11_Introduction_to_Coding_as_Literacy_20170228.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Driven Modeling\n",
    "### (Theme of this semester: CODING AS LITERACY)\n",
    "<hr style=\"height:3px;border:none;color:stillblue;background-color:black;\" />\n",
    "### PhD seminar series at Chair for Computer Aided Architectural Design (CAAD), ETH Zurich\n",
    "\n",
    "[Vahid Moosavi](https://vahidmoosavi.com)\n",
    "<hr style=\"height:1px;border:none;color:stillblue;background-color:black;\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a></a>\n",
    "\n",
    "# First Session: Introduction \n",
    "<hr style=\"height:1px;border:none;color:stillblue;background-color:steelblue;\" />\n",
    "<hr style=\"height:1px;border:none;color:stillblue;background-color:black;\" />\n",
    "\n",
    "<span style=\"color:steelblue;font-size: 1em;\"> 28th February 2017</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What do I mean by \"Modeling\"?\n",
    "\n",
    "<a></a>\n",
    "## Formally speaking...\n",
    "### <a></a>\n",
    "![](Images/RosenModel.png)\n",
    "\n",
    "<hr style=\"height:3px;border:none;color:stillblue;background-color:black;\" />\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Why Data Driven Modeling and what is Coding as Literacy? \n",
    "\n",
    "<hr style=\"height:3px;border:none;color:steelblue;background-color:black;\" />\n",
    "\n",
    "![](Images/Theory_Data_Modeling1.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# They are not opposing each other\n",
    "\n",
    "<hr style=\"height:3px;border:none;color:steelblue;background-color:black;\" />\n",
    "![](Images/DDMTDM.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# This literacy will turn the classical notion of expertise as \n",
    "\n",
    "<span style=\"color:steelblue;font-size: 2.5em;line-height: 100%;\">“Having the Answers to the Known Questions”  </span>\n",
    "\n",
    "# to\n",
    "<span style=\"color:steelblue;font-size: 2.5em;line-height: 100%;\">“Learning to Ask Good Questions”</span> \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a></a>\n",
    "<hr style=\"height:3px;border:none;color:stillblue;background-color:black;\" />\n",
    "# But now how to learn this  if it is like a new language?\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## This is how I framed it few months ago...\n",
    "![](Images/DataDrivenModelingElements.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# With different approaches?\n",
    "## Resource Based --> Forward but slow and might take a BSc.\n",
    "## Market Oriented --> Backward, following the fashion: itmight work, but mostly we get simply puzzled\n",
    "## Connecting the dots in a guided path --- Wiki-based approach\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a></a>\n",
    "![](Images/DataDrivenModelingKW.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# But now I prefer this more as my own image of the field...\n",
    "### Green color indicates we previously discussed the topic to an extent.\n",
    "<a></a>\n",
    "![](Images/KW2016_1.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# This semester I would like to offer two scenarios:\n",
    "# 1- Discussions on algorithms and their implementations\n",
    "\n",
    "* **Theories**\n",
    "    * Convolutional Neural Networks\n",
    "    * Recurrent Neural Networks\n",
    "    * Ensemble Models\n",
    "    * Resampling and Data Completion \n",
    "    * Dimensionality Reduction and Topological Data Analysis\n",
    "    * How to compare objects? On similarity measures\n",
    "    * Intro to Reinforcement Learning\n",
    "    * Support Vector Machines\n",
    "\n",
    "\n",
    "# 2- Workshop style sessions around applications\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "* **Applications**\n",
    "    * Image Modeling: Big Data and Urban Design \n",
    "    * Time Series Analysis and Forecasting\n",
    "    * Text Modeling and sentiment analysis\n",
