EMD_Quantile_PostTrain.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import h5py\n",
    "import glob\n",
    "import itertools\n",
    "import sys\n",
    "from sklearn.utils import shuffle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the data file is here https://cernbox.cern.ch/index.php/s/uw6z7ZvaqoYZXZ5\n",
    "# you need to copy it to /data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(991824, 100, 6, 1) (991824,)\n"
     ]
    }
   ],
   "source": [
    "X = np.array([])\n",
    "Y = np.array([])\n",
    "i = 0\n",
    "fileIN = \"/data/shared/EMD/qcd_sqrtshatTeV_13TeV_PU40_SIDEBAND_List.h5\" \n",
    "f = h5py.File(fileIN, 'r')\n",
    "X = np.array(f.get(\"X_val\"), dtype=np.float32)\n",
    "Y = np.array(f.get(\"Y_val\"), dtype=np.float32)\n",
    "print(X.shape, Y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Custom Loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(991824, 100, 6, 1)\n"
     ]
    }
   ],
   "source": [
    "# load the model\n",
    "from keras.models import model_from_json\n",
    "# read encoder\n",
    "json_file = open(\"models/EMD_Dense.json\", 'r')\n",
    "encoder_json = json_file.read()\n",
    "json_file.close()\n",
    "model = model_from_json(encoder_json)\n",
    "model.load_weights(\"models/EMD_Dense.h5\")\n",
    "\n",
    "model.compile(optimizer='adam', loss='mse', metrics=['acc'])\n",
    "model.summary()\n",
    "Yhat = model.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import LogNorm\n",
    "%matplotlib inline\n",
    "# compare prediction to true value\n",
    "plt.hist2d(Y, Yhat[:,0], bins=100, range=[[0.,1000],[0.,1000]], norm=LogNorm())\n",
    "plt.colorbar()\n",
    "plt.ylabel('Regressed EMD')\n",
    "plt.xlabel('True EDM')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import LogNorm\n",
    "%matplotlib inline\n",
    "# compare prediction to true value\n",
    "plt.hist2d(Y, Yhat[:,0]/Y, bins=100, norm=LogNorm())\n",
    "plt.colorbar()\n",
    "plt.ylabel('Regressed EMD/True EDM')\n",
    "plt.xlabel('True EDM')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": [
    "# compare prediction to true value\n",
    "plt.hist(Y-Yhat[:,0], bins=100)\n",
    "plt.ylabel('Regressed EMD-True EDM')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ABSOLUTE: (0.376241 $\\pm$ 74.624359) GeV\n",
      "RELATIVE: -0.329056 $\\pm$ 0.751866\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "# compare prediction to true value\n",
    "plt.hist((Y-Yhat[:,0])/Y, bins=100, range=[-1., 1.])\n",
    "plt.ylabel('Regressed EMD-True EDM')\n",
    "plt.show()\n",
    "print(\"ABSOLUTE: (%f $\\pm$ %f) GeV\" %(np.mean(Y-Yhat[:,0]), np.sqrt(np.var(Y-Yhat[:,0]))))\n",
    "rel = (Y-Yhat[:,0])/Y\n",
    "varRel = math.sqrt(np.var(rel))\n",
    "print(\"RELATIVE: %f $\\pm$ %f\" %(np.mean(rel), varRel))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MSE "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load the model\n",
    "from keras.models import model_from_json\n",
    "# read encoder\n",
    "json_file = open(\"models/EMD_Dense_MSE.json\", 'r')\n",
    "encoder_json = json_file.read()\n",
    "json_file.close()\n",
    "model = model_from_json(encoder_json)\n",
    "model.load_weights(\"models/EMD_Dense_MSE.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         (None, 100, 6, 1)         0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 100, 6, 1)         4         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 10, 6, 1)          6010      \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 10, 6, 1)          4         \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 10, 6, 1)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 5, 6, 1)           505       \n",
      "_________________________________________________________________\n",
      "batch_normalization_3 (Batch (None, 5, 6, 1)           4         \n",
      "_________________________________________________________________\n",
      "activation_2 (Activation)    (None, 5, 6, 1)           0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 2, 6, 1)           52        \n",
      "_________________________________________________________________\n",
      "batch_normalization_4 (Batch (None, 2, 6, 1)           4         \n",
      "_________________________________________________________________\n",
      "activation_3 (Activation)    (None, 2, 6, 1)           0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 12)                0         \n",
      "_________________________________________________________________\n",
      "dense_relu1 (Dense)          (None, 80)                1040      \n",
      "_________________________________________________________________\n",
      "dense_relu2 (Dense)          (None, 40)                3240      \n",
      "_________________________________________________________________\n",
      "dense_relu3 (Dense)          (None, 20)                820       \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 21        \n",
      "=================================================================\n",
      "Total params: 11,704\n",
      "Trainable params: 11,696\n",
      "Non-trainable params: 8\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.compile(optimizer='adam', loss='mse', metrics=['acc'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "Yhat = model.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(991824,)\n"
     ]
    }
   ],
   "source": [
    "print(Y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import LogNorm\n",
    "%matplotlib inline\n",
    "# compare prediction to true value\n",
    "plt.hist2d(Y, Yhat[:,0], bins=100, range=[[0.,1000],[0.,1000]], norm=LogNorm())\n",
    "plt.colorbar()\n",
    "plt.ylabel('Regressed EMD')\n",
    "plt.xlabel('True EDM')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import LogNorm\n",
    "%matplotlib inline\n",
    "# compare prediction to true value\n",
    "plt.hist2d(Y, Yhat[:,0]/Y, bins=100, norm=LogNorm())\n",
    "plt.colorbar()\n",
    "plt.ylabel('Regressed EMD/True EDM')\n",
    "plt.xlabel('True EDM')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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": [
    "# compare prediction to true value\n",
    "plt.hist(Y-Yhat[:,0], bins=100)\n",
    "plt.ylabel('Regressed EMD-True EDM')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ABSOLUTE: (2.86 +/- 20.17) GeV\n",
      "RELATIVE: -0.006 +/- 0.167\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "# compare prediction to true value\n",
    "plt.hist((Y-Yhat[:,0])/Y, bins=100, range=[-1., 1.])\n",
    "plt.ylabel('Regressed EMD-True EDM')\n",
    "plt.show()\n",
    "print(\"ABSOLUTE: (%.2f +/- %.2f) GeV\" %(np.mean(Y-Yhat[:,0]), np.sqrt(np.var(Y-Yhat[:,0]))))\n",
    "rel = (Y-Yhat[:,0])/Y\n",
    "varRel = math.sqrt(np.var(rel))\n",
    "print(\"RELATIVE: %.3f +/- %.3f\" %(np.mean(rel), varRel))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(133.594574 $\\pm$ 74.754921) GeV\n"
     ]
    }
   ],
   "source": [
    "# compare prediction to true value\n",
    "plt.hist(Y, bins=100)\n",
    "plt.ylabel('True EDM')\n",
    "plt.show()\n",
    "print(\"(%f $\\pm$ %f) GeV\" %(np.mean(Y), np.sqrt(np.var(Y))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MAE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load the model\n",
    "from keras.models import model_from_json\n",
    "# read encoder\n",
    "json_file = open(\"models/EMD_Dense_MAE.json\", 'r')\n",
    "encoder_json = json_file.read()\n",
    "json_file.close()\n",
    "model = model_from_json(encoder_json)\n",
    "model.load_weights(\"models/EMD_Dense_MAE.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         (None, 100, 6, 1)         0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 100, 6, 1)         4         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 10, 6, 1)          6010      \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 10, 6, 1)          4         \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 10, 6, 1)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 5, 6, 1)           505       \n",
      "_________________________________________________________________\n",
      "batch_normalization_3 (Batch (None, 5, 6, 1)           4         \n",
      "_________________________________________________________________\n",
      "activation_2 (Activation)    (None, 5, 6, 1)           0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 2, 6, 1)           52        \n",
      "_________________________________________________________________\n",
      "batch_normalization_4 (Batch (None, 2, 6, 1)           4         \n",
      "_________________________________________________________________\n",
      "activation_3 (Activation)    (None, 2, 6, 1)           0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 12)                0         \n",
      "_________________________________________________________________\n",
      "dense_relu1 (Dense)          (None, 80)                1040      \n",
      "_________________________________________________________________\n",
      "dense_relu2 (Dense)          (None, 40)                3240      \n",
