t81_558_class_08_5_kaggle_project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://colab.research.google.com/github/jeffheaton/t81_558_deep_learning/blob/master/t81_558_class_08_5_kaggle_project.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# T81-558: Applications of Deep Neural Networks\n",
    "**Module 8: Kaggle Data Sets**\n",
    "* Instructor: [Jeff Heaton](https://sites.wustl.edu/jeffheaton/), McKelvey School of Engineering, [Washington University in St. Louis](https://engineering.wustl.edu/Programs/Pages/default.aspx)\n",
    "* For more information visit the [class website](https://sites.wustl.edu/jeffheaton/t81-558/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Module 8 Material\n",
    "\n",
    "* Part 8.1: Introduction to Kaggle [[Video]](https://www.youtube.com/watch?v=v4lJBhdCuCU&list=PLjy4p-07OYzulelvJ5KVaT2pDlxivl_BN) [[Notebook]](t81_558_class_08_1_kaggle_intro.ipynb)\n",
    "* Part 8.2: Building Ensembles with Scikit-Learn and Keras [[Video]](https://www.youtube.com/watch?v=LQ-9ZRBLasw&list=PLjy4p-07OYzulelvJ5KVaT2pDlxivl_BN) [[Notebook]](t81_558_class_08_2_keras_ensembles.ipynb)\n",
    "* Part 8.3: How Should you Architect Your Keras Neural Network: Hyperparameters [[Video]](https://www.youtube.com/watch?v=1q9klwSoUQw&list=PLjy4p-07OYzulelvJ5KVaT2pDlxivl_BN) [[Notebook]](t81_558_class_08_3_keras_hyperparameters.ipynb)\n",
    "* Part 8.4: Bayesian Hyperparameter Optimization for Keras [[Video]](https://www.youtube.com/watch?v=sXdxyUCCm8s&list=PLjy4p-07OYzulelvJ5KVaT2pDlxivl_BN) [[Notebook]](t81_558_class_08_4_bayesian_hyperparameter_opt.ipynb)\n",
    "* **Part 8.5: Current Semester's Kaggle** [[Video]](https://www.youtube.com/watch?v=PHQt0aUasRg&list=PLjy4p-07OYzulelvJ5KVaT2pDlxivl_BN) [[Notebook]](t81_558_class_08_5_kaggle_project.ipynb)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Google CoLab Instructions\n",
    "\n",
    "The following code ensures that Google CoLab is running the correct version of TensorFlow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note: not using Google CoLab\n"
     ]
    }
   ],
   "source": [
    "# Start CoLab\n",
    "try:\n",
    "    %tensorflow_version 2.x\n",
    "    COLAB = True\n",
    "    print(\"Note: using Google CoLab\")\n",
    "except:\n",
    "    print(\"Note: not using Google CoLab\")\n",
    "    COLAB = False\n",
    "\n",
    "# Nicely formatted time string\n",
    "def hms_string(sec_elapsed):\n",
    "    h = int(sec_elapsed / (60 * 60))\n",
    "    m = int((sec_elapsed % (60 * 60)) / 60)\n",
    "    s = sec_elapsed % 60\n",
    "    return \"{}:{:>02}:{:>05.2f}\".format(h, m, s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 8.5: Current Semester's Kaggle\n",
    "\n",
    "Kaggke competition site for current semester (Fall 2020):\n",
    "\n",
    "* [Fall 2020 Kaggle Assignment](https://www.kaggle.com/c/applications-of-deep-learning-wustl-fall-2020/data)\n",
    "\n",
    "Previous Kaggle competition sites for this class (NOT this semester's assignment, feel free to use code):\n",
    "* [Spring 2020 Kaggle Assignment](https://www.kaggle.com/c/applications-of-deep-learningwustl-spring-2020)\n",
    "* [Fall 2019 Kaggle Assignment](https://kaggle.com/c/applications-of-deep-learningwustl-fall-2019)\n",
