CNN approach on Jet images.ipynb

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   "metadata": {},
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   "source": [
    "import pandas as pd\n",
    "from datasets import load_dataset\n",
    "from energyflow.utils import (\n",
    "    center_ptyphims,\n",
    "    phi_fix,\n",
    "    pixelate,\n",
    "    ptyphims_from_p4s,\n",
    "    reflect_ptyphims,\n",
    "    rotate_ptyphims,\n",
    ")\n",
    "from fastai.vision.all import *\n",
    "from huggingface_hub import from_pretrained_fastai\n",
    "from sklearn.metrics import accuracy_score, auc, roc_auc_score, roc_curve\n",
    "from torch.utils.data import Dataset, TensorDataset\n",
    "from torchvision.transforms import ToTensor\n",
    "from scipy.interpolate import interp1d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "92fb9f96-a7bd-4335-be68-e9f91bd32e91",
   "metadata": {},
   "outputs": [],
   "source": [
    "import datasets\n",
    "\n",
    "# Suppress logs to keep things tidy\n",
    "datasets.logging.set_verbosity_error()"
   ]
  },
  {
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   "execution_count": 3,
   "id": "b7996433-6cb8-4f0c-b9f4-184fcd45edad",
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   "outputs": [
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       "          E_0        PX_0        PY_0        PZ_0         E_1       PX_1  \\\n",
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_events = load_dataset(\"dl4phys/top_tagging\", split=\"validation\")\n",
    "sample_df = raw_events.shuffle(seed=42).select(range(10_000)).to_pandas()\n",
    "sample_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "18b68cb2-9f03-46b2-8760-f4ed68a37ca5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((10000, 200, 4), (10000,))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reshape to size (num_jets, num_consituents, 4)\n",
    "events = sample_df.iloc[:, :800].values.reshape(-1, 200, 4)\n",
    "# Extract array of labels\n",
    "labels = sample_df[\"is_signal_new\"].values\n",
    "\n",
    "events.shape, labels.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c0059cf9-453e-4192-825c-bc149347ec38",
   "metadata": {},
   "outputs": [],
   "source": [
    "events_hadronic = ptyphims_from_p4s(events, phi_ref=\"hardest\", mass=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9996bd4e-39f1-4216-b98f-778d80c8a7ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "events_centered = [center_ptyphims(event) for event in events_hadronic]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "73cc9f2f-5692-457c-9fbf-39732426a0c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "events_reflected_and_rotated = [\n",
    "    reflect_ptyphims(rotate_ptyphims(event)) for event in events_centered\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5d8c1234-f963-47e3-a2bb-f32b274858d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "images = [\n",
    "    pixelate(\n",
    "        event,\n",
    "        npix=40,\n",
    "        img_width=0.8,\n",
    "        nb_chan=1,\n",
    "        norm=False,\n",
    "        charged_counts_only=False,\n",
    "    )\n",
    "    for event in events_reflected_and_rotated\n",
    "]\n",
    "\n",
    "images = np.array(images).reshape(len(images), 40, 40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "edba1a15-8d35-4dcd-979a-411fdfab07de",
   "metadata": {},
   "outputs": [],
   "source": [
    "top_images = images[labels == 1]\n",