      "_________________________________________________________________\n",
      "dense_relu3 (Dense)          (None, 20)                820       \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 21        \n",
      "=================================================================\n",
      "Total params: 11,704\n",
      "Trainable params: 11,696\n",
      "Non-trainable params: 8\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.compile(optimizer='adam', loss='mae', metrics=['acc'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "Yhat = model.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import LogNorm\n",
    "%matplotlib inline\n",
    "# compare prediction to true value\n",
    "plt.hist2d(Y, Yhat[:,0], bins=100, range=[[0.,1000],[0.,1000]], norm=LogNorm())\n",
    "plt.colorbar()\n",
    "plt.ylabel('Regressed EMD')\n",
    "plt.xlabel('True EDM')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import LogNorm\n",
    "%matplotlib inline\n",
    "# compare prediction to true value\n",
    "plt.hist2d(Y, Yhat[:,0]/Y, bins=100, norm=LogNorm())\n",
    "plt.colorbar()\n",
    "plt.ylabel('Regressed EMD/True EDM')\n",
    "plt.xlabel('True EDM')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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": [
    "# compare prediction to true value\n",
    "plt.hist(Y-Yhat[:,0], bins=100)\n",
    "plt.ylabel('Regressed EMD-True EDM')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lqa6ZmbVIURff/B/kL/fa1nu9lq8Afwu8Ma0fAjwXETvTeg+75yYZTzZWFxGxU9Lzqf54ID+fe36fjb3KT64WhKR5wDyAww8/vM7QzcysL0VdfH84kANL+jNga0Q8KOldAznWQEXEYmAxQEdHR3+a4szMrEDhsCcDdApwjqSzgf2Bg4BrgFGSRqarkQnAplR/EzAR6JE0EjgYeDZXXpHfp1a5mZm1QGkPFEbEZRExISImkd0YvzciPgTcB5ybqs0B7kjLnWmdtP3eiIhUPiv13poMTAEeANYCU1Jvr33TZxTOgWJmZs1V5pVILZcCyyV9AfgpcEMqvwG4SVI3sJ0sKRAR6yTdCjwG7ATmpxGGkfRJYCUwAljiYerNzFqraADG71HQlbc/MxtGxP3A/Wl5A1nPqt51XgLOq7H/lcCVVcpXkA0QaWZmg6DoSqTSA+v9ZBNUfSutXwBsKTMoMzNrD332zpL0vyOiI7fpe5K6So/MzAp5giobCuq5sX6gpCMrK+nm9oHlhWRmZu2inhvrf0M2Le4GsjGzjgA+VmpUZmbWFvpMIhFxt6QpwFtT0X9ExMvlhmVmZu2gzyQi6fXAZ4AjIuIiSVMkHRUR3y8/PLP2kr9PYTYc1HNP5JvAK8Db0/om4AulRWRmZm2jniTy5oj4EvAqQES8SD/mEzEzs71XPUnkFUkHkB48lPRmwPdEzMysrt5ZC4G7gYmSvk02sOJHywzKzMzaQz29s1ZJ+gkwlawZ65KI+FXpkZmZ2ZDXZ3OWpFOAlyLiTmAU8FlJR5QemZmZDXn13BO5DnhR0rFkXX2fBJaVGpWZmbWFepLIzjSvxwzg2oi4lt3T3ZqZ2TBWz431X0u6DPgwcKqkfYDXlRuWmZm1g3qSyAeADwJzI+IZSYcD/6vcsMysPzyirw2Wepqzfg1cExH/JumPgOOAm/vaSdL+kh6Q9DNJ6yR9PpVPlrRGUrekW9LUtqTpb29J5WskTcod67JU/oSkM3Pl01NZt6QF/fvSzcxsoOpJIj8C9pM0HvhX4CPAjXXs9zJwWkQcS5Z4pkuaCnwRuDoi3gLsAOam+nOBHan86lQPSUeTTZX7NmA68DVJIySNAK4FzgKOBi5Idc3MrEXqSSJKQ528H/haRJwHHNPXTpH5TVp9XXoFcBpwWypfCsxMyzPSOmn76ZKUypdHxMsR8RTQTTa97klAd0RsiIhXgOWprpmZtUhdSUTS24EPAZWG13r2I10xPARsBVaRdQ9+LiJ2pio9wPi0PB7YCJC2Pw8cki/vtU+t8mpxzJPUJalr27Zt9YRuZmZ1qOfG+qeBy4DbI2JdmuXwvnoOHhGvAcdJGgXczu45SVoqIhYDiwE6OjpiMGKwvZeHf7fhrJ5hT34I/DDNK0JEbAA+1Z8PiYjnJN1HNpz8KEkj09XGBLKh5UnvE4EeSSOBg4Fnc+UV+X1qlZuZWQvUM+zJ2yU9BvxHWj9W0tfq2G9sugIhjQJ8BvA42VXMuanaHOCOtNyZ1knb700POXYCs1LvrcnAFOABYC0wJfX22pfs5ntnHV+zmZk1ST3NWV8BziT9gY6In0k6tY79DgOWpl5U+wC3RsT3U0JaLukLwE+BG1L9G4CbJHUD28mSAqkJ7VbgMWAnMD81kyHpk8BKYASwJCLW1fNFm5lZc9STRIiIjVlHqV1eq2Ofh4Hjq5RvIOtZ1bv8JeC8Gse6EriySvkKYEVfsZgNJ37w0FqpniSyUdKfAiHpdcAlZM1SZmY2zNXTVffjwHyy7rObyB4cnF9mUGZm1h4Kr0TS/YyPRMSHWhSPmZm1kcIrkXQD+4MtisXMzNpMPfdEfizpq8AtwG8rhRHxk9KiMjOztlBPEjkuvV+eK6uMgWVmZsNYPU+sv7sVgZiZWfvpM4lI+kyV4ueBByPioeaHZGZm7aKeLr4dZN18KyPnfoxsXo9vSPrbEmMzM7Mhrp57IhOAEypzg0haSDYk/KnAg8CXygvPbGhql5F7/fS6la2eK5E/IJulsOJVYFxE/L9e5WZmNszUcyXybWCNpMpou38O/LOkA8kGRTQzs2Gqnt5ZV0i6CzglFX08IrrSsp9kNzMbxuqa5hbYH3ghIq4Bnk7zepiZ2TBXz6RUC4FLyabIBXgd8K0ygzIzs/ZQz5XI+4BzSEOeRMR/Am/saydJEyXdJ+kxSeskXZLKx0haJWl9eh+dyiVpkaRuSQ9LOiF3rDmp/npJc3LlJ0p6JO2zSL0mPTEzs3LVk0ReSdPUBkC6oV6PncB/jYijganAfElHAwuAeyJiCnBPWgc4i2zq2ynAPOC69HljgIXAyWSTWS2sJJ5U56LcftPrjM1s2Jm04M5dL7NmqSeJ3Crp68AoSRcBPwCu72uniNhcGaQxIn5NNpHVeGAGsDRVWwrMTMszgGWRWZ0+7zCyqXlXRcT2iNgBrAKmp20HRcTqlOSW5Y5lZmYtUE/vrC9LOgN4ATgK+PuIWNWfD5E0iWyq3DVkz5hsTpueAcal5fHAxtxuPex+Sr5WeU+V8mqfP4/s6obDDz+8P6Gb7eL/4M32VO8c66vIrgCQtI+kD0XEt+vZV9IbgO8An46IF/K3LSIiJEX/w+6fiFgMLAbo6Ogo/fPMzIaLms1Zkg6SdJmkr0qalm58fxLYAJxfz8HTnOzfAb4dEd9NxVtSUxTpfWsq3wRMzO0+IZUVlU+oUm5mZi1SdE/kJrLmq0eAvwLuA84DZkbEjL4OnHpK3QA8HhH/mNvUCVR6WM0B7siVz07JairwfGr2WglMkzQ63VCfBqxM216QNDV91uzcsczMrAWKmrOOjIg/AZB0PbAZODwiXqrz2KcAHwEekVQZMv6zwFVkN+vnAk+z+6pmBXA20A28CFwIEBHbJV0BrE31Lo+I7Wn5YuBG4ADgrvQyM7MWKUoir1YWIuI1ST39SCBExI+BWs9tnF6lfgDzaxxrCbCkSnkXcEy9MZlZxqP7WrMUJZFjJb2QlgUckNZF9jf/oNKjMzOzIa1mEomIEa0MxMzM2k+9AzCamZntoa7nRMyGq+HwgKHvj9hA+ErEzMwa5iRiZmYNcxIxM7OGOYmYmVnDnETMzKxh7p1lZru4p5b1l5OIWS/DoVuvWbO4OcvMzBrmJGJmZg1zc5aZVdW7Wc/3SKwaX4mYmVnDfCViw55vpJs1rrQrEUlLJG2V9GiubIykVZLWp/fRqVySFknqlvSwpBNy+8xJ9ddLmpMrP1HSI2mfRWmKXDMza6Eym7NuBKb3KlsA3BMRU4B70jrAWcCU9JoHXAdZ0gEWAicDJwELK4kn1bkot1/vzzKzJpq04M5dL7OK0pJIRPwI2N6reAawNC0vBWbmypdFZjUwStJhwJnAqojYHhE7gFXA9LTtoIhYnabVXZY7lpmZtUirb6yPi4jNafkZYFxaHg9szNXrSWVF5T1VyquSNE9Sl6Subdu2DewrMDOzXQbtxnpEhKRo0WctBhYDdHR0tOQzbWhzk8zAeHgUq2j1lciW1BRFet+ayjcBE3P1JqSyovIJVcrNzKyFWp1EOoFKD6s5wB258tmpl9ZU4PnU7LUSmCZpdLqhPg1Ymba9IGlq6pU1O3csMzNrkdKasyTdDLwLOFRSD1kvq6uAWyXNBZ4Gzk/VVwBnA93Ai8CFABGxXdIVwNpU7/KIqNysv5isB9gBwF3pZWYt5qat4a20JBIRF9TYdHqVugHMr3GcJcCSKuVdwDEDidGGF98HMWs+D3tiZmYN87AnZtY0btoafpxEbK/mJiyzcjmJmFkpfFUyPPieiJmZNcxXIrbXcRPW0OOrkr2Xr0TMzKxhvhKxvYKvPtqHr0r2Lr4SMTOzhvlKxNqWrz7an69K2p+TiLUVJ469lxNKe3ISsSHPicNs6HISMbMhx1cl7cNJxIYkX31YhRPK0OYkYoPKycL6wwll6HESsZZwsrBmq/Uz5eTSWm2fRCRNB64BRgDXR8RVgxzSsOZkYYPNVyut1dZJRNII4FrgDKAHWCupMyIeG9zI9h5OCtbOfLVSvrZOIsBJQHdEbACQtByYAexVScR/yM2aa6C/U05Cu7V7EhkPbMyt9wAn964kaR4wL63+RtITDX7eocCvGty3TI6rfxxX/ziuXvTFws174/k6otaGdk8idYmIxcDigR5HUldEdDQhpKZyXP3juPrHcfXPcIur3Qdg3ARMzK1PSGVmZtYC7Z5E1gJTJE2WtC8wC+gc5JjMzIaNtm7Oioidkj4JrCTr4rskItaV+JEDbhIriePqH8fVP46rf4ZVXIqIMo5rZmbDQLs3Z5mZ2SByEjEzs4Y5ifQi6TxJ6yT9TlLN7nCSpkt6QlK3pAW58smS1qTyW9IN/2bENUbSKknr0/voKnXeLemh3OslSTPTthslPZXbdlyr4kr1Xst9dmeufDDP13GS/j19vx+W9IHctqaer1o/L7nt+6Wvvzudj0m5bZel8icknTmQOBqI6zOSHkvn5x5JR+S2Vf2etiiuj0ralvv8v8ptm5O+7+slzWlxXFfnYvq5pOdy20o5X5KWSNoq6dEa2yVpUYr5YUkn5LYN/FxFhF+5F/DHwFHA/UBHjTojgCeBI4F9gZ8BR6dttwKz0vI/AZ9oUlxfAhak5QXAF/uoPwbYDrw+rd8InFvC+aorLuA3NcoH7XwBfwRMSctvAjYDo5p9vop+XnJ1Lgb+KS3PAm5Jy0en+vsBk9NxRrQwrnfnfoY+UYmr6Hvaorg+Cny1yr5jgA3pfXRaHt2quHrV/2uyzj5ln69TgROAR2tsPxu4CxAwFVjTzHPlK5FeIuLxiOjrifZdw61ExCvAcmCGJAGnAbelekuBmU0KbUY6Xr3HPRe4KyJebNLn19LfuHYZ7PMVET+PiPVp+T+BrcDYJn1+XtWfl4J4bwNOT+dnBrA8Il6OiKeA7nS8lsQVEfflfoZWkz2LVbZ6zlctZwKrImJ7ROwAVgHTBymuC4Cbm/TZNUXEj8j+YaxlBrAsMquBUZIOo0nnykmkMdWGWxkPHAI8FxE7e5U3w7iI2JyWnwHG9VF/Fnv+AF+ZLmevlrRfi+PaX1KXpNWVJjaG0PmSdBLZf5dP5oqbdb5q/bxUrZPOx/Nk56eefcuMK28u2X+0FdW+p62M6y/S9+c2SZWHjofE+UrNfpOBe3PFZZ2vvtSKuynnqq2fE2mUpB8Af1hl0+ci4o5Wx1NRFFd+JSJCUs2+2em/jD8he36m4jKyP6b7kvUXvxS4vIVxHRERmyQdCdwr6RGyP5QNa/L5ugmYExG/S8UNn6+9kaQPAx3AO3PFe3xPI+LJ6kdouu8BN0fEy5I+RnYVd1qLPrses4DbIuK1XNlgnq/SDMskEhHvGeAhag238izZpeLI9N9kv4ZhKYpL0hZJh0XE5vRHb2vBoc4Hbo+IV3PHrvxX/rKkbwL/rZVxRcSm9L5B0v3A8cB3GOTzJekg4E6yfyBW547d8Pmqop7heSp1eiSNBA4m+3kqc2ifuo4t6T1kifmdEfFypbzG97QZfxT7jCsins2tXk92D6yy77t67Xt/E2KqK66cWcD8fEGJ56svteJuyrlyc1Zjqg63EtndqvvI7kcAzAGadWXTmY5Xz3H3aItNf0gr9yFmAlV7cpQRl6TRleYgSYcCpwCPDfb5St+728nai2/rta2Z56ue4Xny8Z4L3JvOTycwS1nvrcnAFOCBAcTSr7gkHQ98HTgnIrbmyqt+T1sY12G51XOAx9PySmBaim80MI3fvyIvNa4U21vJblT/e66szPPVl05gduqlNRV4Pv2T1JxzVUZvgXZ+Ae8jaxt8GdgCrEzlbwJW5OqdDfyc7D+Jz+XKjyT7Je8G/i+wX5PiOgS4B1gP/AAYk8o7yGZ0rNSbRPYfxj699r8XeITsj+G3gDe0Ki7gT9Nn/yy9zx0K5wv4MPAq8FDudVwZ56vazwtZ89g5aXn/9PV3p/NxZG7fz6X9ngDOavLPe19x/SD9HlTOT2df39MWxfUPwLr0+fcBb83t+5fpPHYDF7YyrrT+P4Greu1X2vki+4dxc/pZ7iG7d/Vx4ONpu8gm73syfXZHbt8BnysPe2JmZg1zc5aZmTXMScTMzBrmJGJmZg1zEjEzs4Y5iQgVdZgAAAAWSURBVJiZWcOcRMzMrGFOImZm1rD/D63FssNg/ih+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ABSOLUTE: (5.136188 $\\pm$ 21.678022) GeV\n",
      "RELATIVE: 0.015429 $\\pm$ 0.168394\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "# compare prediction to true value\n",
    "plt.hist((Y-Yhat[:,0])/Y, bins=100, range=[-1., 1.])\n",
    "plt.ylabel('Regressed EMD-True EDM')\n",
    "plt.show()\n",
    "print(\"ABSOLUTE: (%f $\\pm$ %f) GeV\" %(np.mean(Y-Yhat[:,0]), np.sqrt(np.var(Y-Yhat[:,0]))))\n",
    "rel = (Y-Yhat[:,0])/Y\n",
    "varRel = math.sqrt(np.var(rel))\n",
    "print(\"RELATIVE: %f $\\pm$ %f\" %(np.mean(rel), varRel))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(133.594574 $\\pm$ 74.754921) GeV\n"
     ]
    }
   ],
   "source": [
    "# compare prediction to true value\n",
    "plt.hist(Y, bins=100)\n",
    "plt.ylabel('True EDM')\n",
    "plt.show()\n",
    "print(\"(%f $\\pm$ %f) GeV\" %(np.mean(Y), np.sqrt(np.var(Y))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MAPE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load the model\n",
    "from keras.models import model_from_json\n",
    "# read encoder\n",
    "json_file = open(\"models/EMD_Dense_MAPE.json\", 'r')\n",
    "encoder_json = json_file.read()\n",
    "json_file.close()\n",
    "model = model_from_json(encoder_json)\n",
    "model.load_weights(\"models/EMD_Dense_MAPE.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         (None, 100, 6, 1)         0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 100, 6, 1)         4         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 10, 6, 1)          6010      \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 10, 6, 1)          4         \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 10, 6, 1)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 5, 6, 1)           505       \n",