    "* [Spring 2019 Kaggle Assignment](https://www.kaggle.com/c/applications-of-deep-learningwustl-spring-2019)\n",
    "* [Fall 2018 Kaggle Assignment](https://www.kaggle.com/c/wustl-t81-558-washu-deep-learning-fall-2018)\n",
    "* [Spring 2018 Kaggle Assignment](https://www.kaggle.com/c/wustl-t81-558-washu-deep-learning-spring-2018)\n",
    "* [Fall 2017 Kaggle Assignment](https://www.kaggle.com/c/wustl-t81-558-washu-deep-learning-fall-2017)\n",
    "* [Spring 2017 Kaggle Assignment](https://inclass.kaggle.com/c/applications-of-deep-learning-wustl-spring-2017)\n",
    "* [Fall 2016 Kaggle Assignment](https://inclass.kaggle.com/c/wustl-t81-558-washu-deep-learning-fall-2016)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Iris as a Kaggle Competition\n",
    "\n",
    "If the Iris data were used as a Kaggle, you would be given the following three files:\n",
    "\n",
    "* [kaggle_iris_test.csv](https://data.heatonresearch.com/data/t81-558/datasets/kaggle_iris_test.csv) - The data that Kaggle will evaluate you on.  Contains only input, you must provide answers.  (contains x)\n",
    "* [kaggle_iris_train.csv](https://data.heatonresearch.com/data/t81-558/datasets/kaggle_iris_train.csv) - The data that you will use to train. (contains x and y)\n",
    "* [kaggle_iris_sample.csv](https://data.heatonresearch.com/data/t81-558/datasets/kaggle_iris_sample.csv) - A sample submission for Kaggle. (contains x and y)\n",
    "\n",
    "Important features of the Kaggle iris files (that differ from how we've previously seen files):\n",
    "\n",
    "* The iris species is already index encoded.\n",
    "* Your training data is in a separate file.\n",
    "* You will load the test data to generate a submission file.\n",
    "\n",
    "The following program generates a submission file for \"Iris Kaggle\".  You can use it as a starting point for assignment 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of classes: 3\n",
      "Restoring model weights from the end of the best epoch.\n",
      "Epoch 00055: early stopping\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x178e5493fc8>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Activation\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "\n",
    "df_train = pd.read_csv(\n",
    "    \"https://data.heatonresearch.com/data/t81-558/datasets/\"+\\\n",
    "    \"kaggle_iris_train.csv\", na_values=['NA','?'])\n",
    "\n",
    "# Encode feature vector\n",
    "df_train.drop('id', axis=1, inplace=True)\n",
    "\n",
    "num_classes = len(df_train.groupby('species').species.nunique())\n",
    "\n",
    "print(\"Number of classes: {}\".format(num_classes))\n",
    "\n",
    "# Convert to numpy - Classification\n",
    "x = df_train[['sepal_l', 'sepal_w', 'petal_l', 'petal_w']].values\n",
    "dummies = pd.get_dummies(df_train['species']) # Classification\n",
    "species = dummies.columns\n",
    "y = dummies.values\n",
    "    \n",
    "# Split into train/test\n",
    "x_train, x_test, y_train, y_test = train_test_split(    \n",
    "    x, y, test_size=0.25, random_state=45)\n",
    "\n",
    "# Train, with early stopping\n",
    "model = Sequential()\n",
    "model.add(Dense(50, input_dim=x.shape[1], activation='relu'))\n",
    "model.add(Dense(25))\n",
    "model.add(Dense(y.shape[1],activation='softmax'))\n",
    "model.compile(loss='categorical_crossentropy', optimizer='adam')\n",
