader_val)\n",
    "    print(f\"{epoch}/{nepochs}\\t {val_loss}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+SyOWeCV0/S4gKcF1nFPaRcKK/uH+9y/bsHBTeOYUCicVRKl7xOSleOSLVX7Pne7H9fux61C++2KbUEFF/ILCEhzND7ztRaLuEhTddhzMj3QIcSEuEn5SECV8O1k5Rzzm4tFLmUkJruPM+MNZSfWZb9fh5g9/D2ls0UBv9wgk6eUeD+yWfvTUDFzyysKyEr7JoW54fymm/74zoP16G/bGYnQdP7dc+yCKFvGR8BNCW9q66p3fkP7sPDz77Toopdwl2kDuJIxtAqeLnZVqY8XYL1cDqPh69cLiUuhn0ayE/9uWgxj31epyHWNbOcddRP+Y8/iUsf0Q8gIsZFQGcZHwg6nD9+fYqWJ88Ms2tB43B6XatzrR5MLyTdYe098/ZUjyL3znvK5bKYXX5m/C3qMFAUYcnILCEjw3ex0KCp1flNbtPQYguHr10gAzZKlNCT9Qv2UfQNrY2dh1qPzVC9HWiBytIjULyzWTluDKd+JvqdM4SfgV++3TC+tmH92vLRJ+viGB7jpsn2CufOdX3DbV1c983rr9eG3+ZvR5foHHNnNW7w0oKTv14a/b8P7ibfhg8Va/2874fSeyc0+UPWHIevmFxY6+3IGuL+9uLwhBnflnWqOxk0b1HQdPuqv1TheX4OTp6OoWGgsKi0vx9k/ZETt+zmHzQtMVb/2Cj37dFuZowiMuEr6xDn9kz1Yh3/+XK6xXtpq3br/pgirGNgB/NU4rdx7B/PWuXh93frLC5/Wvs/bgzk9W4Olv1vq8NitzN37NPmB/ABvFJa6MWuhgFPHYr1Zj6OuL3D8nCHDm49/j7k9XoPMTP2DaUuuBafrV0l8J/4vlOThaUNaIalXCD6bk6PRXjp0qwoUv/Yyx2t/1yrd/w1lP/mC+z4CjiB/Tlu7Av+ZuinQYPlblHMXT36wLyb4+WbbD9HsZKXGR8PUSfoir8h0zW1Alv7CsROi0h8nLczeadvm8d/pKAK5Rp97um5GJGz9YZjuOwMwd05bjvhkrfZ4/XVyC699bgtU55gvBF5WUHUfgGqPw7aq9AIBvs/b6bF9aqvDL5gPuhl5lc125+OWf8dDnWRj7ZdkFtKxHkOc59O5JBQBH8gsxZ7VvDN78/Tn0O6nFm10XUr0Ky2Mf7viY8q0UFFb+u6LHZq7BR79uj3QYbnGR8PXeM6UKqFElscKOE8h3e+ArZSVhp/W9by7wvP19z2taZz3pPf/denznldju+M9y/GQyEKq4pNR0DqDv1+7DrEzf6qi1e45h2bZD+OesNcgvLHZPOmeW2LyTcHGp73GmLtmOv/x7mftCYVfC35rnakA1DtLRL4DeF3OzC+Ndn67AnZ+s8Gn/eH3+Zmzef9ynNO5RPWUwK3M3gNA0yO4/dgppY2fjiwivf7xs60H3qGV/3vxxM0ZMXlLBEZXPqaISj0KVFaUUlm09WO4Lc2mpwokYqNaLi4Svl/D7tWuIBy7tgPsuaV8hxwn2I3OsoBhDX1+M7NzjOFVUglun/IHnHazI9fx3Gzx+LtQagt9buBV/+2SFRyKfu24/bvnId76ZC178CWc9YV4dAZR1sdS/D3rJOSlBcMP7y9yTzpl9X7yvY2ZJ2Hv20RIHXzzjHdGf33Y1vHlfNM2OtVNrjDVWp508XYxX52/Cte/5JrDVu4/4PJe56wgmzNHPe/kz/pY810Xli+W78NGv24IafOYtO/cE5q/b73j7lTsP4/rJS/HyPGfVKy/P24SlWw8FG54tpRS+WJ5T7p5rPZ6dj842n2vd5xk5uH7yUnyzyv+dn503F2Sjy5M/4Ei+9Zxb0SAuEr6I4McHL8TkUeehRkoSHri0A847o17Ij+O0hOTtl+wDWL/3GN5ckI1X52/Cgg25eG+R/0ZSAFhvqE7w/hI+ZLKI+uGThVi2tWxBsr1HT9nWzx8/5Sq1zF/vSiB6nX6CuJIf4LoImJXMfZJwie823nnZSS8ds95Q3tViJSbHcsdl0sB7JL8Im/cf94zF67SUlipcZ7gw2IaqxePv3eixlCrg6W/W4ZEvfNt7AjXwlYW47eMMx9vr3RO9378/h08W4r4ZK90l24zth8o1QeDLczdiwYZcPPR5Fl6d5zvg0c7IyUsx0VAAOm5T2jaW5rdqBY7y9syaleW66zt4kgk/KrRNrYnqVcrWe3nh6nNCfozyTo88K3MP9h4pG4qvlP/bxKGvL/b42VhXb1Ylc+4z83D95KU+dfqvzd+EtLGzkTZ2tsdgMH1qiQ37jmPjvuPuEv4f28t6shzJLzTtXeOdWM2qdLwTvJM7a7OLwiqvNgXjsY7kF1ouWm+8o9iwzyvhex3n2KkizwF3XvvaFGDCBAxVUVFR1e/59zp44jQmL9piWd3x5oJszMrcgxm/70RBYQmumbQEt9tcaLzPn9n+TmuvB7ruxJKtBx2vXGf2dl76YSO25p0IfuS7ts/DJwvxv5W7g9tHGDhZ4vBDEckVkTV+tushIsUick3owqs4wUyo5k+RTanSqT+2l5XS3/l5C7pY9P6wUuBwaoKfNuYibexs98/GKSSMH/pPl5WNVB382iLTetEJczaYJuEN+zwbMzft960TD6aEb1WdcFgrXR06WYhVu8suAN3Gz8OYaeaJyKwxW/87+hv9650IB726yGJLa3o7R6DjD5zQq3XumLbc429tTeG37APuNpKHPs/ChDkb3Hdy3ozjT/QLp7EgYPTVihyc89Rc3PjBUtsIqmltbPkWn2Pvu+hDQZSorc71mwuyfUa+7zlS4Gjwnb7P/5u2HPd/loklW6JzWW8nJfwpAIbYbSAiiQBeABDXY9DLOyoTcFWx6Oy6e1ox1tPbNQaPnur8lt/osEkd5ZcrckxLTcdO+V4cSkoV/rdyt/tOwTtpBtoP3yhPS1RXvPWLT3vFzxvzsOuQby8ms948D32ehbSxs/1O/mYWqj5DaFkvHdf/hcWlGPLaIncX2d+2HMCXy3PcJXy7hP/IF1n48Bdn/cLPfqqsgLBsmyvpfL/WfOK/79fsxbQl2z0a12/4YBmum+SqttKr837bchCDXl2IU0UlpqX9BBH3efQ+nyWlCqWlyt3NUb8gZO06YtoPXi9gmPWo237gJNo99p27nSNr1xF0f2aeuxFd13qc+cXtxOlivD5/s0f7jjL8FWd6lcw/WbYD509cgItMFj3ypu9Fr9IZ+b79hS1SnKxpu0hE0vxsdg+ALwH0CEVQ0eqV67ri7//1rRevKKeCGEj1u+EOoSJ6BB43SeKA8+khpi3Zjqe+WYdTRSUY0bOVz7xDxSWlyNh+COlp9QGUNeK9+7P/23W9Ss2YSBLE9yKioDDm4wwM6NgIbVJrOIrbjNndwdeZezCgYyOfi23O4Xxs2Hccj81cjYs7NcaH2sCeL/92vhaTtf9muC78V3dvgTrVk21jMv59ShVMq1BmZe5Gaq0U3PEf15iOyTedBwDI3OW6K9LrtfV2Ef3Cl517Ap2a1HLvR682SxDzC+fj/1uDaUt3oH2jmj7nY/jb5qNc52l3JWbToWzWek098sUqj7YO4x3pzJXmhQ8AeOn7DZi6ZAea1q1qvoGXx2aWVWocLShCnWr25z4WlLsOX0SaA7gSwLsOth0jIhkikpGXFz2zRNZMcV337r24ne12V3VvEY5w3KxuayPJqjfDBAe9igC4++Qbq1yMJi3cgmsmLcGSLQexdOtBfJ21Bw9/scqdhOyYNQqbNfAWlZRi7rr9eHTmajwYwAXcO5GY5ZW56/bjpw2+3V/1OLYfzHcne6DsLsyYL2dl7sbV7/6Gy99c7FGifvgLV6y7DuXjtqkZfkdWlyqFayaVTTucNnY2lu84hPtmZOKG95f5bG/s7pqde9yj9Kszlo7nr3O9z4QE8WgzWbb1IGb8vtM90G5z7gn3ynH6+/NHP18fL9mOtLGz8Z+lO5CSZJ6uTheVHfuBzzz/nl2fnot1e45h3Z5j7ouhx+fEYaGo69NzcfaTP1jOv+OvcLVxn6sH3uEIN+qGotH2NQD/UMpuyIyLUmqyUipdKZWempoagkOHRt92DQAAnZvVdj/3n9G9UC254vrsO3EkiGl5K1qWRX3uxn3OGiwztGkLjG0DRvogtdFT/8CIyUtx34xMx7GZNQqbtavkHS/70jntfz16yh8+A9uOnyrGXK/qkhOni3HLFGN1kt6rybx+zX14Qxz3zcjE8h2HsWb3MY9qBj0hT5izHvPX70ffF8qm1zh2qggZ2z3bNmZl7vFpzDZbhtPsczbwlUWm9fHGjgn7tDEYAsHf/lM2Avz6yUsx1mbSOic9kfSxM0/Mco1S/ef/1lgm/P02iwodLSjCsDcWY9gbi91VjMYywMYAGtqPny5Gj+fmY/mOwz4XW7MqOeMd4LivVuH6yUtx7jPzIrrYi98qHQfSAczQ6gEbAhgmIsVKqf+FYN9hZfyb9Wvf0HSb+X/v7zFoilzsFn2xctO/fUuZuvwgqrNyDhfgdJH/aSSyc8u+5E6j/nFDrmn31THTlptu712Hn2AxzFtPFFkWI5eNVYje1wZjg+WQVxdhj9fqWmYNmmY9t/5hMvWHFbMR1iLO5h/SnbbpqaMz7XprcQ4zHB77aIHrfBgvvsZecU7p61lsn3gZcg7no7QUpknc+J0oUWWFpYGvLETmE4MCPm4olDvhK6Va649FZAqAb2Mp2VdLTnR3H1Rw1anqPQHMbmnbNarl8xy5RuAGSp+aIFSc3g08PqtsbpNA2jnMGqGtHPYqNZutcwyY131bUQpYs/uoRyPsuj3H0LFJLZ9kHwin50ApV6Out8ydR4I+thWzOnyru0un9L/Jmz+V9UgLpIRvpt8LP1m+ZvzbGmM/kl+EpVsPonebBuU6djD8JnwRmQ5gAICGIpID4EkAyQCglJpUodFVsHXjB0MgeG6OqwfB0YIiDDu7aYSjonAKpDukXR9yb3qPrWsmLcHfL+2AVyxGsQZyfKUUHp3pWVUy7I3F2PTsUMf7KA+rWV0/C8HoYG9frdztM0fRs7OdtRNZ0afKMOutVRE251pfTD5YvC06E75SaqTTnSml/lquaMJMH4j18KBOKCwuxWXnRD7ZPzqsk2HofnBG9mxpOmEb+QqkJmq9ySRpTlgle8B3NK8dq2qfYPqiB8NsptaK5D0QLtr4W0Dlires59uvgCU6HImbkbZ26lRPxovXdEXtqp7driIx0eEF7cvfmP2PIZ0iNjNorIn0bJahWO2s9/M/hiASCpRVNZ0TeqN0uDHhO9ChcU2M7tfa/4Z+pDWo7nebUKyUlJggIV/Ht7KK9Nwnp4rKNx0HRY73bLCBsGqArmjMCjb6tHXVsX17zwV4/PLOlttdl+7bP//s5nV8nnNSlnRyi3/tefbjAZISEpDo4MNYq2ooOmlReZyKwrEW5IyT6ZetVPAifJaY8G28c2N3/HB/f1Tx6v/bvVVdj5+Hd2uONU8PBgAM6twYm58bill39fXZn5NGP3+NeC3qVcP44V2w6inrbl2JCeLoTmH88LP8b0QVyrh6F8WWnzcGP3g0UiV8FvFsVK+ShI5NfLthTh6VjvRn57t/FrhG6659ejBSkhJ8qlNSkhJwurgUbVJr4J6L2yO1VgpKSktxaecmKCopRafHvwfgSsB2Cf/8tg3w0S09kJKUCFVovV1SQtk8lQsevBAb9x3H37wa3P7UtZnjlbao4oz/NjRL6VH4/dvh/EZmnNyBVwSW8IPgXeLXR+jWSEkyrTt/Y+S5AFz9cm/o1QqXdm6MIV2aIjFBUNUwmndUnzTbXiP/urYrUpJc29sl64QEcb+enJiAoSZdTV+9risTvkGzOs7mVyH/2pZjfqJ4cawc6waUBxN+ELwTZd3qVWy3r6tNuuRkkI1ewu/asq7Pa8bRh2bDzL+//wI8NuxMAGWNv/rvVDcs7VhFuwsxG80IAOOGdvJ57sVrQr9+gJ2pt/YM6/HK0wBHnqJhkrFLOzeOdAi2mtf134GjIjDhB8GYJxc/cpHldi9f2xUfjEp3J1YnCf+c5nVwzXkt8Op1XW23ExFsfHYI1o13tR30adMAnZrUxu3927hi1I6pH9tYU5ScoC/qbp7kOjWt7fOcXUPxWMMFwnv2yU9v62X7Pqz2199iagsnLupo3rX15j5nmD7/4tXn2K76RYHR70IjyWykbjRpWb9aRI7LhB8EY6JsWd/6Sn31eS0wsHPjsoTvoJtOUmIC/nVtV7RJ9V2gpbrXAuwpSYmoXiUJ2ydehuljenu8pkeY4F5qr+zg/rpsmn1VRAR3XNjWdPvLDQPW9IXGda0d3N4/OqwTvr//AvfPl3ZubFni/tsA8xiMbuxlntjNGsq6t6qL63q0DGgUrT+RnnQv0p6Ogs4AVnev0aI86z6UBxN+EAK9+z+zaW10b1UXT/3JvGvn6yO64eu7fXv16OpVT8bm54aiVlXnt8p6wjSbfrebVl3kPejojZHn4onLO5s2VAPAnRf5T7be7BqnujSvjYFnNsKoPmno1KTsrqKKdkEa2bOlz+/8Y4hndVPWk4Ow8VnP9XmsxrSYlfp2aXPnl3d5ylgx94H++M9o37uuqy2m/m5cOyXgYzSuHfn2kIrMp9699IIRqQF/TPhBCLSxs2pyIr66sy/ObWW+cPrwbs1xTou6lr+fmCBIDnAglZ7byqbfdf336e298PaN3Y1PAQBG9myFK7o2w639WqNx7arYPvEyDDu7CQBgwpVnAwCSLTJpPa0No2ZKEi4wVMXUr1HFXaquXdV1J2J0QftUfHBzD4+GawDu9+qdOLynvrigfUPUqZbsU4VQNTnR9KLcqJZvItKnuT1X+xJbVftUtFop5h3m5tx7geVynJP+cp7l/vQ7oUs7N3av9wAAHRrXQiOvJD7rrr6WC8EEMyLUWLr++aEBAf++buXjlwb9u6G8Y/Omd8Ioj0AmzQslJvwghLt3yxitXj4Q+hQNKcmuP/E/L3c15vZu3cAjAQDA0C5N8PxVZ/vsY3i35gDK1gtIshgtUiMlCVsmDMPqpwZh2uhe+PT2Xlg/fgiWjLvYfa68q2g+GJWOBwZ2MN1fTW1AmLEQ9N5N5+FFbeH5BQ9eCADo1bq+6e/3adPA587iX9d2xS1903y2vbhTI23/6Xj3xu648yLfRXA+ua0XptzSA73bmB8vGP07eLYz/DruYqx9ejBa1a+OPoZJtTo3q21TorROGt21woVSCh/d4lqIrn4N14W5ilfhITFBLBNQMFUjiSIY3a81WtWvXq6R4/VqVMGQs1yFjgcv7YCZd57v+HcrMuEbCyLnnVEPU2/tafr9sROpKh32ww9CuKoHNz47BALx6QbqxMSrz8a9l7R3zw80qk8aRvVJ89jGXc9v8YYGn9XEo1SelCAYeGZjzF+/32dbY2I4v21ZKV8vQeu3sP/Xvw1+WLsPA216UdTQ2iqMt72XdGrkbntok1oTv/zjIjSrY97wJaJ3S3X9/sKHB+CMBr4l2CXjLnYnwZopSRh6dlPkHndNM1wrJQnHT7tGUvZt53o/2bknLBdQD1TXFnWwyLA0X62UJIgIFj1yETbsO4Yhry0uez9erSqf39EHCQLsP2Y+eVeiYRxGqQKa1XWdp2Ttgu09wjo5McFj/EfNlCSc0N779T1aupc4/OdlZzqasTIxQfD45Z3x+OWdsTXPd+F6bw1rVsGBE+ZTXLz7l+5Qyvoz+tr13XD/Z5n46JYeHusYWyX8R4Z0xIvf269VbObtG7qja8s6SElK9LjbblW/Oi7skIoci5lEAdcaGokJCR5r41bEovVOsIQfBBHBnQPamo6mDaWUpMSgkr3+u60b2jeY9mvfEH3bNcBtDucJEhF8cHO6uw0AgN9z4J2sxg07Ez8/bN6zqUW9au7jGP8HfEuaLepV90gCWycM83j9DMO8RWbJ/ta+rdG0TjWf6iD9jsTsbmaARe8fb2brKBi9eM057osIALRrVNPjvernTE/M3qXks5rVxnln1Lec3G/LhGFooo0r6Ny0tvvCqVfPNKjpWaVTr0ayR7WR8X3qF4nb+rXGkC5NbN+XzvinclJ1YderR0Qsk/2Tf+qMP5/bHNnPDcVFHRu57wABoECbskK/O9XdOcB+GVMA+OivPdx3RUYt6lVHai3Pc2cWmX5XomvXqJbPd5F1+DHmkSGdTPvKx5JaVZPxyW29LdsWrDwypKP7sV0vpUB9fXc/zH2gv/vn2y4ouxD56yfvnRQ+segO+tWd5+PT23vhCYsGdH1ZukSTumu7xW8Gn+W833ejWike1YLeVTaNtKTyf1pVnvdbN+t55a1L8zqYdVdf3D+wvXuZR2PhYfvEy9zdV+tWq4LLzm6Kj27pgQY1quD+ge3d2x066RogVK9GFUdjFV665hyPXmBm7Sah0qGx6++hH++6HmWN/Me1gU1Wd4F2LurUCBd1bORsY+2UGMceGKcsmX1vP9NfYy8dihnGKpsaKfZdEKtrr99nUV9vVL9GFfeXGEBAvZIAVw+ej7UBW40seop0b1XPI35v+hexdtUk3HFhW7x3k2fD6Id/Tce39/h+id8c2d3nuQtsxhIYr0/ebUL1alTBuvGDcZfWnnDvJe099mU2tsJM15Z1kZSYgAY1XdVWfz0/zTPmG7rj+/svQJWkBIgILurYCMsfv9R9YWvdsAZ6ae0WF3ZIdZf2vb0+opv78bXpnj2r6lRPxrbny+6+vO/UnvlzF/s34UVvv/GndUPXHcsdA9riGj+TDepSkhICrq4tuxtLxuqnBmHrhGHuu4DeberjrGa+kygCkavSYR0+BWXzc0NxJL/I7yCb5MQEn945gdKrevzx7qP/+ohuAY+gbVw7BQ8N6oArujZHK5PprC/u5CrJN6tT1b2s4M19zjCtehs/vItHva1OAWhrGGdhFqO+OA8ANK1TDdNG90Knx7/DqaJSQwnfmdpVk03/BjVTkjy6wxqtGz8YiQmClKREbHp2qPv9vTHyXNw7faV7u8vOborh3ZqjXvUqlg20xvc3666++DprDyYv2goAuKn3GZj08xYArguMvlKYlTapNXF+2wb4bctB2+2euqIzru7eHG1Ta+Jf13bFF8tzbLcHXF18A2W8EBsLKPP/3h9Nve4utk+8DGljZwOI4kZbEfkQwOUAcpVSPpdjERkO4BkApQCKAdyvlPol1IFSdElOTPCpz6wIfzw20GfAmVN6L6NAiAjuvri93+1+engAlAIO5xcitab5eTD2+7+iazN8nVW2gHi9GlUwfvhZeGLWWselyll39cOPG/aHZVCR8YJjvJhd0bWZR8LXc7l3ryNvr4/ohgY1UtCleR10aV4HV3Rt5jOfzNs3dMfRgiKMfH+poxjtCskt6lVHi3rOqhvbNaqJ7NwTPt2DnbBaJc/f2teRqsN3UsKfAuAtAB9bvP4jgK+VUkpEzgHwXwC+k7EQBSEcF5Vg6Hc23qU4I2NvjjdGnoujBUVYaOiZo8+HVNPhugQdm9TyGBTXXmtobVqnKo4WFCG/MPxz6zvtoux98e1iWC9C30Wtqkno3Kw2vrm7H/JOWC/KbnfI90el43OHa+zqd2n/vOxMDDCps596a0+8Mm+T7eLpgc56uWTcxejz/IKI9cN3sqbtIhFJs3nd2O+qBip2kBtR1NMLb1bjFnRXdW+B3GOnMdrQOB2IM5vWxvJ/DkR9rUH102U7w143PNRhzx07r1zXDa/N34SmWs+is1vUAWBe9+3PpZ0bm06c9s3d/Xwauef9/UJ8tSIHF1rcnVzYIRUzft+JLJvrR6Dz2jetUw1VkhKit0rHCRG5EsDzABoBsKywFZExAMYAQKtWrUJxaKKo5T2Vw4SrzsYrczehr9ZonJyYgHsu8V99ZMfYxfKGXuH5TnVqUgtN61TFBzf3CEn1Us/W9fHp7b39b+jFX/dXI9dFxFONlCTc5DU2xVtFjLF8ZvhZ6GjRdlLRQpLwlVIzAcwUkf5w1ecPtNhuMoDJAJCens47AarUvCepa163Gl72MwtqLPj+/v7+N6pAXZrXwa/ZB9HQou0k2l3fI3KF3ZD20tGqf9qISEOl1IFQ7pso1kT5hI0x6+FBHTGsS1OcaTKNtxMzxvTGkXxni9fX0BqvrbqkxppyJ3wRaQdgi9Zo2x1ACgD7PlNEldjN56dh8qKtPnPWUGgkJSaUa9Bj7zYN/G+kefxPnZHWsAYGnhndC6o45aRb5nQAAwA0FJEcAE8CSAYApdQkAFcDGCUiRQAKAFyvItXniCgKjBvaCY8M7uh33QGKfrWrJrsHwBm1qFcNOdrU2rHESS+dkX5efwHACyGLiCjGiYjfHjoU2769px9yj5tPXhfNONKWiChAdatX8buWdTTiPSdRBass9b8U+1jCJ6pAWU8OCnpqCKJQY8InqkDGaXOJIo0JnyhE9AnCiKIVEz5RiAQzOydROLHRlogoTjDhExHFCSZ8IqI4wYRPRBQnmPCJiOIEEz4RUZxgwiciihNM+EREcUIiNXW9iOQB2BHkrzcEEGsrasVizEBsxs2Yw4Mxh4d3zGcopcxXXvcjYgm/PEQkQymVHuk4AhGLMQOxGTdjDg/GHB6hjJlVOkREcYIJn4goTsRqwp8c6QCCEIsxA7EZN2MOD8YcHiGLOSbr8ImIKHCxWsInIqIAMeETEcWJmEv4IjJERDaKSLaIjI10PDoRaSkiP4nIOhFZKyL3ac8/JSK7RSRT+zfM8DvjtPexUUQGRyju7SKyWostQ3uuvojME5HN2v/1tOdFRN7QYl4lIt0jEG9Hw7nMFJFjInJ/tJ1nEflQRHJFZI3huYDPq4jcrG2/WURujkDML4nIBi2umSJSV3s+TUQKDOd7kuF3ztM+U9na+5IwxxzwZyGcecUi5s8M8W4XkUzt+dCeZ6VUzPwDkAhgC4A2AKoAyALQOdJxabE1BdBde1wLwCYAnQE8BeAhk+07a/GnAGitva/ECMS9HUBDr+deBDBWezwWwAva42EAvgMgAHoDWBYFn4d9AM6ItvMMoD+A7gDWBHteAdQHsFX7v572uF6YYx4EIEl7/IIh5jTjdl77+V17H6K9r6Fhjjmgz0K484pZzF6vvwzgiYo4z7FWwu8JIFsptVUpVQhgBoDhEY4JAKCU2quUWqE9Pg5gPQC7Ne+GA5ihlDqtlNoGIBuu9xcNhgOYqj2eCuDPhuc/Vi5LAdQVkaYRiE93CYAtSim7EdsROc9KqUUADpnEEsh5HQxgnlLqkFLqMIB5AIaEM2al1FylVLH241IALez2ocVdWym1VLmy0scoe58hZ3GerVh9FsKaV+xi1krp1wGYbrePYM9zrCX85gB2GX7OgX1SjQgRSQNwLoBl2lN3a7fEH+q38Yie96IAzBWR5SIyRnuusVJqr/Z4H4DG2uNoiVk3Ap5fjGg+z0Dg5zWaYgeAW+EqSepai8hKEVkoIhdozzWHK05dpGIO5LMQTef5AgD7lVKbDc+F7DzHWsKPeiJSE8CXAO5XSh0D8C6AtgC6AdgL1+1aNOmnlOoOYCiAu0Skv/FFrfQQdX13RaQKgCsAfK49Fe3n2UO0nlcrIvIYgGIAn2hP7QXQSil1LoC/A/hURGpHKj4vMfVZ8DISnoWYkJ7nWEv4uwG0NPzcQnsuKohIMlzJ/hOl1FcAoJTar5QqUUqVAngfZdUJUfFelFK7tf9zAcyEK779elWN9n+utnlUxKwZCmCFUmo/EP3nWRPoeY2K2EXkrwAuB3CjdqGCVi1yUHu8HK468A5afMZqn7DHHMRnIVrOcxKAqwB8pj8X6vMcawn/DwDtRaS1VsIbAeDrCMcEwF339m8A65VSrxieN9ZxXwlAb5n/GsAIEUkRkdYA2sPVCBM2IlJDRGrpj+FqoFujxab3CLkZwCxDzKO0XiW9ARw1VFGEm0dJKJrPs0Gg5/UHAINEpJ5WLTFIey5sRGQIgEcAXKGUyjc8nyoiidrjNnCd161a3MdEpLf2nRiFsvcZrpgD/SxES14ZCGCDUspdVRPy81xRLdEV9Q+uHg2b4LrSPRbpeAxx9YPrFn0VgEzt3zAA0wCs1p7/GkBTw+88pr2PjajAngw2MbeBq0dCFoC1+vkE0ADAjwA2A5gPoL72vAB4W4t5NYD0CJ3rGgAOAqhjeC6qzjNcF6O9AIrgql8dHcx5havePFv7d0sEYs6Gq35b/0xP0ra9WvvMZAJYAeBPhv2kw5VktwB4C9qI/jDGHPBnIZx5xSxm7fkpAO7w2jak55lTKxARxYlYq9IhIqIgMeETEcUJJnwiojjBhE9EFCeY8ImI4gQTPhFRnGDCJyKKE/8PJBnrNTHav78AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(losses)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(50000, 3) (50000, 8)\n"