    "qcd_images = images[labels == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3eb1260f-32f8-4783-b118-68f26b49f3e9",
   "metadata": {},
   "outputs": [
    {
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XX31VEyZM0K233qqoqCi99NJLys/P19/+9jfPzef9+vXTpZdeqiVLlujkyZOKiorSmjVrKl09qYs5c+boySef1IwZM7Rv3z7FxcXp5ZdfVmRkZL33CQD+NmnSJI0ZM0b33HOPPv30Uw0aNEj/+Mc/tGnTJi1cuLDSo8D79++v1NRUr0fKStIDDzxQ43Hq8p34y1/+UuvWrdP48eP1s5/9THl5eXrllVcq1XLllVdq/fr1uvrqq/XTn/5U+fn5euaZZ9S3b1/PvYV1NX/+fLlcLt1zzz1yOp36zW9+U6/9AB4Be+4Umoz333/fXH/99SY2NtaEhYUZSSYiIsJ8+OGH1b7n448/NnPmzDHdunUz4eHhpl27dmb48OHmL3/5iyktLfWMO/8IVUnGZrMZh8Nh+vXrZ+bMmWP27NlT6xrPP5p269atZuDAgcZut5sLL7zQrF271mtcaWmpuf32201cXJxp3bq1GT58uMnOzq70eD9jjDlz5oy55557TFJSkmnVqpWJjY01U6dONXl5ecYY70fKft/TTz9tJJk77rjDs27//v1mxIgRxm63my5dupiMjAzzxBNPGEmmoKCg0ueoSl5enpk6dapp3769iYiIMEOHDjWbN2+uclxKSoqx2+0mJibG/OY3vzHbtm2r8pGy/fr1q/T+mTNnmsTERK91n332mbnqqqtMZGSkiY6ONrfddpvZsmULj5QFEDA/fKSsMd89anvRokUmPj7etGrVyvTq1cs8/PDDnkeBnyfJpKenm1deecX06tXL2O1286Mf/ajW32d1+U787//+b3PBBRcYu91uhg8fbvbu3Vup51RUVJg//vGPJjEx0VPL5s2bK30fV9d3jPF+pOz33XnnnUaSefLJJ2v12YDq2Izhjkv418qVKzVr1izdcMMNWrlyZaDLCVkLFy70zEpe06V2AIB/2Ww2paenV/kT1/rKzMzUmDFjtGvXriqfBAiEOn7+BL+bMWOGjh8/rrvvvltdunTRH//4x0CXFPS+/fZbr6d1nDhxQi+//LIuv/xyAgUAAAh6hAo0iLvuuoubv+ogOTlZo0eP1kUXXaTCwkItX75cLpdLv/vd7wJdGgAAgE+ECiAITJw4UevWrdNzzz0nm82mH//4x1q+fLlGjhwZ6NIAAAB84p4KAAAAAJYwTwUAAAAASwgVAAAAACwJunsqKioqdOzYMbVr165Wk34BAKwxxqi4uFjx8fGeiRKDFT0CABpXrXtEQ02A8eSTT3omaRk6dGitJyo7evSoZ7IzFhYWFpbGW44ePdpQLcFLffuDMfQIFhYWlkAtvnpEg1ypeO2117R48WI988wzGjZsmB577DGlpqYqNzdXnTt3rvG97dq1kyRdrolqqVYNUR4A4HvOqUxv6w3P929DstIfJHoEmr6W8bE+x5w7VtAIlQDfqW2PaJCnPw0bNkyXXHKJZybKiooKJSQkaMGCBbr77rtrfK/L5ZLT6dRopamljYYBAA3tnClTpjapqKhIDoejQY9lpT9I9Ag0fS0viPc55twXxxqhEuA7te0Rfv/x7NmzZ7Vv3z6lpKT85yBhYUpJSVF2dra/DwcACBH0BwBouvz+86evv/5a5eXliomJ8VofExOjjz76qNJ4t9stt9vtee1yufxdEgAgCNS1P0j0CAAIFQF/zEdGRoacTqdnSUhICHRJAIAgQY8AgNDg91ARHR2tFi1aqLCw0Gt9YWGhYmMr33y0ZMkSFRUVeZajR4/6uyQAQBCoa3+Q6BEAECr8HirCw8M1ZMgQ7dixw7OuoqJCO3bsUHJycqXxdrtdDofDawEAND117Q8SPQIAQkWDPFJ28eLFmjlzpi6++GINHTpUjz32mEpKSvSLX/yiIQ4HAAgR9AeEqpZxvh/1ajrUHHrLD37scx882QmhqkFCxfTp0/XVV1/p3nvvVUFBgQYPHqwtW7ZUujkPANC80B8AoGlqkHkqrOAZ5ADQuBpzngqr6BEIlMa6UgEEm4DNUwEAAACgeSFUAAAAALCEUAEAAADAEkIFAAAAAEsIFQAAAAAsIVQAAAAAsKRB5qkAgIZga+n7K8ucO9cIlQBobkyp2+eY4j4datzeusNgn/uw/e+BWlYEBBeuVAAAAACwhFABAAAAwBJCBQAAAABLCBUAAAAALCFUAAAAALCEUAEAAADAEkIFAAAAAEuYpwJAyGAOCiA42Oz2Grcbt+85HUJN+Tff+BwTuWGP5eO0jI3xOeZcQaHl40iSwlrUvL2i3D/HQbPAlQoAAAAAlhAqAAAAAFhCqAAAAABgCaECAAAAgCWECgAAAACWECoAAAAAWEKoAAAAAGAJoQIAAACAJUx+BwAA6qQpTm4XLPw2sV1tMLkd/IgrFQAAAAAsIVQAAAAAsIRQAQAAAMASQgUAAAAASwgVAAAAACwhVAAAAACwhFABAAAAwBJCBQAAAABLmPwOAACgEXz8wsU+x/S+aW8jVAL4n9+vVNx///2y2Wxey4UXXujvwwAAQgz9AQCarga5UtGvXz9t3779PwdpyQURAAD9AQCaqgb5Nm/ZsqViY2MbYtcAgBBGfwCApqlBbtQ+dOiQ4uPj1b17d/385z/XkSNHqh3rdrvlcrm8FgBA01SX/iDRIwAgVPg9VAwbNkwvvviitmzZomXLlik/P18jRoxQcXFxleMzMjLkdDo9S0JCgr9LAgAEgbr2B4keAQChwmaMMQ15gFOnTikxMVGPPPKIZs+eXWm72+2W2+32vHa5XEpISNBopamlrVVDlgYAkHTOlClTm1RUVCSHw9Fox/XVHyR6BJoWnv6EUFTbHtHgd8i1b99evXv31uHDh6vcbrfbZbfbG7oMAECQ8dUfJHoEAISKBp/87vTp08rLy1NcXFxDHwoAEELoDwDQdPj9SsUdd9yhSZMmKTExUceOHdN9992nFi1a6LrrrvP3odCQbLaatzfsr+YANEH0BzR3/LQJTZnfQ8Xnn3+u6667TidOnFCnTp10+eWXa/fu3erUqZO/DwUACCH0BwBouvweKtasWePvXQIAmgD6AwA0XQ1+TwUAAACApo1QAQAAAMASQgUAAAAASwgVAAAAACwhVAAAAACwpMFn1EaIYh4KAAAA1BJXKgAAAABYQqgAAAAAYAmhAgAAAIAlhAoAAAAAlhAqAAAAAFhCqAAAAABgCaECAAAAgCWECgAAAACWECoAAAAAWEKoAAAAAGAJoQIAAACAJYQKAAAAAJYQKgAAAABYQqgAAAAAYAmhAgAAAIAlhAoAAAAAlhAqAAAAAFhCqAAAAABgCaECAAAAgCWECgAAAACWECoAAAAAWEKoAAAAAGAJoQIAAACAJYQKAAAAAJYQKgAAAABYQqgAAAAAYAmhAgAAAIAldQ4Vb731liZNmqT4+HjZbDZt3LjRa7sxRvfee6/i4uLUunVrpaSk6NChQ/6qFwAQpOgPANB81TlUlJSUaNCgQXrqqaeq3L506VI98cQTeuaZZ7Rnzx61adNGqampKi0ttVwsACB40R8AoPlqWdc3TJgwQRMmTKhymzFGjz32mH77298qLS1NkrRy5UrFxMRo48aNuvbaa61VCwAIWvQHAGi+/HpPRX5+vgoKCpSSkuJZ53Q6NWzYMGVnZ1f5HrfbLZfL5bUAAJqW+vQHiR4BAKHCr6GioKBAkhQTE+O1PiYmxrPthzIyMuR0Oj1LQkKCP0sCAASB+vQHiR4BAKEi4E9/WrJkiYqKijzL0aNHA10SACBI0CMAIDT4NVTExsZKkgoLC73WFxYWerb9kN1ul8Ph8FoAAE1LffqDRI8AgFDh11CRlJSk2NhY7dixw7PO5XJpz549Sk5O9uehAAAhhP4AAE1bnZ/+dPr0aR0+fNjzOj8/XwcOHFBUVJS6du2qhQsX6ve//7169eqlpKQk/e53v1N8fLwmT57sz7oBAEGG/gAAzVedQ8XevXs1ZswYz+vFixdLkmbOnKkXX3xRd955p0pKSjR37lydOnVKl19+ubZs2aKIiAj/VQ0ACDr0BwBovmzGGBPoIr7P5XLJ6XRqtNLU0tYq0OUAQJN3zpQpU5tUVFQU9Pcs0CMAoHHVtkcE/OlPAAAAAEIboQIAAACAJYQKAAAAAJYQKgAAAABYQqgAAAAAYAmhAgAAAIAlhAoAAAAAlhAqAAAAAFhCqAAAAABgCaECAAAAgCWECgAAAACWECoAAAAAWEKoAAAAAGAJoQIAAACAJS0DXQAAAAAan81ur3G7cbsbqRI0BVypAAAAAGAJoQIAAACAJYQKAAAAAJYQKgAAAABYQqgAAAAAYAmhAgAAAIAlhAoAAAAAlhAqAAAAAFjC5HcAGofNVvN2YxqnDgAIEF+TzUmNO+Eck9vBn7hSAQAAAMASQgUAAAAASwgVAAAAACwhVAAAAACwhFABAAAAwBJCBQAAAABLCBUAAAAALCFUAAAAALCEye8ANA4mtwPQzDHZXNMRFhFR4/aK0tJGqiR41PlKxVtvvaVJkyYpPj5eNptNGzdu9No+a9Ys2Ww2r2X8+PH+qhcAEKToDwDQfNU5VJSUlGjQoEF66qmnqh0zfvx4HT9+3LO8+uqrlooEAAQ/+gMANF91/vnThAkTNGHChBrH2O12xcbG1rsoAEDooT8AQPPVIDdqZ2ZmqnPnzurTp49uueUWnThxotqxbrdbLpfLawEANE116Q8SPQIAQoXfQ8X48eO1cuVK7dixQ3/605+UlZWlCRMmqLy8vMrxGRkZcjqdniUhIcHfJQEAgkBd+4NEjwCAUGEzpv6PZLHZbNqwYYMmT55c7ZhPPvlEPXr00Pbt2zV27NhK291ut9zfexqCy+VSQkKCRitNLW2t6lsaAKCWzpkyZWqTioqK5HA4/LJPf/QHiR4BIDg1p6c/1bZHNPg8Fd27d1d0dLQOHz5c5Xa73S6Hw+G1AACaPl/9QaJHAECoaPB5Kj7//HOdOHFCcXFxDX0oNFO2VuE+x5iys41QCYC6oD8A9dOiFuG6nPuPGlRTuhLhL3UOFadPn/b6q1J+fr4OHDigqKgoRUVF6YEHHtCUKVMUGxurvLw83XnnnerZs6dSU1P9WjgAILjQHwCg+apzqNi7d6/GjBnjeb148WJJ0syZM7Vs2TLl5OTopZde0qlTpxQfH69x48bpv/7rv2S32/1XNQAg6NAfAKD5qnOoGD16tGq6t3vr1q2WCgIAhCb6AwA0Xw1+ozYAAACApo1QAQAAAMASQgUAAAAASwgVAAAAACwhVAAAAACwpMEnvwMamr8mtrvnkwM+x/yh+2C/HAsAgPoKpontbC19/1PSnDtneT+12QcCiysVAAAAACwhVAAAAACwhFABAAAAwBJCBQAAAABLCBUAAAAALCFUAAAAALCEUAEAAADAEkIFAAAAAEuY/A74P0xsBwBA3fhrUrpQm9zOZrfXuN243Y1USfDgSgUAAAAASwgVAAAAACwhVAAAAACwhFABAAAAwBJCBQAAAABLCBUAAAAALCFUAAAAALCEUAEAAADAEia/A5qxsIgIn2MqSksboRIAAEJHc5zczheuVAAAAACwhFABAAAAwBJCBQAAAABLCBUAAAAALCFUAAAAALCEUAEAAADAEkIFAAAAAEuYpwLwo4oRP/I5Juyf+xuhktphDgoACC4tOnXyOab8q68aoRKphcPhc0y5y9UIlSAU1OlKRUZGhi655BK1a9dOnTt31uTJk5Wbm+s1prS0VOnp6erYsaPatm2rKVOmqLCw0K9FAwCCDz0CAJqvOoWKrKwspaena/fu3dq2bZvKyso0btw4lZSUeMYsWrRIr7/+utauXausrCwdO3ZM11xzjd8LBwAEF3oEADRfNmOMqe+bv/rqK3Xu3FlZWVkaOXKkioqK1KlTJ61evVpTp06VJH300Ue66KKLlJ2drUsvvdTnPl0ul5xOp0YrTS1trepbGhAQofbzJ0CSzpkyZWqTioqK5KjFzx1qix4B1B0/f0KwqW2PsHSjdlFRkSQpKipKkrRv3z6VlZUpJSXFM+bCCy9U165dlZ2dbeVQAIAQQ48AgOaj3jdqV1RUaOHChRo+fLj69+8vSSooKFB4eLjat2/vNTYmJkYFBQVV7sftdsvtdnteu0i8ABDy6BEA0LzU+0pFenq6PvjgA61Zs8ZSARkZGXI6nZ4lISHB0v4AAIFHjwCA5qVeoWL+/PnavHmzdu3apS5dunjWx8bG6uzZszp16pTX+MLCQsXGxla5ryVLlqioqMizHD16tD4lAQCCBD0CAJqfOoUKY4zmz5+vDRs2aOfOnUpKSvLaPmTIELVq1Uo7duzwrMvNzdWRI0eUnJxc5T7tdrscDofXAgAIPfQIAGi+6nRPRXp6ulavXq1NmzapXbt2nt/AOp1OtW7dWk6nU7Nnz9bixYsVFRUlh8OhBQsWKDk5uVZP9QBC3bbXVvgc4zZlPsdM7jXK55iKM2dqVRPQWOgRaO5atHfWuL38VJHPfTTWk51qI9Se7BTWpo3PMRXfe8Q1/KtOoWLZsmWSpNGjR3utX7FihWbNmiVJevTRRxUWFqYpU6bI7XYrNTVVTz/9tF+KBQAEL3oEADRfdQoVtZnSIiIiQk899ZSeeuqpehcFAAg99AgAaL4szVMBAAAAAIQKAAAAAJYQKgAAAABYQqgAAAAAYAmhAgAAAIAlhAoAAAAAltTpkbJAUxYWEeFzTEVpaY3bU+MH+9xH6aShPsdEnHnX5xgAQHDxNbldWGSkz33UZmJT2yUDfI4x773vc0xTw8R2gcWVCgAAAACWECoAAAAAWEKoAAAAAGAJoQIAAACAJYQKAAAAAJYQKgAAAABYQqgAAAAAYAmhAgAAAIAlTH4H/B9fE9v5S8TrTGwHhASbzfcYYxq+jhDUold3n2PKD33SCJUEl9pMbFcboTaxXcuELj7HnDv6eSNUgobElQoAAAAAlhAqAAAAAFhCqAAAAABgCaECAAAAgCWECgAAAACWECoAAAAAWEKoAAAAAGAJoQIAAACAJUx+BzQyW0vf/7cz5841QiW1ExYR4XNMY00cCDQqJrart+Y4sV1zdXb8Jb4HbXmv4QtBwHGlAgAAAIAlhAoAAAAAlhAqAAAAAFhCqAAAAABgCaECAAAAgCWECgAAAACWECoAAAAAWMI8FUAd+JpjojbzSwTTHBS1wRwUANB4WnTo4HNM+TffNEIltRPOHBT4P3W6UpGRkaFLLrlE7dq1U+fOnTV58mTl5uZ6jRk9erRsNpvXMm/ePL8WDQAIPvQIAGi+6hQqsrKylJ6ert27d2vbtm0qKyvTuHHjVFJS4jVuzpw5On78uGdZunSpX4sGAAQfegQANF91+vnTli1bvF6/+OKL6ty5s/bt26eRI0d61kdGRio2NtY/FQIAQgI9AgCaL0s3ahcVFUmSoqKivNavWrVK0dHR6t+/v5YsWaIzZ85Uuw+32y2Xy+W1AABCHz0CAJqPet+oXVFRoYULF2r48OHq37+/Z/3111+vxMRExcfHKycnR3fddZdyc3O1fv36KveTkZGhBx54oL5lAACCED0CAJoXmzHG1OeNt9xyi9588029/fbb6tKlS7Xjdu7cqbFjx+rw4cPq0aNHpe1ut1tut9vz2uVyKSEhQaOVppa2VvUpDWgw/nj6ExBszpkyZWqTioqK5HA4/LJPegRQP6H29Cc0fbXtEfW6UjF//nxt3rxZb731Vo3NQpKGDRsmSdU2DLvdLrvdXp8yAABBiB4BAM1PnUKFMUYLFizQhg0blJmZqaSkJJ/vOXDggCQpLi6uXgUCAEIDPQIAmq86hYr09HStXr1amzZtUrt27VRQUCBJcjqdat26tfLy8rR69WpNnDhRHTt2VE5OjhYtWqSRI0dq4MCBDfIBgMbEz5uA6tEjAOv4aRMCIaxdu+q3mbNSse991OmeCpvNVuX6FStWaNasWTp69KhuuOEGffDBByopKVFCQoKuvvpq/fa3v63173RdLpecTie/lwWARuKveyroEQAQmmoKFefMWe0sXuXfeyp85Y+EhARlZWXVZZcAgCaCHgEAzZeleSoAAAAAgFABAAAAwBJCBQAAAABLCBUAAAAALCFUAAAAALCEUAEAAADAkjo9UhYAAABAzcIiI32OqThzphEqqZ2K4upnt6swZbXaB1cqAAAAAFhCqAAAAABgCaECAAAAgCWECgAAAACWECoAAAAAWEKoAAAAAGAJoQIAAACAJYQKAAAAAJYw+R0AAADgR7WZ2K5FdEefY8q/PuGPchoFVyoAAAAAWEKoAAAAAGAJoQIAAACAJYQKAAAAAJYQKgAAAABYQqgAAAAAYAmhAgAAAIAlzFMBAADQDNns9hq3G7e7kSppnkJpDora4EoFAAAAAEsIFQAAAAAsIVQAAAAAsIRQAQAAAMASQgUAAAAASwgVAAAAACwhVAAAAACwhFABAAAAwBImvwMAAH619dgBn2NS4wc3eB2oWYtO0TVuP/f5F41UiW9hERE+x1SUlvre0aUDfY/ZnVOLiqxr0aGDzzHl33zTCJX4R52uVCxbtkwDBw6Uw+GQw+FQcnKy3nzzTc/20tJSpaenq2PHjmrbtq2mTJmiwsJCvxcNAAg+9AgAaL7qFCq6dOmihx56SPv27dPevXv1k5/8RGlpafrwww8lSYsWLdLrr7+utWvXKisrS8eOHdM111zTIIUDAIILPQIAmi+bMcZY2UFUVJQefvhhTZ06VZ06ddLq1as1depUSdJHH32kiy66SNnZ2br00ktrtT+XyyWn06nRSlNLWysrpQEAauGcKVOmNqmoqEgOh8Ov+6ZHNE/8/Ck0tOxyQY3b+flTwwqVnz/VtkfU+0bt8vJyrVmzRiUlJUpOTta+fftUVlamlJQUz5gLL7xQXbt2VXZ2drX7cbvdcrlcXgsAILTRIwCgealzqHj//ffVtm1b2e12zZs3Txs2bFDfvn1VUFCg8PBwtW/f3mt8TEyMCgoKqt1fRkaGnE6nZ0lISKjzhwAABAd6BAA0T3UOFX369NGBAwe0Z88e3XLLLZo5c6YOHjxY7wKWLFmioqIiz3L06NF67wsAEFj0CABonur8SNnw8HD17NlTkjRkyBC99957evzxxzV9+nSdPXtWp06d8vpLVGFhoWJjY6vdn91ul91ur3vlAICgQ48AgObJ8uR3FRUVcrvdGjJkiFq1aqUdO3Z4tuXm5urIkSNKTk62ehgAQAiiRwBA81CnKxVLlizRhAkT1LVrVxUXF2v16tXKzMzU1q1b5XQ6NXv2bC1evFhRUVFyOBxasGCBkpOTa/1UDwBA6KJHNB9fzr+sxu2p8Y1UCCwJpqc7+VKrJzvVRiM92ak2guHJTv5Up1Dx5ZdfasaMGTp+/LicTqcGDhyorVu36oorrpAkPfroowoLC9OUKVPkdruVmpqqp59+ukEKBwAEF3oEADRfluep8DeeQQ4Ajash56nwN3pEcPB1paLzk+80UiUAGlqDz1MBAAAAABKhAgAAAIBFhAoAAAAAlhAqAAAAAFhCqAAAAABgCaECAAAAgCV1mqcCAAAgmB4Za2sVXuN2U3a2kSoBmjeuVAAAAACwhFABAAAAwBJCBQAAAABLCBUAAAAALCFUAAAAALCEUAEAAADAEkIFAAAAAEsIFQAAAAAsYfI7AAAQspjcrmlwT7ikxu32N99rpEqap7B27arfZs5KxbXYhx/rAQAAANAMESoAAAAAWEKoAAAAAGAJoQIAAACAJYQKAAAAAJYQKgAAAABYQqgAAAAAYAnzVAAAACCgmIcisCqKq5+IosKU1WofXKkAAAAAYAmhAgAAAIAlhAoAAAAAlhAqAAAAAFhCqAAAAABgCaECAAAAgCWECgAAAACWECoAAAAAWEKoAAAAAGBJnULFsmXLNHDgQDkcDjkcDiUnJ+vNN9/0bB89erRsNpvXMm/ePL8XDQAIPvQIAGi+WtZlcJcuXfTQQw+pV69eMsbopZdeUlpamvbv369+/fpJkubMmaMHH3zQ857IyEj/VgwACEr0CABovuoUKiZNmuT1+g9/+IOWLVum3bt3expGZGSkYmNj/VchACAk0CMAoPmq9z0V5eXlWrNmjUpKSpScnOxZv2rVKkVHR6t///5asmSJzpw5U+N+3G63XC6X1wIACG30CABoXup0pUKS3n//fSUnJ6u0tFRt27bVhg0b1LdvX0nS9ddfr8TERMXHxysnJ0d33XWXcnNztX79+mr3l5GRoQceeKD+nwAAEDToEQDQPNmMMaYubzh79qyOHDmioqIirVu3Ts8//7yysrI8TeP7du7cqbFjx+rw4cPq0aNHlftzu91yu92e1y6XSwkJCRqtNLW0tarjxwEA1NU5U6ZMbVJRUZEcDoelfdEjAKBpqW2PqPOVivDwcPXs2VOSNGTIEL333nt6/PHH9eyzz1YaO2zYMEmqsWHY7XbZ7fa6lgEACEL0CABonizPU1FRUeH1V6TvO3DggCQpLi7O6mEAACGIHgEAzUOdrlQsWbJEEyZMUNeuXVVcXKzVq1crMzNTW7duVV5enlavXq2JEyeqY8eOysnJ0aJFizRy5EgNHDiwoeoHAAQJegQAhKawGh7vHWbOSjU/U0NSHUPFl19+qRkzZuj48eNyOp0aOHCgtm7dqiuuuEJHjx7V9u3b9dhjj6mkpEQJCQmaMmWKfvvb39blEACAEEWPAIDmq843ajc0l8slp9PJTXgA0Ej8eaN2Q6NHAID/1XSl4pw5q51n1vjsEZbvqQAAAADQvBEqAAAAAFhCqAAAAABgCaECAAAAgCWECgAAAACWECoAAAAAWFKneSoAAAAANC0VZ6qf3a7ClNVqH1ypAAAAAGAJoQIAAACAJYQKAAAAAJYQKgAAAABYQqgAAAAAYAmhAgAAAIAlhAoAAAAAljBPBQAAABCEwiIjfY6paY6JxsSVCgAAAACWECoAAAAAWEKoAAAAAGAJoQIAAACAJYQKAAAAAJYQKgAAAABYQqgAAAAAYAmhAgAAAIAlTH4HAAAABKFgmdiuNrhSAQAAAMASQgUAAAAASwgVAAAAACwhVAAAAACwhFABAAAAwBJCBQAAAABLCBUAAAAALAm6eSqMMZKkcyqTTICLAYBm4JzKJP3n+zeY0SMAoHHVtkcEXagoLi6WJL2tNwJcCQA0L8XFxXI6nYEuo0b0CAAIDF89wmaC7E9TFRUVOnbsmNq1ayebzSZJcrlcSkhI0NGjR+VwOAJcYc1CqVaJehsa9TacUKpVCu56jTEqLi5WfHy8wsKC+1exP+wRwXxeq0K9DSuU6g2lWiXqbWjBXG9te0TQXakICwtTly5dqtzmcDiC7kRXJ5Rqlai3oVFvwwmlWqXgrTfYr1CcV12PCNbzWh3qbVihVG8o1SpRb0ML1npr0yOC+09SAAAAAIIeoQIAAACAJSERKux2u+677z7Z7fZAl+JTKNUqUW9Do96GE0q1SqFXb6gItfNKvQ0rlOoNpVol6m1ooVZvVYLuRm0AAAAAoSUkrlQAAAAACF6ECgAAAACWECoAAAAAWEKoAAAAAGBJ0IeKp556St26dVNERISGDRumd999N9AlVen++++XzWbzWi688MJAl+Xx1ltvadKkSYqPj5fNZtPGjRu9thtjdO+99youLk6tW7dWSkqKDh06FJhi5bveWbNmVTrf48ePD0itGRkZuuSSS9SuXTt17txZkydPVm5urteY0tJSpaenq2PHjmrbtq2mTJmiwsLCoK139OjRlc7vvHnzAlLvsmXLNHDgQM+EQMnJyXrzzTc924Pp3PqqNZjOa1NBj/CPUOoRodQfpNDqEfSHwNYbTOe2PoI6VLz22mtavHix7rvvPv3rX//SoEGDlJqaqi+//DLQpVWpX79+On78uGd5++23A12SR0lJiQYNGqSnnnqqyu1Lly7VE088oWeeeUZ79uxRmzZtlJqaqtLS0kau9Du+6pWk8ePHe53vV199tREr/I+srCylp6dr9+7d2rZtm8rKyjRu3DiVlJR4xixatEivv/661q5dq6ysLB07dkzXXHNN0NYrSXPmzPE6v0uXLg1IvV26dNFDDz2kffv2ae/evfrJT36itLQ0ffjhh5KC69z6qlUKnvPaFNAj/CeUekQo9QcptHoE/SGw9UrBc27rxQSxoUOHmvT0dM/r8vJyEx8fbzIyMgJYVdXuu+8+M2jQoECXUSuSzIYNGzyvKyoqTGxsrHn44Yc9606dOmXsdrt59dVXA1Chtx/Wa4wxM2fONGlpaQGpx5cvv/zSSDJZWVnGmO/OZatWrczatWs9Y/79738bSSY7OztQZXr8sF5jjBk1apS57bbbAleUDx06dDDPP/980J9bY/5TqzHBf15DDT2iYYRSjwi1/mBMaPUI+kPDa0o9ImivVJw9e1b79u1TSkqKZ11YWJhSUlKUnZ0dwMqqd+jQIcXHx6t79+76+c9/riNHjgS6pFrJz89XQUGB17l2Op0aNmxY0J5rScrMzFTnzp3Vp08f3XLLLTpx4kSgS5IkFRUVSZKioqIkSfv27VNZWZnX+b3wwgvVtWvXoDi/P6z3vFWrVik6Olr9+/fXkiVLdObMmUCU56W8vFxr1qxRSUmJkpOTg/rc/rDW84LxvIYiekTjCcUeEaz9QQqtHkF/aDhNsUe0DHQB1fn6669VXl6umJgYr/UxMTH66KOPAlRV9YYNG6YXX3xRffr00fHjx/XAAw9oxIgR+uCDD9SuXbtAl1ejgoICSaryXJ/fFmzGjx+va665RklJScrLy9NvfvMbTZgwQdnZ2WrRokXA6qqoqNDChQs1fPhw9e/fX9J35zc8PFzt27f3GhsM57eqeiXp+uuvV2JiouLj45WTk6O77rpLubm5Wr9+fUDqfP/995WcnKzS0lK1bdtWGzZsUN++fXXgwIGgO7fV1SoF33kNZfSIxhNqPSJY+4MUWj2C/tAwmnKPCNpQEWomTJjg+e+BAwdq2LBhSkxM1F//+lfNnj07gJU1Tddee63nvwcMGKCBAweqR48eyszM1NixYwNWV3p6uj744IOg+q10Taqrd+7cuZ7/HjBggOLi4jR27Fjl5eWpR48ejV2m+vTpowMHDqioqEjr1q3TzJkzlZWV1eh11EZ1tfbt2zfozisaDz2i8QRrf5BCq0fQHxpGU+4RQfvzp+joaLVo0aLSXfqFhYWKjY0NUFW11759e/Xu3VuHDx8OdCk+nT+foXquJal79+6Kjo4O6PmeP3++Nm/erF27dqlLly6e9bGxsTp79qxOnTrlNT7Q57e6eqsybNgwSQrY+Q0PD1fPnj01ZMgQZWRkaNCgQXr88ceD8txWV2tVAn1eQxk9ovGEeo8Ihv4ghVaPoD80nKbcI4I2VISHh2vIkCHasWOHZ11FRYV27Njh9duzYHX69Gnl5eUpLi4u0KX4lJSUpNjYWK9z7XK5tGfPnpA415L0+eef68SJEwE538YYzZ8/Xxs2bNDOnTuVlJTktX3IkCFq1aqV1/nNzc3VkSNHAnJ+fdVblQMHDkhS0PzvuaKiQm63O+jObVXO11qVYDuvoYQe0XhCvUcEsj9IodUj6A+Nr0n1iMDeJ16zNWvWGLvdbl588UVz8OBBM3fuXNO+fXtTUFAQ6NIquf32201mZqbJz883//u//2tSUlJMdHS0+fLLLwNdmjHGmOLiYrN//36zf/9+I8k88sgjZv/+/eazzz4zxhjz0EMPmfbt25tNmzaZnJwck5aWZpKSksy3334bdPUWFxebO+64w2RnZ5v8/Hyzfft28+Mf/9j06tXLlJaWNnqtt9xyi3E6nSYzM9McP37cs5w5c8YzZt68eaZr165m586dZu/evSY5OdkkJyc3eq21qffw4cPmwQcfNHv37jX5+flm06ZNpnv37mbkyJEBqffuu+82WVlZJj8/3+Tk5Ji7777b2Gw2849//MMYE1zntqZag+28