MomentInstability.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import energyflow as ef\n",
    "import energyflow.archs\n",
    "from energyflow.archs import PFN\n",
    "from matplotlib import gridspec\n",
    "from sklearn.model_selection import train_test_split\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.layers import Dense, Input, Layer, concatenate\n",
    "from tensorflow.keras.models import Model\n",
    "import tensorflow.keras.backend as K\n",
    "from tensorflow.keras.layers import BatchNormalization\n",
    "\n",
    "plt.rc('font', size=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<__array_function__ internals>:5: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n"
     ]
    }
   ],
   "source": [
    "#These are the same datasets from the OmniFold paper https://arxiv.org/abs/1911.09107.  More detail at https://energyflow.network/docs/datasets/.\n",
    "#Pythia and Herwig are two generators; one will be treated here as the \"simulation\" and one as \"data\".\n",
    "datasets = {'Pythia26': ef.zjets_delphes.load('Pythia26', num_data=1000),\n",
    "            'Herwig': ef.zjets_delphes.load('Herwig', num_data=1000)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "w_true = datasets['Pythia26']['gen_widths'] #gen = particle level\n",
    "w_reco = datasets['Pythia26']['sim_widths'] #sim = detector level\n",
    "w_true_alt = datasets['Herwig']['gen_widths']\n",
    "w_reco_alt = datasets['Herwig']['sim_widths']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "initializer = tf.keras.initializers.RandomUniform(minval=-5., maxval=5.)\n",
    "n = 1\n",
    "\n",
    "class MyLayer(Layer):\n",
    "\n",
    "    def __init__(self, myc, **kwargs):\n",
    "        self.myinit = myc\n",
    "        super(MyLayer, self).__init__(**kwargs)\n",
    "\n",
    "    def build(self, input_shape):\n",
    "        # Create a trainable weight variable for this layer.\n",
    "        self._lambda = self.add_weight(name='lambda', \n",
    "                                    shape=(n,),\n",
    "                                    initializer=tf.keras.initializers.Constant(self.myinit), \n",
    "                                    trainable=True)\n",
    "        \n",
    "        super(MyLayer, self).build(input_shape)  # Be sure to call this at the end\n",
    "\n",
    "    def call(self, x):\n",
    "        #return tf.exp(self._lambda1 * x + self._lambda0)\n",
    "        return tf.exp(sum([self._lambda[i]* x**(i+1) for i in range(n)]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def weighted_binary_crossentropy(y_true, y_pred):\n",
    "    weights = tf.gather(y_true, [1], axis=1) # event weights\n",
    "    y_true = tf.gather(y_true, [0], axis=1) # actual y_true for loss\n",
    "\n",
    "    weights_1 = K.sum(y_true*weights)\n",
    "    weights_0 = K.sum((1-y_true)*weights)\n",
    "\n",
    "    # Clip the prediction value to prevent NaN's and Inf's\n",
    "    epsilon = K.epsilon()\n",
    "    y_pred = K.clip(y_pred, epsilon, 1. - epsilon)\n",
    "    t_loss = -weights * ((y_true) * K.log(y_pred)/weights_1 +\n",
    "                         (1 - y_true) * K.log(1 - y_pred)/weights_0)\n",
    "    return K.mean(t_loss)\n",
    "\n",
    "def weighted_binary_crossentropy_GAN(y_true, y_pred):\n",
    "    weights = tf.gather(y_pred, [1], axis=1) # event weights\n",
    "    y_pred = tf.gather(y_pred, [0], axis=1) # actual y_pred for loss\n",
    "\n",
    "    weights_1 = K.sum(y_true*weights)\n",
    "    weights_0 = K.sum((1-y_true)*weights)\n",
    "\n",
    "    #tf.print(\"weights\",weights_0,weights_1)\n",
    "\n",
    "    # Clip the prediction value to prevent NaN's and Inf's\n",
    "    epsilon = K.epsilon()\n",
    "    y_pred = K.clip(y_pred, epsilon, 1. - epsilon)\n",
    "    t_loss = weights * ((1 - y_true) * K.log(1 - y_pred)/weights_0)\n",
    "    return K.mean(t_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n = 1\n",
      "on epoch= 0 [[0.09971608]]\n",
      "on epoch= 1 [[0.09936266]]\n",
      "Moment  1\n",
      "0.1465040121205129 0.11008277856846732 0.11072115448980732\n",
      "\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "xvals_1 = np.concatenate([w_true_alt,w_true])\n",
    "yvals_1 = np.concatenate([np.ones(len(w_true_alt)),np.zeros(len(w_true))])\n",
    "\n",
    "X_train_1, X_test_1, Y_train_1, Y_test_1 = train_test_split(xvals_1, yvals_1)\n",
    "\n",
    "\n",
    "N = 2\n",
    "n = 1\n",
    "errors_weighted = np.zeros((N, N))\n",
    "errors_unweighted = np.zeros((N, N))\n",
    "\n",
    "while n < N:\n",
    "    print(f\"{n = }\")\n",
    "    myc = 0.1\n",
    "    mymodel_inputtest = Input(shape=(1,))\n",
    "    mymodel_test = MyLayer(myc)(mymodel_inputtest)\n",
    "    model_generator = Model(mymodel_inputtest, mymodel_test)\n",
    "\n",
    "    inputs_disc = Input((1, ))\n",
    "    hidden_layer_1_disc = Dense(50, activation='relu')(inputs_disc)\n",
    "    hidden_layer_2_disc = Dense(50, activation='relu')(hidden_layer_1_disc)\n",
    "    hidden_layer_3_disc = Dense(50, activation='relu')(hidden_layer_2_disc)\n",
    "    outputs_disc = Dense(1, activation='sigmoid')(hidden_layer_3_disc)\n",
