001_intro_to_ml.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Section 01: Introduction to Machine Learning\n",
    "    * What is Machine Learning?\n",
    "    * Types of Machine Learning\n",
    "    * Thinking about Machine Learning\n",
    "    * The Machine Learning Workflow\n",
    "    * Toy Datasets\n",
    "- Section 02: Introduction to sklearn\n",
    "    * Designing a Machine Learning API\n",
    "    * Estimators\n",
    "    * Transformers\n",
    "    * Towards a Pipeline\n",
    "- Section 03: Example\n",
    "    * Preprocessing\n",
    "    * Normalization\n",
    "    * Standardization\n",
    "    * Imputation\n",
    "    * Modelig\n",
    "- Section 04: Feature Selection\n",
    "    * Univariate Feature Selection Methods\n",
    "    * Multivariate Feature Selection Methods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Section 02: Introduction to Machine Learning "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1 What is Machine Learning?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E.\n",
    "\n",
    "    - Tom Mitchell"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2 Types of Machine Learning"
   ]
  },
  {
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   "source": [
    "### 2.2.1 Supervised Machine Learning\n",
    "\n",
    "* the data comes with a target attribute that we want to predict\n",
    "* there are two types of Supervised Machine Learning problem\n",
    "    - Classification: the target variable is categorical\n",
    "    - Regression: the target variable is numeric\n",
    "* Note: there can be multiple target variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2.2 Unsupervised Machine Learning\n",
    "\n",
    "* the training data consists of a set of input vectors without any corresponding target values\n",
    "* the goal is to explore the structure\n",
    "    - Clustering: discover groups of similar examples within the data\n",
    "    - Density Estimation: determine the distribution of data within the input space\n",
    "    - Dimension Reduction: for example, project the data from a high-dimensional space down to two or three dimensions for the purpose of visualization\n",
    "    - Manifold learning (or nonlinear dimension reduction): Finding a low-dimensional manifold containing the data points"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.3 Thinking about Machine Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task\n",
    "\n",
    "* Classification\n",
    "* Regression\n",
    "* Clustering\n",
    "\n",
    "### Learner\n",
    "\n",
    "* Naive Bayes\n",
    "* Linear Regression\n",
    "* Support Vector Machines\n",
    "* Logistic Regression\n",
    "* Random Forest\n",
    "* Gradient Boosting Machines\n",
    "\n",
    "### Performace Measure (from testing/validation set)\n",
    "\n",
    "* RMSE\n",
    "* F1-score\n",
    "\n",
    "### Experience (from training set)\n",
    "\n",
    "* The Training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.4 The Machine Learning Workflow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Business Understanding is crucial for the success of any Data Mining/Machine Learning project.\n",
    "\n",
    "1. Data Collection and Preparation\n",
    "2. Exploratory Data Analysis\n",
    "    - Statistical Summary\n",
    "    - Univariate Explorations of Data\n",
    "        * Histogram\n",
    "        * Box plot\n",
    "        * Q-Q plots\n",
    "    - Multivariate Explorations of Data\n",
    "        * Scatter plot (jitter)\n",
    "        * Correlation and Correllograms\n",
    "        * Facet plots\n",
    "3. Data Preprocessing\n",
    "    - Centering and Scaling\n",
    "    - Missing Value Treatment\n",
    "        * Univariate methods\n",
    "        * Multivariate methods\n",
    "    - Outlier Treatment\n",
    "        * Univariate methods\n",
    "        * Multivariate Supervised methods\n",
    "        * Multivariate Unsupervised methods\n",
    "4. Feature Engineering\n",
    "    - Feature Transformation\n",
    "        * Rescaling\n",
    "            - Centering\n",
    "            - Scaling\n",
    "            - Normalization, Standardization\n",
    "        * Algebraic Transformations\n",
    "            - Polynomial\n",
    "            - Exponential and Logarithmic\n",
    "        * Combining Existing Features\n",
    "    - Feature Creation/Extraction\n",
    "        * Recoding\n",
    "            - Calibration\n",
    "            - Categorical to Ordinal/Numeric\n",
    "                * Hashing Trick\n",
    "                * One-hot Encoding\n",
    "                * Label Encoding\n",
    "            - Numeric to Categorical\n",