      "_________________________________________________________________\n",
      "batch_normalization_3 (Batch (None, 5, 6, 1)           4         \n",
      "_________________________________________________________________\n",
      "activation_2 (Activation)    (None, 5, 6, 1)           0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 2, 6, 1)           52        \n",
      "_________________________________________________________________\n",
      "batch_normalization_4 (Batch (None, 2, 6, 1)           4         \n",
      "_________________________________________________________________\n",
      "activation_3 (Activation)    (None, 2, 6, 1)           0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 12)                0         \n",
      "_________________________________________________________________\n",
      "dense_relu1 (Dense)          (None, 80)                1040      \n",
      "_________________________________________________________________\n",
      "dense_relu2 (Dense)          (None, 40)                3240      \n",
      "_________________________________________________________________\n",
      "dense_relu3 (Dense)          (None, 20)                820       \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 21        \n",
      "=================================================================\n",
      "Total params: 11,704\n",
      "Trainable params: 11,696\n",
      "Non-trainable params: 8\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.compile(optimizer='adam', loss='mape', metrics=['acc'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "Yhat = model.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import LogNorm\n",
    "%matplotlib inline\n",
    "# compare prediction to true value\n",
    "plt.hist2d(Y, Yhat[:,0], bins=100, range=[[0.,1000],[0.,1000]], norm=LogNorm())\n",
    "plt.colorbar()\n",
    "plt.ylabel('Regressed EMD')\n",
    "plt.xlabel('True EDM')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import LogNorm\n",
    "%matplotlib inline\n",
    "# compare prediction to true value\n",
    "plt.hist2d(Y, Yhat[:,0]/Y, bins=100, norm=LogNorm())\n",
    "plt.colorbar()\n",
    "plt.ylabel('Regressed EMD/True EDM')\n",
    "plt.xlabel('True EDM')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ABSOLUTE: (8.870861 $\\pm$ 24.087114) GeV\n",
      "RELATIVE: 0.047715 $\\pm$ 0.172854\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "# compare prediction to true value\n",
    "plt.hist((Y-Yhat[:,0])/Y, bins=100, range=[-1., 1.])\n",
    "plt.ylabel('Regressed EMD-True EDM')\n",
    "plt.show()\n",
    "print(\"ABSOLUTE: (%f $\\pm$ %f) GeV\" %(np.mean(Y-Yhat[:,0]), np.sqrt(np.var(Y-Yhat[:,0]))))\n",
    "rel = (Y-Yhat[:,0])/Y\n",
    "varRel = math.sqrt(np.var(rel))\n",
    "print(\"RELATIVE: %f $\\pm$ %f\" %(np.mean(rel), varRel))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(133.594574 $\\pm$ 74.754921) GeV\n"
     ]
    }
   ],
   "source": [
    "# compare prediction to true value\n",
    "plt.hist(Y, bins=100)\n",
    "plt.ylabel('True EDM')\n",
    "plt.show()\n",
    "print(\"(%f $\\pm$ %f) GeV\" %(np.mean(Y), np.sqrt(np.var(Y))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Asymmetric MAE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load the model\n",
    "from keras.models import model_from_json\n",
    "# read encoder\n",
    "json_file = open(\"models/EMD_Dense_MAE_Asymmetric.json\", 'r')\n",
    "encoder_json = json_file.read()\n",
    "json_file.close()\n",
    "model = model_from_json(encoder_json)\n",
    "model.load_weights(\"models/EMD_Dense_MAE_Asymmetric.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "W0726 22:00:43.159321 4615525824 deprecation_wrapper.py:119] From /usr/local/lib/python3.7/site-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         (None, 100, 6, 1)         0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 100, 6, 1)         4         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 10, 6, 1)          6010      \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 10, 6, 1)          4         \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 10, 6, 1)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 5, 6, 1)           505       \n",
      "_________________________________________________________________\n",
      "batch_normalization_3 (Batch (None, 5, 6, 1)           4         \n",
      "_________________________________________________________________\n",
      "activation_2 (Activation)    (None, 5, 6, 1)           0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 2, 6, 1)           52        \n",
      "_________________________________________________________________\n",
      "batch_normalization_4 (Batch (None, 2, 6, 1)           4         \n",
      "_________________________________________________________________\n",
      "activation_3 (Activation)    (None, 2, 6, 1)           0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 12)                0         \n",
      "_________________________________________________________________\n",
      "dense_relu1 (Dense)          (None, 80)                1040      \n",
      "_________________________________________________________________\n",
      "dense_relu2 (Dense)          (None, 40)                3240      \n",
      "_________________________________________________________________\n",
      "dense_relu3 (Dense)          (None, 20)                820       \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 21        \n",
      "=================================================================\n",
      "Total params: 11,704\n",
      "Trainable params: 11,696\n",
      "Non-trainable params: 8\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.compile(optimizer='adam', loss='mape', metrics=['acc'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "Yhat = model.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import LogNorm\n",
    "%matplotlib inline\n",
    "# compare prediction to true value\n",
    "plt.hist2d(Y, Yhat[:,0], bins=100, range=[[0.,1000],[0.,1000]], norm=LogNorm())\n",
    "plt.colorbar()\n",
    "plt.ylabel('Regressed EMD')\n",
    "plt.xlabel('True EDM')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import LogNorm\n",
    "%matplotlib inline\n",
    "# compare prediction to true value\n",
    "plt.hist2d(Y, Yhat[:,0]/Y, bins=100, norm=LogNorm())\n",
    "plt.colorbar()\n",
    "plt.ylabel('Regressed EMD/True EDM')\n",
    "plt.xlabel('True EDM')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ABSOLUTE: (2.955375 $\\pm$ 15.021564) GeV\n",
      "RELATIVE: 0.007148 $\\pm$ 0.130587\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "# compare prediction to true value\n",
    "plt.hist((Y-Yhat[:,0])/Y, bins=100, range=[-1., 1.])\n",
    "plt.ylabel('Regressed EMD-True EDM')\n",
    "plt.show()\n",
    "print(\"ABSOLUTE: (%f $\\pm$ %f) GeV\" %(np.mean(Y-Yhat[:,0]), np.sqrt(np.var(Y-Yhat[:,0]))))\n",
    "rel = (Y-Yhat[:,0])/Y\n",
    "varRel = math.sqrt(np.var(rel))\n",
    "print(\"RELATIVE: %f $\\pm$ %f\" %(np.mean(rel), varRel))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(133.594574 $\\pm$ 74.754921) GeV\n"
     ]
    }
   ],
   "source": [
    "# compare prediction to true value\n",
    "plt.hist(Y, bins=100)\n",
    "plt.ylabel('True EDM')\n",
    "plt.show()\n",
    "print(\"(%f $\\pm$ %f) GeV\" %(np.mean(Y), np.sqrt(np.var(Y))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Asymmetric MAE with penalty=100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         (None, 100, 6, 1)         0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 100, 6, 1)         4         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 10, 6, 1)          6010      \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 10, 6, 1)          4         \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 10, 6, 1)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 5, 6, 1)           505       \n",
      "_________________________________________________________________\n",
      "batch_normalization_3 (Batch (None, 5, 6, 1)           4         \n",
      "_________________________________________________________________\n",
      "activation_2 (Activation)    (None, 5, 6, 1)           0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 2, 6, 1)           52        \n",
      "_________________________________________________________________\n",
      "batch_normalization_4 (Batch (None, 2, 6, 1)           4         \n",
      "_________________________________________________________________\n",
      "activation_3 (Activation)    (None, 2, 6, 1)           0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 12)                0         \n",
      "_________________________________________________________________\n",
      "dense_relu1 (Dense)          (None, 80)                1040      \n",
      "_________________________________________________________________\n",