NgX0CP8JpR4RSv3BmNDqEfSHwNUbbOe2PoI6VBhjzF/+8hfTtWtXEx4eboYOHWp2794d6JKqNH36dBMXF2fCw8PNBRdcYKZPn24OHz4c6LI8du3aZSRVWmbOnGmM+e6Rgb/73e9MTEyMsdvtZuzYsSY3Nzco6z1z5owZN26c6dSpk2nVqpVJTEw0c+bMCdg/JKqqU5JZsWKFZ8y3335rfvWrX5kOHTqYyMhIc/XVV5vjx48HZb1HjhwxI0eONFFRUcZut5uePXuaX//616aoqCgg9d50000mMTHRhIeHm06dOpmxY8d6GoYxwXVua6o12M5rU0GP8I9Q6hGh1B+MCa0eQX8IXL3Bdm7rw2aMMf6//gEAAACguQjaeyoAAAAAhAZCBQAAAABLCBUAAAAALCFUAAAAALCEUAEAAADAEkIFAAAAAEsIFQAAAAAsIVQAAAAAsIRQAQAAAMASQgUAAAAASwgVAAAAACwhVAAAAACw5P8D7JfuOrZktpEAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(2):\n",
    "    plt.figure(figsize=(8, 4))\n",
    "    plt.subplot(1, 2, 1)\n",
    "    plt.title(\"QCD background\")\n",
    "    plt.imshow(qcd_images[i])\n",
    "    plt.subplot(1, 2, 2)\n",
    "    plt.title(\"Top quark\")\n",
    "    plt.imshow(top_images[i])\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ea6dd301-3b85-4dea-bae0-7b41d9396d5b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<>:5: SyntaxWarning: invalid escape sequence '\\e'\n",
      "<>:6: SyntaxWarning: invalid escape sequence '\\p'\n",
      "<>:11: SyntaxWarning: invalid escape sequence '\\e'\n",
      "<>:12: SyntaxWarning: invalid escape sequence '\\p'\n",
      "<>:5: SyntaxWarning: invalid escape sequence '\\e'\n",
      "<>:6: SyntaxWarning: invalid escape sequence '\\p'\n",
      "<>:11: SyntaxWarning: invalid escape sequence '\\e'\n",
      "<>:12: SyntaxWarning: invalid escape sequence '\\p'\n",
      "C:\\Users\\Ayush\\AppData\\Local\\Temp\\ipykernel_19140\\2118213832.py:5: SyntaxWarning: invalid escape sequence '\\e'\n",
      "  plt.xlabel(\"$\\eta$\")\n",
      "C:\\Users\\Ayush\\AppData\\Local\\Temp\\ipykernel_19140\\2118213832.py:6: SyntaxWarning: invalid escape sequence '\\p'\n",
      "  plt.ylabel(\"$\\phi$\")\n",
      "C:\\Users\\Ayush\\AppData\\Local\\Temp\\ipykernel_19140\\2118213832.py:11: SyntaxWarning: invalid escape sequence '\\e'\n",
      "  plt.xlabel(\"$\\eta$\")\n",
      "C:\\Users\\Ayush\\AppData\\Local\\Temp\\ipykernel_19140\\2118213832.py:12: SyntaxWarning: invalid escape sequence '\\p'\n",
      "  plt.ylabel(\"$\\phi$\")\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 4))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.imshow(np.sum(qcd_images, axis=0))\n",
    "plt.title(\"Average QCD\")\n",
    "plt.xlabel(\"$\\eta$\")\n",
    "plt.ylabel(\"$\\phi$\")\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.imshow(np.sum(top_images, axis=0))\n",
    "plt.title(\"Average top\")\n",
    "plt.xlabel(\"$\\eta$\")\n",
    "plt.ylabel(\"$\\phi$\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f7655595-ec53-40a3-ae76-841f8fb6eb91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['image', 'label'],\n",
       "        num_rows: 1211000\n",
       "    })\n",
       "    validation: Dataset({\n",
       "        features: ['image', 'label'],\n",
       "        num_rows: 403000\n",
       "    })\n",
       "    test: Dataset({\n",
       "        features: ['image', 'label'],\n",
       "        num_rows: 404000\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "images_ds = load_dataset(\"dl4phys/top_tagging_images\")\n",
    "images_ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8c8f8a32-5b3a-43f9-9327-a9b1ca8a6105",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = images_ds[\"train\"][0][\"image\"]\n",
    "plt.imshow(img);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2e0a133c-2ae9-4f3a-ac6e-8b964ba77d0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 40, 40])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = ToTensor()(img)\n",