     ]
    }
   ],
   "source": [
    "with torch.no_grad():\n",
    "    samples_prepd = model.sample(50000)\n",
    "samples = undo_preprocess(samples_prepd, mean, std)\n",
    "print(samples_prepd.shape, samples.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(data_tst, samples)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3) Unfolding with Normalizing Flows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this exercise you we have to extend the INN of sheet 10, exercise 2 to allow for extra, conditional inputs. The following code demonstrates how to implement a 1-dimensional random condition for INNs within the FrEIA framework. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# standard imports\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from sklearn.datasets import make_moons\n",
    "\n",
    "# FrEIA imports\n",
    "import FrEIA.framework as Ff\n",
    "import FrEIA.modules as Fm\n",
    "\n",
    "BATCHSIZE = 100\n",
    "N_DIM = 2\n",
    "\n",
    "# we define a subnet for use inside an affine coupling block\n",
    "# for more detailed information see the full tutorial\n",
    "def subnet_fc(dims_in, dims_out):\n",
    "    return nn.Sequential(nn.Linear(dims_in, 512), nn.ReLU(),\n",
    "                         nn.Linear(512,  dims_out))\n",
    "\n",
    "# a simple chain of operations is collected by ReversibleSequential\n",
    "inn = Ff.SequenceINN(N_DIM)\n",
    "for k in range(8):\n",
    "    inn.append(Fm.AllInOneBlock, subnet_constructor=subnet_fc, permute_soft=True,\n",
    "              cond=0, cond_shape=(1,))\n",
    "\n",
    "optimizer = torch.optim.Adam(inn.parameters(), lr=0.001)\n",
    "\n",
    "# a very basic training loop\n",
    "for i in range(500):\n",
    "    optimizer.zero_grad()\n",
    "    # sample data from the moons distribution\n",
    "    data, label = make_moons(n_samples=BATCHSIZE, noise=0.05)\n",
    "    x = torch.Tensor(data)\n",
    "    c = torch.randn(BATCHSIZE, 1)\n",
    "    # pass to INN and get transformed variable z and log Jacobian determinant\n",
    "    z, log_jac_det = inn(x, c=(c,))\n",
    "    # calculate the negative log-likelihood of the model with a standard normal prior\n",
    "    loss = 0.5*torch.sum(z**2, 1) - log_jac_det\n",
    "    loss = loss.mean() / N_DIM\n",
    "    # backpropagate and update the weights\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "\n",
    "# sample from the INN by sampling from a standard normal and transforming\n",
    "# it in the reverse direction\n",
    "z = torch.randn(BATCHSIZE, N_DIM)\n",
    "c = torch.randn(BATCHSIZE, 1)\n",
    "samples, _ = inn(z, c=(c,), rev=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the dataset (same as exercise 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1384081, 8) (296588, 8) (296588, 8)\n"
     ]
    }