      "dense_relu2 (Dense)          (None, 40)                3240      \n",
      "_________________________________________________________________\n",
      "dense_relu3 (Dense)          (None, 20)                820       \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 21        \n",
      "=================================================================\n",
      "Total params: 11,704\n",
      "Trainable params: 11,696\n",
      "Non-trainable params: 8\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# load the model\n",
    "from keras.models import model_from_json\n",
    "# read encoder\n",
    "json_file = open(\"models/EMD_Dense_MAE_AsymmetryLarge.json\", 'r')\n",
    "encoder_json = json_file.read()\n",
    "json_file.close()\n",
    "model = model_from_json(encoder_json)\n",
    "model.load_weights(\"models/EMD_Dense_MAE_AsymmetryLarge.h5\")\n",
    "\n",
    "model.compile(optimizer='adam', loss='mape', metrics=['acc'])\n",
    "model.summary()\n",
    "\n",
    "Yhat = model.predict(X)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ABSOLUTE: (102.728172 $\\pm$ 46.940735) GeV\n",
      "RELATIVE: 1.106994 $\\pm$ 0.907242\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(133.594574 $\\pm$ 74.754921) GeV\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import LogNorm\n",
    "%matplotlib inline\n",
    "# compare prediction to true value\n",
    "plt.hist2d(Y, Yhat[:,0], bins=100, range=[[0.,1000],[0.,1000]], norm=LogNorm())\n",
    "plt.colorbar()\n",
    "plt.ylabel('Regressed EMD')\n",
    "plt.xlabel('True EDM')\n",
    "plt.show()\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import LogNorm\n",
    "%matplotlib inline\n",
    "# compare prediction to true value\n",
    "plt.hist2d(Y, Yhat[:,0]/Y, bins=100, norm=LogNorm())\n",
    "plt.colorbar()\n",
    "plt.ylabel('Regressed EMD/True EDM')\n",
    "plt.xlabel('True EDM')\n",
    "plt.show()\n",
    "\n",
    "import math\n",
    "# compare prediction to true value\n",
    "plt.hist((Yhat[:,0]-Y)/Y, bins=100, range=[-1., 5.])\n",
    "plt.ylabel('(Regressed EMD-True EDM)/True EMD')\n",
    "plt.show()\n",
    "print(\"ABSOLUTE: (%f $\\pm$ %f) GeV\" %(np.mean(Yhat[:,0]-Y), np.sqrt(np.var(Y-Yhat[:,0]))))\n",
    "rel = (Yhat[:,0]-Y)/Y\n",
    "varRel = math.sqrt(np.var(rel))\n",
    "print(\"RELATIVE: %f $\\pm$ %f\" %(np.mean(rel), varRel))\n",
    "\n",
    "# compare prediction to true value\n",
    "plt.hist(Y, bins=100)\n",
    "plt.ylabel('True EDM')\n",
    "plt.show()\n",
    "print(\"(%f $\\pm$ %f) GeV\" %(np.mean(Y), np.sqrt(np.var(Y))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Asymmetric MAE with penalty=50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         (None, 100, 6, 1)         0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 100, 6, 1)         4         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 10, 6, 1)          6010      \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 10, 6, 1)          4         \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 10, 6, 1)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 5, 6, 1)           505       \n",
      "_________________________________________________________________\n",
      "batch_normalization_3 (Batch (None, 5, 6, 1)           4         \n",
      "_________________________________________________________________\n",
      "activation_2 (Activation)    (None, 5, 6, 1)           0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 2, 6, 1)           52        \n",
      "_________________________________________________________________\n",
      "batch_normalization_4 (Batch (None, 2, 6, 1)           4         \n",
      "_________________________________________________________________\n",
      "activation_3 (Activation)    (None, 2, 6, 1)           0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 12)                0         \n",
      "_________________________________________________________________\n",
      "dense_relu1 (Dense)          (None, 80)                1040      \n",
      "_________________________________________________________________\n",
      "dense_relu2 (Dense)          (None, 40)                3240      \n",
      "_________________________________________________________________\n",
      "dense_relu3 (Dense)          (None, 20)                820       \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 21        \n",
      "=================================================================\n",
      "Total params: 11,704\n",
      "Trainable params: 11,696\n",
      "Non-trainable params: 8\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# load the model\n",
    "from keras.models import model_from_json\n",
    "# read encoder\n",
    "json_file = open(\"models/EMD_Dense_MAE_AsymmetryLarge_50.json\", 'r')\n",
    "encoder_json = json_file.read()\n",
    "json_file.close()\n",
    "model = model_from_json(encoder_json)\n",
    "model.load_weights(\"models/EMD_Dense_MAE_AsymmetryLarge_50.h5\")\n",
    "\n",
    "model.compile(optimizer='adam', loss='mape', metrics=['acc'])\n",
    "model.summary()\n",
    "\n",
    "Yhat = model.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ABSOLUTE: (96.828194 $\\pm$ 46.518017) GeV\n",
      "RELATIVE: 1.049238 $\\pm$ 0.871551\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(133.594574 $\\pm$ 74.754921) GeV\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import LogNorm\n",
    "%matplotlib inline\n",
    "# compare prediction to true value\n",
    "plt.hist2d(Y, Yhat[:,0], bins=100, range=[[0.,1000],[0.,1000]], norm=LogNorm())\n",
    "plt.colorbar()\n",
    "plt.ylabel('Regressed EMD')\n",
    "plt.xlabel('True EDM')\n",
    "plt.show()\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import LogNorm\n",
    "%matplotlib inline\n",
    "# compare prediction to true value\n",
    "plt.hist2d(Y, Yhat[:,0]/Y, bins=100, norm=LogNorm())\n",
    "plt.colorbar()\n",
    "plt.ylabel('Regressed EMD/True EDM')\n",
    "plt.xlabel('True EDM')\n",
    "plt.show()\n",
    "\n",
    "import math\n",
    "# compare prediction to true value\n",
    "plt.hist((Yhat[:,0]-Y)/Y, bins=100, range=[-1., 5.])\n",
    "plt.ylabel('(Regressed EMD-True EDM)/True EMD')\n",
    "plt.show()\n",
    "print(\"ABSOLUTE: (%f $\\pm$ %f) GeV\" %(np.mean(Yhat[:,0]-Y), np.sqrt(np.var(Y-Yhat[:,0]))))\n",
    "rel = (Yhat[:,0]-Y)/Y\n",
    "varRel = math.sqrt(np.var(rel))\n",
    "print(\"RELATIVE: %f $\\pm$ %f\" %(np.mean(rel), varRel))\n",
    "\n",
    "# compare prediction to true value\n",
    "plt.hist(Y, bins=100)\n",
    "plt.ylabel('True EDM')\n",
    "plt.show()\n",
    "print(\"(%f $\\pm$ %f) GeV\" %(np.mean(Y), np.sqrt(np.var(Y))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Asymmetric MAE with penalty=10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n",
      "WARNING: Logging before flag parsing goes to stderr.\n",
      "W0727 11:49:14.827538 4537976256 deprecation_wrapper.py:119] From /usr/local/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
      "\n",
      "W0727 11:49:14.859516 4537976256 deprecation_wrapper.py:119] From /usr/local/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:245: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
      "\n",
      "W0727 11:49:14.860401 4537976256 deprecation_wrapper.py:119] From /usr/local/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:174: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
      "\n",
      "W0727 11:49:14.861032 4537976256 deprecation_wrapper.py:119] From /usr/local/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:181: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
      "\n",
      "W0727 11:49:14.861699 4537976256 deprecation_wrapper.py:119] From /usr/local/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:186: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
      "\n",
      "W0727 11:49:14.883983 4537976256 deprecation_wrapper.py:119] From /usr/local/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:1834: The name tf.nn.fused_batch_norm is deprecated. Please use tf.compat.v1.nn.fused_batch_norm instead.\n",
      "\n",
      "W0727 11:49:15.459896 4537976256 deprecation_wrapper.py:119] From /usr/local/lib/python3.7/site-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         (None, 100, 6, 1)         0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 100, 6, 1)         4         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 10, 6, 1)          6010      \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 10, 6, 1)          4         \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 10, 6, 1)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 5, 6, 1)           505       \n",
      "_________________________________________________________________\n",
      "batch_normalization_3 (Batch (None, 5, 6, 1)           4         \n",
      "_________________________________________________________________\n",
      "activation_2 (Activation)    (None, 5, 6, 1)           0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 2, 6, 1)           52        \n",
      "_________________________________________________________________\n",
