06. Make all models.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create all the models we are going to use\n",
    "\n",
    "Most of them aren't going to work particularly well. But, we are gonna build all the frameworks out, so as we update things, we can just rerun this notebook to get better performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T21:17:23.975176Z",
     "start_time": "2020-02-27T21:17:21.398629Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "# basic stuff\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# sklearn for logreg, RF, GBT, and SVM\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "\n",
    "# other stuff from sklearn we might need\n",
    "from sklearn import metrics\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "from sklearn.utils import resample # we have imbalanced classes\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import preprocessing\n",
    "\n",
    "# keras for the NN\n",
    "from keras import Sequential, layers, models\n",
    "\n",
    "# Stuff that I wrote\n",
    "from utils.strings import TARGET, FEATURES, CAT_FEATURES, DATE\n",
    "from utils.utilities import select_by_date\n",
    "\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load in the data\n",
    "\n",
    "we also need to do some simple preprocessing (for now) before we can train all the models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T21:17:25.341998Z",
     "start_time": "2020-02-27T21:17:24.016939Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 111854 entries, 0 to 111853\n",
      "Data columns (total 29 columns):\n",
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      "time_since_last          111854 non-null float64\n",
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      "dsny_heat_score          111854 non-null float64\n",
      "dep_heat_score           111854 non-null float64\n",
      "dohmh_heat_score         111854 non-null float64\n",
      "is_chain                 111854 non-null int64\n",
      "init_inspec              111854 non-null int64\n",
      "dtypes: datetime64[ns](1), float64(11), int64(6), object(11)\n",
      "memory usage: 24.7+ MB\n"
     ]
    }
   ],
   "source": [
    "inspecs = pd.read_csv('./data/inspec_scores.csv', parse_dates=[DATE])\n",
    "inspecs.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Let's do some feature pre-processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T21:17:25.830587Z",
     "start_time": "2020-02-27T21:17:25.552677Z"
    }
   },
   "outputs": [],
   "source": [
    "# encode the catagorical features. For some reason I couldn't just use the onehot.\n",
    "# i needed to actually add new columns to the DF for each new feature\n",
    "\n",
    "# enc = preprocessing.OneHotEncoder(sparse=False)\n",
    "# for feat in CAT_FEATURES:\n",
    "#     enc.fit(inspecs[feat].unique().reshape(-1, 1))\n",
    "#     inspecs[f'{feat}_enc'] = enc.transform(inspecs[feat].values.reshape(-1, 1)).tolist()\n",
    "\n",
    "for feat in CAT_FEATURES:\n",
    "    try:\n",
    "        new_df = pd.concat([new_df, pd.get_dummies(inspecs[feat])], axis=1)\n",
    "    except NameError:\n",
    "        new_df = pd.DataFrame(index=inspecs.index)\n",
    "        new_df = pd.concat([new_df, pd.get_dummies(inspecs[feat])], axis=1)\n",
    "#     new_df = new_df.drop([feat], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T21:17:26.096556Z",
     "start_time": "2020-02-27T21:17:26.078354Z"
    }
   },
   "outputs": [
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       "   A  B  C  Bronx  Brooklyn  Manhattan  Queens  Staten Island  10000.0  \\\n",
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   "source": [
    "new_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T21:17:26.404046Z",
     "start_time": "2020-02-27T21:17:26.349576Z"
    }
   },
   "outputs": [],
   "source": [
    "# merge the two frames together\n",
    "\n",
    "FEATURES += new_df.columns.tolist()\n",
    "inspecs = pd.concat([inspecs, new_df], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T21:17:27.850332Z",
     "start_time": "2020-02-27T21:17:27.743153Z"
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      "text/plain": [
       "   tmax    tmax_3d  time_since_last  past_critical  past_score  \\\n",
       "0    92  79.000000              0.0            0.0        12.0   \n",
       "1    73  72.333333            358.0            1.0         7.0   \n",
       "2    73  56.333333            370.0            0.0         5.0   \n",
