01-DNN-Regression.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"text-align: left\">\n",
    "<!--     <img src=\"../fidle/img/00-Fidle-header-01.svg\" style=\"width:800\" /> -->\n",
    "</div>\n",
    "\n",
    "![this is a test](../fidle/img/00-Fidle-header-01.png)\n",
    "\n",
    "Deep Neural Network (DNN) - BHPD dataset\n",
    "========================================\n",
    "\n",
    "A very simple and classic example of **regression** :\n",
    "\n",
    "## Objectives :\n",
    "Predicts **housing prices** from a set of house features. \n",
    "\n",
    "The **[Boston Housing Dataset](https://www.cs.toronto.edu/~delve/data/boston/bostonDetail.html)** consists of price of houses in various places in Boston.  \n",
    "Alongside with price, the dataset also provide information such as Crime, areas of non-retail business in the town,  \n",
    "age of people who own the house and many other attributes...\n",
    "\n",
    "What we're going to do:\n",
    "\n",
    " - Retrieve data\n",
    " - Preparing the data\n",
    " - Build a model\n",
    " - Train the model\n",
    " - Evaluate the result\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import Image, SVG\n",
    "display(SVG(filename='../fidle/img/00-Fidle-header-01.svg'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1 - Import and init"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       "\n",
       "div.warn {    \n",
       "    background-color: #fcf2f2;\n",
       "    border-color: #dFb5b4;\n",
       "    border-left: 5px solid #dfb5b4;\n",
       "    padding: 0.5em;\n",
       "    font-weight: bold;\n",
       "    font-size: 1.1em;;\n",
       "    }\n",
       "\n",
       "\n",
       "\n",
       "div.nota {    \n",
       "    background-color: #DAFFDE;\n",
       "    border-left: 5px solid #92CC99;\n",
       "    padding: 0.5em;\n",
       "    }\n",
       "\n",
       "\n",
       "\n",
       "</style>\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "FIDLE 2020 - Practical Work Module\n",
      "Version              : 0.2.9\n",
      "Run time             : Monday 17 February 2020, 22:08:38\n",
      "TensorFlow version   : 2.0.0\n",
      "Keras version        : 2.2.4-tf\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import os,sys\n",
    "\n",
    "from IPython.display import display, Markdown\n",
    "from importlib import reload\n",
    "\n",
    "sys.path.append('..')\n",
    "import fidle.pwk as ooo\n",
    "\n",
    "ooo.init()\n",
    "os.makedirs('./run/models',   mode=0o750, exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2 - Retrieve data\n",
    "\n",
    "### 2.1 - Option 1  : From Keras\n",
    "Boston housing is a famous historic dataset, so we can get it directly from [Keras datasets](https://www.tensorflow.org/api_docs/python/tf/keras/datasets)  "
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "(x_train, y_train), (x_test, y_test) = keras.datasets.boston_housing.load_data(test_split=0.2, seed=113)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 - Option 2 : From a csv file\n",
    "More fun !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >crim</th>        <th class=\"col_heading level0 col1\" >zn</th>        <th class=\"col_heading level0 col2\" >indus</th>        <th class=\"col_heading level0 col3\" >chas</th>        <th class=\"col_heading level0 col4\" >nox</th>        <th class=\"col_heading level0 col5\" >rm</th>        <th class=\"col_heading level0 col6\" >age</th>        <th class=\"col_heading level0 col7\" >dis</th>        <th class=\"col_heading level0 col8\" >rad</th>        <th class=\"col_heading level0 col9\" >tax</th>        <th class=\"col_heading level0 col10\" >ptratio</th>        <th class=\"col_heading level0 col11\" >b</th>        <th class=\"col_heading level0 col12\" >lstat</th>        <th class=\"col_heading level0 col13\" >medv</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row0_col0\" class=\"data row0 col0\" >0.01</td>\n",
       "                        <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row0_col1\" class=\"data row0 col1\" >18.00</td>\n",
       "                        <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row0_col2\" class=\"data row0 col2\" >2.31</td>\n",
       "                        <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row0_col3\" class=\"data row0 col3\" >0.00</td>\n",
       "                        <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row0_col4\" class=\"data row0 col4\" >0.54</td>\n",
       "                        <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row0_col5\" class=\"data row0 col5\" >6.58</td>\n",
       "                        <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row0_col6\" class=\"data row0 col6\" >65.20</td>\n",
       "                        <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row0_col7\" class=\"data row0 col7\" >4.09</td>\n",
       "                        <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row0_col8\" class=\"data row0 col8\" >1.00</td>\n",
       "                        <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row0_col9\" class=\"data row0 col9\" >296.00</td>\n",
       "                        <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row0_col10\" class=\"data row0 col10\" >15.30</td>\n",
       "                        <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row0_col11\" class=\"data row0 col11\" >396.90</td>\n",
       "                        <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row0_col12\" class=\"data row0 col12\" >4.98</td>\n",
       "                        <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row0_col13\" class=\"data row0 col13\" >24.00</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row1_col0\" class=\"data row1 col0\" >0.03</td>\n",
       "                        <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row1_col1\" class=\"data row1 col1\" >0.00</td>\n",
       "                        <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row1_col2\" class=\"data row1 col2\" >7.07</td>\n",
       "                        <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row1_col3\" class=\"data row1 col3\" >0.00</td>\n",
       "                        <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row1_col4\" class=\"data row1 col4\" >0.47</td>\n",
       "                        <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row1_col5\" class=\"data row1 col5\" >6.42</td>\n",
       "                        <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row1_col6\" class=\"data row1 col6\" >78.90</td>\n",
       "                        <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row1_col7\" class=\"data row1 col7\" >4.97</td>\n",
       "                        <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row1_col8\" class=\"data row1 col8\" >2.00</td>\n",
       "                        <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row1_col9\" class=\"data row1 col9\" >242.00</td>\n",
       "                        <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row1_col10\" class=\"data row1 col10\" >17.80</td>\n",
       "                        <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row1_col11\" class=\"data row1 col11\" >396.90</td>\n",