    "                * Binarization\n",
    "                * Discretization\n",
    "        * Brainstorming\n",
    "            - Combining Feature\n",
    "            - Applying Domain Expertise\n",
    "        * Feature Space Reengineering\n",
    "            - Dimensionality Reduction\n",
    "            - Dimensionality Explosion\n",
    "    - Feature Selection\n",
    "        * Model-based\n",
    "            - Regularization\n",
    "            - Ensemble\n",
    "        * Filter\n",
    "            - Variance\n",
    "            - Pearson Correlation Coefficient\n",
    "            - Mutual Information\n",
    "            - embedded methods (feature selection is a part of model constructin)\n",
    "        * Wrappers\n",
    "            - Forwards\n",
    "            - Backwrds\n",
    "5. Modeling\n",
    "    * Selecting right Algorithm\n",
    "        - Classification vs Regression\n",
    "        - Dimensionality of Data (p>>n vs n>>p)\n",
    "        - Linear Separability\n",
    "        - Interpretability vs Predictive Power\n",
    "        - Online vs Offline\n",
    "        - Start by choosing the simplest hypothesis\n",
    "    * Sampling\n",
    "        - Dealing with Imbalanced Data\n",
    "        - Simple Validation\n",
    "        - Cross-Validation\n",
    "    * Training\n",
    "    * Ensembling\n",
    "        - Naive Aggregation\n",
    "            * Voting\n",
    "            * Averaging\n",
    "        - Stacking\n",
    "        - Bagging\n",
    "        - Boosting\n",
    "    * Hyper-parameter Tuning\n",
    "        - Grid Search\n",
    "        - Random Search\n",
    "        - Bayesian Strategies\n",
    "6. Model Assessment\n",
    "    * Model Evaluation and Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.5 Toy Datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.5.1 The iris Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://en.wikipedia.org/wiki/Iris_flower_data_set\n",
    "\n",
    "\n",
    "The Iris flower data set or Fisher's Iris data set is a multivariate data set introduced by Ronald Fisher in his 1936 paper The use of multiple measurements in taxonomic problems as an example of linear discriminant analysis.\n",
    "\n",
    "The data set consists of 50 samples from each of three species of Iris\n",
    "* Iris setosa\n",
    "* Iris virginica\n",
    "* Iris versicolor\n",
    "\n",
    "Four features were measured (in centimeters) from each sample:\n",
    "* Sepal Length\n",
    "* Sepal Width\n",
    "* Petal Length\n",
    "* Petal Width\n",
    "\n",
    "Based on the combination of these four features, Fisher developed a linear discriminant model to distinguish the species from each other."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
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   "source": [
    "# import iris data\n",
    "from sklearn import datasets\n",
    "iris = datasets.load_iris()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These datasets are dictionary-like object, the interesting attributes are\n",
    "\n",
    "* **data** - the data to learn\n",
    "* **target** - the classification labels\n",
    "* **target_names** - the meaning of the labels\n",
    "* **feature_names** - the meaning of the features\n",
    "* **DESCR** - the full description of the dataset"
   ]
  },
  {
   "cell_type": "code",
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    {
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       "array(['setosa', 'versicolor', 'virginica'], \n",
       "      dtype='<U10')"
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     },
     "execution_count": 18,
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    "iris.target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sepal length (cm)',\n",
       " 'sepal width (cm)',\n",
       " 'petal length (cm)',\n",
       " 'petal width (cm)']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "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",
      "    ============== ==== ==== ======= ===== ====================\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",
      "\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": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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ouo5SqVRnDKAo262prXU0g/o5E9JLKGZI17QqrG+UAmYdxIMorHei3RQwoDN7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Vy6eIETB4swG/Skpmd0OF9KvzQsNjtNTU2kpKQ4Pp80aRKLq6tZ9p/lFG1eByKQS8QkJSURl5DU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\nVAXpAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11034df28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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yknnz5pm6LSIqoZKTk+WFfv1EUZSc/UbTJiESHR1t9FoJCQnSq2fPnDoApHWrlnLr1i2j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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1100fd358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "X = iris.data[:, :2]  # we only take the first two features.\n",
