Assignment 3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "_You are currently looking at **version 1.2** of this notebook. To download notebooks and datafiles, as well as get help on Jupyter notebooks in the Coursera platform, visit the [Jupyter Notebook FAQ](https://www.coursera.org/learn/python-machine-learning/resources/bANLa) course resource._\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Assignment 3 - Evaluation\n",
    "\n",
    "In this assignment you will train several models and evaluate how effectively they predict instances of fraud using data based on [this dataset from Kaggle](https://www.kaggle.com/dalpozz/creditcardfraud).\n",
    " \n",
    "Each row in `fraud_data.csv` corresponds to a credit card transaction. Features include confidential variables `V1` through `V28` as well as `Amount` which is the amount of the transaction. \n",
    " \n",
    "The target is stored in the `class` column, where a value of 1 corresponds to an instance of fraud and 0 corresponds to an instance of not fraud."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 1\n",
    "Import the data from `fraud_data.csv`. What percentage of the observations in the dataset are instances of fraud?\n",
    "\n",
    "*This function should return a float between 0 and 1.* "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.016410823768035772"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def answer_one():\n",
    "    \n",
    "    # Your code here\n",
    "    clas= pd.read_csv('fraud_data.csv')['Class']\n",
    "    return np.bincount(clas)[1]/clas.size\n",
    "\n",
    "answer_one()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Use X_train, X_test, y_train, y_test for all of the following questions\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "df = pd.read_csv('fraud_data.csv')\n",
    "\n",
    "X = df.iloc[:,:-1]\n",
    "y = df.iloc[:,-1]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 2\n",
    "\n",
    "Using `X_train`, `X_test`, `y_train`, and `y_test` (as defined above), train a dummy classifier that classifies everything as the majority class of the training data. What is the accuracy of this classifier? What is the recall?\n",
    "\n",
    "*This function should a return a tuple with two floats, i.e. `(accuracy score, recall score)`.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.98525073746312686, 0.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def answer_two():\n",
    "    from sklearn.dummy import DummyClassifier\n",
    "    from sklearn.metrics import recall_score,accuracy_score , confusion_matrix\n",
    "    \n",
    "    # Your code here\n",
    "    clf_dummy = DummyClassifier(strategy='most_frequent').fit(X_train,y_train)\n",
    "    accuracy, recal = accuracy_score(y_test,clf_dummy.predict(X_test)), recall_score(y_test,clf_dummy.predict(X_test))\n",
    "\n",
    "    return accuracy, recal#, confusion_matrix(y_test,clf_dummy.predict(X_test))\n",
    "\n",
    "answer_two()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 3\n",
    "\n",
    "Using X_train, X_test, y_train, y_test (as defined above), train a SVC classifer using the default parameters. What is the accuracy, recall, and precision of this classifier?\n",
    "\n",
    "*This function should a return a tuple with three floats, i.e. `(accuracy score, recall score, precision score)`.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.99078171091445433, 0.375, 1.0)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def answer_three():\n",
    "    from sklearn.metrics import recall_score, precision_score,accuracy_score,confusion_matrix\n",
    "    from sklearn.svm import SVC\n",
    "\n",
    "    # Your code here\n",
    "    clf_svm = SVC().fit(X_train,y_train) \n",
    "    accuracy,recal,precision = accuracy_score(y_test,clf_svm.predict(X_test)),\\\n",
    "                               recall_score(y_test,clf_svm.predict(X_test)),\\\n",
    "                               precision_score(y_test,clf_svm.predict(X_test))\n",
    "    return accuracy,recal,precision#,confusion_matrix(y_test,clf_svm.predict(X_test))  # Return your answer\n",
    "answer_three()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 4\n",
    "\n",
    "Using the SVC classifier with parameters `{'C': 1e9, 'gamma': 1e-07}`, what is the confusion matrix when using a threshold of -220 on the decision function. Use X_test and y_test.\n",
    "\n",
    "*This function should return a confusion matrix, a 2x2 numpy array with 4 integers.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5320,   24],\n",
       "       [  14,   66]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def answer_four():\n",
    "    from sklearn.metrics import confusion_matrix\n",
    "    from sklearn.svm import SVC\n",