   ],
   "source": [
    "data_trn = np.load(\"data09/tutorial-11-data/dy_trn_data.npy\")\n",
    "data_tst = np.load(\"data09/tutorial-11-data/dy_tst_data.npy\")\n",
    "data_val = np.load(\"data09/tutorial-11-data/dy_val_data.npy\")\n",
    "print(data_trn.shape, data_tst.shape, data_val.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_mass(particle):\n",
    "    return np.sqrt(np.clip(particle[:,0]**2 - np.sum(particle[:,1:]**2, axis=-1), 0, None))\n",
    "\n",
    "def get_pt(particle):\n",
    "    return np.sqrt(particle[:,1]**2 + particle[:,2]**2)\n",
    "\n",
    "def get_eta(particle):\n",
    "    p_absolute = np.sqrt(np.sum(particle[:,1:]**2, axis=-1))\n",
    "    return np.arctanh(particle[:,3] / p_absolute)\n",
    "\n",
    "def get_phi(particle):\n",
    "    return np.arctan2(particle[:,2], particle[:,1])\n",
    "\n",
    "def get_pt_phi_eta_mass(particle):\n",
    "    pt = get_pt(particle)\n",
    "    phi = get_phi(particle)\n",
    "    eta = get_eta(particle)\n",
    "    mass = get_mass(particle)\n",
    "    return np.stack((pt, phi, eta, mass), axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cuts in the dataset\n",
    "get_pt(data_tst[:,:4]).min(), get_pt(data_tst[:,4:]).min() # pt cuts\n",
    "get_eta(data_tst[:,:4]).min(), get_eta(data_tst[:,:4]).max()\n",
    "\n",
    "eta_cut = 2.5 + 1e-5 # should be 2.5 but one event is weird\n",
    "pt_cut = 10."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess(events, mean=None, std=None):\n",
    "    particle1, particle2 = events[:,:4], events[:,4:]\n",
    "    events_jetcoordinates = np.stack((get_pt(particle1), get_phi(particle1), get_eta(particle1), get_mass(particle1), get_pt(particle2), get_phi(particle2), get_eta(particle2), get_mass(particle2)), axis=-1)\n",
    "    events_reduced = events_jetcoordinates[:,[0,2,6]]\n",
    "    \n",
    "    events_reduced[:,0] = np.log(events_reduced[:,0] - pt_cut)\n",
    "    events_reduced[:,1:] = np.arctanh(events_reduced[:,1:] / eta_cut)\n",
    "    \n",
    "    if mean is None or std is None:\n",
    "        mean = events_reduced.mean(axis=0)\n",
    "        std = events_reduced.std(axis=0)\n",
    "    events_reduced = (events_reduced - mean) / std\n",
    "    \n",
    "    assert np.isfinite(events_reduced).all()\n",
    "    events_reduced = torch.tensor(events_reduced).float()\n",
    "    return events_reduced, mean, std\n",
    "    \n",
    "def undo_preprocess(events_reduced, mean, std):\n",
    "    events_reduced = events_reduced * std + mean\n",
    "    \n",
    "    events_reduced[:,0] = np.exp(events_reduced[:,0]) + pt_cut\n",
    "    events_reduced[:,[1,2]] = np.tanh(events_reduced[:,[1,2]]) * eta_cut\n",
    "    \n",
    "    pt1, eta1, eta2 = events_reduced.T\n",
    "    phi1 = np.random.uniform(0, 2*np.pi, events_reduced.shape[0])\n",
    "    mass1, mass2 = np.ones((2, events_reduced.shape[0])) * 0.105\n",
    "    px1 = pt1 * np.cos(phi1)\n",
    "    py1 = pt1 * np.sin(phi1)\n",
    "    pz1 = pt1 * np.sinh(eta1)\n",
    "    e1 = np.sqrt(mass1**2 + px1**2 + py1**2 + pz1**2)\n",