    "monitor = EarlyStopping(monitor='val_loss', min_delta=1e-3, \n",
    "                        patience=5, verbose=1, mode='auto',\n",
    "                       restore_best_weights=True)\n",
    "\n",
    "model.fit(x_train,y_train,validation_data=(x_test,y_test),\n",
    "          callbacks=[monitor],verbose=0,epochs=1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we've trained the neural network, we can check its log loss."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log loss score: 0.3136451941728592\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "\n",
    "# Calculate multi log loss error\n",
    "pred = model.predict(x_test)\n",
    "score = metrics.log_loss(y_test, pred)\n",
    "print(\"Log loss score: {}\".format(score))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are ready to generate the Kaggle submission file.  We will use the iris test data that does not contain a $y$ target value.  It is our job to predict this value and submit to Kaggle."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     id  species-0  species-1  species-2\n",
      "0   100   0.022236   0.533230   0.444534\n",
      "1   101   0.003699   0.394908   0.601393\n",
      "2   102   0.004600   0.420394   0.575007\n",
      "3   103   0.956168   0.040161   0.003672\n",
      "4   104   0.975333   0.022761   0.001906\n",
      "5   105   0.966681   0.030938   0.002381\n",
      "6   106   0.992637   0.007049   0.000314\n",
      "7   107   0.002810   0.358485   0.638705\n",
      "8   108   0.026152   0.557480   0.416368\n",
      "9   109   0.001194   0.350682   0.648124\n",
      "10  110   0.000649   0.268023   0.731328\n",
      "11  111   0.994907   0.004923   0.000170\n",
      "12  112   0.072954   0.587299   0.339747\n",
      "13  113   0.000571   0.258208   0.741221\n",
      "14  114   0.977138   0.021400   0.001463\n",
      "15  115   0.004665   0.449740   0.545596\n",
      "16  116   0.073553   0.567955   0.358493\n",
      "17  117   0.968778   0.029240   0.001982\n",
      "18  118   0.983742   0.015341   0.000918\n",
      "19  119   0.986016   0.013193   0.000792\n",
      "20  120   0.023752   0.583601   0.392647\n",
      "21  121   0.032858   0.584882   0.382260\n",
      "22  122   0.004007   0.395656   0.600338\n",
      "23  123   0.000885   0.240763   0.758352\n",
      "24  124   0.000531   0.271212   0.728256\n",
      "25  125   0.985742   0.013471   0.000787\n",
      "26  126   0.001298   0.320333   0.678369\n",
      "27  127   0.001753   0.342856   0.655391\n",
      "28  128   0.001147   0.317827   0.681026\n",
      "29  129   0.981223   0.017589   0.001188\n",
      "30  130   0.036438   0.578421   0.385140\n",
      "31  131   0.976528   0.021834   0.001638\n",
      "32  132   0.003681   0.405441   0.590878\n",
      "33  133   0.024478   0.539376   0.436146\n",
      "34  134   0.012039   0.466313   0.521649\n",
      "35  135   0.963704   0.033453   0.002844\n",
      "36  136   0.000614   0.244336   0.755050\n",
      "37  137   0.008160   0.490362   0.501478\n",
      "38  138   0.976859   0.021646   0.001495\n",
      "39  139   0.003789   0.317224   0.678987\n",
      "40  140   0.962254   0.034885   0.002861\n",
      "41  141   0.000792   0.289380   0.709828\n",
      "42  142   0.000253   0.239028   0.760719\n",
