{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
    "\n",
    "# <!-- TITLE --> [LOGR1] - Logistic regression, in pure Tensorflow\n",
    "<!-- DESC --> Logistic Regression with Mini-Batch Gradient Descent using pure TensorFlow. \n",
    "<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
    "\n",
    "## Objectives :\n",
    " - A logistic regression has the objective of providing a probability of belonging to a class.  \n",
    " - Découvrir une implémentation 100% Tensorflow ..et apprendre à aimer Keras\n",
    "\n",
    "## What we're going to do :\n",
    "\n",
    "X contains characteristics  \n",
    "y contains the probability of membership (1 or 0)  \n",
    "\n",
    "We'll look for a value of $\\theta$ such that the linear regression $\\theta^{T}X$ can be used to calculate our probability:  \n",
    "\n",
    "$\\hat{p} = h_\\theta(X) = \\sigma(\\theta^T{X})$  \n",
    "\n",
    "Where $\\sigma$ is the logit function, typically a sigmoid (S) function:  \n",
    "\n",
    "$\n",
    "\\sigma(t) = \\dfrac{1}{1 + \\exp(-t)}\n",
    "$  \n",
    "\n",
    "The predicted value $\\hat{y}$ will then be calculated as follows:\n",
    "\n",
    "$\n",
    "\\hat{y} =\n",
    "\\begin{cases}\n",
    "  0 & \\text{if } \\hat{p} < 0.5 \\\\\n",
    "  1 & \\text{if } \\hat{p} \\geq 0.5\n",
    "\\end{cases}\n",
    "$\n",
    "\n",
    "**Calculation of the cost of the regression:**  \n",
    "For a training observation x, the cost can be calculated as follows:  \n",
    "\n",
    "$\n",
    "c(\\theta) =\n",
    "\\begin{cases}\n",
    "  -\\log(\\hat{p}) & \\text{if } y = 1 \\\\\n",
    "  -\\log(1 - \\hat{p}) & \\text{if } y = 0\n",
    "\\end{cases}\n",
    "$\n",
    "\n",
    "The regression cost function (log loss) over the whole training set can be written as follows:  \n",
    "\n",
    "$\n",
    "J(\\theta) = -\\dfrac{1}{m} \\sum_{i=1}^{m}{\\left[ y^{(i)} log\\left(\\hat{p}^{(i)}\\right) + (1 - y^{(i)}) log\\left(1 - \\hat{p}^{(i)}\\right)\\right]}\n",
    "$\n",
    "## Step 1 - Import and init"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/pjluc/anaconda3/envs/fidle/lib/python3.7/site-packages/tensorflow_core/python/compat/v2_compat.py:65: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "non-resource variables are not supported in the long term\n"
     ]
    },
    {
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       "div.warn {    \n",
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       "    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",
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       "    }\n",
       "\n",
       "\n",
       "\n",
       "</style>\n",
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     "text": [
      "\n",
      "FIDLE 2020 - Practical Work Module\n",
      "Version              : 0.4.0\n",
      "Run time             : Saturday 22 February 2020, 15:41:52\n",
      "TensorFlow version   : 2.0.0\n",
      "Keras version        : 2.2.4-tf\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import sklearn as sl\n",
    "from sklearn import metrics\n",
    "\n",
    "import tensorflow.compat.v1 as tf\n",
    "tf.disable_v2_behavior()\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "import random\n",
    "import os\n",
    "import sys\n",
    "\n",
    "sys.path.append('..')\n",
    "import fidle.pwk as ooo\n",
    "\n",
    "ooo.init()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1 - Usefull stuff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def vector_infos(name,V):\n",
    "    '''Displaying some information about a vector'''\n",
    "    with np.printoptions(precision=4, suppress=True):\n",
    "        print(\"{:16} : ndim={}  shape={:10}  Mean = {}  Std = {}\".format( name,V.ndim, str(V.shape), V.mean(axis=0), V.std(axis=0)))\n",
    "\n",
    "def random_batch(X_train, y_train, batch_size):\n",
