Chapter 2 - Supervised learning - Explaining Titanic Hypothesis with Decision Trees.ipynb

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  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Learning Scikit-learn: Machine Learning in Python"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "IPython Notebook for Chapter 2: Supervised Learning: Explaining Titanic Hypothesis with Decision Trees"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "_The problem we would like to solve is to determine if a Titanic's passenger would have survived, given her age, passenger class, and sex. We will work with the Titanic dataset that can be downloaded from http://biostat.mc.vanderbilt.edu/wiki/pub/Main/DataSets/titanic.txt._"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Start by importing numpy, scikit-learn, and pyplot, the Python libraries we will be using in this chapter. Show the versions we will be using (in case you have problems running the notebooks)."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import IPython\n",
      "import sklearn as sk\n",
      "import numpy as np\n",
      "import matplotlib\n",
      "import matplotlib.pyplot as plt\n",
      "import pydot\n",
      "import pyparsing\n",
      "\n",
      "print 'IPython version:', IPython.__version__\n",
      "print 'numpy version:', np.__version__\n",
      "print 'scikit-learn version:', sk.__version__\n",
      "print 'matplotlib version:', matplotlib.__version__\n",
      "print 'pydot version:', pydot.__version__\n",
      "print 'pyparsing version:', pyparsing.__version__"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "IPython version: 2.1.0\n",
        "numpy version: 1.8.2\n",
        "scikit-learn version: 0.15.1\n",
        "matplotlib version: 1.3.1\n",
        "pydot version: 1.0.28\n",
        "pyparsing version: 1.5.6\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "_Please note the pyparsing version. pydot has problems with pyparsing >= 2.0, you should have pyparsing 1.5.x. See the problem and a workaround at: http://stackoverflow.com/questions/15951748/pydot-and-graphviz-error-couldnt-import-dot-parser-loading-of-dot-files-will/21462609#21462609_"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Preprocessing"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "As usual, we should first load the dataset. The list of attributes it includes is: Ordinal, Class, Survived (0=no, 1=yes), Name, Age, Port of Embarkation, Home/Destination, Room, Ticket, Boat, and Sex. We will start by loading the dataset into a numpy array.. We assume it is located in the data/titanic.csv file"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import csv\n",
      "with open('data/titanic.csv', 'rb') as csvfile:\n",
      "    titanic_reader = csv.reader(csvfile, delimiter=',', quotechar='\"')\n",
      "    \n",
      "    # Header contains feature names\n",
      "    row = titanic_reader.next()\n",
      "    feature_names = np.array(row)\n",
      "    \n",
      "    # Load dataset, and target classes\n",
      "    titanic_X, titanic_y = [], []\n",
      "    for row in titanic_reader:  \n",
      "        titanic_X.append(row)\n",
      "        titanic_y.append(row[2]) # The target value is \"survived\"\n",
      "    \n",
      "    titanic_X = np.array(titanic_X)\n",
      "    titanic_y = np.array(titanic_y)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's inspect how data looks. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print feature_names, titanic_X[0], titanic_y[0]\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "['row.names' 'pclass' 'survived' 'name' 'age' 'embarked' 'home.dest' 'room'\n",
        " 'ticket' 'boat' 'sex'] ['1' '1st' '1' 'Allen, Miss Elisabeth Walton' '29.0000' 'Southampton'\n",
        " 'St Louis, MO' 'B-5' '24160 L221' '2' 'female'] 1\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We will keep only class (1st,2nd,3rd), age (float), and sex (masc, fem) for our study. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# we keep the class, the age and the sex\n",
      "titanic_X = titanic_X[:, [1, 4, 10]]\n",
      "feature_names = feature_names[[1, 4, 10]]\n",
      "print feature_names\n",
      "print titanic_X[12], titanic_y[12]\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "['pclass' 'age' 'sex']\n",
        "['1st' 'NA' 'female'] 1\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We have some problems with missing values ('NA') for the 'age' feature. To avoid this, we will use the mean value whenever we do not have data available."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ages = titanic_X[:, 1]\n",
      "mean_age = np.mean(titanic_X[ages != 'NA', 1].astype(np.float))\n",
      "titanic_X[titanic_X[:, 1] == 'NA', 1] = mean_age\n",
      "print feature_names\n",
      "print titanic_X[12], titanic_y[12]\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "['pclass' 'age' 'sex']\n",
        "['1st' '31.1941810427' 'female'] 1\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now, class and sex are categorical classes, but most scikit-learn classifiers (in particular the Decision Trees we plan to use), expect real-valued attributes. We can easily convert sex  to a binary value (0=female,1=male). We will use the LabelEncoder class from scikit-learn:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.preprocessing import LabelEncoder\n",