      "43  143   0.001390   0.298506   0.700104\n",
      "44  144   0.968422   0.029224   0.002354\n",
      "45  145   0.029218   0.524128   0.446654\n",
      "46  146   0.130497   0.579122   0.290381\n",
      "47  147   0.023003   0.499443   0.477553\n",
      "48  148   0.022195   0.527769   0.450036\n",
      "49  149   0.983695   0.015325   0.000980\n",
      "50  150   0.942703   0.052154   0.005144\n"
     ]
    }
   ],
   "source": [
    "# Generate Kaggle submit file\n",
    "\n",
    "# Encode feature vector\n",
    "df_test = pd.read_csv(\n",
    "    \"https://data.heatonresearch.com/data/t81-558/datasets/\"+\\\n",
    "    \"kaggle_iris_test.csv\", na_values=['NA','?'])\n",
    "\n",
    "# Convert to numpy - Classification\n",
    "ids = df_test['id']\n",
    "df_test.drop('id', axis=1, inplace=True)\n",
    "x = df_test[['sepal_l', 'sepal_w', 'petal_l', 'petal_w']].values\n",
    "y = dummies.values\n",
    "\n",
    "# Generate predictions\n",
    "pred = model.predict(x)\n",
    "#pred\n",
    "\n",
    "# Create submission data set\n",
    "\n",
    "df_submit = pd.DataFrame(pred)\n",
    "df_submit.insert(0,'id',ids)\n",
    "df_submit.columns = ['id','species-0','species-1','species-2']\n",
    "\n",
    "# Write submit file locally\n",
    "df_submit.to_csv(\"iris_submit.csv\", index=False) \n",
    "\n",
    "print(df_submit)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MPG as a Kaggle Competition (Regression)\n",
    "\n",
    "If the Auto MPG data were used as a Kaggle, you would be given the following three files:\n",
    "\n",
    "* [kaggle_mpg_test.csv](https://data.heatonresearch.com/data/t81-558/datasets/kaggle_auto_test.csv) - The data that Kaggle will evaluate you on.  Contains only input, you must provide answers.  (contains x)\n",
    "* [kaggle_mpg_train.csv](https://data.heatonresearch.com/data/t81-558/datasets/kaggle_auto_test.csv) - The data that you will use to train. (contains x and y)\n",
    "* [kaggle_mpg_sample.csv](https://data.heatonresearch.com/data/t81-558/datasets/kaggle_auto_sample.csv) - A sample submission for Kaggle. (contains x and y)\n",
    "\n",
    "Important features of the Kaggle iris files (that differ from how we've previously seen files):\n",
    "\n",
    "The following program generates a submission file for \"MPG Kaggle\".  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 261 samples, validate on 88 samples\n",
      "Epoch 1/1000\n",
      "261/261 - 0s - loss: 382597.1196 - val_loss: 246687.4858\n",
      "Epoch 2/1000\n",
      "261/261 - 0s - loss: 192257.0072 - val_loss: 98804.3558\n",
      "Epoch 3/1000\n",
      "261/261 - 0s - loss: 67605.7908 - val_loss: 28617.0703\n",
      "Epoch 4/1000\n",
      "261/261 - 0s - loss: 15922.8367 - val_loss: 3325.1682\n",
      "Epoch 5/1000\n",
      "261/261 - 0s - loss: 1270.3832 - val_loss: 512.5387\n",
      "Epoch 6/1000\n",
      "261/261 - 0s - loss: 1118.9636 - val_loss: 1651.5679\n",
      "Epoch 7/1000\n",
      "261/261 - 0s - loss: 1703.0441 - val_loss: 1161.2368\n",
      "Epoch 8/1000\n",
      "261/261 - 0s - loss: 900.1420 - val_loss: 452.0660\n",
      "Epoch 9/1000\n",
      "261/261 - 0s - loss: 355.7248 - val_loss: 304.3305\n",
      "Epoch 10/1000\n",
      "261/261 - 0s - loss: 336.1776 - val_loss: 353.2767\n",
