INTRO ML 01 - Machine learning - a very basic introduction.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning - a very basic introduction\n",
    "\n",
    "#### An over simplified 'in-a-nutshell' of how it works, sort of -- Geoff French\n",
    "\n",
    "In this notebook we show how samples are positioned in N-dimensional space. Dividing this space into regions allows us to classify (guess the type) of novel, as-of-yet unseen samples.\n",
    "\n",
    "In this example we are going to train a classifier on Fisher's Iris Dataset and plot its decision surface."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, some imports:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.datasets import load_iris\n",
    "import seaborn as sns\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the data\n",
    "\n",
    "The feature vectors for each sample can be found in the `data` attribute and the ground truth classes (of which there are 3) can be found in the `target` attribute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Load\n",
    "iris = load_iris()\n",
    "\n",
    "# Get features and ground truth\n",
    "X = iris.data\n",
    "Y = iris.target\n",
    "\n",
    "# Extract sepal length and petal width features (0 and 3)\n",
    "X03 = np.append(X[:,0:1], X[:,3:4], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scatter plot of data\n",
    "\n",
    "First, lets produce a scatter plot of sepal length and petal width, with the colour of the points indicating ground truth class:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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EiYklB94Y2L599vKiovufr06/TlXsYxBrGkZ1+P4+EPuul7r23Y1+nWk7mB00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\n6RUTNZECKZYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118ffcc10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Figure\n",
    "plt.figure(figsize=(10,8))\n",
    "\n",
    "# Scatter plot of the data points\n",
    "plt.scatter(X[Y==0,0], X[Y==0,3], color='red')\n",
    "plt.scatter(X[Y==1,0], X[Y==1,3], color='blue')\n",
    "plt.scatter(X[Y==2,0], X[Y==2,3], color='green')\n",
    "\n",
    "# Labels\n",
    "plt.title('Fisher\\'s iris datset')\n",
    "plt.xlabel('Sepal length')\n",
    "plt.ylabel('Petal width')\n",
    "\n",
    "# Show\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train a classifier\n",
    "\n",
    "If we were given measurements of a flower of unknown species, how could we predict its species from its measurements?\n",
    "\n",
    "We would need to divide the space seen in the plot above into regions in which a flower is most likely to be member of that species.\n",
    "\n",
    "The classifiers in Scikit-learn do just this; they compute parameters to control the way in which an algorithm divides the space up into regions.\n",
    "\n",
    "#### Define classifier plotting helper function\n",
    "\n",
    "This function plots the data points in the data set in addition to plotting the decision surface that shows the regions of space that the classifier will assign to the 3 classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def clf_decision_surface_plot(clf):\n",
    "    # Generate the co-ordinates at which we will ask the classifier to predict\n",
    "    # the probabilities of the classes\n",
    "    xx, yy = np.meshgrid(np.arange(4.0, 8.5, 0.01),\n",
    "                         np.arange(-0.5, 3.0, 0.01))\n",
    "\n",
    "    # Predict classes for all grid points\n",
    "    Y_pred = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "\n",
    "    # New figure\n",
    "    plt.figure(figsize=(10,8))\n",
    "\n",
    "    # Plot the contours of the boundaries corresponding to each class\n",
    "    plt.contourf(xx, yy, (Y_pred == 0).reshape(xx.shape), 1, cmap='OrRd', alpha=.4)\n",
    "    plt.contourf(xx, yy, (Y_pred == 1).reshape(xx.shape), 1, cmap='Blues', alpha=.4)\n",
    "    plt.contourf(xx, yy, (Y_pred == 2).reshape(xx.shape), 1, cmap='Greens', alpha=.4)\n",
    "\n",
    "    # Plot the data points\n",
    "    plt.scatter(X[Y==0,0], X[Y==0,3], color='red')\n",
    "    plt.scatter(X[Y==1,0], X[Y==1,3], color='blue')\n",
    "    plt.scatter(X[Y==2,0], X[Y==2,3], color='green')\n",
    "\n",
    "    # Labels\n",
    "    plt.title('Fisher\\'s iris datset')\n",
    "    plt.xlabel('Sepal length')\n",
    "    plt.ylabel('Petal width')\n",
    "\n",
    "    # Show\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Nearest neighbour classifier\n",
    "\n",
    "In this case, we're going to train a nearest neighbours classifier on the dataset, only using the sepal length and petal width features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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4/FP3svB5T0947KT750FaLYvy1t7IVmCt56yZstbMchmPAXcC3wS+FWNc147C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\npJz4f8I9E12FFEBsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1031f02d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Build a nearest neighbours classifier that uses the 3 nearest neighbours to make a decision\n",
    "nn_clf = KNeighborsClassifier(3)\n",
    "# Train on the dataset\n",
    "nn_clf.fit(X03, Y)\n",
    "# Plot the decision surface\n",
    "clf_decision_surface_plot(nn_clf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The decision surface of the nearest neighbors classifier is quite complex and attempts to follow the points in the dataset.\n",
    "\n",
    "#### Linear support vector classifier\n",
    "\n",
    "Now, lets try a linear support vector classifier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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S5u/gdPfd5DEcHFxU6fGruPl/b+DrwGkppe9st/oW4FERsRuwgdZlzAum2ufq1Rtmvc7x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\nSeq+KmbMvgg8OyKuay+/OiJ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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1190928d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Linear classifier\n",
    "lclf = LinearSVC()\n",
    "# Train\n",
    "lclf.fit(X03, Y)\n",
    "# Plot the decision surface\n",
    "clf_decision_surface_plot(lclf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The linear support vector classifier finds simple lines to separate the data points\n",
    "\n",
    "#### Random forest classifier\n",
    "\n",
    "Now, lets try a random forest:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5ifsnSf1iJi2ZngpcAhwI3A7sCbwkM79XfXkzY0smzUSvtxHpB45h8zmGzdfr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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117d5af10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fclf = RandomForestClassifier()\n",
    "fclf.fit(X03, Y)\n",
    "\n",
    "clf_plot(fclf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The random forest classifier is composed of decisions trees that split the space along lines that are aligned to the X and Y axes."
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 2",
   "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": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}