01-DNN-Regression.ipynb

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    "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
    "\n",
    "\n",
    "# <!-- TITLE --> [BHP1] - Regression with a Dense Network (DNN)\n",
    "<!-- DESC --> A Simple regression with a Dense Neural Network (DNN) - BHPD dataset\n",
    "<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
    "\n",
    "## Objectives :\n",
    " - Predicts **housing prices** from a set of house features. \n",
    " - Understanding the **principle** and the **architecture** of a regression with a **dense neural network**  \n",
    "\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 theses informations : \n",
    "\n",
    " - CRIM: This is the per capita crime rate by town\n",
    " - ZN: This is the proportion of residential land zoned for lots larger than 25,000 sq.ft\n",
    " - INDUS: This is the proportion of non-retail business acres per town\n",
    " - CHAS: This is the Charles River dummy variable (this is equal to 1 if tract bounds river; 0 otherwise)\n",
    " - NOX: This is the nitric oxides concentration (parts per 10 million)\n",
    " - RM: This is the average number of rooms per dwelling\n",
    " - AGE: This is the proportion of owner-occupied units built prior to 1940\n",
    " - DIS: This is the weighted distances to five Boston employment centers\n",
    " - RAD: This is the index of accessibility to radial highways\n",
    " - TAX: This is the full-value property-tax rate per 10,000 dollars\n",
    " - PTRATIO: This is the pupil-teacher ratio by town\n",
    " - B: This is calculated as 1000(Bk — 0.63)^2, where Bk is the proportion of people of African American descent by town\n",
    " - LSTAT: This is the percentage lower status of the population\n",
    " - MEDV: This is the median value of owner-occupied homes in 1000 dollars\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": "markdown",
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   "source": [
    "## Step 1 - Import and init"
   ]
  },
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   "cell_type": "code",
   "execution_count": 1,
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      "\n",
      "FIDLE 2020 - Practical Work Module\n",
      "Version              : 0.57 DEV\n",
      "Run time             : Friday 11 September 2020, 08:50:07\n",
      "TensorFlow version   : 2.2.0\n",
      "Keras version        : 2.3.0-tf\n",
      "Current place        : Fidle at IDRIS\n",
      "Datasets dir         : /gpfswork/rech/mlh/commun/datasets\n",
      "Update keras cache   : Done\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",
    "sys.path.append('..')\n",
    "import fidle.pwk as ooo\n",
    "\n",
    "place, datasets_dir = ooo.init()"
   ]
  },
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   "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": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "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": [
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       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63b\" ><caption>Few lines of the dataset :</caption><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_0738d7b2_f3fb_11ea_bd64_0cc47af5c63blevel0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow0_col0\" class=\"data row0 col0\" >0.01</td>\n",
       "                        <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow0_col1\" class=\"data row0 col1\" >18.00</td>\n",
       "                        <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow0_col2\" class=\"data row0 col2\" >2.31</td>\n",
       "                        <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow0_col3\" class=\"data row0 col3\" >0.00</td>\n",
       "                        <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow0_col4\" class=\"data row0 col4\" >0.54</td>\n",
       "                        <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow0_col5\" class=\"data row0 col5\" >6.58</td>\n",
       "                        <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow0_col6\" class=\"data row0 col6\" >65.20</td>\n",
       "                        <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow0_col7\" class=\"data row0 col7\" >4.09</td>\n",
       "                        <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow0_col8\" class=\"data row0 col8\" >1.00</td>\n",
       "                        <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow0_col9\" class=\"data row0 col9\" >296.00</td>\n",
       "                        <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow0_col10\" class=\"data row0 col10\" >15.30</td>\n",
       "                        <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow0_col11\" class=\"data row0 col11\" >396.90</td>\n",
       "                        <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow0_col12\" class=\"data row0 col12\" >4.98</td>\n",
       "                        <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow0_col13\" class=\"data row0 col13\" >24.00</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63blevel0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow1_col0\" class=\"data row1 col0\" >0.03</td>\n",
       "                        <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow1_col1\" class=\"data row1 col1\" >0.00</td>\n",
       "                        <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow1_col2\" class=\"data row1 col2\" >7.07</td>\n",
       "                        <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow1_col3\" class=\"data row1 col3\" >0.00</td>\n",