    "    '''Returning a data set for a batch'''\n",
    "    indices = np.random.randint(0, len(X_train), batch_size)\n",
    "    X_batch = X_train[indices]\n",
    "    y_batch = y_train[indices]\n",
    "    return X_batch, y_batch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 - Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_size      = 1000       # Number of observations\n",
    "data_cols      = 2          # observation size\n",
    "data_noise     = 0.2\n",
    "test_ratio     = 0.2        # Ratio of data reserved for validation\n",
    "random_seed    = 123\n",
    "\n",
    "learning_rate  = 0.01\n",
    "n_epochs       = 1000\n",
    "batch_size     = 50\n",
    "\n",
    "epsilon        = 1e-7       # To avoid overflows on some calculations (log())\n",
    "\n",
    "learning_rate2 = 0.01       # Pour la version 2\n",
    "n_epochs2      = 6000\n",
    "batch_size2    = 50\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2 - Data preparation\n",
    "### 2.1 - Get some data\n",
    "The data here are totally fabricated and represent the **examination results** (passed or failed) based on the students' **working** and **sleeping hours** .  \n",
    "X=(working hours, sleeping hours) y={result} where result=0 (failed) or 1 (passed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def do_i_have_it(hours_of_work, hours_of_sleep):\n",
    "    '''Returns the exam result based on work and sleep hours'''\n",
    "    hours_of_sleep_min = 5\n",
    "    hours_of_work_min  = 4\n",
    "    hours_of_game_max  = 3\n",
    "    # ---- Have to sleep and work\n",
    "    if hours_of_sleep < hours_of_sleep_min: return 0\n",
    "    if hours_of_work < hours_of_work_min:   return 0\n",
    "    # ---- Gameboy is not good for you\n",
    "    hours_of_game = 24 - 10 - hours_of_sleep - hours_of_work + random.gauss(0,0.4)\n",
    "    if hours_of_game > hours_of_game_max:   return 0\n",
    "    # ---- Fine, you got it\n",
    "    return 1\n",
    "\n",
    "def make_students_dataset(size, noise):\n",
    "    '''Fabrique un dataset pour <size> étudiants'''\n",
    "    x = []\n",
    "    y = []\n",
    "    for i in range(size):\n",
    "        w = random.gauss(5,1)\n",
    "        s = random.gauss(7,1.5)\n",
    "        r   = do_i_have_it(w,s)\n",
    "        x.append([w,s])\n",
    "        y.append(r)\n",
    "    return (np.array(x), np.array(y))\n",
    "\n",
    "X_data,y_data=make_students_dataset(data_size,data_noise)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 - Show it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset X        : ndim=2  shape=(1000, 2)   Mean = [5.0127 6.9391]  Std = [0.9817 1.4605]\n",
      "Dataset y        : ndim=1  shape=(1000,)     Mean = 0.641  Std = 0.4797072023641087\n"
     ]
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1)\n",
    "fig.set_size_inches(8,6)\n",
    "ax.plot(X_data[y_data == 1, 0], X_data[y_data == 1, 1], 'go', markersize=4, label=\"y=1 (positive)\")\n",
    "ax.plot(X_data[y_data == 0, 0], X_data[y_data == 0, 1], 'ro', markersize=4, label=\"y=0 (negative)\")\n",
    "plt.tick_params(axis='both', which='both', bottom=False, left=False, labelbottom=False, labelleft=False)\n",
    "plt.xlabel('Hours of work')\n",
    "plt.ylabel('Hours of sleep')\n",
    "plt.show()\n",
    "\n",
    "vector_infos('Dataset X',X_data)\n",
    "vector_infos('Dataset y',y_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 - Preparation of data\n",
    "\n",
    "We're going to:\n",
    "- normalize the data\n",
    "- add a column of 1 for bias\n",
    "- Transform y_moons into a vector\n",
    "- split the data to have : :\n",
    "  - a training set\n",
    "  - a test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_scaled         : ndim=2  shape=(1000, 2)   Mean = [0. 0.]  Std = [1. 1.]\n",