    "Y = iris.target\n",
    "\n",
    "# Determine the range of axis\n",
    "x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\n",
    "y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\n",
    "\n",
    "plt.figure(2, figsize=(8, 6))\n",
    "plt.clf()\n",
    "\n",
    "# Plot the training points\n",
    "plt.scatter(X[:, 0], X[:, 1], c=Y, cmap=plt.cm.Paired)\n",
    "plt.xlabel('Sepal length')\n",
    "plt.ylabel('Sepal width')\n",
    "\n",
    "plt.xlim(x_min, x_max)\n",
    "plt.ylim(y_min, y_max)\n",
    "plt.xticks(())\n",
    "plt.yticks(())\n",
    "\n",
    "# To getter a better understanding of interaction of the dimensions\n",
    "# plot the first three PCA dimensions\n",
    "fig = plt.figure(1, figsize=(8, 6))\n",
    "ax = Axes3D(fig, elev=-150, azim=110)\n",
    "X_reduced = PCA(n_components=3).fit_transform(iris.data)\n",
    "ax.scatter(X_reduced[:, 0], X_reduced[:, 1], X_reduced[:, 2], c=Y,\n",
    "           cmap=plt.cm.Paired)\n",
    "ax.set_title(\"First three PCA directions\")\n",
    "ax.set_xlabel(\"1st eigenvector\")\n",
    "ax.w_xaxis.set_ticklabels([])\n",
    "ax.set_ylabel(\"2nd eigenvector\")\n",
    "ax.w_yaxis.set_ticklabels([])\n",
    "ax.set_zlabel(\"3rd eigenvector\")\n",
    "ax.w_zaxis.set_ticklabels([])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.5.2 More datasets from sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_boston\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.datasets import load_diabetes\n",
    "from sklearn.datasets import load_digits\n",
    "from sklearn.datasets import load_linnerud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(506, 13)\n"
     ]
    }
   ],
   "source": [
    "# Boston house prices dataset (13x506, reals, regression)\n",
    "boston = load_boston()\n",
    "print(boston.data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(150, 4)\n"
     ]
    }
   ],
   "source": [
    "# Iris flower dataset (4x150, reals, multi-label classification)\n",
    "iris = load_iris()\n",
    "print(iris.data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(442, 10)\n"
     ]
    }
   ],
   "source": [
    "# Diabetes dataset (10x442, reals, regression)\n",
    "diabetes = load_diabetes()\n",
    "print(diabetes.data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1797, 64)\n"
     ]
    }
   ],
   "source": [
    "# Hand-written digit dataset (64x1797, multi-label classification)\n",
    "digits = load_digits()\n",
    "print(digits.data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20, 3)\n"
     ]
    }
   ],
   "source": [
    "# Linnerud psychological and exercise dataset (3x20,3x20 multivariate regression)\n",
    "linnerud = load_linnerud()\n",
    "print(linnerud.data.shape) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.5.3 Load data from CSV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we load a csv dataset from a URL. The dataset in question is the Pima Indians diabetes classification dataset from the UCI Machine Learning Repository."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Load the Pima Indians diabetes dataset from CSV URL\n",
    "import numpy as np\n",
    "import urllib.request\n",
    "# URL for the Pima Indians Diabetes dataset (UCI Machine Learning Repository)\n",
    "# http://archive.ics.uci.edu/ml/machine-learning-databases/pima-indians-diabetes/pima-indians-diabetes.data\n",
    "url = \"http://goo.gl/j0Rvxq\"\n",
    "# download the file\n",
    "raw_data = urllib.request.urlopen(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(768, 9)\n"
     ]
    }
   ],
   "source": [
    "# load the CSV file as a numpy matrix\n",
    "dataset = np.loadtxt(raw_data, delimiter=\",\")\n",
    "print(dataset.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# separate the data from the target attributes\n",
    "X = dataset[:,0:7]\n",
    "y = dataset[:,8]  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Section 03: Introduction to sklearn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1 Designing a Machine Learning API"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* fit()\n",
    "* transform()\n",
    "* predict()\n",
    "\n",
    "Examples:\n",
    "\n",
    "* Standardization (remember parameters across train, test, validation sets/folds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2 Estimators"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perform \n",
