model_deep_neural_network.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "#from tqdm import tqdm_notebook as tqdm\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Activation, LeakyReLU\n",
    "from keras import backend\n",
    "from keras.callbacks import ModelCheckpoint\n",
    "from keras.regularizers import l2\n",
    "\n",
    "import sklearn\n",
    "from sklearn.metrics import mean_squared_error\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = np.loadtxt('X_train.csv', delimiter=',')  # last column is rating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_val = np.loadtxt('X_val.csv', delimiter=',')  # last column is rating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train=  np.loadtxt('y_train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_val= np.loadtxt('y_val.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = np.loadtxt('X_test.csv', delimiter=',')  # last column is rating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(250874,)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "backend.clear_session()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "nodes = 924\n",
    "model = Sequential()\n",
    "model.add(Dense(nodes, input_dim=X_train.shape[1],activation='relu'))\n",
    "#model.add(LeakyReLU())\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "\n",
    "model.add(Dense(nodes,kernel_regularizer=l2(0.01), activation='relu'))\n",
    "#model.add(Dense(nodes, activation='relu'))\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "#model.add(Dense(nodes,kernel_regularizer=l2(0.01), activation='relu'))\n",
    "#model.add(Dense(nodes, activation='relu'))\n",
    "#model.add(Dropout(0.25))\n",
    "\n",
    "\n",
    "\n",
    "model.add(Dense(nodes,kernel_regularizer=l2(0.01), activation='relu'))\n",
    "#model.add(Dense(nodes, activation='relu'))\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "          \n",
    "#model.compile(loss='mse',optimizer='rmsprop',metrics=['mse', 'mae', 'mape', 'cosine'])\n",
    "\n",
    "#model.compile(optimizer='adam', \n",
    " #             loss='binary_crossentropy', \n",
    " #             metrics=['accuracy'])\n",
    "\n",
    "model.compile(optimizer='adam',loss='mse',metrics=['mse'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dense_1 (None, 924) ----> (None, 924)\n",
      "dropout_1 (None, 924) ----> (None, 924)\n",
      "dense_2 (None, 924) ----> (None, 924)\n",
      "dropout_2 (None, 924) ----> (None, 924)\n",
      "dense_3 (None, 924) ----> (None, 924)\n",
      "dropout_3 (None, 924) ----> (None, 924)\n",
      "dense_4 (None, 924) ----> (None, 1)\n",
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_1 (Dense)              (None, 924)               854700    \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 924)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 924)               854700    \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 924)               0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 924)               854700    \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 924)               0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 1)                 925       \n",
      "=================================================================\n",
      "Total params: 2,565,025\n",
      "Trainable params: 2,565,025\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "for m in model.layers:\n",
    "    print(m.name, m.input_shape,'---->',m.output_shape)\n",
    "\n",
    "print(model.summary())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.utils import plot_model\n",
    "#plot_model(model, to_file='model.png') #install pydot\n",
    "plot_model(model, show_shapes=True, show_layer_names=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 250874 samples, validate on 35918 samples\n",
      "Epoch 1/12\n",
      " - 82s - loss: 0.3283 - mse: 0.0929 - val_loss: 0.0913 - val_mse: 0.0912\n",
      "Epoch 2/12\n",
      " - 80s - loss: 0.0924 - mse: 0.0917 - val_loss: 0.0939 - val_mse: 0.0924\n",
      "Epoch 3/12\n",
