{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Machine Learning (and Numpy Arrays)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Machine Learning is about building programs with **tunable parameters** (typically an\n",
    "array of floating point values) that are adjusted automatically so as to improve\n",
    "their behavior by **adapting to previously seen data.**\n",
    "\n",
    "Machine Learning can be considered a subfield of **Artificial Intelligence** since those\n",
    "algorithms can be seen as building blocks to make computers learn to behave more\n",
    "intelligently by somehow **generalizing** rather that just storing and retrieving data items\n",
    "like a database system would do.\n",
    "\n",
    "We'll take a look at a very simple machine learning tasks here: the **clustering** task"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## *Data* for Machine Learning Algorithms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Data in machine learning algorithms, with very few exceptions, is assumed to be stored as a\n",
    "**two-dimensional array**, of size `[n_samples, n_features]`.\n",
    "\n",
    "The arrays can be\n",
    "either ``numpy`` arrays, or in some cases ``scipy.sparse`` matrices.\n",
    "The size of the array is expected to be `[n_samples, n_features]`\n",
    "\n",
    "- **n_samples:**   The number of samples: each sample is an item to process (e.g. classify).\n",
    "  A sample can be a document, a picture, a sound, a video, an astronomical object,\n",
    "  a row in database or CSV file,\n",
    "  or whatever you can describe with a fixed set of quantitative traits.\n",
    "\n",
    "- **n_features:**  The number of features or distinct traits that can be used to describe each\n",
    "  item in a quantitative manner.  Features are generally real-valued, but may be boolean or\n",
    "  discrete-valued in some cases.\n",
    "\n",
    "The number of features must be fixed in advance. However it can be very high dimensional\n",
    "(e.g. millions of features) with most of them being zeros for a given sample. \n",
    "\n",
    "This is a case where `scipy.sparse` matrices can be useful, in that they are much more memory-efficient than numpy arrays."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Addendum\n",
    "\n",
    "There is a dedicated notebook in the training material, explicitly dedicated to `scipy.sparse`: [07_1_Sparse_Matrices](./07_1_Sparse_Matrices.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## A Simple Example: the Iris Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": "/9j/4AAQSkZJRgABAQEASABIAAD/2wBDAAgFBgcGBQgHBgcJCAgJDBMMDAsLDBgREg4THBgdHRsY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\nR+NS3ibZDjvXJWjaVwZScYNRMasSDjNQMKhEn//Z\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iris Setosa\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/jpeg": "/9j/4AAQSkZJRgABAQEASABIAAD/2wBDAAYEBQYFBAYGBQYHBwYIChAKCgkJChQODwwQFxQYGBcU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\n9a9XqXUjhf/Z\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iris Versicolor\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/jpeg": "/9j/4AAQSkZJRgABAQEASABIAAD/2wBDAAMCAgMCAgMDAwMEAwMEBQgFBQQEBQoHBwYIDAoMDAsK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\nEzRnY6lWLDA/WqdzfXEc0Vtp9oUWNtkcRGWZvU47V9ArWOuHu6n/2Q==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iris Virginica\n"
     ]
    }
   ],
   "source": [
    "from IPython.core.display import Image, display\n",
    "display(Image(filename='images/iris_setosa.jpg'))\n",
    "print(\"Iris Setosa\\n\")\n",
    "\n",
    "display(Image(filename='images/iris_versicolor.jpg'))\n",
    "print(\"Iris Versicolor\\n\")\n",
    "\n",
    "display(Image(filename='images/iris_virginica.jpg'))\n",
    "print(\"Iris Virginica\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "- Features in the Iris dataset:\n",
    "\n",
    "  1. sepal length in cm\n",
    "  2. sepal width in cm\n",
    "  3. petal length in cm\n",
    "  4. petal width in cm\n",
    "\n",
    "- Target classes to predict:\n",
    "\n",
    "  1. Iris Setosa\n",
    "  2. Iris Versicolour\n",
    "  3. Iris Virginica"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "iris = load_iris()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Try by yourself one of the following commands where *'d'* is the variable containing the dataset:\n",
    "\n",
    "    print(iris.keys())           # Structure of the contained data\n",
    "    print(iris.DESCR)            # A complete description of the dataset\n",
    "    print(iris.data.shape)       # [n_samples, n_features]\n",