    "    px2 = -px1\n",
    "    py2 = -py1\n",
    "    pt2 = pt1\n",
    "    pz2 = pt2 * np.sinh(eta2)\n",
    "    e2 = np.sqrt(mass2**2 + px2**2 + py2**2 + pz2**2)\n",
    "    return np.stack((e1, px1, py1, pz1, e2, px2, py2, pz2), axis=-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def toy_detector_sim(data):\n",
    "    split_frac = np.random.uniform(0,1,(len(data),1))\n",
    "    q1 = split_frac * data[:,:4]\n",
    "    q2 = (1.-split_frac) * data[:,:4]\n",
    "    q3 = data[:,4:]\n",
    "    q_all = np.concatenate((q1, q2, q3), axis=1)\n",
    "    q_noise = q_all * np.random.normal(0.9, 0.05, q_all.shape)\n",
    "    return q_noise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "det_trn_data = toy_detector_sim(data_trn)\n",
    "det_tst_data = toy_detector_sim(data_tst)\n",
    "det_val_data = toy_detector_sim(data_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalize detector-level data\n",
    "det_trn_mean = np.mean(det_trn_data, axis=0)\n",
    "det_trn_std = np.std(det_trn_data, axis=0)\n",
    "det_trn_data_p = torch.Tensor((det_trn_data - det_trn_mean) / det_trn_std)\n",
    "det_val_data_p = torch.Tensor((det_val_data - det_trn_mean) / det_trn_std)\n",
    "det_tst_data_p = torch.Tensor((det_tst_data - det_trn_mean) / det_trn_std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import TensorDataset, DataLoader\n",
    "\n",
    "data_trn_prepd, mean, std = preprocess(data_trn)\n",
    "data_tst_prepd, _, _ = preprocess(data_tst, mean, std)\n",
    "data_val_prepd, _, _ = preprocess(data_val, mean, std)\n",
    "\n",
    "dataset_trn = TensorDataset(data_trn_prepd, det_trn_data_p)\n",
    "dataset_tst = TensorDataset(data_tst_prepd, det_tst_data_p)\n",
    "dataset_val = TensorDataset(data_val_prepd, det_val_data_p)\n",
    "\n",
    "batchsize = 1024\n",
    "dataloader_trn = DataLoader(dataset_trn, batch_size=batchsize, shuffle=True)\n",
    "dataloader_tst = DataLoader(dataset_tst, batch_size=batchsize, shuffle=False)\n",
    "dataloader_val = DataLoader(dataset_val, batch_size=batchsize, shuffle=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def inn_loss(z, log_jac_det):\n",
    "    l = 0.5*torch.sum(z**2, dim=1) - log_jac_det\n",
    "    return l.mean() / z.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def batch_loss(model, data):\n",
    "    x, c = data\n",
    "    z, log_jac_det = model(x, c=(c,))\n",
    "    loss = inn_loss(z, log_jac_det)\n",
    "    return loss\n",
    "\n",
    "def train_epoch(model, loader, losses):\n",
    "    model.train()\n",
    "    for data in loader:\n",
    "        loss = batch_loss(model, data)\n",
    "        \n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        losses.append(loss.item())\n",
    "    return losses\n",
    "\n",
    "def val_epoch(model, loader):\n",
    "    losses = []\n",
    "    model.eval()\n",
    "    with torch.no_grad():\n",
    "        for data in loader:\n",
    "            loss = batch_loss(model, data)\n",
    "            losses.append(loss.item())\n",