    "\n",
    "* classification\n",
    "* Regression\n",
    "* Clustering\n",
    "\n",
    "etc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Standardized implementation of a large number of machine learning algorithms\n",
    "* Estimators have two main functions\n",
    "    - *fit()*\n",
    "    - *predict()*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2.1 *fit()*\n",
    "\n",
    "* train the model\n",
    "* set internal parameters\n",
    "* two inputs\n",
    "    - training dataset (X)\n",
    "    - target values (y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2.2 *predict()*\n",
    "\n",
    "* predict target variable for the testing sample\n",
    "* returns an array with the predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.3 Transformers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perform\n",
    "\n",
    "* Preprocessing\n",
    "* Feature Engineering\n",
    "\n",
    "etc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* fit(): Optional\n",
    "* transform(): Mandatory\n",
    "* fit_transform(): Exists if fit() exists"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.4 Towards a Pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Put together workflow into a replicable format.\n",
    "\n",
    "* Chain as many Transformers as you like.\n",
    "* The last one in the chain can be an Estimator."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Section 04: Example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.1 Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(150, 4)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "from sklearn import preprocessing\n",
    "\n",
    "# load the iris dataset\n",
    "iris = load_iris()\n",
    "print(iris.data.shape)\n",
    "\n",
    "# separate the data from the target attributes\n",
    "X = iris.data\n",
    "y = iris.target"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.2 Normalization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Normalization refers to rescaling real valued numeric attributes into the range 0 and 1. It is useful to scale the input attributes for a model that relies on the magnitude of values, such as\n",
    "\n",
    "* distance measures used in k-nearest neighbors, and \n",
    "* linear or logistic regression.\n",
    "\n",
    "(A good rule of thumb is any algorithm that is going to use some version of gradient descent for optimization.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# normalize the data attributes\n",
    "normalized_X = preprocessing.normalize(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.3 Standardization\n",
    "\n",
    "Standardization refers to shifting the distribution of each attribute to have a mean of 0 and a standard deviation of 1. It is useful to standardize attributes for a model that relies on the distribution of attributes such as Gaussian processes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# standardize the data attributes\n",
    "standardized_X = preprocessing.scale(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.4 Imputation\n",
    "\n",
    "* Data can have missing values. \n",
    "* These are values for attributes where a measurement could not be taken or is corrupt for some reason.\n",
    "* It is important to identify and mark this missing data. Once marked, replacement values can be prepared.\n",
    "* This is useful because some algorithms are unable to work with or exploit missing data.  \n",
    " \n",
    "The following code demonstrates marking 0 values from the Pima Indians dataset as NaN and imputing the missing values with the mean of the attribute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(768, 9)\n"
     ]
    }
   ],
   "source": [
    "# Mark 0 values as missing and impute with the mean\n",
    "import numpy as np\n",
    "import urllib.request\n",
    "from sklearn.preprocessing import Imputer\n",
    "\n",
    "# Load the Pima Indians Diabetes dataset\n",
    "url = \"http://goo.gl/j0Rvxq\"\n",
    "raw_data = urllib.request.urlopen(url)\n",
    "dataset = np.loadtxt(raw_data, delimiter=\",\")\n",
    "print(dataset.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   6.   ,  148.   ,   72.   , ...,      nan,   33.6  ,    0.627],\n",
       "       [   1.   ,   85.   ,   66.   , ...,      nan,   26.6  ,    0.351],\n",
       "       [   8.   ,  183.   ,   64.   , ...,      nan,   23.3  ,    0.672],\n",
       "       ..., \n",
       "       [   5.   ,  121.   ,   72.   , ...,  112.   ,   26.2  ,    0.245],\n",
       "       [   1.   ,  126.   ,   60.   , ...,      nan,   30.1  ,    0.349],\n",