    "    print(iris.target.shape)     # [n_samples,]\n",
    "    print(iris.feature_names)\n",
    "    datasets.get_data_home() # This is where the datasets are stored"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['target_names', 'target', 'feature_names', 'DESCR', 'data'])\n"
     ]
    }
   ],
   "source": [
    "print(iris.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iris Plants Database\n",
      "\n",
      "Notes\n",
      "-----\n",
      "Data Set Characteristics:\n",
      "    :Number of Instances: 150 (50 in each of three classes)\n",
      "    :Number of Attributes: 4 numeric, predictive attributes and the class\n",
      "    :Attribute Information:\n",
      "        - sepal length in cm\n",
      "        - sepal width in cm\n",
      "        - petal length in cm\n",
      "        - petal width in cm\n",
      "        - class:\n",
      "                - Iris-Setosa\n",
      "                - Iris-Versicolour\n",
      "                - Iris-Virginica\n",
      "    :Summary Statistics:\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "                    Min  Max   Mean    SD   Class Correlation\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "    sepal length:   4.3  7.9   5.84   0.83    0.7826\n",
      "    sepal width:    2.0  4.4   3.05   0.43   -0.4194\n",
      "    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\n",
      "    petal width:    0.1  2.5   1.20  0.76     0.9565  (high!)\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "    :Missing Attribute Values: None\n",
      "    :Class Distribution: 33.3% for each of 3 classes.\n",
      "    :Creator: R.A. Fisher\n",
      "    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n",
      "    :Date: July, 1988\n",
      "\n",
      "This is a copy of UCI ML iris datasets.\n",
      "http://archive.ics.uci.edu/ml/datasets/Iris\n",
      "\n",
      "The famous Iris database, first used by Sir R.A Fisher\n",
      "\n",
      "This is perhaps the best known database to be found in the\n",
      "pattern recognition literature.  Fisher's paper is a classic in the field and\n",
      "is referenced frequently to this day.  (See Duda & Hart, for example.)  The\n",
      "data set contains 3 classes of 50 instances each, where each class refers to a\n",
      "type of iris plant.  One class is linearly separable from the other 2; the\n",
      "latter are NOT linearly separable from each other.\n",
      "\n",
      "References\n",
      "----------\n",
      "   - Fisher,R.A. \"The use of multiple measurements in taxonomic problems\"\n",
      "     Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\n",
      "     Mathematical Statistics\" (John Wiley, NY, 1950).\n",
      "   - Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.\n",
      "     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\n",
      "   - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\n",
      "     Structure and Classification Rule for Recognition in Partially Exposed\n",
      "     Environments\".  IEEE Transactions on Pattern Analysis and Machine\n",
      "     Intelligence, Vol. PAMI-2, No. 1, 67-71.\n",
      "   - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\n",
      "     on Information Theory, May 1972, 431-433.\n",
      "   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\n",
      "     conceptual clustering system finds 3 classes in the data.\n",
      "   - Many, many more ...\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(iris.DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "print(type(iris.data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "600 (150, 4)\n"
     ]
    }
   ],
   "source": [
    "data = iris.data\n",
    "print(data.size, data.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Spatial and Clustering Analysis\n",
    "\n",
    "**Spatial** and **Clustering** analysis are key to identifying patterns, groups, and clusters in data. \n",
    "In astrophysics, for example, these analysis techniques are used to seek and identify star clusters, galaxy clusters, and large-scale filaments (composed of galaxy clusters). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<img src=\"images/cluster_0.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<img src=\"images/cluster_1.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<img src=\"images/modeling_data_flow.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Machine Learning with SciPy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "**SciPy** provides a spatial analysis class (`scipy.spatial`) and a cluster analysis class (`scipy.cluster`). \n",
    "\n",
    "* The spatial class includes functions to analyze distances between data points (e.g., k-d trees). \n",