    "t.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d12e822f-7977-446a-96dc-db4a39c66db7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([10, 40, 40])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = torch.cat([ToTensor()(img) for img in images_ds[\"train\"][:10][\"image\"]])\n",
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ebff1f41-f76e-4b09-997c-a3c49d3a0914",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([10, 1, 40, 40])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = torch.cat([ToTensor()(img) for img in images_ds[\"train\"][:10][\"image\"]])\n",
    "x.unsqueeze_(dim=1)\n",
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8db9e617-06e1-4bc4-a4e4-1c07111eff66",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_dataset(dataset, num_examples=None):\n",
    "    if num_examples is not None:\n",
    "        dataset = dataset.shuffle(seed=42).select(range(num_examples))\n",
    "\n",
    "    x = torch.cat([ToTensor()(img) for img in dataset[\"image\"]]).unsqueeze(1)\n",
    "    y = torch.cat([torch.tensor(l).unsqueeze(0) for l in dataset[\"label\"]])\n",
    "\n",
    "    return TensorDataset(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "705427ef-395b-4d4b-bbdb-9013590473ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_ds = get_dataset(images_ds[\"train\"], num_examples=350_000)\n",
    "valid_ds = get_dataset(images_ds[\"validation\"], num_examples=50_000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "86d5d2f0-92fd-408a-b6ec-fa3c3ace7309",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dl = DataLoader(train_ds, bs=128, shuffle=True)\n",
    "valid_dl = DataLoader(valid_ds, bs=128)\n",
    "dls = DataLoaders(train_dl, valid_dl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "15234a9d-5147-4055-9b4c-b4fbe5373805",
   "metadata": {},
   "outputs": [],
   "source": [
    "learn = vision_learner(\n",
    "    dls,\n",
    "    resnet34,\n",
    "    metrics=[accuracy, RocAucBinary()],\n",
    "    n_in=1,\n",
    "    n_out=2,\n",
    "    normalize=False,\n",
    "    loss_func=F.cross_entropy,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a0a4c3ca-fbd7-443d-9ce1-38ccee27c7b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.model.to(\"cuda\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2f600e7e-ce1d-437b-b125-f72e08af7401",
   "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": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ayush\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\fastai\\learner.py:53: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  state = torch.load(file, map_location=device, **torch_load_kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SuggestedLRs(valley=0.001737800776027143)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.lr_find()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "79948ee1-6b8b-48e2-b47e-16e9468467d2",
   "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": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>roc_auc_score</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.301891</td>\n",
       "      <td>0.284050</td>\n",
       "      <td>0.878820</td>\n",
       "      <td>0.949528</td>\n",
       "      <td>02:13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>roc_auc_score</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.243680</td>\n",
       "      <td>0.234028</td>\n",
       "      <td>0.901440</td>\n",
