NeuralNetwork.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "32000/32000 [==============================] - 115s 4ms/step - loss: 0.6082 - accuracy: 0.6632 - val_loss: 0.5876 - val_accuracy: 0.6858\n",
      "Epoch 2/50\n",
      "32000/32000 [==============================] - 81s 3ms/step - loss: 0.5784 - accuracy: 0.6926 - val_loss: 0.5716 - val_accuracy: 0.7012\n",
      "Epoch 3/50\n",
      "32000/32000 [==============================] - 84s 3ms/step - loss: 0.5674 - accuracy: 0.7018 - val_loss: 0.5636 - val_accuracy: 0.7062\n",
      "Epoch 4/50\n",
      "32000/32000 [==============================] - 95s 3ms/step - loss: 0.5595 - accuracy: 0.7091 - val_loss: 0.5613 - val_accuracy: 0.7115\n",
      "Epoch 5/50\n",
      "32000/32000 [==============================] - 85s 3ms/step - loss: 0.5536 - accuracy: 0.7140 - val_loss: 0.5575 - val_accuracy: 0.7119\n",
      "Epoch 6/50\n",
      "32000/32000 [==============================] - 83s 3ms/step - loss: 0.5486 - accuracy: 0.7182 - val_loss: 0.5457 - val_accuracy: 0.7207\n",
      "Epoch 7/50\n",
      "32000/32000 [==============================] - 130s 4ms/step - loss: 0.5444 - accuracy: 0.7212 - val_loss: 0.5419 - val_accuracy: 0.7233\n",
      "Epoch 8/50\n",
      "32000/32000 [==============================] - 86s 3ms/step - loss: 0.5409 - accuracy: 0.7237 - val_loss: 0.5435 - val_accuracy: 0.7234\n",
      "Epoch 9/50\n",
      "32000/32000 [==============================] - 83s 3ms/step - loss: 0.5386 - accuracy: 0.7257 - val_loss: 0.5411 - val_accuracy: 0.7243\n",
      "Epoch 10/50\n",
      "32000/32000 [==============================] - 80s 2ms/step - loss: 0.5366 - accuracy: 0.7268 - val_loss: 0.5426 - val_accuracy: 0.7253\n",
      "Epoch 11/50\n",
      "32000/32000 [==============================] - 102s 3ms/step - loss: 0.5345 - accuracy: 0.7282 - val_loss: 0.5386 - val_accuracy: 0.7248\n",
      "Epoch 12/50\n",
      "32000/32000 [==============================] - 136s 4ms/step - loss: 0.5331 - accuracy: 0.7290 - val_loss: 0.5390 - val_accuracy: 0.7241\n",
      "Epoch 13/50\n",
      "32000/32000 [==============================] - 114s 4ms/step - loss: 0.5316 - accuracy: 0.7299 - val_loss: 0.5319 - val_accuracy: 0.7298\n",
      "Epoch 14/50\n",
      "32000/32000 [==============================] - 127s 4ms/step - loss: 0.5305 - accuracy: 0.7308 - val_loss: 0.5360 - val_accuracy: 0.7276\n",
      "Epoch 15/50\n",
      "32000/32000 [==============================] - 110s 3ms/step - loss: 0.5290 - accuracy: 0.7321 - val_loss: 0.5313 - val_accuracy: 0.7327\n",
      "Epoch 16/50\n",
      "32000/32000 [==============================] - 86s 3ms/step - loss: 0.5279 - accuracy: 0.7328 - val_loss: 0.5336 - val_accuracy: 0.7301\n",
      "Epoch 17/50\n",
      "32000/32000 [==============================] - 84s 3ms/step - loss: 0.5269 - accuracy: 0.7331 - val_loss: 0.5296 - val_accuracy: 0.7316\n",
      "Epoch 18/50\n",
      "32000/32000 [==============================] - 88s 3ms/step - loss: 0.5255 - accuracy: 0.7343 - val_loss: 0.5281 - val_accuracy: 0.7330\n",
      "Epoch 19/50\n",
      "32000/32000 [==============================] - 86s 3ms/step - loss: 0.5245 - accuracy: 0.7344 - val_loss: 0.5286 - val_accuracy: 0.7319\n",
      "Epoch 20/50\n",
      "32000/32000 [==============================] - 83s 3ms/step - loss: 0.5233 - accuracy: 0.7356 - val_loss: 0.5293 - val_accuracy: 0.7311\n",
      "Epoch 21/50\n",
      "32000/32000 [==============================] - 85s 3ms/step - loss: 0.5226 - accuracy: 0.7364 - val_loss: 0.5328 - val_accuracy: 0.7272\n",
      "Epoch 22/50\n",
      "32000/32000 [==============================] - 85s 3ms/step - loss: 0.5221 - accuracy: 0.7367 - val_loss: 0.5257 - val_accuracy: 0.7354\n",
      "Epoch 23/50\n",