      "batch_normalization_4 (Batch (None, 2, 6, 1)           4         \n",
      "_________________________________________________________________\n",
      "activation_3 (Activation)    (None, 2, 6, 1)           0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 12)                0         \n",
      "_________________________________________________________________\n",
      "dense_relu1 (Dense)          (None, 80)                1040      \n",
      "_________________________________________________________________\n",
      "dense_relu2 (Dense)          (None, 40)                3240      \n",
      "_________________________________________________________________\n",
      "dense_relu3 (Dense)          (None, 20)                820       \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 21        \n",
      "=================================================================\n",
      "Total params: 11,704\n",
      "Trainable params: 11,696\n",
      "Non-trainable params: 8\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# load the model\n",
    "from keras.models import model_from_json\n",
    "# read encoder\n",
    "json_file = open(\"models/EMD_Dense_MAE_AsymmetryLarge_10.json\", 'r')\n",
    "encoder_json = json_file.read()\n",
    "json_file.close()\n",
    "model = model_from_json(encoder_json)\n",
    "model.load_weights(\"models/EMD_Dense_MAE_AsymmetryLarge_10.h5\")\n",
    "\n",
    "model.compile(optimizer='adam', loss='mape', metrics=['acc'])\n",
    "model.summary()\n",
    "\n",
    "Yhat = model.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ABSOLUTE: (83.689445 $\\pm$ 43.495651) GeV\n",
      "RELATIVE: 0.914349 $\\pm$ 0.781675\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(133.594574 $\\pm$ 74.754921) GeV\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import LogNorm\n",
    "%matplotlib inline\n",
    "# compare prediction to true value\n",
    "plt.hist2d(Y, Yhat[:,0], bins=100, range=[[0.,1000],[0.,1000]], norm=LogNorm())\n",
    "plt.colorbar()\n",
    "plt.ylabel('Regressed EMD')\n",
    "plt.xlabel('True EDM')\n",
    "plt.show()\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import LogNorm\n",
    "%matplotlib inline\n",
    "# compare prediction to true value\n",
    "plt.hist2d(Y, Yhat[:,0]/Y, bins=100, norm=LogNorm())\n",
    "plt.colorbar()\n",
    "plt.ylabel('Regressed EMD/True EDM')\n",
    "plt.xlabel('True EDM')\n",
    "plt.show()\n",
    "\n",
    "import math\n",
    "# compare prediction to true value\n",
    "plt.hist((Yhat[:,0]-Y)/Y, bins=100, range=[-1., 5.])\n",
    "plt.ylabel('(Regressed EMD-True EDM)/True EMD')\n",
    "plt.show()\n",
    "print(\"ABSOLUTE: (%f $\\pm$ %f) GeV\" %(np.mean(Yhat[:,0]-Y), np.sqrt(np.var(Y-Yhat[:,0]))))\n",
    "rel = (Yhat[:,0]-Y)/Y\n",
    "varRel = math.sqrt(np.var(rel))\n",
    "print(\"RELATIVE: %f $\\pm$ %f\" %(np.mean(rel), varRel))\n",
    "\n",
    "# compare prediction to true value\n",
    "plt.hist(Y, bins=100)\n",
    "plt.xlabel('True EDM')\n",
    "plt.show()\n",
    "print(\"(%f $\\pm$ %f) GeV\" %(np.mean(Y), np.sqrt(np.var(Y))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Asymmetric MAE with penalty=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         (None, 100, 6, 1)         0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 100, 6, 1)         4         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 10, 6, 1)          6010      \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 10, 6, 1)          4         \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 10, 6, 1)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 5, 6, 1)           505       \n",
      "_________________________________________________________________\n",
      "batch_normalization_3 (Batch (None, 5, 6, 1)           4         \n",
      "_________________________________________________________________\n",
      "activation_2 (Activation)    (None, 5, 6, 1)           0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 2, 6, 1)           52        \n",
      "_________________________________________________________________\n",
      "batch_normalization_4 (Batch (None, 2, 6, 1)           4         \n",
      "_________________________________________________________________\n",
      "activation_3 (Activation)    (None, 2, 6, 1)           0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 12)                0         \n",
      "_________________________________________________________________\n",
      "dense_relu1 (Dense)          (None, 80)                1040      \n",
      "_________________________________________________________________\n",
      "dense_relu2 (Dense)          (None, 40)                3240      \n",
      "_________________________________________________________________\n",
      "dense_relu3 (Dense)          (None, 20)                820       \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 21        \n",
      "=================================================================\n",
      "Total params: 11,704\n",
      "Trainable params: 11,696\n",
      "Non-trainable params: 8\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# load the model\n",
    "from keras.models import model_from_json\n",
    "# read encoder\n",
    "json_file = open(\"models/EMD_Dense_MAE_AsymmetryLarge_1.json\", 'r')\n",
    "encoder_json = json_file.read()\n",
    "json_file.close()\n",
    "model = model_from_json(encoder_json)\n",
    "model.load_weights(\"models/EMD_Dense_MAE_AsymmetryLarge_1.h5\")\n",
    "\n",
    "model.compile(optimizer='adam', loss='mape', metrics=['acc'])\n",
    "model.summary()\n",
    "\n",
    "Yhat = model.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ehZGZDza/pIaL+Sknsfk/3j+ty4KADMNIxIBo4n9T59iWVk+kHZy55sJpOcarCfFKE4vZyQ3DoDj7kF6hphBX1c91ciL1SOWUeU9MsWdDttxX8M4omWUj5b6xh54OJ+07MOM6lp7WMIxZGZSiECJyXb0TVfXs1k/HMAyj+wyKd8opwCO4KM3b6GK2F03jfMKHos6Mm05urLqre2xGMe3bMIyGGBAhfhRwGi4R1uuAfwOuUVVL2m0YhtEGROSVwO8BC4ErVPWW2c6pZxMvAN8Gvi0iIzhhfquIfEBVL2/RnDvCmWsuBGYWUjYN3TCMarTSnCIiVwJnAbtU9cSo/wxcRtc08BlV/ZCqfhMXVLkEuBSXPbYus+VOGcF9K5wHrAM+DnyjuY/SPJoScmMpCmENk+xu95Qzh8LTjkPvk7oTmiA3DGMaSqtD6q8CLgc+X+oQkTTwCZy1Yztwu4hcp6ql6PX3++OzUm9h8/PAicCNwAdU9d5mZm8YhtF3tFATV9UfiMi6iu7n4UpgPgwgIl8CzhGR+4EPATclLUZfTxN/Ay4y8x3AhSLlbyZx89KFST/EXJGCMry3wL6jwyLm1EIfdp8P35gTq8bK7bFSOH4VV0PDMIx6NGBOWS4icdzMJp+BdTZW4xxHSmwHno9LSXIqLk/5M1X1U7NdqJ5NvF7pNsMwjMEluRB/UlVblkJEVT+OM1snpl4q2pdG7fUVx17d8OzmgKaF3Pw0hXmUt3zWbcWMlLeR3ZPlrVp6WsMwjES0v7LPo8DaaH+N72uYetr2pVH7axXH3o9hGMYAIpp8w1f2UdXrG7zN7cAGEVnvs7meC9QNsKxFPZu41GhX228rmoL8PGFoX9RZ+vqJZpIfC9FA2afHZ1wns2olN21v6E3FMIzDkRZ6p4jINcCLcfbz7cDFqnqFiLwduBnnYnhlszE49YS41mhX2x8ILBmWYRjQ0MLmIl9f4fpa2riqnlej/0ac99+cqCfEj/X5UyRq4/fX1z6tTQhMLQm7Ja1cCqFv/MhIE9/qvFIqA3wMwzBmJbkQ7+lCyedE7UsrjlXu9w1Wns0wjLpoQ5p416nnYvj9Tk5kNiodHtXPPDcabFdjuyK1fOF89/OYk0Lfj+6uew8T6oZhAI1o4rOaU9pN3bB7wzCMwxFJXhSi6+YUC+gxDMPoY/pDExcX1BNTquwzFQX/z98R3oEOHLfU9T34VBhgNTYNw0jCIJhTROR66nyUQazsYy6GhmE0uLDZdXNKPU285IHyalyBiC/4/fOAx9s5qUpUoDACuYXhyabHnWY+EpXV1HTQ1of35Do2P8MwBoxB8k4RkX+qSPByfUXWrr6lpHnX0rhnO24YxoAyCEI8YkxEjo3y3q4HxmY5p6W4sHumPdjikNuZXBa076mnwzqtFNxHS31/W0fmaBjGYCA05J3SdZII8b/ElWV7GPf5jgH+tK2z6jCVtnDDMA5jGrOJ9+7CZglV/baIbACO910PqOpke6c1HVFIHwLNRE/WK92yN9LEF4b2/O151zglBPukJvPTrnvzlktsMdMwjJkMyMImACIyCrwLOEZV/0RENojIcap6Q/un114a1cBrjTfBbxgDxoDZxD8L3AGc4vcfBa4FOibENQW5+VDMBkOV+LJshXlhXD4qpDxxhHMkn/fEVPWLzhKCbxjG4Us/5U5JErH5DFX9MJADUNVxOpxP3DAMo6Mkr+zTdZJo4lMiUvYNEZFnAB21iSMu4VX87ahDTisvjITvoXQufLcURlw7sz9MNTUetPLiyc8ut2/ecsmMW1bLdmhmE8M4DNCGvFN6f2ETuBj4NrBWRK4GXgi8uZ2TMgzD6CqDtLCpqptF5E7gBTgzyjtU9cm2z6yDnLnmwnLbyrcZhtFPNvEk3ikvBO5S1X8TkTcA7xWRy1T1l+2fnkMFikMg8yIXwfxMc/5kVPlHfI28p08MGbIW/DKYVkpmltTOKEFWRMaSZRnG4UsfCfEkC5ufBMZF5CScq+HPgc/P9cYikhaRn4hIT7kqnpZ6rQX/GMbhTNJFzR4R9Els4nlVVRE5B/iEr9J8fgvu/Q7gfmDhbANLpIbCakOpGLXGnyB6qJkJt5N9KlT7SU1F7fGZrodxPc78YztsIdMwDkOEATOnAPtF5CLgDcCLRCQFDM1yTl1EZA3we8D/xmn3PclcEmBZJKhh9C8DFXYP/AHwOuB8Vd0pIkcDH5njfT8G/C2wIOkJmobMUNCkiyn3lHOxdj6ULrcnljtVPZULfflsiAwqZ/BaMsrm/3j/jPuZScUwDmMGyTsF2A9cpqoFEXkWLofKNc3eUETOAnap6h0i8uI64y4ALgDILFpSa1hLOPOE95bbN239YKJx1caa4DeMAWHAzCk/AH5bRJYAtwC347Tz1zd5zxcCZ4vI7wJZYKGIfEFV3xAPUtVNwCaA7Jq1qgJD6aB1pzJOK58aDrH2hXnhyQ8d8EUj9gbtfRo+7D5z/IYmP8bsmAnFMPqQxrIYdp0kQlxUddwvZv6Lqn5YRJpOPKKqFwEXAXhN/K8rBXg3iX3GS5Q07HYKfMMweohBE+IicgpO8y55pSRxTWwpmlGyw6HkWr7gpiAjkZ08sokXsu7n+BGhb/SJMDZ/qitWVMwF7X54W/BOqUb+gW11tWvTvA1jMBi0ohDvxGnO31DV+0TkWOB7rbi5qt4K3NqKaxmGYbSKgTKn+Fqb3/d5xfFl2mbaHAzDMAaBHgrkSUKSsPtTgCuA+cDRPnLzT1X1z9s9uTIKqUlhbDgE6OQKzkxycCSE4udH4o/jjsfVfgrR8cUPuWsVh4JlKH/MinD6Y/VNK4ZhDDB9JMST2LY/BpwO7AZQ1buBF7VzUoZhGN2iFLGZZOsFktjEUdVHRKbVgajht9deRtJB6x5KuSkMRQFAcU6svI/rSR8KfcP7wlPPjzlNPXMwWhgdDoug8bebLVgaxuGFFBNL6L6I2HxERH4TUBEZIuQ8OaypVjTCMIwBoDGbeF9EbL4VuAxYjauveQvwtnZOqioCo5nIxbDodOWhTNCkJ+JUtd4mrtELRCEbdqbmexfFQvhtZbc+Gq5/ykktmbZhGP1Hr5hKklBXiItIGnijqjYbnXlYUSvplWnthtFnDIoQ9/lSXgd8tEPzqY3AcCpo3aMZ510yGgUAjWeDJp5b6BItZsaDdVsiRT0332nl2d3ht1U8amm5nZqMtfqZmDA2jMFlYDRxzw9F5HLgy8DBUqeq3tm2WfUY1cq3JU2aZRhGHzJgQvw3/M//GfUp8NLWT6c2UoDhyDtlOOXaC0aC+8lTqdFwQtr9Fgoh++y08m1p73J+aFl4BJmJkEyr5D8e28lno5Z2blq7YfQRjVW77zpJIjZf0omJ9AtlrXzh/BnH6qWiNUFuGP3BwFX2EZFqlXf2Aneo6l2tn1KVORQhPQmp6B1n8dAEAHuGgvadHQn28UmforYwEtnEx4J3ypA3DGnkEH5oWShYtHBr9QLKJfIPbGvgExiG0Vdo/0jxJOaUjX4rObKfBdwDvFVErlXVD7drcoZhGN1goDRxYA1wsqoeABCRi4F/w4Xe3wEc9kK8VRV9rC6nYfQAg5YAC1gBTEb7OeBIVZ0Qkcka57QWcUE7qejrcX7a3Xp+JkxhQTa0D8xzCcWLB0MofXEqivyp8ktKT83snNqwstxOTQUXx0xkE49NKyZ4DaP/GaiFTeBq4DYR+ZbffwXwRREZA7a2bWZ9igX2GEb/0y0h7us1vA9YpKqvSXJOEu+UfxCRm3C1MQHeqqpbfLsjkZwqUByCVPRkF2XGAVgcLWyODoVUtcM+Re3EaPiIsSY+udS1C8ORdi5Ba+eZzh9xaH9wa5wWALTvQDMfpS4m9A2jB1BaurApIlfi1hJ3qeqJUf8ZuJQmaeAzqvohX6/hfBH5atLrJ8piiCtovE9VPysiR4jIelX9RfKPcXhSqZVX1u8sBQ5VG186xzCMztPihc2rgMuBz5ev71KafAI4DdgO3C4i16lqw9aNJC6GF+O8U44DPgsMAV8gaOZtRwowvA/yxaApL047TfzpzES5b+FwsImPZp1WPpkNboOFbFSDc8Jp4KlweJqdPJ91voeF4TAgvSA8rpG4AIUVkDCMwaKFQlxVfyAi6yq6nwc85DVvRORLwDk0YaJOUhTiVcDZ+JB7VX0MWNDojQzDMPqBBotCLBeRLdGWNC3tauCRaH87sFpElonIp4DniMhFSS6UxJwypaoq4qbsFzQ7iwJFyEeROaMpp2kvijTxI0b2l9tPjLhp7h/OlvtyQ8GmXkpLK8VgEz+0LLRTOdce21G9/sU0+/jJz3Z941NVx5aoNJ9UYuYTw+gBVBspCvGkqm5s3a11Ny79d2KSCPGviMingcUi8ifAHwGfaWJ+hzWnpV5Lygt7gOKd9007bgLcMHqI5OaUZiv7PAqsjfbX+L6GSeKdcqmInAbsw9nF/15VNzdzs6ZJQSEL4/nhcldWnNa7NBO8RJ7MBN/tRcMuMdbeeUET3zcZ+YxPOq2+mAvady5KhzJ/u0+gNRKOx37kU0tDZq1ykqwavuOGYfQXDSxsNlvZ53Zgg4isxwnvc4HXNXGdxDU2NwObAUQkJSKvV9Wrm7mhUZ3TUq8ls2rltL6S+cV8zw2jgyiQ3JwyKyJyDfBinP18O3Cxql4hIm8Hbsa5GF6pqvfVuUxNagpxEVmIK8O2GrgOJ8TfBvw1cDcuCKgjSAFG9sChQphu2n9VLk0HTXzJUDndOUtHvPdKNmjMh0aDp8khr5UXCpEdPB/aB48qFY0IfXEAgOSjX3KVjIaZ4zeEHe9Tnr+zqd+RYRidpoXmFFU9r0b/jcCNTc0vop4m/q/A08CPgD8G3otbuH1lp7IXGoZhdIMOmFNaRj0hfqyq/jcAEfkMsAM4WlUP1TnH6BAWGGQY7aMB75SuU0+Il5Nz+1qb27smwAWKGZjIR4E7vox9KegHYGk6mFOOHNkHwJPDwSNyPBsWRnM+HL8YuRgWpsLjEO9ZOLUwTCMdLYI6M5ZjeIkL/c88HeYSL2yWbN2ZVSur2rnL5zy2o6owNgFtGB2ksSyGzXqntIx6QvwkEdnn2wLM8/sCqKourH2qUYvK0PtKSsK91iKnYRjtxQX7JJbivWtOUdV0rWOdRgowslc5lIs0cR9suiwVtN/d09wNXVDp8mzQzvfnQg3NCR+OP5ELAUSF0agKUMG1U1FMz6HFUWWgyPVwar7T9kefDO6MmTXB/z/1lAtISu0M1YKqCelG8pKbdm4YbWTAUtEaHeD0jZfUPX7mmgvJ18nRsrl4bU07eTUXxWpjzc5uGI4GNPGeNqf0DKkpZWxHjr3FoCnv92Xss8MhLH5FOoTd7/Upap8cDu5/e0Yid8N5ThMvFMI1pwjadd4/GolC/SXSymPzeNpH208uDGOH94R6n+Nr3RxC0tzpIfrF0WEqiQV2pdZuGEYbacwm3rvmFKP/aVXZOMM4vGgod0rX6QshLgqpXJGDh4LG+mTe2bxTkUZ8ROSdsifjtPKVw3vLfRPzomAfHziUj7T7vfkoVe2Qa+cWRMaxaGwmmOJJ+ZeBiWVxYFCwv2cm3DUOrAtvBaM7gqNPfszNKxsHCFVw09YPziqUY/PHbKYR84IxjDoMWLV7ow9IonWbzdswEqCDV2OzpYjIWlyFiyNxlqdNqnpZ3XOKRTL7J8lFmvjevLNv5zRov0tT4dvziLTzjtw3FDxG9heidta1J/PhERTmh2uV/FzyB8PxfORTLtF9S0p35BzDoSXh+Dx/3tB49W/3VG7mX0y1BFq1bOP1FjwNw2gCW9isSx74K1W9U0QWAHeIyOZmyhIZhmG0BVvYrI2q7sCF8KOq+0XkflySLRPidTh94yWJyjDVop65pRSANJsLo2EcLkixf+wpXbWJ+7pzzwFuq3LsAuACgGx6AamdT1HMryof353zlXuKwcSyKh18AI9KuwCb/Zl95b7x4bDYOFksLWyGxcxc1FZvLplIR6H6kTklF8VCZcZdfz5KZpiKCgJNLHfiNxeKEDG5KDgcpnLuaz8/b3G45pEnh/Z+Vzu0eNTScE4cOFRaEN0X2XOonsrWMIxZ8JXE+oWuCXERmQ98DXinqu6rPLPgKv4AABKMSURBVK6qm4BNAIuGj+yfpeIeotkQ/3pjqwULxedbegCj3xG0kWCfrtMVIS4iQzgBfrWqfn228ZrLkX9sB7L3mHLf01NOE99VCDWbnzkUtNOlKffRVkWa+JSGj1vSpIs1jBTbWQRAKlos3ZePQvSjfk27a6Wmgqauke+jLwdKFDdEMROOj+x115pYEeaXGQ+a/tC8mRkQUlFloeFSWD/Rq0CklVcTsrEQrmVGqRXdaRgDjy1s1kZEBLgCuF9V/7nT9z/cmU07bxWzuTPO1d3R3CWNtjIICbDayAuBNwI/FZFScYn3+ioXVXnWc49l85ZrOfajQeY/Pu60zsdyS8p94yNPlNsrUk5TXZUO4e053VNulzTwQqSJFyK3wZR3FN05HjT9dCoYyvZHrouT4mztOhyupZmZ4fqajhJoRQakfNaNze4JfzglOzqE3MaTi0LfgkdmhvVnd4UAolRFKP/NWy6pKcDjAs43b7mk6phKTGgaA4vZxOujqj8EZNaBRkt50dkfYWz2YZx5wnuB2oWeS4m68gtGpvV/93sXzWV6ZZIm6zKMdmLeKW1ieG+Q/eM5p2lunwoeGzsLQbAs9zbrRamgMa+KCkgUCFp5NUZ8DtpMFLq1Kx1cToYirXxv2rUnD4Ww/mJsxz7g2rF2np8XPkvGK9CFbPXvNk2588d2hvtPLo7qjXrvloOrg5180b27y+2DzwxvK/WIk3JVPR5p7LMxm6CdqyA2QW60D7Wwe+Pw47TUa6ctoDYaRdpNoWyavjENxYR4u8g+Gdr7xp2Gvf1Q8K1+bCy016Z3AbAiHTw2lqeDb3ZanEdHmuDRkooMYWMp55udTQXb84KhYHN+fCgUNhrOOK39wEhUdGIyaOWTGdcuHow09aEobL9kS48U8aF9Yaek4E8tDNr92M7wR3ZgsTs/HRXPm3zhEWHs425++15wdLlvdMeKcjvly8rFKXEzVZJx5UfCn8vwtiCkWxn2b4m5jJ4guTXl8PNOMYxqlGztxTvvK/fN5uLYSIGLEnP1aa9ms28H9nbQXQaiPFsvsuDRYBPec8BpvbsmgvfItskjy+21Gadhj0pQT+dH9vEl3nsllQnHxySo+gtTrn9BKhxflA625SVRLtolw04r3z0Zlg53Hwpaf6kU3MFI0z00EdrFKW8zL1TRzoGULyGXHg/H982baT+PXhrIhKy85Edn/pqLQ8F+Xtjg5jpvV4h41czMZdCR3ZPh/Ch6tLjBC8ZIO4/t57Gt/aatH5xx3dmqGhlGxzFzimEYRp+iCgXzTjGMqpS07mmeMFXK00Fwd6xFLZNJ0oCmdkSiztXc0u7oWDPTJMQ08fYw/8GwCMkB9zq/82Awpzw0Fhbr1voQ/AWpR8t9aQnmgHk+QCd2QRwiCJYRcZWBFqdC1qql6RDK/kS0sLli2EXu7BoOfbuzwRxRShHw1GQwYeyfF+57yOc0n5wKv47JiWgR1FccKi6MTChRkc/UQXdcoqRbcVh/uT8+fX4w15QWRCcXh3umw6Oi4GXsWLZ6ioKSi2Pu5LXVjx9yn7uUHqAeJTNMLOSnmWZ84q9Ga5C2KmK0k0LPBGwXMSFuGL1FrNVXs8tX04CrCdFq16mlPdfTyk0j7mEUsBqb7SH+z3fC+z4KwJ5lYQHxF6PLyu2jRlza2gXpWPuLE2Q59XRUgvY5JOnouPs5FuWUXZAKAULLonqeR2Vc/+6h4M74VJSX9qmC08R358Jbw75c0MT35pymOp4Pc9k/FdwVD045VXiqEOZ3aCqMnfI1OotRqtxClKyrpLWnoqRaqclIk/ePMF4YjctTlRJ47TsmXHMoWjgV//eeiSoXFUaiYKYJ1x+7SMbH44pHU/4NYf728CqQWxD+TEe8m2MqWliNXR/50d3unrF2vjBKDFaa0xx82ttJu4W5fVkkQUHNJm4YPUurkoDNZrOPKaf9rVEMO55Tq9L5diqFwcC9VSi2sNkJlv/UucP96uigsW7PhmCfnw6tBiAV1VkqjAZNcrXXnpdGLoRZibRDSfnzQ99odDwVqa1DXm0dk2DHXRbZz/cXnaYd1/vcWxid0S7VDQWYKITFvn15d16svcda+4RvlzR2mK6p57xNvRBpwvmpKC1AybVxKtLeI/t6esI/iyjVbmFeNNTfNnMoen77w/FSvdF0dM9Y058Mno2kD7nf154N4bOmp8LvUPLuZvnRcDwuwCFnPc9dk+qM3fkIUFFgI9bqK3LCVFJ6G4wFeCyYa+WcqSRz/IZwrQ5lljQawGzihtH/lITr1IZgehl+fH+t4U1fv5LKAhy1SKJpz4V2aPLNnF/rnLZ68lg+8fbzg+v+BoAT3vvRct+BTNBuH8B5quSLQbuMS6o9K+vsoEdl9pb7FkZa+ZB36UhHmnxKqntnFH0K25SEsWORp0sp22020tRLYf0AS31kzqGhyCZeDJrmgYJrj0/TzudFx13/oUI4fzwfaeUF92s+mAt945HWPplzx0saO0B+MrJfezt1PrKz5yYi+7rXhHOLyl1MLg5aedpr6Pno/0WcIqAY/RUO73djY009tp/nR4ZmHE8Vot+Rf0HKTFR/HS5p4FNRUY3MwdyMcXFagWpUatwlwVIWzJEdPh6bf2Abm4vXThPg7bLJV/PaaYepo+/NJzNoKAGWRWwaxuFGZbKwWjRiJ+9W9aV6bw2dSlHQchSwVLSdY+WPgkr3WDporwcnnSb0YCFoj3snw/HHFzmf7jXZp8t9Rw4FrXwodrr2pCP1rxDVWit6u3m18m+V7WoU/bVqnXOo6LTPyWJ8/ej+vh0XfY7fQEoaeD7ybinEniz+GcUeLcXIfl1KByBTcSqAuPycfxOJXz5i5dYPzYbsuOTDr2JaYrPhA+4Zx9p39umZ/6HiAhiTy4Idu2Tzzh8TJfiKjO4lP/PszuhiFd4rN2394DQBOrxtR9WydtUEU0mrzhCEdGbVyhnn5x/bUVewJbGTz2bimKtgny0ZWTPXb0aY1zqnVr/IzJQUDWM2ccMwjH7Fwu47SlxR5kVnf6Tc3rvf24EPBi3rF8siTXyJ89k+alGok3bUaFi0yqadKpmO7Nxx+bapSCue8jbnku0ZpnuPTPr+2A4d25/HfTKsQuwxEmnKHPIRmZH2mzkQRVx683pciDnWiodLLxjx8UhTLlmHlz0e3j5ij5DCsMzoy2dDe94TbgKxHTn2/ijfc2fw04/twMXfOTnM1V+jnp24Mkth7E+SZ+6LbdC8m1+rimE0YidvVFNtFe0u/NE1FNT8xA3DMPoYi9g0DMPoY8wm3h1Gbvhxub301I0AjD4ePuKBlcHEcWiFa/9iaXBL/OWCELY/NOIWw2K3QY3MKdMSVHl3PMnHGaaiRUC/8BfXCI3NHSN+aJSifFqwzNC4e7XTVDh/ZG8+GuvnOhXMIdUW82oljUry6l5pwhir8K6oND/EwTAlF7vKF9S5vG638lW9kWt1ykTQt6aIQUDVvFOMw5NYyNcKL2/kGibIjK5hmnh3qJp1LnLVWhy5kh04zi287VsbHkExE7nw+VxVWsNbacG+mX1xNZ04qVMp8CS7O6wmVov8yy8JbwWZX+4K/TU05XpCLhaGxVnGJqWRa5SCWgyj/1C0MNPFuFcZKCFuGIYxZywVbW8R22pj7TT7gPs5P05eFGnCeZ/eNZULtrFYex5fF5Jtjd6z3Z3TgFtYvmK/1fmmu6EFd1rbN4y20SUXQxEZA/4FmAJuVdWrZzunejIQwzCMwxQFtKiJtiSIyJUisktE7q3oP0NEHhSRh0TkPb771cBXVfVPgLOTXH/gNfGYRrLCVXswsfacHQ0lw6oFhvSjJm0YBm5Rs7Wa+FXA5cDnSx0ikgY+AZwGbAduF5HrgDXAT/2wRIb5w0qIG4ZhJKGVC5uq+gMRWVfR/TzgIVV9GEBEvgScgxPoa4C7SGgpEe0DVxoReQL4ZbfnkYDlwJOzjuotbM6dwebcGZYDY6p6RLMXEJFv++skIQtEiZXZpKqbqlxzHXCDqp7o918DnKGqf+z33wg8H3g3Tms/BPwwiU28LzTxufxCOomIbFHVjd2eRyPYnDuDzbkz+Dmvm8s1VPWMFk2nmXsfBN7SyDm2sGkYhtF5HgXWRvtrfF/DmBA3DMPoPLcDG0RkvYgMA+cC1zVzIRPirWWGLawPsDl3BptzZ+i5OYvINcCPgONEZLuInK+qeeDtwM3A/cBXVPW+pq7fDwubhmEYRnVMEzcMw+hjTIgbhmH0MSbEEyIia0XkeyKyVUTuE5F3+P6lIrJZRLb5n0t8v4jIx31I7T0icnL9O7R17mkR+YmI3OD314vIbX5uX/YLK4jIiN9/yB9f16X5LhaRr4rIAyJyv4ic0uvPWUT+0v9d3Csi14hItteec7Xw72aeq4i8yY/fJiJv6sKcP+L/Nu4RkW+IyOLo2EV+zg+KyOlRf7UQ98FAVW1LsAErgZN9ewHwM+AE4MPAe3z/e4B/9O3fBW7C1Xp/AXBbF+f+LuCLuGADgK8A5/r2p4A/8+0/Bz7l2+cCX+7SfD8H/LFvDwOLe/k5A6uBXwDzouf75l57zsCLgJOBe6O+hp4rsBR42P9c4ttLOjznlwMZ3/7HaM4nAHfjyq6uB34OpP32c+BY//d0N3BCN/622/KMuj2Bft2Ab+HyHjwIrPR9K4EHffvTwHnR+PK4Ds9zDfBd4KXADf4/5ZPRf4JTgJt9+2bgFN/O+HHS4fku8gJRKvp79jl7If6IF2wZ/5xP78XnDKyrEIgNPVfgPODTUf+0cZ2Yc8WxVwFX+/ZFwEXRsZv9cy8/+2rj+n0zc0oT+Nff5wC3AUeqaikH7U7gSN8u/ccusd33dZqPAX9LqI62DNijzsWpcl7lOfvje/34TrIeeAL4rDcBfUZces6efc6q+ihwKfArYAfuud1Bbz/nEo0+164/7wr+CPfGAP0z55ZiQrxBRGQ+8DXgnao6rb6Puq/5nvHZFJGzgF2qeke359IAGdzr8ydV9TnAQdxrfpkefM5LcMmL1gOrgDGga6HbzdJrz3U2ROR9uOSis+YXGWRMiDeAiAzhBPjVqvp13/24iKz0x1cCpbpqLQurnQMvBM4Wkf8CvoQzqVwGLBaRUt6ceF7lOfvji4DdnZwwTkvarqq3+f2v4oR6Lz/nU4FfqOoTqpoDvo579r38nEs0+lx74XkjIm8GzgJe7798oMfn3C5MiCdERAS4ArhfVf85OnQdUFqhfxPOVl7q/0O/yv8CYG/02toRVPUiVV2jLiHQucC/q+rrge8Br6kx59JneY0f31HNTFV3Ao+IyHG+62XAVnr4OePMKC8QkVH/d1Kac88+54hGn+vNwMtFZIl/A3m57+sYInIGzkR4tqqOR4euA8713j/rgQ3Aj2lhiHtP0m2jfL9swG/hXjXvweX6vQu3gr8Mt3C4DfgOsNSPF1zS95/jkrxv7PL8X0zwTjkW98f9EHAtMOL7s37/IX/82C7N9TeALf5ZfxPnBdHTzxn4APAAcC/wrzgPiZ56zsA1OJt9DvfGc34zzxVnh37Ib2/pwpwfwtm4S/8PPxWNf5+f84PAmVH/7+I8yn4OvK8bfyPt2izs3jAMo48xc4phGEYfY0LcMAyjjzEhbhiG0ceYEDcMw+hjTIgbhmH0MSbEjbYgIstE5C6/7RSRR6P94Rbe51QR2Rtd+y4ReYmIZESk4Pe3+p/vFJFUdJ76oJHStTb6vne2an6G0W76otq90X+o6m6cvzcicglwQFUvjcf4wBhR1eLMKzTE91T1lRXXzgD7VbU0hyNxUasLgH/ww34K/AFwld8/D5fhzjD6BtPEjY4iIs/0mvHVwH3AWhHZEx0/V0Q+49tHisjXRWSLiPzYRw42hao+Dvwp8BdR98PAQhFZ7jX00+hw9KFhzBXTxI1ucDzwh6q6JcotUo2PAx9W1f/0mSNvAE6sMu4lInJXtP9KXHTfNFT1ZyIyT0TijIFfw4W+34/LSplr6JMYRpcxIW50g5+r6pYE407FVQgv7S8RkXmqOlExrpY5pRpSsf9lXJj8z3Ah3i9NMC/D6BlMiBvd4GDULjJdsGajtgDPU9WpVtxURJ4FjKvq7tIXg6o+6m3zv4OruGNC3OgrzCZudBW/qPm0iGzwdulXRYe/A7yttCMiv9HsfURkBfBJ4P9UOfx3wLtbsMBqGB3HNHGjF3g3bkFxF64izojvfxvwSRF5C+5v9XtEQj2i0ib+AeB6YIHvH8LZuj+Hy6c+DVX9YYs+h2F0HMtiaBiG0ceYOcUwDKOPMSFuGIbRx5gQNwzD6GNMiBuGYfQxJsQNwzD6GBPihmEYfYwJccMwjD7m/wOxXuBGH2LNeQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ABSOLUTE: (58.589905 $\\pm$ 38.070789) GeV\n",