    "    return np.mean(losses)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INN has 12864 parameters\n",
      "0/2\t -2.310439999350186\n",
      "1/2\t -2.661410591520112\n"
     ]
    }
   ],
   "source": [
    "# construct INN\n",
    "import FrEIA.framework as Ff\n",
    "import FrEIA.modules as Fm\n",
    "ndim = 3\n",
    "def subnet_fc(dims_in, dims_out):\n",
    "    return nn.Sequential(\n",
    "        nn.Linear(dims_in, 32), \n",
    "        nn.ReLU(),\n",
    "        nn.Linear(32, 32), \n",
    "        nn.ReLU(),\n",
    "        nn.Linear(32,  dims_out)\n",
    "    )\n",
    "inn = Ff.SequenceINN(ndim)\n",
    "for k in range(8):\n",
    "    inn.append(Fm.AllInOneBlock, subnet_constructor=subnet_fc, permute_soft=True,\n",
    "              cond=0, cond_shape=(12,))\n",
    "total_parameters = sum(p.numel() for p in inn.parameters() if p.requires_grad)\n",
    "print(f\"INN has {total_parameters} parameters\")\n",
    "    \n",
    "optimizer = torch.optim.Adam(inn.parameters(), lr=1e-3)\n",
    "\n",
    "# train INN\n",
    "nepochs = 2\n",
    "losses = []\n",
    "for epoch in range(nepochs):\n",
    "    losses = train_epoch(inn, dataloader_trn, losses)\n",
    "    val_loss = val_epoch(inn, dataloader_val)\n",
    "    print(f\"{epoch}/{nepochs}\\t {val_loss}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(losses)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([296588, 3]) (296588, 8)\n"
     ]
    }
   ],
   "source": [
    "with torch.no_grad():\n",
    "    z = torch.randn(det_tst_data_p.shape[0], ndim)\n",
    "    c = det_tst_data_p\n",
    "    samples_prepd, _ = inn(z, c=(c,), rev=True)\n",
    "samples = undo_preprocess(samples_prepd.detach().numpy(), mean, std)\n",
    "print(samples_prepd.shape, samples.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(data_tst, samples)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_samples = 1000\n",
    "n_events = 10\n",
    "\n",
    "unfolded_events = []\n",
    "for i in range(n_events):\n",
    "    z = torch.randn((n_samples, ndim))\n",
    "    c = det_tst_data_p[i].repeat(n_samples, 1)\n",
    "    x, _ = inn(z, c=(c,), rev=True)\n",
    "    x = undo_preprocess(x.detach().numpy(), mean, std)\n",
    "    unfolded_events.append(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2,5, figsize=(12,5))\n",
    "for i in range(2):\n",
    "    for j in range(5):\n",
    "        k = i*5 + j\n",
    "        axs[i,j].hist(unfolded_events[k][:,0], alpha=.5, density=True, \n",
    "                     bins=30, label=\"unfolded\")\n",
    "        axs[i,j].axvline(data_tst[k,0], label=\"truth\", color=\"r\")\n",
    "        axs[i,j].set_xlabel(\"$E_1$ [GeV]\")\n",
    "        axs[i,j].legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "accelerator": "TPU",
  "colab": {
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  "gpuClass": "standard",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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    "version": 3
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