       "3    77  76.333333             26.0            1.0        14.0   \n",
       "4    67  59.000000            132.0            0.0         6.0   \n",
       "\n",
       "   dsny_heat_score  dep_heat_score  dohmh_heat_score  is_chain  init_inspec  \\\n",
       "0         0.406452        0.117716          0.098959         0            1   \n",
       "1         0.203028        0.074187          0.169137         0            0   \n",
       "2         0.160208        0.027397          0.173178         0            0   \n",
       "3         0.256666        0.399057          0.316888         0            0   \n",
       "4         0.309046        0.031414          0.274051         0            0   \n",
       "\n",
       "   ...  11694.0  11697.0  12345.0  Friday  Monday  Saturday  Sunday  Thursday  \\\n",
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       "4  ...        0        0        0       0       1         0       0         0   \n",
       "\n",
       "   Tuesday  Wednesday  \n",
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       "\n",
       "[5 rows x 246 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inspecs[FEATURES].head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-21T18:32:02.636838Z",
     "start_time": "2020-02-21T18:31:38.482180Z"
    }
   },
   "outputs": [],
   "source": [
    "inspecs.to_csv('./data/inspecs_encoded.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Class Imbalance\n",
    "\n",
    "We know that we have really imbalanced classes. We will need to resample the two classes to make sure that we can train appropriately. Currently, I am both downsampling all the examples where there is a critical violation or upsampling all of the times when there isn't a critical violation.\n",
    "\n",
    "We could use other fancier methods like SMOTE, but not right now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T21:17:32.222677Z",
     "start_time": "2020-02-27T21:17:31.947779Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1    100369\n",
      "0     11485\n",
      "Name: critical, dtype: int64\n",
      "1    11485\n",
      "0    11485\n",
      "Name: critical, dtype: int64\n",
      "1    100369\n",
      "0    100369\n",
      "Name: critical, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Previously we found that we have really imbalanced classes. Here we'll downsample the majority class to match\n",
    "# the number of the minority class\n",
    "# Separate majority and minority classes\n",
    "df_majority = inspecs[inspecs[TARGET]==1]\n",
    "df_minority = inspecs[inspecs[TARGET]==0]\n",
    "\n",
    "# Downsample Majority Class\n",
    "df_majority_dsampled = resample(df_majority, \n",
    "                                 replace=False, # sample without replacement\n",
    "                                 n_samples=len(df_minority)) # to match minority class\n",
    " \n",
    "# Combine minority class with downsampled majority class\n",
    "df_dsampled = pd.concat([df_minority, df_majority_dsampled])\n",
    " \n",
    "# Upsample minority class\n",
    "df_minority_upsampled = resample(df_minority, \n",
    "                                 replace=True,     # sample with replacement\n",
    "                                 n_samples=len(df_majority))    # to match majority class\n",
    " \n",
    "# Combine majority class with upsampled minority class\n",
    "df_upsampled = pd.concat([df_majority, df_minority_upsampled])    \n",
    "    \n",
    "    \n",
    "# Display new class counts\n",
    "print(inspecs[TARGET].value_counts()) # before\n",
    "print(df_dsampled[TARGET].value_counts()) # downsample\n",
    "print(df_upsampled[TARGET].value_counts()) # upsample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-21T18:32:53.951411Z",
     "start_time": "2020-02-21T18:32:03.759675Z"
    }
   },
   "outputs": [],
   "source": [
    "df_dsampled.to_csv('./data/inspecs_encoded_dsample.csv', index=False)\n",
    "df_upsampled.to_csv('./data/inspecs_encoded_usample.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T21:17:35.963163Z",
     "start_time": "2020-02-27T21:17:35.952993Z"
    }
   },
   "outputs": [],
   "source": [
    "#### START HERE FOR RESTARTS ####\n",
    "\n",
    "# inspecs = pd.read_csv('./data/inspecs_encoded.csv', parse_dates=[DATE])\n",
    "# df_upsampled = pd.read_csv('./data/inspecs_encoded_usample.csv', parse_dates=[DATE])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Select the training and testing data\n",
    "\n",