      "RELATIVE: 0.647068 $\\pm$ 0.594578\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYMAAAEGCAYAAACHGfl5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAZmklEQVR4nO3df6xfdZ3n8edrWvkhI7RAp2Fa3NbY1SCJCDdQ42R2BC0FjWUSx4WYbYdl7WTBWZ3dRMuaDatoArNmGNkoDpEOxTACg7p0EafbqUw28wc/LsLwU+wFRNoAvVJ+jLKjou/94/u5eLjc9n5ve3+2z0fyzfdz3udzzvfz6bn9vr/nnM85J1WFJOng9lsz3QBJ0swzGUiSTAaSJJOBJAmTgSQJmD/TDdhXxx57bC1btmymmyFJc8Y999zzk6paNNa8OZsMli1bxuDg4Ew3Q5LmjCRP7mmeh4kkSSYDSZLJQJKEyUCShMlAkoTJQJKEyUCShMlAkoTJQJLEHL4CeaYs2/CdV8s/uuwDM9gSSZo87hlIkkwGkiQPE+0XDxlJOlD0tWeQ5M+SPJTkwSTfSHJYkuVJ7kwylOTGJIe0uoe26aE2f1lnPRe3+KNJzuzEV7fYUJINk91JSdLejZsMkiwB/hMwUFUnAvOAc4HLgSuq6q3A88AFbZELgOdb/IpWjyQntOXeAawGvpJkXpJ5wJeBs4ATgPNaXUnSNOn3nMF84PAk84E3Ak8DpwM3t/mbgHNaeU2bps0/I0la/Iaq+nlVPQEMAae211BVPV5VvwBuaHUlSdNk3GRQVTuBLwI/ppcEXgTuAV6oqldatR3AklZeAjzVln2l1T+mGx+1zJ7ir5NkfZLBJIPDw8P99E+S1Id+DhMtpPdLfTnwu8AR9A7zTLuqurqqBqpqYNGiMZ/cJknaB/2MJnof8ERVDQMk+RbwHmBBkvnt1/9SYGervxM4HtjRDisdBTzXiY/oLrOn+KzQHTUkSQeifs4Z/BhYmeSN7dj/GcDDwO3Ah1uddcAtrby5TdPmf6+qqsXPbaONlgMrgLuAu4EVbXTSIfROMm/e/65Jkvo17p5BVd2Z5Gbg+8ArwL3A1cB3gBuSfL7FrmmLXAN8PckQsJvelztV9VCSm+glkleAi6rqVwBJPg5soTdSaWNVPTR5XZQkjaevi86q6hLgklHhx+mNBBpd91+AP9rDer4AfGGM+G3Abf20RZI0+bwCeZJ4NbKkucx7E0mSTAaSJJOBJAmTgSQJk4EkCZOBJAmTgSQJk4EkCZOBJAmTgSQJk4EkCZOBJAmTgSQJk4EkCW9hPSW8nbWkuWbcPYMkb0tyX+f1UpJPJjk6ydYk29v7wlY/Sa5MMpTk/iQnd9a1rtXfnmRdJ35KkgfaMle2x2tKkqbJuMmgqh6tqpOq6iTgFOBl4NvABmBbVa0AtrVpgLPoPd94BbAeuAogydH0npZ2Gr0npF0ykkBanY91lls9Kb2TJPVloucMzgAeq6ongTXAphbfBJzTymuA66rnDmBBkuOAM4GtVbW7qp4HtgKr27wjq+qOqirgus66JEnTYKLJ4FzgG628uKqebuVngMWtvAR4qrPMjhbbW3zHGPHXSbI+yWCSweHh4Qk2XZK0J30ngySHAB8C/nb0vPaLviaxXWOqqquraqCqBhYtWjTVHydJB42J7BmcBXy/qp5t08+2Qzy0910tvhM4vrPc0hbbW3zpGHFJ0jSZSDI4j98cIgLYDIyMCFoH3NKJr22jilYCL7bDSVuAVUkWthPHq4Atbd5LSVa2UURrO+uSJE2Dvq4zSHIE8H7gTzrhy4CbklwAPAl8pMVvA84GhuiNPDofoKp2J7kUuLvV+1xV7W7lC4FrgcOB77bXjOpeKyBJB7q+kkFV/Qw4ZlTsOXqji0bXLeCiPaxnI7BxjPggcGI/bZEkTT5vRyFJMhlIkkwGkiRMBpIkTAaSJLyF9ZTzdtaS5gL3DCRJJgNJkslAkoTJQJKEyUCShMlAkoTJQJKEyUCShMlAkoTJQJJEn8kgyYIkNyf5QZJHkrw7ydFJtibZ3t4XtrpJcmWSoST3Jzm5s551rf72JOs68VOSPNCWubI9/lKSNE363TP4EvB3VfV24J3AI8AGYFtVrQC2tWmAs4AV7bUeuAogydHAJcBpwKnAJSMJpNX5WGe51fvXLUnSRIybDJIcBfw+cA1AVf2iql4A1gCbWrVNwDmtvAa4rnruABYkOQ44E9haVbur6nlgK7C6zTuyqu5oj8y8rrMuSdI06GfPYDkwDPx1knuTfC3JEcDiqnq61XkGWNzKS4CnOsvvaLG9xXeMEX+dJOuTDCYZHB4e7qPpkqR+9JMM5gMnA1dV1buAn/GbQ0IAtF/0NfnNe62qurqqBqpqYNGiRVP9cZJ00OgnGewAdlTVnW36ZnrJ4dl2iIf2vqvN3wkc31l+aYvtLb50jLgkaZqMmwyq6hngqSRva6EzgIeBzcDIiKB1wC2tvBlY20YVrQRebIeTtgCrkixsJ45XAVvavJeSrGyjiNZ21iVJmgb9PunsT4HrkxwCPA6cTy+R3JTkAuBJ4COt7m3A2cAQ8HKrS1XtTnIpcHer97mq2t3KFwLXAocD322vA45PPZM0W/WVDKrqPmBgjFlnjFG3gIv2sJ6NwMYx4oPAif20RZI0+bwCWZJkMpAkmQwkSZgMJEmYDCRJmAwkSZgMJEmYDCRJmAwkSZgMJEmYDCRJmAwkSZgMJEmYDCRJmAwkSZgMJEn0mQyS/CjJA0nuSzLYYkcn2Zpke3tf2OJJcmWSoST3Jzm5s551rf72JOs68VPa+ofaspnsjs42yzZ859WXJM20iewZvLeqTqqqkSeebQC2VdUKYFubBjgLWNFe64GroJc8gEuA04BTgUtGEkir87HOcqv3uUeSpAnbn8NEa4BNrbwJOKcTv6567gAWJDkOOBPYWlW7q+p5YCuwus07sqruaI/MvK6zLknSNOg3GRTwf5Lck2R9iy2uqqdb+RlgcSsvAZ7qLLujxfYW3zFG/HWSrE8ymGRweHi4z6ZLksYzv896v1dVO5P8DrA1yQ+6M6uqktTkN++1qupq4GqAgYGBKf88STpY9LVnUFU72/su4Nv0jvk/2w7x0N53teo7geM7iy9tsb3Fl44RlyRNk3GTQZIjkrxppAysAh4ENgMjI4LWAbe08mZgbRtVtBJ4sR1O2gKsSrKwnTheBWxp815KsrKNIlrbWZckaRr0c5hoMfDtNtpzPvA3VfV3Se4GbkpyAfAk8JFW/zbgbGAIeBk4H6Cqdie5FLi71ftcVe1u5QuBa4HDge+2lyRpmoybDKrqceCdY8SfA84YI17ARXtY10Zg4xjxQeDEPtorSZoCXoEsSTIZSJL6H1p6UPDWEJIOVu4ZSJJMBpIkk4EkCZOBJAmTgSQJRxPNCt1RTD+67AMz2BJJByv3DCRJJgNJkslAkoTJQJKEyUCShMlAkoTJQJLEBJJBknlJ7k1ya5tenuTOJENJbkxySIsf2qaH2vxlnXVc3OKPJjmzE1/dYkNJNkxe9yRJ/ZjInsEngEc605cDV1TVW4HngQta/ALg+Ra/otUjyQnAucA7gNXAV1qCmQd8GTgLOAE4r9WVJE2TvpJBkqXAB4CvtekApwM3tyqbgHNaeU2bps0/o9VfA9xQVT+vqifoPSP51PYaqqrHq+oXwA2triRpmvS7Z/CXwKeAX7fpY4AXquqVNr0DWNLKS4CnANr8F1v9V+OjltlT/HWSrE8ymGRweHi4z6ZLksYzbjJI8kFgV1XdMw3t2auqurqqBqpqYNGiRTPdHEk6YPRzo7r3AB9KcjZwGHAk8CVgQZL57df/UmBnq78TOB7YkWQ+cBTwXCc+orvMnuKSpGkw7p5BVV1cVUurahm9E8Dfq6qPArcDH27V1gG3tPLmNk2b/72qqhY/t402Wg6sAO4C7gZWtNFJh7TP2DwpvZMk9WV/bmH9aeCGJJ8H7gWuafFrgK8nGQJ20/typ6oeSnIT8DDwCnBRVf0KIMnHgS3APGBjVT20H+2SJE1Qej/a556BgYEaHByc1HV2nyswG/hsA0mTKck9VTUw1jyvQJYkmQwkSSYDSRImA0kSJgNJEiYDSRImA0kSJgNJEiYDSRImA0kSJgNJEiYDSRImA0kS+3cLa02x7l1UvYOppKnknoEkyWQgSeojGSQ5LMldSf4pyUNJPtviy5PcmWQoyY3tkZW0x1re2OJ3JlnWWdfFLf5okjM78dUtNpRkw+R3U5K0N/3sGfwcOL2q3gmcBKxOshK4HLiiqt4KPA9c0OpfADzf4le0eiQ5gd4jMN8BrAa+kmReknnAl4GzgBOA81pdSdI0GTcZVM9P2+Qb2quA04GbW3wTcE4rr2nTtPlnJEmL31BVP6+qJ4Ah4NT2Gqqqx6vqF8ANra4kaZr0dc6g/YK/D9gFbAUeA16oqldalR3AklZeAjwF0Oa/CBzTjY9aZk/xsdqxPslgksHh4eF+mi5J6kNfyaCqflVVJwFL6f2Sf/uUtmrP7bi6qgaqamDRokUz0QRJOiBNaDRRVb0A3A68G1iQZOQ6haXAzlbeCRwP0OYfBTzXjY9aZk9xSdI06Wc00aIkC1r5cOD9wCP0ksKHW7V1wC2tvLlN0+Z/r6qqxc9to42WAyuAu4C7gRVtdNIh9E4yb56MzkmS+tPPFcjHAZvaqJ/fAm6qqluTPAzckOTzwL3ANa3+NcDXkwwBu+l9uVNVDyW5CXgYeAW4qKp+BZDk48AWYB6wsaoemrQeSpLGNW4yqKr7gXeNEX+c3vmD0fF/Af5oD+v6AvCFMeK3Abf10V5J0hTwCmRJkjeqmyu8aZ2kqeSegSTJZCBJMhlIkjAZSJIwGUiSMBlIkjAZSJIwGUiSMBlIkvAK5DmpezUyeEWypP3nnoEkyWQgSTIZSJIwGUiS6O+xl8cnuT3Jw0keSvKJFj86ydYk29v7whZPkiuTDCW5P8nJnXWta/W3J1nXiZ+S5IG2zJVJMhWdlSSNrZ89g1eA/1JVJwArgYuSnABsALZV1QpgW5sGOIve841XAOuBq6CXPIBLgNPoPSHtkpEE0up8rLPc6v3vmiSpX+Mmg6p6uqq+38r/DDwCLAHWAJtatU3AOa28Briueu4AFiQ5DjgT2FpVu6vqeWArsLrNO7Kq7qiqAq7rrEuSNA0mdJ1BkmX0nod8J7C4qp5us54BFrfyEuCpzmI7Wmxv8R1jxMf6/PX09jZ485vfPJGmH9B8Cpqk/dX3CeQkvw18E/hkVb3Undd+0dckt+11qurqqhqoqoFFixZN9cdJ0kGjr2SQ5A30EsH1VfWtFn62HeKhve9q8Z3A8Z3Fl7bY3uJLx4hLkqZJP6OJAlwDPFJVf9GZtRkYGRG0DrilE1/bRhWtBF5sh5O2AKuSLGwnjlcBW9q8l5KsbJ+1trMuSdI06OecwXuAfwc8kOS+FvuvwGXATUkuAJ4EPtLm3QacDQwBLwPnA1TV7iSXAne3ep+rqt2tfCFwLXA48N32kiRNk3GTQVX9I7Cncf9njFG/gIv2sK6NwMYx4oPAieO1RZI0NQ76u5aOvgOoJB2MvB2FJMlkIEnyMNEBxwvQJO0L9wwkSSYDSZLJQJKEyUCShMlAkoSjiQ5ojiyS1C/3DCRJJgNJkslAkoTJQJKEJ5APGp5MlrQ37hlIkvp67OXGJLuSPNiJHZ1ka5Lt7X1hiyfJlUmGktyf5OTOMuta/e1J1nXipyR5oC1zZXv0pSRpGvWzZ3AtsHpUbAOwrapWANvaNMBZwIr2Wg9cBb3kAVwCnAacClwykkBanY91lhv9WZKkKdbPYy//b5Jlo8JrgD9o5U3APwCfbvHr2qMv70iyIMlxre7WkWceJ9kKrE7yD8CRVXVHi18HnIPPQJ5Snj+QNNq+njNYXFVPt/IzwOJWXgI81am3o8X2Ft8xRnxMSdYnGUwyODw8vI9NlySNtt8nkNteQE1CW/r5rKuraqCqBhYtWjQdHylJB4V9TQbPtsM/tPddLb4TOL5Tb2mL7S2+dIy4JGka7Wsy2AyMjAhaB9zSia9to4pWAi+2w0lbgFVJFrYTx6uALW3eS0lWtlFEazvr0jRYtuE7r74kHbzGPYGc5Bv0TgAfm2QHvVFBlwE3JbkAeBL4SKt+G3A2MAS8DJwPUFW7k1wK3N3qfW7kZDJwIb0RS4fTO3HsyWNJmmb9jCY6bw+zzhijbgEX7WE9G4GNY8QHgRPHa4ckaep4Owq9yiGn0sHL21FIkkwGkiQPE2kPPGQkHVzcM5AkuWeg8bmXIB343DOQJJkMJEkeJtIEechIOjC5ZyBJcs9A+869BOnAYTLQpDAxSHObh4kkSe4ZaPK5lyDNPSYDTSkTgzQ3HJTJwKd6zQwTgzR7zZpkkGQ18CVgHvC1qrpshpukKbSnhGySkGbGrEgGSeYBXwbeD+wA7k6yuaoentmWabr1s9dmwpAm36xIBsCpwFBVPQ6Q5AZgDWAy0OtM92E+k48OBrMlGSwBnupM7wBOG10pyXpgfZv8aZJH+1z/scBP9quFs4d9mWa5vO+qc6I/fbIvs9P+9uVf7WnGbEkGfamqq4GrJ7pcksGqGpiCJk07+zJ7HUj9sS+z01T2ZbZcdLYTOL4zvbTFJEnTYLYkg7uBFUmWJzkEOBfYPMNtkqSDxqw4TFRVryT5OLCF3tDSjVX10CR+xIQPLc1i9mX2OpD6Y19mpynrS6pqqtYtSZojZsthIknSDDIZSJIO7GSQZHWSR5MMJdkw0+3pR5Ljk9ye5OEkDyX5RIsfnWRrku3tfWGLJ8mVrY/3Jzl5ZnvwWknmJbk3ya1tenmSO1t7b2wDBkhyaJseavOXzWS7x5JkQZKbk/wgySNJ3j2Ht8uftb+vB5N8I8lhc2nbJNmYZFeSBzuxCW+LJOta/e1J1s2ivvyP9nd2f5JvJ1nQmXdx68ujSc7sxPfv+66qDsgXvRPRjwFvAQ4B/gk4Yabb1Ue7jwNObuU3AT8ETgD+HNjQ4huAy1v5bOC7QICVwJ0z3YdR/fnPwN8At7bpm4BzW/mrwH9s5QuBr7byucCNM932MfqyCfgPrXwIsGAubhd6F3k+ARze2SZ/PJe2DfD7wMnAg53YhLYFcDTweHtf2MoLZ0lfVgHzW/nyTl9OaN9lhwLL23fcvMn4vpvxP8wp/Ad+N7ClM30xcPFMt2sf+nELvXs2PQoc12LHAY+28l8B53Xqv1pvpl/0rhfZBpwO3Nr+M/6k80f+6jaiN5Ls3a08v9XLTPeh05ej2hdoRsXn4nYZueL/6PZvfStw5lzbNsCyUV+gE9oWwHnAX3Xir6k3k30ZNe8Pgetb+TXfYyPbZjK+7w7kw0Rj3eJiyQy1ZZ+03fF3AXcCi6vq6TbrGWBxK8/mfv4l8Cng1236GOCFqnqlTXfb+mo/2vwXW/3ZYjkwDPx1O+z1tSRHMAe3S1XtBL4I/Bh4mt6/9T3M3W0zYqLbYtZuo1H+Pb09G5jCvhzIyWBOS/LbwDeBT1bVS9151Uv9s3pMcJIPAruq6p6ZbsskmU9vV/6qqnoX8DN6hyJeNRe2C0A7lr6GXoL7XeAIYPWMNmqSzZVtMZ4knwFeAa6f6s86kJPBnL3FRZI30EsE11fVt1r42STHtfnHAbtafLb28z3Ah5L8CLiB3qGiLwELkoxc7Nht66v9aPOPAp6bzgaPYwewo6rubNM300sOc227ALwPeKKqhqvql8C36G2vubptRkx0W8zmbUSSPwY+CHy0JTeYwr4cyMlgTt7iIkmAa4BHquovOrM2AyOjHdbRO5cwEl/bRkysBF7s7CrPmKq6uKqWVtUyev/236uqjwK3Ax9u1Ub3Y6R/H271Z80vu6p6Bngqydta6Ax6t1ifU9ul+TGwMskb29/bSF/m5LbpmOi22AKsSrKw7S2tarEZl97Dvj4FfKiqXu7M2gyc20Z4LQdWAHcxGd93M30SaIpPypxNbzTOY8BnZro9fbb59+jt3t4P3NdeZ9M7RrsN2A78PXB0qx96DwZ6DHgAGJjpPozRpz/gN6OJ3tL+eIeAvwUObfHD2vRQm/+WmW73GP04CRhs2+Z/0RuBMie3C/BZ4AfAg8DX6Y1OmTPbBvgGvfMdv6S313bBvmwLesfjh9rr/FnUlyF65wBGvgO+2qn/mdaXR4GzOvH9+r7zdhSSpAP6MJEkqU8mA0mSyUCSZDKQJGEykCRhMtBBKMkxSe5rr2eS7OxMHzKJn/O+JC921n1fkvcmmZ/kV2364fb+ySS/1Vmu2kVHI+saaLFPTlb7pK5Z8dhLaTpV1XP0rhkgyX8HflpVX+zWaRdjpap+/fo1TMjtVXXOqHXPB/65qkbasJjeVdpvAi5t1R4A/i1wbZs+j96dKKUp4Z6B1CR5a/ulfj3wEHB8khc6889N8rVWXpzkW0kGk9zVrmzdJ1X1LPAnwJ92wo8DRyY5tu0xvJ9ZcnWsDkzuGUiv9XZgbVUNdu7TM5YrgT+vqjva3WVvBU4co957k9zXmT6H3lWmr1FVP0xyeJLu3UC/Se/2D4/Qu3PtLyfUE2kCTAbSaz1WVYN91Hsf8Lbe0SQAFiY5vKr+36h6ezpMNJaMmr6R3q0ifkjvlgWn99EuaZ+YDKTX+lmn/Gte+wV9WKcc4NSq+sVkfGiSfw28XFXPjSSYqtrZzl38G3pPGzMZaMp4zkDag3by+PkkK9px+z/szP574KKRiSQn7evnJPkd4Crgf44x+78Bn56EE9nSXrlnIO3dp+mduN1F72lgh7b4RcBVSc6n9//odjrJoWP0OYPPAv8beFOLv4HeuYBN9J738BpV9Y+T1A9pr7xrqSTJw0SSJJOBJAmTgSQJk4EkCZOBJAmTgSQJk4EkCfj/62KwNpXn8FIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(133.594574 $\\pm$ 74.754921) GeV\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import LogNorm\n",
    "%matplotlib inline\n",
    "# compare prediction to true value\n",
    "plt.hist2d(Y, Yhat[:,0], bins=100, range=[[0.,1000],[0.,1000]], norm=LogNorm())\n",
    "plt.colorbar()\n",
    "plt.ylabel('Regressed EMD')\n",
    "plt.xlabel('True EDM')\n",
    "plt.show()\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import LogNorm\n",