      "enc = LabelEncoder()\n",
      "label_encoder = enc.fit(titanic_X[:, 2])\n",
      "print \"Categorical classes:\", label_encoder.classes_\n",
      "integer_classes = label_encoder.transform(label_encoder.classes_)\n",
      "print \"Integer classes:\", integer_classes\n",
      "t = label_encoder.transform(titanic_X[:, 2])\n",
      "titanic_X[:, 2] = t\n",
      "print 'Feature names:',feature_names\n",
      "print 'Features for instance number 12:',titanic_X[12], titanic_y[12]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Categorical classes: ['female' 'male']\n",
        "Integer classes: [0 1]\n",
        "Feature names: ['pclass' 'age' 'sex']\n",
        "Features for instance number 12: ['1st' '31.1941810427' '0'] 1\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now, we have to convert the class. Since we have three different classes, we cannot convert to binary values (and using 0/1/2 values would imply an order, something we do not want). We use OneHotEncoder to get three different attributes:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.preprocessing import OneHotEncoder\n",
      "\n",
      "enc = LabelEncoder()\n",
      "label_encoder = enc.fit(titanic_X[:, 0])\n",
      "print \"Categorical classes:\", label_encoder.classes_\n",
      "integer_classes = label_encoder.transform(label_encoder.classes_).reshape(3, 1)\n",
      "print \"Integer classes:\", integer_classes\n",
      "enc = OneHotEncoder()\n",
      "one_hot_encoder = enc.fit(integer_classes)\n",
      "# First, convert clases to 0-(N-1) integers using label_encoder\n",
      "num_of_rows = titanic_X.shape[0]\n",
      "t = label_encoder.transform(titanic_X[:, 0]).reshape(num_of_rows, 1)\n",
      "# Second, create a sparse matrix with three columns, each one indicating if the instance belongs to the class\n",
      "new_features = one_hot_encoder.transform(t)\n",
      "# Add the new features to titanix_X\n",
      "titanic_X = np.concatenate([titanic_X, new_features.toarray()], axis = 1)\n",
      "#Eliminate converted columns\n",
      "titanic_X = np.delete(titanic_X, [0], 1)\n",
      "# Update feature names\n",
      "feature_names = ['age', 'sex', 'first_class', 'second_class', 'third_class']\n",
      "# Convert to numerical values\n",
      "titanic_X = titanic_X.astype(float)\n",
      "titanic_y = titanic_y.astype(float)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Categorical classes: ['1st' '2nd' '3rd']\n",
        "Integer classes: [[0]\n",
        " [1]\n",
        " [2]]\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print 'New feature names:',feature_names\n",
      "print 'Values:',titanic_X[0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "New feature names: ['age', 'sex', 'first_class', 'second_class', 'third_class']\n",
        "Values: [ 29.   0.   1.   0.   0.]\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Split, as usual, training and testing sets"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.cross_validation import train_test_split\n",
      "X_train, X_test, y_train, y_test = train_test_split(titanic_X, titanic_y, test_size=0.25, random_state=33)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Decision Trees"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now, we can create a new DecisionTreeClassifier and use the fit method of the classifier to do the learning job."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn import tree\n",
      "clf = tree.DecisionTreeClassifier(criterion='entropy', max_depth=3,min_samples_leaf=5)\n",
      "clf = clf.fit(X_train,y_train)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The decision tree we have built represents a series of decisions based on the training data. To classify an instance, we should answer the question at each node. For example, at our root node, the question is: Is sex<=0.5? (are we talking about a woman?). If the answer is yes, you go to the left child node in the tree; otherwise you go to the right child node. You keep answering questions (was she in the third class?, was she in the first class?, and was she below 13 years old?), until you reach a leaf. When you are there, the prediction corresponds to the target class that has most instances (that is if the answers are given to the previous questions). In our case, if she was a woman from second class, the answer would be 1 (that is she survived), and so on. Let's drawit, using pyplot:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import StringIO\n",
      "dot_data = StringIO.StringIO() \n",
      "tree.export_graphviz(clf, out_file=dot_data, feature_names=['age','sex','1st_class','2nd_class','3rd_class']) \n",
      "graph = pydot.graph_from_dot_data(dot_data.getvalue()) \n",
      "graph.write_png('titanic.png') \n",
      "from IPython.core.display import Image \n",
