Assignment+3 (1).ipynb

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    "---\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
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   "outputs": [
    {
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       "0.016410823768035772"
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     "execution_count": 2,
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     "output_type": "execute_result"
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   ],
   "source": [
    "def answer_one():\n",
    "    \n",
    "    df = pd.read_csv(\"fraud_data.csv\")\n",
    "    x = df['Class'].value_counts()[1]\n",
    "    y = df.shape[0]\n",
    "    z = x/y\n",
    "    \n",
    "    return z\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('readonly/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\n",
    "    \n",
    "    clf = DummyClassifier(strategy = 'most_frequent').fit(X_train,y_train)    # Your code here\n",
    "    y_clf_pre = clf.predict(X_test)\n",
    "    return (accuracy_score(y_test,y_clf_pre),recall_score(y_test,y_clf_pre))\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\n",
    "    from sklearn.svm import SVC\n",
    "    \n",
    "    clf = SVC().fit(X_train,y_train)\n",
    "    y_clf_pre = clf.predict(X_test)\n",
    "    # Your code here\n",
    "    \n",
    "    return (accuracy_score(y_test,y_clf_pre), recall_score(y_test,y_clf_pre), precision_score(y_test,y_clf_pre))\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 integes.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
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   "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",
    "    \n",
    "    clf = SVC(C = 1e9, gamma = 1e-07).fit(X_train, y_train)\n",
    "   \n",
    "    clf_dec = clf.decision_function(X_test) > -220\n",
    "    confusion = confusion_matrix(y_test,clf_dec)\n",
    "    return confusion\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": [
       "(0.825, 0.9375)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def answer_five():\n",
    "    from sklearn.linear_model import LogisticRegression\n",
    "    from sklearn.metrics import precision_recall_curve,roc_curve\n",
    "    \n",
    "    clf = LogisticRegression().fit(X_train, y_train)\n",
    "    \n",
    "    y_clf_dec = clf.decision_function(X_test)\n",
    "    \n",
    "    precision, recall, thresholds = precision_recall_curve(y_test,y_clf_dec)\n",
    "    \n",
    "    fpr, tpr, _ = roc_curve(y_test,y_clf_dec)\n",
    "    \n",
    "    recall_value = float(recall[np.where(precision==0.75)])\n",
    "    tpr_value = float(tpr[np.where((fpr >= 0.159) & (fpr <= 0.16))][0])\n",
    "    \n",
    "    return recall_value,tpr_value\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": 8,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.99422214,  0.99539001],\n",
       "       [ 0.99606614,  0.99618907],\n",
       "       [ 0.99606614,  0.9961276 ],\n",
       "       [ 0.99588174,  0.9959432 ],\n",
       "       [ 0.99588174,  0.99588174]])"
      ]
     },
     "execution_count": 8,
     "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",
    "    clf = LogisticRegression()\n",
    "    \n",
    "    params = {'C':[0.01, 0.1, 1, 10, 100],'penalty': ['l1', 'l2']}\n",
    "        \n",
    "    grid_clf = GridSearchCV(clf, params, cv = 3)\n",
    "    \n",
    "    grid_clf_acc = grid_clf.fit(X_train, y_train) \n",
    "    \n",
    "    output = grid_clf_acc.cv_results_[\"mean_test_score\"].reshape(-1,2)\n",
    "    return output\n",
    "answer_six()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "        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": {
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\" 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      "text/plain": [
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "GridSearch_Heatmap(answer_six())"
   ]
  }
 ],
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