    "%matplotlib inline\n",
    "# compare prediction to true value\n",
    "plt.hist2d(Y, Yhat[:,0]/Y, bins=100, norm=LogNorm())\n",
    "plt.colorbar()\n",
    "plt.ylabel('Regressed EMD/True EDM')\n",
    "plt.xlabel('True EDM')\n",
    "plt.show()\n",
    "\n",
    "import math\n",
    "# compare prediction to true value\n",
    "plt.hist((Yhat[:,0]-Y)/Y, bins=100, range=[-1., 5.])\n",
    "plt.ylabel('(Regressed EMD-True EDM)/True EMD')\n",
    "plt.show()\n",
    "print(\"ABSOLUTE: (%f $\\pm$ %f) GeV\" %(np.mean(Yhat[:,0]-Y), np.sqrt(np.var(Y-Yhat[:,0]))))\n",
    "rel = (Yhat[:,0]-Y)/Y\n",
    "varRel = math.sqrt(np.var(rel))\n",
    "print(\"RELATIVE: %f $\\pm$ %f\" %(np.mean(rel), varRel))\n",
    "\n",
    "# compare prediction to true value\n",
    "plt.hist(Y, bins=100)\n",
    "plt.xlabel('True EDM')\n",
    "plt.show()\n",
    "print(\"(%f $\\pm$ %f) GeV\" %(np.mean(Y), np.sqrt(np.var(Y))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Asymmetric MAE with penalty=0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         (None, 100, 6, 1)         0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 100, 6, 1)         4         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 10, 6, 1)          6010      \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 10, 6, 1)          4         \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 10, 6, 1)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 5, 6, 1)           505       \n",
      "_________________________________________________________________\n",
      "batch_normalization_3 (Batch (None, 5, 6, 1)           4         \n",
      "_________________________________________________________________\n",
      "activation_2 (Activation)    (None, 5, 6, 1)           0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 2, 6, 1)           52        \n",
      "_________________________________________________________________\n",
      "batch_normalization_4 (Batch (None, 2, 6, 1)           4         \n",
      "_________________________________________________________________\n",
      "activation_3 (Activation)    (None, 2, 6, 1)           0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 12)                0         \n",
      "_________________________________________________________________\n",
      "dense_relu1 (Dense)          (None, 80)                1040      \n",
      "_________________________________________________________________\n",
      "dense_relu2 (Dense)          (None, 40)                3240      \n",
      "_________________________________________________________________\n",
      "dense_relu3 (Dense)          (None, 20)                820       \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 21        \n",
      "=================================================================\n",
      "Total params: 11,704\n",
      "Trainable params: 11,696\n",
      "Non-trainable params: 8\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# load the model\n",
    "from keras.models import model_from_json\n",
    "# read encoder\n",
    "json_file = open(\"models/EMD_Dense_MAE_AsymmetryLarge_0.1.json\", 'r')\n",
    "encoder_json = json_file.read()\n",
    "json_file.close()\n",
    "model = model_from_json(encoder_json)\n",
    "model.load_weights(\"models/EMD_Dense_MAE_AsymmetryLarge_0.1.h5\")\n",
    "\n",
    "model.compile(optimizer='adam', loss='mape', metrics=['acc'])\n",
    "model.summary()\n",
    "\n",
    "Yhat = model.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ABSOLUTE: (6.794277 $\\pm$ 18.139107) GeV\n",
      "RELATIVE: 0.079871 $\\pm$ 0.175535\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(133.594574 $\\pm$ 74.754921) GeV\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import LogNorm\n",
    "%matplotlib inline\n",
    "# compare prediction to true value\n",
    "plt.hist2d(Y, Yhat[:,0], bins=100, range=[[0.,1000],[0.,1000]], norm=LogNorm())\n",
    "plt.colorbar()\n",
    "plt.ylabel('Regressed EMD')\n",
    "plt.xlabel('True EDM')\n",
    "plt.show()\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import LogNorm\n",
    "%matplotlib inline\n",
    "# compare prediction to true value\n",
    "plt.hist2d(Y, Yhat[:,0]/Y, bins=100, norm=LogNorm())\n",
    "plt.colorbar()\n",
    "plt.ylabel('Regressed EMD/True EDM')\n",
    "plt.xlabel('True EDM')\n",
    "plt.show()\n",
    "\n",
    "import math\n",
    "# compare prediction to true value\n",
    "plt.hist((Yhat[:,0]-Y)/Y, bins=100, range=[-1., 5.])\n",
    "plt.ylabel('(Regressed EMD-True EDM)/True EMD')\n",
    "plt.show()\n",
    "print(\"ABSOLUTE: (%f $\\pm$ %f) GeV\" %(np.mean(Yhat[:,0]-Y), np.sqrt(np.var(Y-Yhat[:,0]))))\n",
    "rel = (Yhat[:,0]-Y)/Y\n",
    "varRel = math.sqrt(np.var(rel))\n",
    "print(\"RELATIVE: %f $\\pm$ %f\" %(np.mean(rel), varRel))\n",
    "\n",
    "# compare prediction to true value\n",
    "plt.hist(Y, bins=100)\n",
    "plt.xlabel('True EDM')\n",
    "plt.show()\n",
    "print(\"(%f $\\pm$ %f) GeV\" %(np.mean(Y), np.sqrt(np.var(Y))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Quantile Regression at 0.1%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         (None, 100, 6, 1)         0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 100, 6, 1)         4         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 10, 6, 1)          6010      \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 10, 6, 1)          4         \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 10, 6, 1)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 5, 6, 1)           505       \n",
      "_________________________________________________________________\n",
      "batch_normalization_3 (Batch (None, 5, 6, 1)           4         \n",
      "_________________________________________________________________\n",
      "activation_2 (Activation)    (None, 5, 6, 1)           0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 2, 6, 1)           52        \n",
      "_________________________________________________________________\n",
      "batch_normalization_4 (Batch (None, 2, 6, 1)           4         \n",
      "_________________________________________________________________\n",
      "activation_3 (Activation)    (None, 2, 6, 1)           0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 12)                0         \n",
      "_________________________________________________________________\n",
      "dense_relu1 (Dense)          (None, 80)                1040      \n",
      "_________________________________________________________________\n",
      "dense_relu2 (Dense)          (None, 40)                3240      \n",
      "_________________________________________________________________\n",
      "dense_relu3 (Dense)          (None, 20)                820       \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 21        \n",
      "=================================================================\n",
      "Total params: 11,704\n",
      "Trainable params: 11,696\n",
      "Non-trainable params: 8\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ABSOLUTE: (100.368080 $\\pm$ 44.551941) GeV\n",
      "RELATIVE: 1.079920 $\\pm$ 0.870613\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(133.594574 $\\pm$ 74.754921) GeV\n"
     ]
    }
   ],
   "source": [
    "# load the model\n",
    "from keras.models import model_from_json\n",
    "# read encoder\n",
    "json_file = open(\"models/EMD_Dense_QuantileLoss.json\", 'r')\n",
    "encoder_json = json_file.read()\n",
    "json_file.close()\n",
    "model = model_from_json(encoder_json)\n",
    "model.load_weights(\"models/EMD_Dense_QuantileLoss.h5\")\n",
    "\n",
    "model.compile(optimizer='adam', loss='mape', metrics=['acc'])\n",
    "model.summary()\n",
    "\n",
    "Yhat = model.predict(X)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import LogNorm\n",
    "%matplotlib inline\n",
    "# compare prediction to true value\n",
    "plt.hist2d(Y, Yhat[:,0], bins=100, range=[[0.,1000],[0.,1000]], norm=LogNorm())\n",
    "plt.colorbar()\n",
    "plt.ylabel('Regressed EMD')\n",
    "plt.xlabel('True EDM')\n",
    "plt.show()\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import LogNorm\n",
    "%matplotlib inline\n",
    "# compare prediction to true value\n",
    "plt.hist2d(Y, Yhat[:,0]/Y, bins=100, norm=LogNorm())\n",
    "plt.colorbar()\n",
    "plt.ylabel('Regressed EMD/True EDM')\n",
    "plt.xlabel('True EDM')\n",
    "plt.show()\n",
    "\n",
    "import math\n",
    "# compare prediction to true value\n",
    "plt.hist((Yhat[:,0]-Y)/Y, bins=100, range=[-1., 5.])\n",
    "plt.ylabel('(Regressed EMD-True EDM)/True EMD')\n",
    "plt.show()\n",
    "print(\"ABSOLUTE: (%f $\\pm$ %f) GeV\" %(np.mean(Yhat[:,0]-Y), np.sqrt(np.var(Y-Yhat[:,0]))))\n",
    "rel = (Yhat[:,0]-Y)/Y\n",
    "varRel = math.sqrt(np.var(rel))\n",
    "print(\"RELATIVE: %f $\\pm$ %f\" %(np.mean(rel), varRel))\n",
    "\n",
    "# compare prediction to true value\n",
    "plt.hist(Y, bins=100)\n",
    "plt.xlabel('True EDM')\n",
    "plt.show()\n",
    "print(\"(%f $\\pm$ %f) GeV\" %(np.mean(Y), np.sqrt(np.var(Y))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}