      "32000/32000 [==============================] - 79s 2ms/step - loss: 0.5214 - accuracy: 0.7376 - val_loss: 0.5282 - val_accuracy: 0.7340\n",
      "Epoch 24/50\n",
      "32000/32000 [==============================] - 85s 3ms/step - loss: 0.5205 - accuracy: 0.7374 - val_loss: 0.5223 - val_accuracy: 0.7359\n",
      "Epoch 25/50\n",
      "32000/32000 [==============================] - 155s 5ms/step - loss: 0.5200 - accuracy: 0.7384 - val_loss: 0.5242 - val_accuracy: 0.7357\n",
      "Epoch 26/50\n",
      "32000/32000 [==============================] - 140s 4ms/step - loss: 0.5194 - accuracy: 0.7386 - val_loss: 0.5236 - val_accuracy: 0.7347\n",
      "Epoch 27/50\n",
      "32000/32000 [==============================] - 134s 4ms/step - loss: 0.5193 - accuracy: 0.7386 - val_loss: 0.5278 - val_accuracy: 0.7344\n",
      "Epoch 28/50\n",
      "32000/32000 [==============================] - 140s 4ms/step - loss: 0.5187 - accuracy: 0.7390 - val_loss: 0.5271 - val_accuracy: 0.7345\n",
      "Epoch 29/50\n",
      "32000/32000 [==============================] - 146s 5ms/step - loss: 0.5183 - accuracy: 0.7390 - val_loss: 0.5215 - val_accuracy: 0.7354\n",
      "Epoch 30/50\n",
      "32000/32000 [==============================] - 140s 4ms/step - loss: 0.5181 - accuracy: 0.7392 - val_loss: 0.5251 - val_accuracy: 0.7343\n",
      "Epoch 31/50\n",
      "32000/32000 [==============================] - 134s 4ms/step - loss: 0.5175 - accuracy: 0.7396 - val_loss: 0.5283 - val_accuracy: 0.7328\n",
      "Epoch 32/50\n",
      "32000/32000 [==============================] - 156s 5ms/step - loss: 0.5168 - accuracy: 0.7408 - val_loss: 0.5254 - val_accuracy: 0.7352\n",
      "Epoch 33/50\n",
      "32000/32000 [==============================] - 114s 4ms/step - loss: 0.5165 - accuracy: 0.7400 - val_loss: 0.5224 - val_accuracy: 0.7366\n",
      "Epoch 34/50\n",
      "32000/32000 [==============================] - 125s 4ms/step - loss: 0.5163 - accuracy: 0.7406 - val_loss: 0.5212 - val_accuracy: 0.7377\n",
      "Epoch 35/50\n",
      "32000/32000 [==============================] - 122s 4ms/step - loss: 0.5161 - accuracy: 0.7411 - val_loss: 0.5216 - val_accuracy: 0.7377\n",
      "Epoch 36/50\n",
      "32000/32000 [==============================] - 124s 4ms/step - loss: 0.5156 - accuracy: 0.7414 - val_loss: 0.5225 - val_accuracy: 0.7373\n",
      "Epoch 37/50\n",
      "32000/32000 [==============================] - 113s 4ms/step - loss: 0.5155 - accuracy: 0.7405 - val_loss: 0.5202 - val_accuracy: 0.7371\n",
      "Epoch 38/50\n",
      "32000/32000 [==============================] - 116s 4ms/step - loss: 0.5152 - accuracy: 0.7414 - val_loss: 0.5189 - val_accuracy: 0.7392\n",
      "Epoch 39/50\n",
      "32000/32000 [==============================] - 126s 4ms/step - loss: 0.5153 - accuracy: 0.7413 - val_loss: 0.5276 - val_accuracy: 0.7351\n",
      "Epoch 40/50\n",
      "32000/32000 [==============================] - 103s 3ms/step - loss: 0.5147 - accuracy: 0.7414 - val_loss: 0.5242 - val_accuracy: 0.7370\n",
      "Epoch 41/50\n",
      "32000/32000 [==============================] - 95s 3ms/step - loss: 0.5144 - accuracy: 0.7420 - val_loss: 0.5180 - val_accuracy: 0.7394\n",
      "Epoch 42/50\n",
      "32000/32000 [==============================] - 84s 3ms/step - loss: 0.5145 - accuracy: 0.7420 - val_loss: 0.5211 - val_accuracy: 0.7388\n",
      "Epoch 43/50\n",
      "32000/32000 [==============================] - 119s 4ms/step - loss: 0.5139 - accuracy: 0.7423 - val_loss: 0.5236 - val_accuracy: 0.7355\n",
      "Epoch 44/50\n",
      "32000/32000 [==============================] - 106s 3ms/step - loss: 0.5133 - accuracy: 0.7422 - val_loss: 0.5204 - val_accuracy: 0.7401\n",
      "Epoch 45/50\n",