       "       [   1.   ,   93.   ,   70.   , ...,      nan,   30.4  ,    0.315]])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# separate the data and target attributes\n",
    "X = dataset[:,0:7]\n",
    "y = dataset[:,8]\n",
    "\n",
    "# Mark all zero values as NaN\n",
    "X[X==0] = np.nan\n",
    "\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   6.        ,  148.        ,   72.        , ...,  155.54822335,\n",
       "          33.6       ,    0.627     ],\n",
       "       [   1.        ,   85.        ,   66.        , ...,  155.54822335,\n",
       "          26.6       ,    0.351     ],\n",
       "       [   8.        ,  183.        ,   64.        , ...,  155.54822335,\n",
       "          23.3       ,    0.672     ],\n",
       "       ..., \n",
       "       [   5.        ,  121.        ,   72.        , ...,  112.        ,\n",
       "          26.2       ,    0.245     ],\n",
       "       [   1.        ,  126.        ,   60.        , ...,  155.54822335,\n",
       "          30.1       ,    0.349     ],\n",
       "       [   1.        ,   93.        ,   70.        , ...,  155.54822335,\n",
       "          30.4       ,    0.315     ]])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Impute all missing values with the mean of the attribute\n",
    "imp = Imputer(missing_values='NaN', strategy='mean')\n",
    "imputed_X = imp.fit_transform(X)\n",
    "\n",
    "imputed_X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.5 Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.5.1 Linear Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Classical Regression\n",
    "* Handling Nonlinearity\n",
    "* Regularization\n",
    "* Dimensionality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)\n"
     ]
    }
   ],
   "source": [
    "# Linear Regression\n",
    "import numpy as np\n",
    "from sklearn import datasets\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "# load the diabetes datasets\n",
    "dataset = datasets.load_diabetes()\n",
    "\n",
    "# fit a linear regression model to the data\n",
    "model = LinearRegression()\n",
    "model.fit(dataset.data, dataset.target)\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2859.69039877\n",
      "0.517749425413\n"
     ]
    }
   ],
   "source": [
    "# make predictions\n",
    "expected = dataset.target\n",
    "predicted = model.predict(dataset.data)\n",
    "\n",
    "# summarize the fit of the model\n",
    "mse = np.mean((predicted-expected)**2)\n",
    "print(mse)\n",
    "print(model.score(dataset.data, dataset.target))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* *score* returns the *R^2* of the model (http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html)\n",
    "* generally, higher the *score*, the better"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.5.2 Logistic Regression\n",
    "\n",
    "* Logistic Regression fits a logistic model to data and makes predictions about the probability of an event (between 0 and 1)\n",
    "* Logistic Regression is more robust to outliers compared to Linear Regression\n",
    "\n",
    "We show the fitting of a logistic regression algorithm to the iris dataset. Because this is a mutliclass classification problem and logistic regression makes predictions between 0 and 1, a one-vs-all scheme is used (one model is created per class)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
      "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
      "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
      "          verbose=0, warm_start=False)\n"
     ]
    }
   ],
   "source": [
    "# Logistic Regression\n",
    "from sklearn import datasets\n",
    "from sklearn import metrics\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "# load the iris datasets\n",
    "dataset = datasets.load_iris()\n",
    "\n",
    "# fit a logistic regression model to the data\n",
    "model = LogisticRegression()\n",
    "model.fit(dataset.data, dataset.target)\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      1.00      1.00        50\n",
      "          1       0.98      0.90      0.94        50\n",
      "          2       0.91      0.98      0.94        50\n",
      "\n",
      "avg / total       0.96      0.96      0.96       150\n",
      "\n",
      "[[50  0  0]\n",
      " [ 0 45  5]\n",
      " [ 0  1 49]]\n"
     ]
    }
   ],
   "source": [
    "# make predictions\n",
    "expected = dataset.target\n",
    "predicted = model.predict(dataset.data)\n",
    "\n",
    "# summarize the fit of the model\n",
    "print(metrics.classification_report(expected, predicted))\n",
    "print(metrics.confusion_matrix(expected, predicted))"
   ]
  }
 ],
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