    "    * Real Estate Market \n",
    "    * Traffic Modeling \n",
    "    * Urban Air pollution modeling\n",
    "    * Water flow Emulations\n",
    "    * Analyzing Food Recipes \n",
    "\n",
    "\n",
    "* **Data Wrangling with Pandas** \n",
    "* **Data Collection and use of APIs**\n",
    "* **Web Application Development**\n",
    "\n",
    "\n",
    "* **Frameworks to be used**\n",
    "    * Scikit-learn: Classical Machine Learning\n",
    "    * Gensim: Text Modeling\n",
    "    * Tensorflow: Deep Learning\n",
    "    * Flask: Web Application Framework\n",
    "    * BS4: HTML Parsing\n",
    "    * Leaflet: Geo Mapping\n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:3px;border:none;color:steelblue;background-color:red;\" />\n",
    "\n",
    "# Back to the original idea of coding as litercy\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Does it make sense to use data driven modeling if we have a valid descriptive theory or an explicit logic of a phenomenon?\n",
    "## This seems silly!\n",
    "## But, as we can see in the following example, we can solve logical games without explicit implementation of the logic of the game.\n",
    "<hr style=\"height:3px;border:none;color:steelblue;background-color:black;\" />\n",
    "![](Images/DDMTDM.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A very funny intro to TensorFlow and Machine Learning for the logical game of fizzbuzz!\n",
    "http://joelgrus.com/2016/05/23/fizz-buzz-in-tensorflow/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def binary_encode(i, num_digits):\n",
    "    return np.array([i >> d & 1 for d in range(num_digits)][::-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 1, 0, 0, 1])"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "binary_encode(9,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def fizz_buzz_encode(i):\n",
    "    if   i % 15 == 0: return np.array([0, 0, 0, 1])\n",
    "    elif i % 5  == 0: return np.array([0, 0, 1, 0])\n",
    "    elif i % 3  == 0: return np.array([0, 1, 0, 0])\n",
    "    else:             return np.array([1, 0, 0, 0])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "NUM_DIGITS = 11\n",
    "test_number = 200\n",
    "trX = np.array([binary_encode(i, NUM_DIGITS) for i in range(test_number+1, 2 ** NUM_DIGITS)])\n",
    "trY = np.array([fizz_buzz_encode(i) for i in range(test_number+1, 2 ** NUM_DIGITS)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "NUM_HIDDEN = 400"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X = tf.placeholder(\"float\", [None, NUM_DIGITS])\n",
    "Y = tf.placeholder(\"float\", [None, 4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def init_weights(shape):\n",
    "    return tf.Variable(tf.random_normal(shape, stddev=0.01))\n",
    "\n",
    "w_h = init_weights([NUM_DIGITS, NUM_HIDDEN])\n",
    "w_o = init_weights([NUM_HIDDEN, 4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(11, 400)\n",
      "(400, 4)\n"
     ]
    }
   ],
   "source": [
    "print w_h.get_shape()\n",
    "print w_o.get_shape()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def model(X, w_h, w_o):\n",
    "    h = tf.nn.relu(tf.matmul(X, w_h))\n",
    "    return tf.matmul(h, w_o)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "py_x = model(X, w_h, w_o)\n",
    "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(py_x, Y))\n",
    "train_op = tf.train.GradientDescentOptimizer(0.05).minimize(cost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predict_op = tf.argmax(py_x, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def fizz_buzz(i, prediction):\n",
    "    return [str(i), \"fizz\", \"buzz\", \"fizzbuzz\"][prediction]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0, 0.53329723876556578)\n",
      "(500, 0.57011369788846777)\n",
      "(1000, 0.92528424472116944)\n",
      "(1500, 0.9821331889550623)\n",
      "(2000, 0.99133730373578777)\n",
      "(2500, 0.99891716296697342)\n",