    "    model_discrimantor = Model(inputs=inputs_disc, outputs=outputs_disc)\n",
    "    \n",
    "    model_discrimantor.compile(loss=weighted_binary_crossentropy, optimizer='adam')\n",
    "\n",
    "    model_discrimantor.trainable = False\n",
    "    mymodel_gan = Input(shape=(1,))\n",
    "    gan_model = Model(inputs=mymodel_gan,outputs=concatenate([model_discrimantor(mymodel_gan),model_generator(mymodel_gan)]))\n",
    "\n",
    "    gan_model.compile(loss=weighted_binary_crossentropy_GAN, optimizer='adam')\n",
    "    n_epochs = 2\n",
    "    n_batch = 128*10\n",
    "    n_batches = len(X_train_1) // n_batch\n",
    "\n",
    "    for i in range(n_epochs):\n",
    "        #print(\"  \",np.sum(model_generator.predict(X_train_1,batch_size=1000)))\n",
    "        for j in range(n_batches):\n",
    "            X_batch = X_train_1[j*n_batch:(j+1)*n_batch]\n",
    "            Y_batch = Y_train_1[j*n_batch:(j+1)*n_batch]\n",
    "            W_batch = model_generator(X_batch)\n",
    "            W_batch = np.array(W_batch).flatten()\n",
    "            W_batch[Y_batch==1] = 1\n",
    "            #W_batch[Y_batch==0] = 1\n",
    "\n",
    "            Y_batch_2 = np.stack((Y_batch, W_batch), axis=1)\n",
    "\n",
    "            model_discrimantor.train_on_batch(X_batch, Y_batch_2)\n",
    "\n",
    "            gan_model.train_on_batch(X_batch[Y_batch==0],np.zeros(len(X_batch[Y_batch==0])))\n",
    "            \n",
    "        mylambda = np.array(model_generator.layers[-1].get_weights())\n",
    "        print(\"on epoch=\",i, mylambda)\n",
    "\n",
    "    arr = np.array([mylambda[:, k]*w_true**(k+1) for k in range(n)])\n",
    "    exponent = np.exp(np.sum(arr, axis=0))\n",
    "    weights_1 = np.concatenate([np.ones(len(w_true_alt)),exponent*len(w_true_alt)/np.sum(exponent)])\n",
    "\n",
    "    X_train_1, X_test_1, Y_train_1, Y_test_1, w_train_1, w_test_1 = train_test_split(xvals_1, \n",
    "                                                                                    yvals_1, weights_1)\n",
    "\n",
    "    #data, simulation w/o weights, weighted simulation\n",
    "    for i in range(n):\n",
    "        print(\"Moment \", i+1)\n",
    "        print(np.mean(X_test_1[Y_test_1==1]**(i+1)),np.mean(X_test_1[Y_test_1==0]**(i+1)),np.average(X_test_1[Y_test_1==0]**(i+1),weights=w_test_1[Y_test_1==0]))\n",
    "    errors_weighted[:, n] = [(np.average(X_test_1[Y_test_1==0]**(i+1),weights=w_test_1[Y_test_1==0]) - np.mean(X_test_1[Y_test_1==1]**(i+1)))/np.average(X_test_1[Y_test_1==0]**(i+1),weights=w_test_1[Y_test_1==0]) for i in range(N)]\n",
    "    errors_unweighted[:, n] = [(np.mean(X_test_1[Y_test_1==0]**(i+1)) - np.mean(X_test_1[Y_test_1==1]**(i+1)))/np.mean(X_test_1[Y_test_1==0]**(i+1)) for i in range(N)]\n",
    "    n += 1\n",
    "    print(\"\\n\\n\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_2 (InputLayer)         [(None, 1)]               0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 50)                100       \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 50)                2550      \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 50)                2550      \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 1)                 51        \n",
      "=================================================================\n",
      "Total params: 5,251\n",
      "Trainable params: 0\n",
      "Non-trainable params: 5,251\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(model_discrimantor.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x1008 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(20, 14))\n",
    "for i in range(3):\n",
    "    plt.plot(range(1, N), np.abs(errors_weighted[i])[1:], color='r')\n",
    "    plt.text(range(1, N)[-1], np.abs(errors_weighted[i])[-1], f'Moment {i+1} weighted', color='r')\n",
    "    plt.plot(range(1, N), np.abs(errors_unweighted[i])[1:], label=f'Moment {i+1} unweighted', color='cyan')\n",
    "    plt.text(range(1, N)[-1], np.abs(errors_unweighted[i])[-1], f'Moment {i+1} unweighted', color='cyan')\n",
    "    \n",
    "ax.xaxis.label.set_color('white')        #setting up X-axis label color to yellow\n",
    "ax.yaxis.label.set_color('white')          #setting up Y-axis label color to blue\n",
    "\n",
    "ax.tick_params(axis='x', colors='white')    #setting up X-axis tick color to red\n",
    "ax.tick_params(axis='y', colors='white')  #setting up Y-axis tick color to black\n",
    "\n",
    "ax.spines['left'].set_color('white')        # setting up Y-axis tick color to red\n",
    "ax.spines['top'].set_color('white')\n",
    "ax.spines['right'].set_color('white') \n",
    "ax.spines['bottom'].set_color('white') \n",
    "plt.xlabel(\"Number of moments learned\")\n",
    "plt.ylabel(\"Error\")\n",
    "#plt.title(\"Unfolding Errors\")\n",
    "ax.xaxis.set_ticks([0,5,10, 15, 20])\n",
    "plt.savefig(\"One_level_unfolding.pdf\", bbox_inches='tight', transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "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": "3.8.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}