      "Epoch 11/1000\n",
      "261/261 - 0s - loss: 364.7770 - val_loss: 337.0882\n",
      "Epoch 12/1000\n",
      "261/261 - 0s - loss: 334.1086 - val_loss: 301.5655\n",
      "Epoch 13/1000\n",
      "261/261 - 0s - loss: 318.2330 - val_loss: 295.2506\n",
      "Epoch 14/1000\n",
      "261/261 - 0s - loss: 315.3628 - val_loss: 294.1454\n",
      "Epoch 15/1000\n",
      "261/261 - 0s - loss: 313.4151 - val_loss: 292.0427\n",
      "Epoch 16/1000\n",
      "261/261 - 0s - loss: 310.5834 - val_loss: 290.4511\n",
      "Epoch 17/1000\n",
      "261/261 - 0s - loss: 308.1132 - val_loss: 289.9176\n",
      "Epoch 18/1000\n",
      "261/261 - 0s - loss: 307.3153 - val_loss: 287.1054\n",
      "Epoch 19/1000\n",
      "261/261 - 0s - loss: 305.2746 - val_loss: 285.1501\n",
      "Epoch 20/1000\n",
      "261/261 - 0s - loss: 303.8164 - val_loss: 283.2582\n",
      "Epoch 21/1000\n",
      "261/261 - 0s - loss: 302.2492 - val_loss: 281.4607\n",
      "Epoch 22/1000\n",
      "261/261 - 0s - loss: 300.0016 - val_loss: 279.4577\n",
      "Epoch 23/1000\n",
      "261/261 - 0s - loss: 296.3905 - val_loss: 279.2795\n",
      "Epoch 24/1000\n",
      "261/261 - 0s - loss: 296.2508 - val_loss: 278.0922\n",
      "Epoch 25/1000\n",
      "261/261 - 0s - loss: 295.3600 - val_loss: 275.6349\n",
      "Epoch 26/1000\n",
      "261/261 - 0s - loss: 291.1920 - val_loss: 271.5592\n",
      "Epoch 27/1000\n",
      "261/261 - 0s - loss: 293.0040 - val_loss: 270.6060\n",
      "Epoch 28/1000\n",
      "261/261 - 0s - loss: 288.8120 - val_loss: 267.5230\n",
      "Epoch 29/1000\n",
      "261/261 - 0s - loss: 285.0153 - val_loss: 267.6846\n",
      "Epoch 30/1000\n",
      "261/261 - 0s - loss: 284.5063 - val_loss: 267.5903\n",
      "Epoch 31/1000\n",
      "261/261 - 0s - loss: 283.2598 - val_loss: 263.2579\n",
      "Epoch 32/1000\n",
      "261/261 - 0s - loss: 279.1897 - val_loss: 259.1413\n",
      "Epoch 33/1000\n",
      "261/261 - 0s - loss: 278.0727 - val_loss: 257.1468\n",
      "Epoch 34/1000\n",
      "261/261 - 0s - loss: 275.0580 - val_loss: 255.3159\n",
      "Epoch 35/1000\n",
      "261/261 - 0s - loss: 275.2246 - val_loss: 257.6078\n",
      "Epoch 36/1000\n",
      "261/261 - 0s - loss: 273.1009 - val_loss: 253.1600\n",
      "Epoch 37/1000\n",
      "261/261 - 0s - loss: 268.6169 - val_loss: 248.6043\n",
      "Epoch 38/1000\n",
      "261/261 - 0s - loss: 266.2035 - val_loss: 246.5989\n",
      "Epoch 39/1000\n",
      "261/261 - 0s - loss: 263.9700 - val_loss: 245.5532\n",
      "Epoch 40/1000\n",
      "261/261 - 0s - loss: 262.1468 - val_loss: 242.2550\n",
      "Epoch 41/1000\n",
      "261/261 - 0s - loss: 259.1994 - val_loss: 239.2889\n",
      "Epoch 42/1000\n",
      "261/261 - 0s - loss: 258.9926 - val_loss: 237.0006\n",
      "Epoch 43/1000\n",
      "261/261 - 0s - loss: 253.8787 - val_loss: 239.7331\n",
      "Epoch 44/1000\n",
      "261/261 - 0s - loss: 255.4787 - val_loss: 234.9061\n",
      "Epoch 45/1000\n",
      "261/261 - 0s - loss: 251.2081 - val_loss: 231.0518\n",
      "Epoch 46/1000\n",
      "261/261 - 0s - loss: 248.3354 - val_loss: 228.7012\n",
      "Epoch 47/1000\n",
      "261/261 - 0s - loss: 246.8801 - val_loss: 225.7509\n",
      "Epoch 48/1000\n",
      "261/261 - 0s - loss: 243.6159 - val_loss: 224.8320\n",
      "Epoch 49/1000\n",