      "X_train          : ndim=2  shape=(800, 3)    Mean = [ 1.      0.009  -0.0045]  Std = [0.     1.0073 1.0062]\n",
      "y_train          : ndim=2  shape=(800, 1)    Mean = [0.6362]  Std = [0.4811]\n",
      "X_test           : ndim=2  shape=(200, 3)    Mean = [ 1.     -0.0358  0.0181]  Std = [0.     0.9696 0.9747]\n",
      "y_test           : ndim=2  shape=(200, 1)    Mean = [0.66]  Std = [0.4737]\n"
     ]
    }
   ],
   "source": [
    "# ----- Normalisation des données\n",
    "scaler = sl.preprocessing.StandardScaler()\n",
    "X_scaled   = scaler.fit_transform(X_data)\n",
    "\n",
    "# ----- Ajout de la colonne de 1\n",
    "X_scaled_1 = np.c_[np.ones((data_size, 1)), X_scaled]\n",
    "\n",
    "# ----- Verticalisation de y_moons\n",
    "y_data_v = y_data.reshape(-1,1)\n",
    "\n",
    "# ----- Partage des données\n",
    "test_size = int(data_size * test_ratio)\n",
    "X_train = X_scaled_1[:-test_size]\n",
    "X_test  = X_scaled_1[-test_size:]\n",
    "y_train = y_data_v[:-test_size]\n",
    "y_test  = y_data_v[-test_size:]\n",
    "\n",
    "vector_infos('X_scaled',X_scaled)\n",
    "vector_infos('X_train',X_train)\n",
    "vector_infos('y_train',y_train)\n",
    "vector_infos('X_test',X_test)\n",
    "vector_infos('y_test',y_test)\n",
    "\n",
    "y_train_h = y_train.reshape(-1,) # nécessaire pour la visu."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4 - Have a look"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "#### Train data :"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "#### Test data :"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ooo.display_md(\"#### Train data :\")\n",
    "fig, axs = plt.subplots()\n",
    "fig.set_size_inches(8,6)\n",
    "axs.plot(X_train[y_train_h == 1, 1], X_train[y_train_h == 1, 2], 'o', color='green', markersize=4, label=\"Train / Positifs\")\n",
    "axs.plot(X_train[y_train_h == 0, 1], X_train[y_train_h == 0, 2], 'o', color='red',   markersize=4, label=\"Train / Négatifs\")\n",
    "plt.tick_params(axis='both', which='both', bottom=False, left=False, labelbottom=False, labelleft=False)\n",
    "plt.xlabel('Hours of work')\n",
    "plt.ylabel('Hours of sleep')\n",
    "plt.show()\n",
    "\n",
    "ooo.display_md(\"#### Test data :\")\n",
    "fig, axs = plt.subplots()\n",
    "fig.set_size_inches(8,6)\n",
    "axs.plot(X_test[:, 1], X_test[:, 2], 'o',color='gray', markersize=4, label=\"A classer !\")\n",
    "plt.tick_params(axis='both', which='both', bottom=False, left=False, labelbottom=False, labelleft=False)\n",
    "plt.xlabel('Hours of work')\n",
    "plt.ylabel('Hours of sleep')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3 - Logistic model #1\n",
    "### 3.1 - Build model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf.reset_default_graph()\n",
    "\n",
    "X = tf.placeholder(tf.float32, shape=(None, data_cols + 1), name=\"X\")\n",
    "y = tf.placeholder(tf.float32, shape=(None, 1), name=\"y\")\n",
    "\n",
    "initializer = tf.random_uniform([data_cols + 1, 1], -1.0, 1.0, seed=random_seed)\n",
    "theta = tf.Variable(initializer, name=\"theta\")\n",
    "\n",
    "logits = tf.matmul(X, theta, name=\"logits\")\n",
    "\n",
    "#y_proba = tf.sigmoid(logits)\n",
    "y_proba = 1 / (1 + tf.exp(-logits))\n",
    "\n",
    "#loss = tf.losses.log_loss(y, y_proba)\n",
    "loss = -tf.reduce_mean(y * tf.log(y_proba + epsilon) + (1 - y) * tf.log(1 - y_proba + epsilon))\n",
    "\n",
    "optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n",
    "training_op = optimizer.minimize(loss)\n",
    "\n",
    "init = tf.global_variables_initializer()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 - Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0 \tLoss: 1.270164\n",
      "Epoch: 100 \tLoss: 0.29649493\n",
      "Epoch: 200 \tLoss: 0.25434545\n",
      "Epoch: 300 \tLoss: 0.23806633\n",
      "Epoch: 400 \tLoss: 0.2301672\n",