    "\n",
    "* The cluster class provides two overarching subclasses: \n",
    "\n",
    "    - vector quantization (`vq`); \n",
    "    - hierarchical clustering (`hierarchy`). \n",
    "    \n",
    "**Vector quantization** groups large sets of data points (`vectors`) where each group is represented by centroids. \n",
    "\n",
    "The **hierarchy** subclass contains functions to construct clusters and analyze their substructures.\n",
    "\n",
    "(*We won't see examples of hierarchical clustering in this notebook*)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Simple Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy.cluster import vq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# Creating randomly generated data\n",
    "c1 = np.random.randn(100, 2) + 5 \n",
    "c2 = np.random.randn(30, 2) - 5 \n",
    "c3 = np.random.randn(50, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# Pooling all the data into one 180 x 2 array \n",
    "data = np.vstack([c1, c2, c3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "# Calculating the cluster centroids and variance # from kmeans\n",
    "centroids, variance = vq.kmeans(data, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "# The identified variable contains the information \n",
    "# we need to separate the points in clusters\n",
    "# based on the vq function.\n",
    "identified, distance = vq.vq(data, centroids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# Retrieving coordinates for points in each vq # identified core\n",
    "vqc1 = data[identified == 0]\n",
    "vqc2 = data[identified == 1]\n",
    "vqc3 = data[identified == 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "%load utility/plot_clustering.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAACtCAYAAAATbawaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n5QUAsIjoAoBFRBcALCK6AGAR0QUAi/4faqGQHajNZKkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111a4db70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_kmeans_clustering_results(c1, c2, c3, vqc1, vqc2, vqc3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Iris Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sepal length (cm)',\n",
       " 'sepal width (cm)',\n",
       " 'petal length (cm)',\n",
       " 'petal width (cm)']"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(150, 2)\n"
     ]
    }
   ],
   "source": [
    "sepals = iris.data[:, 0:2]\n",
    "print(sepals.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "mask = iris.target == 0\n",
    "sepals = data[:,0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# Creating Data\n",
    "c1 = sepals[iris.target == 0]\n",
    "c2 = sepals[iris.target == 1]\n",
    "c3 = sepals[iris.target == 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(50, 2) (50, 2) (50, 2)\n"
     ]
    }
   ],
   "source": [
    "print(c1.shape, c2.shape, c3.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "data = np.vstack([c1, c2, c3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "# Calculating the cluster centroids and variance # from kmeans\n",
    "centroids, variance = vq.kmeans(data, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "identified, distance = vq.vq(data, centroids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "vqc1 = data[identified == 0]\n",
    "vqc2 = data[identified == 1]\n",
    "vqc3 = data[identified == 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAACtCAYAAAATbawaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n5QUAsIjoAoBFRBcALCK6AGAR0QUAi/4faqGQHajNZKkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111a55278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_kmeans_clustering_results(c1, c2, c3, vqc1, vqc2, vqc3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Note about `kmeans` in `scipy.cluster`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "In `scipy.cluster` we have **two** routines to divide data into clusters using the **k-means technique**:\n",
    "\n",
    "* `kmeans`;\n",
    "* `kmeans2`. \n",
    "\n",
    "They correspond to two different implementations. \n",
    "\n",
    "The former has a very simple syntax:\n",
    "   \n",
    "       kmeans(obs, k_or_guess, iter=20, thresh=1e-05)\n",
    "       \n",
    "The obs parameter is an `ndarray` with the data we wish to cluster. \n",
    "\n",
    "If the dimensions of the array are m x n, the algorithm interprets this data as m points in the n-dimensional Euclidean space. \n",
    "\n",
    "If we know the number of clusters in which this data should be divided, we enter so with `the k_or_guess` option. \n",
    "\n",