      " - 79s - loss: 0.0899 - mse: 0.0880 - val_loss: 0.0878 - val_mse: 0.0865\n",
      "Epoch 4/12\n",
      " - 78s - loss: 0.0889 - mse: 0.0876 - val_loss: 0.0875 - val_mse: 0.0863\n",
      "Epoch 5/12\n",
      " - 78s - loss: 0.0886 - mse: 0.0874 - val_loss: 0.0887 - val_mse: 0.0870\n",
      "Epoch 6/12\n",
      " - 84s - loss: 0.0886 - mse: 0.0872 - val_loss: 0.0873 - val_mse: 0.0863\n",
      "Epoch 7/12\n",
      " - 81s - loss: 0.0884 - mse: 0.0871 - val_loss: 0.0877 - val_mse: 0.0864\n",
      "Epoch 8/12\n",
      " - 84s - loss: 0.0881 - mse: 0.0870 - val_loss: 0.0876 - val_mse: 0.0864\n",
      "Epoch 9/12\n",
      " - 82s - loss: 0.0880 - mse: 0.0869 - val_loss: 0.0878 - val_mse: 0.0865\n",
      "Epoch 10/12\n",
      " - 79s - loss: 0.0880 - mse: 0.0868 - val_loss: 0.0905 - val_mse: 0.0887\n",
      "Epoch 11/12\n",
      " - 82s - loss: 0.0879 - mse: 0.0866 - val_loss: 0.0877 - val_mse: 0.0865\n",
      "Epoch 12/12\n",
      " - 90s - loss: 0.0877 - mse: 0.0864 - val_loss: 0.0875 - val_mse: 0.0861\n"
     ]
    }
   ],
   "source": [
    "#filepath=\"epoch-{epoch:02d}-{val_loss:.4f}.hdf5\"\n",
    "#checkpoint = ModelCheckpoint(filepath, monitor='val_loss', verbose=1,save_best_only=False, mode='auto', period=1)\n",
    "history= model.fit(X_train, y_train, batch_size=200, epochs=12, verbose=2,validation_data = [X_val,y_val])#,callbacks=[checkpoint])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12 ,10))\n",
    "plt.plot(history.history['loss'],linewidth=1.3)\n",
    "plt.plot(history.history['val_loss'],linewidth=1.3)\n",
    "plt.title('Model loss',fontsize=20,fontname='Times New Roman')\n",
    "plt.ylabel('Loss',fontsize=20,fontname='Times New Roman')\n",
    "plt.xlabel('Epoch',fontsize=20,fontname='Times New Roman')\n",
    "plt.legend(['Train', 'Validation'], loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "probs_val = model.predict(X_val)\n",
    "probs_val = np.squeeze(probs_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.11095157, 0.11095157, 0.11095157, 0.11095157, 0.11095157,\n",
       "       0.11095157, 0.11095157, 0.11095157, 0.11095157, 0.11095157],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "probs_val[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics\n",
    "\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import average_precision_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7188968812350833"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fpr_w,tpr_w,threshold_w = metrics.roc_curve(y_val, probs_val)\n",
    "auc_w = metrics.auc(fpr_w,tpr_w)\n",
    "auc_w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.213156196163639"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ap=average_precision_score(y_val, probs_val)\n",
    "ap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7389576192257393"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Training AUC check for overfitting\n",
    "p = model.predict(X_train)  # probability for label=1\n",
    "p=np.squeeze(p)\n",
    "f,t,thres = metrics.roc_curve(y_train, p)\n",
    "auc_train = metrics.auc(f,t)\n",
    "auc_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a72c1edd0>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12 ,10))\n",
    "#plt.plot(fpr, tpr, color='red', label='( ROC_LR, AUC=%0.3f)'%auc)\n",
    "plt.plot(fpr_w, tpr_w, color='Red', label='( Deep Neural Network ROC, AUC=%0.3f)'%auc_w)\n",
    "\n",
    "plt.plot([0, 1], [0, 1], color='black', linestyle='--')\n",
    "plt.xlabel('False Positive Rate(FPR)')\n",
    "plt.ylabel('True Positive Rate(TPR)')\n",
    "plt.title('Receiver Operating Characteristic (ROC) Curve')\n",
    "plt.legend(loc=4) #4:lower right legend\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(72165, 924)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "probs_test = model.predict(X_test)\n",
    "probs_test = np.squeeze(probs_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savetxt('predictions.csv', probs_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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