      "Epoch: 500 \tLoss: 0.22440842\n",
      "Epoch: 600 \tLoss: 0.22117954\n",
      "Epoch: 700 \tLoss: 0.21890724\n",
      "Epoch: 800 \tLoss: 0.21674454\n",
      "Epoch: 900 \tLoss: 0.21477516\n",
      "Epoch: 1000 \tLoss: 0.21442997\n"
     ]
    }
   ],
   "source": [
    "nb_batches = int(np.ceil(data_size / batch_size))\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    sess.run(init)\n",
    "\n",
    "    for epoch in range(n_epochs+1):\n",
    "        for batch_index in range(nb_batches):\n",
    "            X_batch, y_batch = random_batch(X_train, y_train, batch_size)\n",
    "            sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\n",
    "            \n",
    "        loss_val = loss.eval({X: X_test, y: y_test})\n",
    "        \n",
    "        if epoch % 100 == 0:\n",
    "            print(\"Epoch:\", epoch, \"\\tLoss:\", loss_val)\n",
    "\n",
    "    y_proba_val = y_proba.eval(feed_dict={X: X_test, y: y_test})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### 3.3 - Evaluation\n",
    "\n",
    "Accuracy = Ability to avoid false positives = $\\frac{Tp}{Tp+Fp}$  \n",
    "Recall = Ability to find the right positives = $\\frac{Tp}{Tp+Fn}$  \n",
    "Avec :  \n",
    "$T_p$ (true positive) Correct positive answer  \n",
    "$F_p$ (false positive) False positive answer  \n",
    "$T_n$ (true negative) Correct negative answer  \n",
    "$F_n$ (false negative) Wrong negative answer  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy = 0.946    Recall = 0.932\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def show_results(y_proba_val):\n",
    "    y_pred = (y_proba_val >= 0.5)\n",
    "\n",
    "    precision = metrics.precision_score(y_test, y_pred)\n",
    "    recall    = metrics.recall_score(y_test, y_pred)\n",
    "\n",
    "    print(\"Accuracy = {:5.3f}    Recall = {:5.3f}\".format(precision, recall))\n",
    "\n",
    "    y_pred_1d = y_pred.reshape(-1)\n",
    "    y_test_1d = y_test.reshape(-1)\n",
    "\n",
    "    X_pred_positives = X_test[ y_pred_1d == True]   # items prédits    positifs\n",
    "    X_real_positives = X_test[ y_test_1d == 1 ]     # items réellement positifs\n",
    "    X_pred_negatives = X_test[ y_pred_1d == False]  # items prédits    négatifs\n",
    "    X_real_negatives = X_test[ y_test_1d == 0 ]     # items réellement négatifs\n",
    "\n",
    "    fig, axs = plt.subplots(2, 2)\n",
    "    fig.subplots_adjust(wspace=.1,hspace=0.2)\n",
    "    fig.set_size_inches(14,10)\n",
    "    \n",
    "    axs[0,0].plot(X_pred_positives[:,1], X_pred_positives[:,2], 'o',color='lightgreen', markersize=10, label=\"Prédits positifs\")\n",
    "    axs[0,0].plot(X_real_positives[:,1], X_real_positives[:,2], 'o',color='green',      markersize=4,  label=\"Réels positifs\")\n",
    "    axs[0,0].legend()\n",
    "    axs[0,0].tick_params(axis='both', which='both', bottom=False, left=False, labelbottom=False, labelleft=False)\n",
    "    axs[0,0].set_xlabel('$x_1$')\n",
    "    axs[0,0].set_ylabel('$x_2$')\n",
    "\n",
    "\n",
    "    axs[0,1].plot(X_pred_negatives[:,1], X_pred_negatives[:,2], 'o',color='lightsalmon', markersize=10, label=\"Prédits négatifs\")\n",
    "    axs[0,1].plot(X_real_negatives[:,1], X_real_negatives[:,2], 'o',color='red',        markersize=4,  label=\"Réels négatifs\")\n",
    "    axs[0,1].legend()\n",
    "    axs[0,1].tick_params(axis='both', which='both', bottom=False, left=False, labelbottom=False, labelleft=False)\n",
    "    axs[0,1].set_xlabel('$x_1$')\n",
    "    axs[0,1].set_ylabel('$x_2$')\n",
    "    \n",
    "    axs[1,0].plot(X_pred_positives[:,1], X_pred_positives[:,2], 'o',color='lightgreen', markersize=10, label=\"Prédits positifs\")\n",
    "    axs[1,0].plot(X_pred_negatives[:,1], X_pred_negatives[:,2], 'o',color='lightsalmon', markersize=10, label=\"Prédits négatifs\")\n",