      "261/261 - 0s - loss: 242.0351 - val_loss: 222.3293\n",
      "Epoch 50/1000\n",
      "261/261 - 0s - loss: 240.8072 - val_loss: 218.9842\n",
      "Epoch 51/1000\n",
      "261/261 - 0s - loss: 237.3082 - val_loss: 216.6910\n",
      "Epoch 52/1000\n",
      "261/261 - 0s - loss: 236.4236 - val_loss: 219.1308\n",
      "Epoch 53/1000\n",
      "261/261 - 0s - loss: 233.8834 - val_loss: 213.7722\n",
      "Epoch 54/1000\n",
      "261/261 - 0s - loss: 229.9621 - val_loss: 209.7647\n",
      "Epoch 55/1000\n",
      "261/261 - 0s - loss: 227.2555 - val_loss: 207.4864\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "261/261 - 0s - loss: 28.9162 - val_loss: 23.7909\n",
      "Epoch 253/1000\n",
      "261/261 - 0s - loss: 29.2801 - val_loss: 23.8153\n",
      "Epoch 254/1000\n",
      "261/261 - 0s - loss: 29.3222 - val_loss: 23.5515\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 255/1000\n",
      "261/261 - 0s - loss: 28.5132 - val_loss: 23.8399\n",
      "Epoch 256/1000\n",
      "261/261 - 0s - loss: 28.9835 - val_loss: 23.3674\n",
      "Epoch 257/1000\n",
      "261/261 - 0s - loss: 28.2271 - val_loss: 23.4548\n",
      "Epoch 258/1000\n",
      "261/261 - 0s - loss: 27.8565 - val_loss: 23.1535\n",
      "Epoch 259/1000\n",
      "261/261 - 0s - loss: 27.8770 - val_loss: 23.1761\n",
      "Epoch 260/1000\n",
      "261/261 - 0s - loss: 27.5445 - val_loss: 22.9507\n",
      "Epoch 261/1000\n",
      "261/261 - 0s - loss: 27.6223 - val_loss: 22.8882\n",
      "Epoch 262/1000\n",
      "261/261 - 0s - loss: 27.3854 - val_loss: 22.9048\n",
      "Epoch 263/1000\n",
      "261/261 - 0s - loss: 27.3946 - val_loss: 22.6476\n",
      "Epoch 264/1000\n",
      "261/261 - 0s - loss: 27.0089 - val_loss: 22.5546\n",
      "Epoch 265/1000\n",
      "261/261 - 0s - loss: 26.9027 - val_loss: 22.4856\n",
      "Epoch 266/1000\n",
      "261/261 - 0s - loss: 26.7630 - val_loss: 22.4675\n",
      "Epoch 267/1000\n",
      "261/261 - 0s - loss: 27.0150 - val_loss: 22.3077\n",
      "Epoch 268/1000\n",
      "261/261 - 0s - loss: 26.3339 - val_loss: 22.1958\n",
      "Epoch 269/1000\n",
      "261/261 - 0s - loss: 26.5861 - val_loss: 22.3650\n",
      "Epoch 270/1000\n",
      "261/261 - 0s - loss: 26.3245 - val_loss: 22.0337\n",
      "Epoch 271/1000\n",
      "261/261 - 0s - loss: 26.0610 - val_loss: 21.9219\n",
      "Epoch 272/1000\n",
      "261/261 - 0s - loss: 25.9908 - val_loss: 22.0404\n",
      "Epoch 273/1000\n",
      "261/261 - 0s - loss: 25.7291 - val_loss: 22.0628\n",
      "Epoch 274/1000\n",
      "261/261 - 0s - loss: 28.5037 - val_loss: 22.0770\n",
      "Epoch 275/1000\n",
      "261/261 - 0s - loss: 26.8031 - val_loss: 21.6196\n",
      "Epoch 276/1000\n",
      "261/261 - 0s - loss: 26.1467 - val_loss: 21.5624\n",
      "Epoch 277/1000\n",
      "261/261 - 0s - loss: 25.3375 - val_loss: 22.5604\n",
      "Epoch 278/1000\n",
      "261/261 - 0s - loss: 25.9187 - val_loss: 21.3272\n",
      "Epoch 279/1000\n",
      "261/261 - 0s - loss: 25.1155 - val_loss: 21.4415\n",
      "Epoch 280/1000\n",
      "261/261 - 0s - loss: 24.9094 - val_loss: 21.2730\n",
      "Epoch 281/1000\n",
      "261/261 - 0s - loss: 24.6625 - val_loss: 21.0808\n",
      "Epoch 282/1000\n",
      "261/261 - 0s - loss: 24.9405 - val_loss: 21.0107\n",
      "Epoch 283/1000\n",