      "(3000, 1.0)\n",
      "(3500, 1.0)\n",
      "(4000, 1.0)\n",
      "(4500, 1.0)\n",
      "(5000, 1.0)\n",
      "(5500, 1.0)\n",
      "(6000, 1.0)\n",
      "(6500, 1.0)\n",
      "(7000, 1.0)\n",
      "(7500, 1.0)\n",
      "(8000, 1.0)\n",
      "(8500, 1.0)\n",
      "(9000, 1.0)\n",
      "(9500, 1.0)\n"
     ]
    }
   ],
   "source": [
    "BATCH_SIZE = 128\n",
    "\n",
    "# Launch the graph in a session\n",
    "with tf.Session() as sess:\n",
    "    tf.global_variables_initializer().run()\n",
    "\n",
    "    for epoch in range(10000):\n",
    "        # Shuffle the data before each training iteration.\n",
    "        p = np.random.permutation(range(len(trX)))\n",
    "        trX, trY = trX[p], trY[p]\n",
    "\n",
    "        # Train in batches of 128 inputs.\n",
    "        for start in range(0, len(trX), BATCH_SIZE):\n",
    "            end = start + BATCH_SIZE\n",
    "            sess.run(train_op, feed_dict={X: trX[start:end], Y: trY[start:end]})\n",
    "\n",
    "        # And print the current accuracy on the training data.\n",
    "        if epoch%500 ==0:\n",
    "            print(epoch, np.mean(np.argmax(trY, axis=1) ==\n",
    "                             sess.run(predict_op, feed_dict={X: trX, Y: trY})))\n",
    "\n",
    "    # And now for some fizz buzz\n",
    "    numbers = np.arange(1, test_number)\n",
    "    teX = np.transpose(binary_encode(numbers, NUM_DIGITS))\n",
    "    pred_label = sess.run(predict_op, feed_dict={X: teX})\n",
    "    output = np.vectorize(fizz_buzz)(numbers, pred_label)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 1, 0, 2, 1, 0, 0, 1, 2, 0, 1, 0, 0, 3, 0, 0, 1, 0, 2, 1, 0, 0,\n",
       "       1, 2, 0, 1, 0, 0, 3, 0, 0, 1, 0, 2, 1, 0, 0, 1, 2, 0, 1, 0, 0, 3, 0,\n",
       "       0, 1, 0, 2, 1, 0, 0, 1, 2, 0, 1, 0, 0, 3, 0, 0, 1, 0, 2, 1, 0, 0, 1,\n",
       "       2, 0, 1, 0, 0, 3, 0, 0, 1, 0, 2, 1, 0, 0, 1, 2, 0, 1, 0, 0, 3, 0, 0,\n",
       "       1, 0, 2, 1, 0, 0, 1, 2, 0, 1, 0, 0, 3, 0, 0, 1, 0, 2, 1, 0, 0, 1, 2,\n",
       "       0, 1, 0, 0, 3, 0, 0, 1, 0, 2, 1, 0, 0, 1, 2, 0, 1, 0, 0, 3, 0, 0, 1,\n",
       "       0, 2, 1, 0, 0, 1, 2, 0, 1, 0, 0, 3, 0, 0, 1, 0, 2, 1, 0, 0, 1, 2, 0,\n",
       "       1, 0, 0, 3, 0, 0, 1, 0, 2, 1, 0, 0, 1, 2, 0, 1, 0, 0, 3, 0, 0, 1, 0,\n",
       "       2, 1, 0, 0, 1, 2, 0, 1, 0, 0, 3, 0, 0, 1, 0])"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def setlabel(i):\n",
    "    if   i % 15 == 0: return 3\n",
    "    elif i % 5  == 0: return 2\n",
    "    elif i % 3  == 0: return 1\n",
    "    else:             return 0\n",
    "numbers = np.arange(1, test_number)\n",
    "correct_label = np.vectorize(setlabel)(numbers)\n",
    "correct_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "%matplotlib inline\n",
    "import itertools\n",
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "\n",
    "    print(cm)\n",
    "\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, cm[i, j],\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')\n",
    "    font = {'size'   : 8}\n",
    "    plt.rc('font', **font)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[106   1   0   0]\n",
      " [  0  53   0   0]\n",
      " [  0   0  26   0]\n",
      " [  0   1   0  12]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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B2a2p5SUtClwE7Ah8DYyLiEMlrQKMIBk1bDpweES8UUzcdeWVDCNiwQ28JHUA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115fe7250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute confusion matrix\n",
    "cnf_matrix = confusion_matrix(correct_label, pred_label)\n",
    "np.set_printoptions(precision=1)\n",
    "class_names = ['number','fizz','buzz','fizbuzz']\n",
    "# Plot non-normalized confusion matrix\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cnf_matrix, classes=class_names,\n",
    "                      title='Confusion matrix, without normalization')\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}