    "    axs[1,0].plot(X_real_positives[:,1], X_real_positives[:,2], 'o',color='green',      markersize=4,  label=\"Réels positifs\")\n",
    "    axs[1,0].plot(X_real_negatives[:,1], X_real_negatives[:,2], 'o',color='red',        markersize=4,  label=\"Réels négatifs\")\n",
    "    axs[1,0].tick_params(axis='both', which='both', bottom=False, left=False, labelbottom=False, labelleft=False)\n",
    "    axs[1,0].set_xlabel('$x_1$')\n",
    "    axs[1,0].set_ylabel('$x_2$')\n",
    "\n",
    "    axs[1,1].pie([precision,1-precision], explode=[0,0.1], labels=[\"\",\"Errors\"], \n",
    "                 autopct='%1.1f%%', shadow=False, startangle=70, colors=[\"lightsteelblue\",\"coral\"])\n",
    "    axs[1,1].axis('equal')\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "show_results(y_proba_val)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 4 - Bending the space to a model #2 ;-)\n",
    "\n",
    "We're going to increase the characteristics of our observations, with : ${x_1}^2$, ${x_2}^2$, ${x_1}^3$ et ${x_2}^3$  \n",
    "\n",
    "$\n",
    "X=\n",
    "\\begin{bmatrix}1 & x_{11} & x_{12} \\\\\n",
    "\\vdots & \\dots\\\\\n",
    "1 & x_{m1} & x_{m2}  \\end{bmatrix}\n",
    "\\text{et }\n",
    "X_{ng}=\\begin{bmatrix}1 & x_{11} & x_{12} & x_{11}^2 & x_{12}^2& x_{11}^3 & x_{12}^3 \\\\\n",
    "\\vdots & & & \\dots \\\\\n",
    "1 & x_{m1} & x_{m2} & x_{m1}^2 & x_{m2}^2& x_{m1}^3 & x_{m2}^3 \\end{bmatrix}\n",
    "$\n",
    "\n",
    "Note : `sklearn.preprocessing.PolynomialFeatures` can do that for us, but we'll do it ourselves:\n",
    "### 4.1 - Extend data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_enhanced = np.c_[X_train,\n",
    "                         X_train[:, 1] ** 2,\n",
    "                         X_train[:, 2] ** 2,\n",
    "                         X_train[:, 1] ** 3,\n",
    "                         X_train[:, 2] ** 3]\n",
    "X_test_enhanced = np.c_[X_test,\n",
    "                        X_test[:, 1] ** 2,\n",
    "                        X_test[:, 2] ** 2,\n",
    "                        X_test[:, 1] ** 3,\n",
    "                        X_test[:, 2] ** 3]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 - A more readable version of our model. Yes it is.\n",
    "...and with Tensorboard tracking and checkpoint recording."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def logistic_regression(X, y, initializer=None, seed=42, learning_rate=0.01):\n",
    "\n",
    "    n_inputs_including_bias = int(X.get_shape()[1])\n",
    "    \n",
    "    with tf.name_scope(\"logistic_regression\"):\n",
    "        \n",
    "        # ----- Construction du modèle\n",
    "        with tf.name_scope(\"model\"):\n",
    "            if initializer is None:\n",
    "                initializer = tf.random_uniform([n_inputs_including_bias, 1], -1.0, 1.0, seed=seed)\n",
    "            theta = tf.Variable(initializer, name=\"theta\")\n",
    "            logits = tf.matmul(X, theta, name=\"logits\")\n",
    "            y_proba = tf.sigmoid(logits)\n",
    "            \n",
    "        with tf.name_scope(\"train\"):\n",
    "            loss = tf.losses.log_loss(y, y_proba, scope=\"loss\")\n",
    "            optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate2)\n",
    "            training_op = optimizer.minimize(loss)\n",
    "            loss_summary = tf.summary.scalar('log_loss', loss)\n",
    "            \n",
    "        with tf.name_scope(\"init\"):\n",
    "            init = tf.global_variables_initializer()\n",
    "            \n",
    "        with tf.name_scope(\"save\"):\n",
    "            saver = tf.train.Saver(max_to_keep=4)\n",
    "            \n",
    "    return y_proba, loss, training_op, loss_summary, init, saver\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3 - Build the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf.reset_default_graph()\n",