    "**Note**: The data we pass to kmeans need to be *normalized* with the `whiten` routine. \n",
    "\n",
    "The second function is much more flexible, as its syntax indicates:\n",
    "   \n",
    "       kmeans2(data, k, iter=10, thresh=1e-05, minit='random', missing='warn')\n",
    "       \n",
    "The `data` and `k` parameters are the same as `obs` and `k_or_guess`, respectively. The difference in this routine is the possibility of choosing among different **initialization algorithms**, hence providing us with the possibility to speed up the process and use fewer resources if we know some properties of our data. \n",
    "\n",
    "We do so by passing to the `minit` parameter, one of the strings such as:\n",
    "\n",
    "* `'random'` (initialization centroids are constructed randomly using a Gaussian);\n",
    "* `'points'` (initialization is done by choosing points belonging to our data)- **Kmedoids**; \n",
    "* `'uniform'` (if we prefer uniform distribution to Gaussian).\n",
    "\n",
    "In case we would like to provide the initialization centroids ourselves with the `k` parameter, we must indicate our choice to the algorithm by passing 'matrix' to the minit option as well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Machine Learning with scikit-learn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### What is scikit-learn?\n",
    "\n",
    "<img src=\"images/scikit-learn.png\" />"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(numpy.ndarray, numpy.ndarray)"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import make_blobs\n",
    "blobs, classes = make_blobs(500, centers=3)\n",
    "\n",
    "type(blobs), type(classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Plotting the (randomly generated) data\n",
    "\n",
    "#### Scatter Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x111a410f0>"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAcoAAAHNCAYAAACemTtkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nKBVFURQlCv8PNapWwJzVv84AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111e8cf28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "f, ax = plt.subplots(figsize=(7.5, 7.5))\n",
    "rgb = np.array(['r', 'g', 'b'])\n",
    "\n",
    "ax.scatter(blobs[:, 0], blobs[:, 1], color=rgb[classes])\n",
    "ax.set_title(\"Blobs\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Now we can use **KMeans** to find the centers of these clusters.\n",
    "\n",
    "(In this example, we'll pretend we know that there are three centers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.95420345,  3.92087883],\n",
       "       [-2.75450026, -4.13335675],\n",
       "       [ 1.26593243, -9.56682583]])"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "kmean = KMeans(n_clusters=3)\n",
    "kmean.fit(blobs)\n",
    "kmean.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAcoAAAHNCAYAAACemTtkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niYIKSkVRFEWJggpKRVEURYnC/wOQdVC8a41zvQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11152ceb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(7.5, 7.5))\n",
    "ax.scatter(blobs[:, 0], blobs[:, 1], color=rgb[classes])\n",
    "ax.scatter(kmean.cluster_centers_[:, 0],\n",
    "           kmean.cluster_centers_[:, 1], marker='*', s=250,\n",
    "           color='black', label='Centers')\n",
    "ax.set_title(\"Blobs\")\n",
    "ax.legend(loc='best')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 1, 1, 1], dtype=int32)"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Clustering Results\n",
    "kmean.labels_[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 2, 2, 2])"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Expected Results\n",
    "classes[:5]\n",
    "\n",
    "## Please note that KMeans does not know in advance cluster ids, thus it cannot assign the same as in `classes`.\n",
    "## In any case, please note that same elements correspond to the same numbers (IDs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "The `transform` function is quite useful in the sense that it will output the distance between\n",
    "each point and centroid:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0.47214034,   8.23399873,  13.82242774],\n",
       "       [ 15.79717782,   8.67945871,   2.12920933],\n",
       "       [  7.82124055,   0.69958497,   6.68488045],\n",
       "       [  7.86106348,   0.53780226,   6.770957  ],\n",
       "       [  8.41224735,   1.02961051,   5.95129219]])"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmean.transform(blobs)[:5]"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "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": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