       "      <td>0.966077</td>\n",
       "      <td>03:46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.218904</td>\n",
       "      <td>0.219371</td>\n",
       "      <td>0.908780</td>\n",
       "      <td>0.970012</td>\n",
       "      <td>03:46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.195318</td>\n",
       "      <td>0.209968</td>\n",
       "      <td>0.913780</td>\n",
       "      <td>0.971643</td>\n",
       "      <td>03:46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.fine_tune(3, 3e-3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "445b546b-ce79-4df0-93f5-143b67471f0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_metrics(learn, test_ds):\n",
    "    test_dl = learn.dls.test_dl(test_items=test_ds)\n",
    "    preds, targs = learn.get_preds(dl=test_dl)\n",
    "    fpr, tpr, _ = roc_curve(y_true=targs, y_score=preds[:, 1])\n",
    "    acc_test = accuracy_score(targs, preds.argmax(dim=-1))\n",
    "    auc_test = auc(fpr, tpr)\n",
    "    background_eff = interp1d(tpr, fpr)\n",
    "    background_eff_at_30 = background_eff(0.3)\n",
    "\n",
    "    print(f\"Accuracy: {acc_test:.4f}\")\n",
    "    print(f\"AUC: {auc_test:.4f}\")\n",
    "    print(\n",
    "        f\"Backround rejection at signal efficiency 0.3: {1/background_eff_at_30:0.3f}\"\n",
    "    )\n",
    "    return fpr, tpr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6241ec16-79d3-4340-8daf-eb9014fbc447",
   "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": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9140\n",
      "AUC: 0.9725\n",
      "Backround rejection at signal efficiency 0.3: 458.895\n"
     ]
    }
   ],
   "source": [
    "test_ds = get_dataset(images_ds[\"test\"])\n",
    "fpr, tpr = compute_metrics(learn, test_ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "418be0ac-cc82-44f3-94c6-9aef155772d2",
   "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(fpr, tpr)\n",
    "plt.plot([0, 1], [0, 1], ls=\"--\", color=\"k\")\n",
    "plt.xlabel(r\"$\\epsilon_B$\")\n",
    "plt.ylabel(r\"$\\epsilon_S$\")\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8e805986-4b3f-404c-9061-52c65edad2ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<>:4: SyntaxWarning: invalid escape sequence '\\e'\n",
      "<>:5: SyntaxWarning: invalid escape sequence '\\e'\n",
      "<>:4: SyntaxWarning: invalid escape sequence '\\e'\n",
      "<>:5: SyntaxWarning: invalid escape sequence '\\e'\n",
      "C:\\Users\\Ayush\\AppData\\Local\\Temp\\ipykernel_19140\\4132169030.py:4: SyntaxWarning: invalid escape sequence '\\e'\n",
      "  plt.xlabel(\"Signal befficiency $\\epsilon_{S}$\")\n",
      "C:\\Users\\Ayush\\AppData\\Local\\Temp\\ipykernel_19140\\4132169030.py:5: SyntaxWarning: invalid escape sequence '\\e'\n",
      "  plt.ylabel(\"Background rejection $1/\\epsilon_{B}$\")\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "plt.plot(tpr, 1 / (fpr + 1e-6), label=\"CNN\")\n",
    "plt.xlabel(\"Signal befficiency $\\epsilon_{S}$\")\n",
    "plt.ylabel(\"Background rejection $1/\\epsilon_{B}$\")\n",
    "plt.xlim(0, 1)\n",
    "plt.yscale(\"log\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "fb44b43e-eb7e-4d8a-b82e-efa116a0c2ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Path('models/CNN for Jet images.pth')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learn.save('CNN for Jet images')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a7a8f883-2991-441d-a1d0-fd9f1bfbcd20",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}