    "\n",
    "log_dir = './run/logs'\n",
    "chk_dir = './run/models'\n",
    "os.makedirs(log_dir, mode=0o750, exist_ok=True)\n",
    "os.makedirs(chk_dir, mode=0o750, exist_ok=True)\n",
    "\n",
    "X = tf.placeholder(tf.float32, shape=(None, data_cols + 1 + 4), name=\"X\")\n",
    "y = tf.placeholder(tf.float32, shape=(None, 1), name=\"y\")\n",
    "\n",
    "# Build model\n",
    "y_proba, loss, training_op, loss_summary, init, saver = logistic_regression(X, y)\n",
    "\n",
    "# Save model\n",
    "file_writer = tf.summary.FileWriter(log_dir, tf.get_default_graph())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.4 - Train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:      0  Loss:   0.7085    checkpoint: ./run/models/model-ckpt-0\n",
      "Epoch:    500  Loss:   0.1654    checkpoint: ./run/models/model-ckpt-500\n",
      "Epoch:   1000  Loss:   0.1470    checkpoint: ./run/models/model-ckpt-1000\n",
      "Epoch:   1500  Loss:   0.1382    checkpoint: ./run/models/model-ckpt-1500\n",
      "Epoch:   2000  Loss:   0.1328    checkpoint: ./run/models/model-ckpt-2000\n",
      "WARNING:tensorflow:From /home/pjluc/anaconda3/envs/fidle/lib/python3.7/site-packages/tensorflow_core/python/training/saver.py:963: remove_checkpoint (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use standard file APIs to delete files with this prefix.\n",
      "Epoch:   2500  Loss:   0.1300    checkpoint: ./run/models/model-ckpt-2500\n",
      "Epoch:   3000  Loss:   0.1272    checkpoint: ./run/models/model-ckpt-3000\n",
      "Epoch:   3500  Loss:   0.1262    checkpoint: ./run/models/model-ckpt-3500\n",
      "Epoch:   4000  Loss:   0.1253    checkpoint: ./run/models/model-ckpt-4000\n",
      "Epoch:   4500  Loss:   0.1245    checkpoint: ./run/models/model-ckpt-4500\n",
      "Epoch:   5000  Loss:   0.1239    checkpoint: ./run/models/model-ckpt-5000\n",
      "Epoch:   5500  Loss:   0.1235    checkpoint: ./run/models/model-ckpt-5500\n"
     ]
    }
   ],
   "source": [
    "n_batches = int(np.ceil(data_size / batch_size2))\n",
    "\n",
    "model_file  = chk_dir + \"/model-ckpt\"\n",
    "model_final = chk_dir + \"/model-final\"\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    \n",
    "    sess.run(init)\n",
    "\n",
    "    for epoch in range(n_epochs2):\n",
    "        \n",
    "        for batch_index in range(n_batches):\n",
    "            # get a batch\n",
    "            X_batch, y_batch = random_batch(X_train_enhanced, y_train, batch_size)\n",
    "            # train\n",
    "            sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\n",
    "        \n",
    "        # Calculation of logistic loss and logs\n",
    "        loss_val, summary_str = sess.run([loss, loss_summary], feed_dict={X: X_test_enhanced, y: y_test})\n",
    "        # Logging\n",
    "        file_writer.add_summary(summary_str, epoch)\n",
    "        \n",
    "        if epoch % 500 == 0:\n",
    "            print('Epoch: {:6d}  Loss: {:8.4f}    checkpoint: {}-{}'.format(epoch,loss_val,model_file,epoch))\n",
    "            # Save checkpoint\n",
    "            saver.save(sess, model_file, global_step=epoch)\n",
    "\n",
    "    # Save the final model\n",
    "    saver.save(sess, model_final)\n",
    "    # Evaluation with test data\n",
    "    y_proba_val2 = y_proba.eval(feed_dict={X: X_test_enhanced, y: y_test})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.5 - Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy = 0.962    Recall = 0.970\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1008x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_results(y_proba_val2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "<img width=\"80px\" src=\"../fidle/img/00-Fidle-logo-01.svg\"></img>"
   ]
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}