      "32000/32000 [==============================] - 89s 3ms/step - loss: 0.5138 - accuracy: 0.7422 - val_loss: 0.5280 - val_accuracy: 0.7345\n",
      "Epoch 46/50\n",
      "32000/32000 [==============================] - 88s 3ms/step - loss: 0.5134 - accuracy: 0.7422 - val_loss: 0.5180 - val_accuracy: 0.7401\n",
      "Epoch 47/50\n",
      "32000/32000 [==============================] - 87s 3ms/step - loss: 0.5133 - accuracy: 0.7426 - val_loss: 0.5209 - val_accuracy: 0.7389\n",
      "Epoch 48/50\n",
      "32000/32000 [==============================] - 112s 3ms/step - loss: 0.5131 - accuracy: 0.7420 - val_loss: 0.5209 - val_accuracy: 0.7383\n",
      "Epoch 49/50\n",
      "32000/32000 [==============================] - 89s 3ms/step - loss: 0.5129 - accuracy: 0.7426 - val_loss: 0.5184 - val_accuracy: 0.7391\n",
      "Epoch 50/50\n",
      "32000/32000 [==============================] - 84s 3ms/step - loss: 0.5127 - accuracy: 0.7438 - val_loss: 0.5180 - val_accuracy: 0.7398\n",
      "3750/3750 [==============================] - 8s 2ms/step - loss: nan - accuracy: 0.7413\n",
      "Accuracy: 0.7413416504859924\n",
      "3750/3750 [==============================] - 6s 2ms/step\n",
      "Recall: 0.7844982850699285\n",
      "Precision: 0.7435208549799615\n",
      "F1 score: 0.7634601169020203\n",
      "Confusion matrix:\n",
      "[[38870 17279]\n",
      " [13760 50091]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from tensorflow import keras\n",
    "from sklearn.model_selection import train_test_split\n",
    "import pandas as pd\n",
    "from sklearn.metrics import confusion_matrix, f1_score, precision_score, recall_score\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# load data\n",
    "data = pd.read_csv(\"HIGGS_train.csv\", header=None)\n",
    "X = data.drop(columns=[0])  # drop target column\n",
    "y = data[0]\n",
    "\n",
    "# split data into train and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# build model architecture\n",
    "model = keras.Sequential([\n",
    "    keras.layers.Dense(70, activation='relu', input_shape=(28,)),\n",
    "    keras.layers.Dense(43, activation='relu'),\n",
    "    keras.layers.Dense(1, activation='sigmoid')\n",
    "])\n",
    "\n",
    "# compile model\n",
    "model.compile(optimizer='adam',\n",
    "              loss='binary_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "# define early stopping callback\n",
    "early_stopping = keras.callbacks.EarlyStopping(\n",
    "    monitor='val_loss',\n",
    "    patience=5,\n",
    "    restore_best_weights=True\n",
    ")\n",
    "\n",
    "# train model\n",
    "history = model.fit(X_train, y_train, epochs=50,\n",
    "                    batch_size=12, validation_split=0.2)\n",
    "\n",
    "# evaluate model on test set\n",
    "test_loss, test_acc = model.evaluate(X_test, y_test)\n",
    "print('Accuracy:', test_acc)\n",
    "\n",
    "# print recall, percision, f-1 score, confusion matrix\n",
    "y_pred = model.predict(X_test)\n",
    "y_pred = [1 if y >= 0.5 else 0 for y in y_pred]\n",
    "print('Recall:', recall_score(y_test, y_pred))\n",
    "print('Precision:', precision_score(y_test, y_pred))\n",
    "print('F1 score:', f1_score(y_test, y_pred))\n",
    "print('Confusion matrix:')\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "\n",
    "# plot confusion matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "ax = plt.subplot()\n",
    "sns.heatmap(cm, annot=True, ax=ax, fmt='g')\n",
    "ax.set_xlabel('Predicted labels')\n",
    "ax.set_ylabel('True labels')\n",
    "ax.set_title('Confusion Matrix')\n",
    "ax.xaxis.set_ticklabels(['bkg', 'sig'])\n",
    "ax.yaxis.set_ticklabels(['bkg', 'sig'])\n",
    "plt.show()\n",
    "\n",
    "# plot loss and accuracy\n",
    "plt.plot(history.history['loss'], label='loss')\n",
    "plt.plot(history.history['val_loss'], label='val_loss')\n",