      "261/261 - 0s - loss: 24.4015 - val_loss: 21.2510\n",
      "Epoch 284/1000\n",
      "261/261 - 0s - loss: 24.8920 - val_loss: 20.8377\n",
      "Epoch 285/1000\n",
      "261/261 - 0s - loss: 24.1691 - val_loss: 20.7580\n",
      "Epoch 286/1000\n",
      "261/261 - 0s - loss: 24.4140 - val_loss: 20.6990\n",
      "Epoch 287/1000\n",
      "261/261 - 0s - loss: 24.0014 - val_loss: 20.6096\n",
      "Epoch 288/1000\n",
      "261/261 - 0s - loss: 23.7787 - val_loss: 20.5134\n",
      "Epoch 289/1000\n",
      "261/261 - 0s - loss: 23.7206 - val_loss: 20.6378\n",
      "Epoch 290/1000\n",
      "261/261 - 0s - loss: 24.6226 - val_loss: 20.3711\n",
      "Epoch 291/1000\n",
      "261/261 - 0s - loss: 23.3452 - val_loss: 20.4992\n",
      "Epoch 292/1000\n",
      "261/261 - 0s - loss: 23.6446 - val_loss: 20.4952\n",
      "Epoch 293/1000\n",
      "261/261 - 0s - loss: 24.2056 - val_loss: 20.6786\n",
      "Epoch 294/1000\n",
      "261/261 - 0s - loss: 25.1895 - val_loss: 20.4868\n",
      "Epoch 295/1000\n",
      "Restoring model weights from the end of the best epoch.\n",
      "261/261 - 0s - loss: 23.6036 - val_loss: 20.3795\n",
      "Epoch 00295: early stopping\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Activation\n",
    "from sklearn.model_selection import train_test_split\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "import pandas as pd\n",
    "import io\n",
    "import os\n",
    "import requests\n",
    "import numpy as np\n",
    "from sklearn import metrics\n",
    "\n",
    "save_path = \".\"\n",
    "\n",
    "df = pd.read_csv(\n",
    "    \"https://data.heatonresearch.com/data/t81-558/datasets/\"+\\\n",
    "    \"kaggle_auto_train.csv\", \n",
    "    na_values=['NA', '?'])\n",
    "\n",
    "cars = df['name']\n",
    "\n",
    "# Handle missing value\n",
    "df['horsepower'] = df['horsepower'].fillna(df['horsepower'].median())\n",
    "\n",
    "# Pandas to Numpy\n",
    "x = df[['cylinders', 'displacement', 'horsepower', 'weight',\n",
    "       'acceleration', 'year', 'origin']].values\n",
    "y = df['mpg'].values # regression\n",
    "\n",
    "# Split into train/test\n",
    "x_train, x_test, y_train, y_test = train_test_split(    \n",
    "    x, y, test_size=0.25, random_state=42)\n",
    "\n",
    "# Build the neural network\n",
    "model = Sequential()\n",
    "model.add(Dense(25, input_dim=x.shape[1], activation='relu')) # Hidden 1\n",
    "model.add(Dense(10, activation='relu')) # Hidden 2\n",
    "model.add(Dense(1)) # Output\n",
    "model.compile(loss='mean_squared_error', optimizer='adam')\n",
    "monitor = EarlyStopping(monitor='val_loss', min_delta=1e-3, patience=5, \n",
    "                        verbose=1, mode='auto', restore_best_weights=True)\n",
    "model.fit(x_train,y_train,validation_data=(x_test,y_test),\n",
    "          verbose=2,callbacks=[monitor],epochs=1000)\n",
    "\n",
    "# Predict\n",
    "pred = model.predict(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we've trained the neural network, we can check its RMSE error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final score (RMSE): 4.5134384517538795\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Measure RMSE error.  RMSE is common for regression.\n",
    "score = np.sqrt(metrics.mean_squared_error(pred,y_test))\n",