      "Image(filename='titanic.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "png": 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0NBQ/Pz+qV6+OxWJhwIABzJs3r9hr+qtTp05x5MiRIt8jk8mEp6fnde3XCte/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xkZGdW5n1FaWho8PDyky3JycoKenuyhX11djcOHD2PixIky0ysqKrB8+XKsXbu2QduzbNkyXLp0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\n/Tt6OgAAAABJRU5ErkJggg==\n",
       "prompt_number": 12,
       "text": [
        "<IPython.core.display.Image at 0x107a4eed0>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's measure Accuracy, precision, recall, f1 in the training set"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn import metrics\n",
      "def measure_performance(X,y,clf, show_accuracy=True, show_classification_report=True, show_confusion_matrix=True):\n",
      "    y_pred=clf.predict(X)   \n",
      "    if show_accuracy:\n",
      "        print \"Accuracy:{0:.3f}\".format(metrics.accuracy_score(y,y_pred)),\"\\n\"\n",
      "\n",
      "    if show_classification_report:\n",
      "        print \"Classification report\"\n",
      "        print metrics.classification_report(y,y_pred),\"\\n\"\n",
      "        \n",
      "    if show_confusion_matrix:\n",
      "        print \"Confusion matrix\"\n",
      "        print metrics.confusion_matrix(y,y_pred),\"\\n\"\n",
      "        \n",
      "measure_performance(X_train,y_train,clf, show_classification_report=False, show_confusion_matrix=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Accuracy:0.838 \n",
        "\n"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "For this example, we will use an extreme case of cross- validation, named leave-one-out cross-validation. For each instance in the training sample, we train on the rest of the sample, and evaluate the model built on the only instance left out. After performing as many classifications as training instances, we calculate the accuracy simply as the proportion of times our method correctly predicted the class of the left-out instance, and found it is a little lower (as we expected) than the resubstitution accuracy on the training set."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.cross_validation import cross_val_score, LeaveOneOut\n",
      "from scipy.stats import sem\n",
      "\n",
      "def loo_cv(X_train,y_train,clf):\n",
      "    # Perform Leave-One-Out cross validation\n",
      "    # We are preforming 1313 classifications!\n",
      "    loo = LeaveOneOut(X_train[:].shape[0])\n",
      "    scores=np.zeros(X_train[:].shape[0])\n",
      "    for train_index,test_index in loo:\n",
      "        X_train_cv, X_test_cv= X_train[train_index], X_train[test_index]\n",
      "        y_train_cv, y_test_cv= y_train[train_index], y_train[test_index]\n",
      "        clf = clf.fit(X_train_cv,y_train_cv)\n",
      "        y_pred=clf.predict(X_test_cv)\n",
      "        scores[test_index]=metrics.accuracy_score(y_test_cv.astype(int), y_pred.astype(int))\n",
      "    print (\"Mean score: {0:.3f} (+/-{1:.3f})\").format(np.mean(scores), sem(scores))\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "loo_cv(X_train, y_train,clf)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Mean score: 0.837 (+/-0.012)\n"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Random Forests"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "A common criticism to decision trees is that once the training set is divided after answering a question, it is not possible to reconsider this decision. For example, if we divide men and women, every subsequent question would be only about men or women, and the method could not consider another type of question (say, age less than a year, irrespective of the gender). Random Forests try to introduce some level of randomization in each step, proposing alternative trees and combining them to get the final prediction. These types of algorithms that consider several classifiers answering the same question are called ensemble methods. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.ensemble import RandomForestClassifier\n",
      "clf = RandomForestClassifier(n_estimators=10,random_state=33)\n",
      "clf = clf.fit(X_train,y_train)\n",
      "loo_cv(X_train,y_train,clf)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Mean score: 0.817 (+/-0.012)\n"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "To evaluate performance on future data, evaluate on the training set and test on the evaluation set"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clf_dt=tree.DecisionTreeClassifier(criterion='entropy', max_depth=3,min_samples_leaf=5)\n",
      "clf_dt.fit(X_train,y_train)\n",
      "measure_performance(X_test,y_test,clf_dt)\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Accuracy:0.793 \n",
        "\n",
        "Classification report\n",
        "             precision    recall  f1-score   support\n",
        "\n",
        "        0.0       0.77      0.96      0.85       202\n",
        "        1.0       0.88      0.54      0.67       127\n",
        "\n",
        "avg / total       0.81      0.79      0.78       329\n",
        "\n",
        "\n",
        "Confusion matrix\n",
        "[[193   9]\n",
        " [ 59  68]] \n",
        "\n"
       ]
      }
     ],
     "prompt_number": 17
    }
   ],
   "metadata": {}
  }
 ]
}