    "plt.plot(history.history['accuracy'], label='accuracy')\n",
    "plt.plot(history.history['val_accuracy'], label='val_accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss/Accuracy')\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18750/18750 [==============================] - 33s 2ms/step\n",
      "Accuracy:  0.7446837234186171\n",
      "F1-score:  0.7655498877405316\n",
      "Confusion matrix:  [[196704  85724]\n",
      " [ 67465 250104]]\n",
      "Recall:  0.7875579795257094\n",
      "Precision:  0.7447383779792036\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pickle\n",
    "from sklearn.metrics import accuracy_score, f1_score, confusion_matrix, recall_score, precision_score\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "# save the model .h5\n",
    "model.save(\"FAM_best_model.h5\")\n",
    "\n",
    "# save the model using pickle\n",
    "pickle.dump(model, open(\"FAM_model_one.pkl\", \"wb\"))\n",
    "\n",
    "# load the model using pickle\n",
    "FAM_model_one = pickle.load(open(\"FAM_model_one.pkl\", \"rb\"))\n",
    "\n",
    "# make predictions on \"HIGGS_train.csv\"\n",
    "data = pd.read_csv(\"HIGGS_train.csv\", header=None).dropna()\n",
    "X = data.drop(columns=[0])  # drop target column\n",
    "y = data[0]\n",
    "\n",
    "# make predictions\n",
    "predictions = FAM_model_one.predict(X)\n",
    "\n",
    "# print accuracy, F1-score, confusion matrix, recall, percision\n",
    "print(\"Accuracy: \", accuracy_score(y, predictions.round()))\n",
    "print(\"F1-score: \", f1_score(y, predictions.round()))\n",
    "print(\"Confusion matrix: \", confusion_matrix(y, predictions.round()))\n",
    "print(\"Recall: \", recall_score(y, predictions.round()))\n",
    "print(\"Precision: \", precision_score(y, predictions.round()))\n",
    "\n",
    "# plot confusion matrix\n",
    "sns.set(font_scale=1.4)\n",
    "cm = confusion_matrix(y, predictions.round())\n",
    "sns.heatmap(cm, annot=True, annot_kws={\"size\": 16}, cmap='Blues', fmt='g')\n",
    "plt.xlabel('Predicted labels')\n",
    "plt.ylabel('True labels')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "343750/343750 [==============================] - 746s 2ms/step\n",
      "Accuracy:  0.7416663636363636\n",
      "F1-score:  0.7629833008099539\n",
      "Confusion matrix:  [[3584503 1586374]\n",
      " [1255296 4573827]]\n",
      "Recall:  0.7846509672209696\n",
      "Precision:  0.7424801560858161\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, f1_score, confusion_matrix, recall_score, precision_score\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# make predictions on \"HIGGS_train.csv\"\n",
    "data = pd.read_csv(\"HIGGS.csv\", header=None).dropna()\n",
    "X = data.drop(columns=[0])  # drop target column\n",
    "y = data[0]\n",
    "\n",
    "# make predictions\n",
    "predictions = FAM_model_one.predict(X)\n",
    "\n",
    "# print accuracy, F1-score, confusion matrix, recall, percision\n",
    "print(\"Accuracy: \", accuracy_score(y, predictions.round()))\n",
    "print(\"F1-score: \", f1_score(y, predictions.round()))\n",
    "print(\"Confusion matrix: \", confusion_matrix(y, predictions.round()))\n",
    "print(\"Recall: \", recall_score(y, predictions.round()))\n",
    "print(\"Precision: \", precision_score(y, predictions.round()))\n",
    "\n",
    "# plot confusion matrix\n",
    "sns.set(font_scale=1.4)\n",
    "cm = confusion_matrix(y, predictions.round())\n",
    "sns.heatmap(cm, annot=True, annot_kws={\"size\": 16}, cmap='Blues', fmt='g')\n",
    "plt.xlabel('Predicted labels')\n",
    "plt.ylabel('True labels')\n",
    "plt.show()\n",
    "\n"
   ]
  }
 ],
 "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": "3.11.1"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}