    "print(\"Final score (RMSE): {}\".format(score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are ready to generate the Kaggle submission file.  We will use the MPG test data that does not contain a $y$ target value.  It is our job to predict this value and submit to Kaggle."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     id        mpg\n",
      "0   350  29.112602\n",
      "1   351  27.803200\n",
      "2   352  27.981804\n",
      "3   353  30.487831\n",
      "4   354  27.227440\n",
      "5   355  26.438324\n",
      "6   356  27.886986\n",
      "7   357  29.103935\n",
      "8   358  26.447609\n",
      "9   359  30.027260\n",
      "10  360  30.312553\n",
      "11  361  30.712151\n",
      "12  362  23.952263\n",
      "13  363  24.858467\n",
      "14  364  23.459129\n",
      "15  365  22.638985\n",
      "16  366  26.032127\n",
      "17  367  26.197884\n",
      "18  368  28.448906\n",
      "19  369  28.138954\n",
      "20  370  27.352821\n",
      "21  371  27.313377\n",
      "22  372  26.464119\n",
      "23  373  26.689583\n",
      "24  374  26.546562\n",
      "25  375  27.829781\n",
      "26  376  27.466354\n",
      "27  377  30.343369\n",
      "28  378  29.985909\n",
      "29  379  27.807251\n",
      "30  380  28.450882\n",
      "31  381  26.574844\n",
      "32  382  28.199501\n",
      "33  383  29.615051\n",
      "34  384  29.048317\n",
      "35  385  29.320534\n",
      "36  386  29.582710\n",
      "37  387  24.533165\n",
      "38  388  24.426888\n",
      "39  389  24.658607\n",
      "40  390  21.805504\n",
      "41  391  26.026482\n",
      "42  392  24.947670\n",
      "43  393  26.902489\n",
      "44  394  26.575218\n",
      "45  395  33.546684\n",
      "46  396  24.233910\n",
      "47  397  28.609993\n",
      "48  398  28.913261\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Generate Kaggle submit file\n",
    "\n",
    "# Encode feature vector\n",
    "df_test = pd.read_csv(\n",
    "    \"https://data.heatonresearch.com/data/t81-558/datasets/\"+\\\n",
    "    \"kaggle_auto_test.csv\", na_values=['NA','?'])\n",
    "\n",
    "# Convert to numpy - regression\n",
    "ids = df_test['id']\n",
    "df_test.drop('id', axis=1, inplace=True)\n",
    "\n",
    "# Handle missing value\n",
    "df_test['horsepower'] = df_test['horsepower'].\\\n",
    "    fillna(df['horsepower'].median())\n",
    "\n",
    "x = df_test[['cylinders', 'displacement', 'horsepower', 'weight',\n",
    "       'acceleration', 'year', 'origin']].values\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Generate predictions\n",
    "pred = model.predict(x)\n",
    "#pred\n",
    "\n",
    "# Create submission data set\n",
    "\n",
    "df_submit = pd.DataFrame(pred)\n",
    "df_submit.insert(0,'id',ids)\n",
    "df_submit.columns = ['id','mpg']\n",
    "\n",
    "# Write submit file locally\n",
    "df_submit.to_csv(\"auto_submit.csv\", index=False) \n",
    "\n",
    "print(df_submit)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Module 8 Assignment\n",
    "\n",
    "You can find the first assignment here: [assignment 8](https://github.com/jeffheaton/t81_558_deep_learning/blob/master/assignments/assignment_yourname_class8.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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