    "    \n",
    "    # Your code here\n",
    "    clf_svm = SVC(C=1e9, gamma=1e-07).fit(X_train,y_train)\n",
    "    y_score_svm = clf_svm.decision_function(X_test)\n",
    "    y_pred = np.where(y_score_svm> -220,1,0)\n",
    "    \n",
    "    return confusion_matrix(y_test,y_pred)# Return your answer\n",
    "answer_four()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 5\n",
    "\n",
    "Train a logisitic regression classifier with default parameters using X_train and y_train.\n",
    "\n",
    "For the logisitic regression classifier, create a precision recall curve and a roc curve using y_test and the probability estimates for X_test (probability it is fraud).\n",
    "\n",
    "Looking at the precision recall curve, what is the recall when the precision is `0.75`?\n",
    "\n",
    "Looking at the roc curve, what is the true positive rate when the false positive rate is `0.16`?\n",
    "\n",
    "*This function should return a tuple with two floats, i.e. `(recall, true positive rate)`.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.82499999999999996, 0.90000000000000002)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def answer_five():\n",
    "        \n",
    "    # Your code here\n",
    "    from sklearn.linear_model import LogisticRegression\n",
    "    from sklearn.metrics import precision_recall_curve, roc_curve,auc\n",
    "\n",
    "    clf_lr = LogisticRegression(max_iter=1000).fit(X_train,y_train)\n",
    "    y_score_lr = clf_lr.decision_function(X_test)\n",
    "    precision, recall, threshsold = precision_recall_curve(y_test,y_score_lr)\n",
    "    fpr, tpr, roc_threshold = roc_curve(y_test,y_score_lr)\n",
    "    roc_auc_lr = auc(fpr, tpr)\n",
    "\n",
    "    pre_index = np.where(precision==0.75)\n",
    "    fpr_index = np.where((fpr>=0.1) & (fpr<=0.16))\n",
    "\n",
    "    return  recall[pre_index][0],tpr[fpr_index][0] # Return your answer\n",
    "\n",
    "answer_five()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 6\n",
    "\n",
    "Perform a grid search over the parameters listed below for a Logisitic Regression classifier, using recall for scoring and the default 3-fold cross validation.\n",
    "\n",
    "`'penalty': ['l1', 'l2']`\n",
    "\n",
    "`'C':[0.01, 0.1, 1, 10, 100]`\n",
    "\n",
    "From `.cv_results_`, create an array of the mean test scores of each parameter combination. i.e.\n",
    "\n",
    "|      \t| `l1` \t| `l2` \t|\n",
    "|:----:\t|----\t|----\t|\n",
    "| **`0.01`** \t|    ?\t|   ? \t|\n",
    "| **`0.1`**  \t|    ?\t|   ? \t|\n",
    "| **`1`**    \t|    ?\t|   ? \t|\n",
    "| **`10`**   \t|    ?\t|   ? \t|\n",
    "| **`100`**   \t|    ?\t|   ? \t|\n",
    "\n",
    "<br>\n",
    "\n",
    "*This function should return a 5 by 2 numpy array with 10 floats.* \n",
    "\n",
    "*Note: do not return a DataFrame, just the values denoted by '?' above in a numpy array. You might need to reshape your raw result to meet the format we are looking for.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.66666667,  0.76086957],\n",
       "       [ 0.80072464,  0.80434783],\n",
       "       [ 0.8115942 ,  0.8115942 ],\n",
       "       [ 0.80797101,  0.8115942 ],\n",
       "       [ 0.80797101,  0.80797101]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def answer_six():    \n",
    "    from sklearn.model_selection import GridSearchCV\n",
    "    from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "    # Your code here\n",
    "    grid_value = {'penalty': ['l1', 'l2'],'C':[0.01, 0.1, 1, 10, 100]}\n",
    "    clf = LogisticRegression(max_iter=10000)\n",
    "    grid_clf_recal = GridSearchCV(clf,param_grid=grid_value,scoring='recall',cv=3).fit(X_train,y_train)\n",
    "    \n",
    "    return grid_clf_recal.cv_results_['mean_test_score'].reshape(-1,2)\n",
    "answer_six()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"data:image/png;base64,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\" width=\"800\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use the following function to help visualize results from the grid search\n",
    "def GridSearch_Heatmap(scores):\n",
    "    %matplotlib notebook\n",
    "    import seaborn as sns\n",
    "    import matplotlib.pyplot as plt\n",
    "    plt.figure()\n",
    "    sns.heatmap(scores.reshape(5,2), xticklabels=['l1','l2'], yticklabels=[0.01, 0.1, 1, 10, 100])\n",
    "    plt.yticks(rotation=0);\n",
    "\n",
    "# GridSearch_Heatmap(answer_six())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "coursera": {
   "course_slug": "python-machine-learning",
   "graded_item_id": "5yX9Z",
   "launcher_item_id": "eqnV3",
   "part_id": "Msnj0"
  },
  "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": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}