    "We are going to train on the balanced data, but we are going to test on the imbalanced data, because that is the what the world actully looks like. \n",
    "\n",
    "\n",
    "I've picked two months toward the middle of 2019, but it can be anything (in 2019)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T21:17:37.123162Z",
     "start_time": "2020-02-27T21:17:36.958514Z"
    }
   },
   "outputs": [],
   "source": [
    "train = select_by_date(df_upsampled, '2018-01-01', '2018-12-31')\n",
    "train_imbalanced = select_by_date(inspecs, '2018-01-01', '2019-01-01')\n",
    "test = select_by_date(inspecs, '2019-05-01', '2019-06-30')\n",
    "test_balanced = select_by_date(df_upsampled, '2019-05-01', '2019-06-30')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T21:17:38.415260Z",
     "start_time": "2020-02-27T21:17:38.407291Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    33407\n",
      "1    31605\n",
      "Name: critical, dtype: int64\n",
      "1    6981\n",
      "0     906\n",
      "Name: critical, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(train[TARGET].value_counts()) # train\n",
    "print(test[TARGET].value_counts()) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T21:17:39.722225Z",
     "start_time": "2020-02-27T21:17:39.692942Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1    6981\n",
      "0    6981\n",
      "Name: critical, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "df_major = test[test[TARGET]==1]\n",
    "df_minor = test[test[TARGET]==0]\n",
    "\n",
    "df_minor_up = resample(df_minor, \n",
    "                                 replace=True,     # sample with replacement\n",
    "                                 n_samples=len(df_major))\n",
    "\n",
    "test_balanced = pd.concat([df_major, df_minor_up])\n",
    "\n",
    "print(test_balanced[TARGET].value_counts()) # upsample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T21:17:41.215502Z",
     "start_time": "2020-02-27T21:17:41.212049Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(65012, 265)\n",
      "(7887, 265)\n"
     ]
    }
   ],
   "source": [
    "print(train.shape)\n",
    "print(test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Build Models\n",
    "\n",
    "We are gonna build a bunch of them. Mostly, because I am interested in their performance. We can also think about trying to combine models in some fancy-ish way. Like using a interpretable model to do feature ranking and then dumping those features into a less interpretable model (but better?) to make the actual predictions.\n",
    "\n",
    "I am gonna builld a lot of models, because they are easy to do. If they were harder to actually impliment, then I would need to think harder about which models we actually care about... It would be some sort of a decision tree. It is both interpretable and will provide probabilities (what we actually care about) for the individual predictions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T21:17:46.648101Z",
     "start_time": "2020-02-27T21:17:42.653114Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.16      0.57      0.25       906\n",
      "           1       0.92      0.62      0.74      6981\n",
      "\n",
      "    accuracy                           0.61      7887\n",
      "   macro avg       0.54      0.59      0.49      7887\n",
      "weighted avg       0.83      0.61      0.68      7887\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/boada/.local/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
     ]
    }
   ],
   "source": [
    "log_base = LogisticRegression(solver='lbfgs')\n",
    "log_base.fit(train[FEATURES], train[TARGET])\n",
    "predictions = log_base.predict(test[FEATURES])\n",
    "\n",
    "with open('./models/logmodel.pkl', 'wb') as model:\n",
    "    pickle.dump(log_base, model)\n",
    "\n",
    "print(metrics.classification_report(test[TARGET], predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T21:18:13.192851Z",
     "start_time": "2020-02-27T21:17:48.131390Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.41      0.09      0.15       906\n",
      "           1       0.89      0.98      0.94      6981\n",
      "\n",
      "    accuracy                           0.88      7887\n",
      "   macro avg       0.65      0.54      0.54      7887\n",
      "weighted avg       0.84      0.88      0.85      7887\n",
      "\n"
     ]
    }
   ],
   "source": [
    "RF_base = RandomForestClassifier()\n",
    "RF_base.fit(train[FEATURES], train[TARGET])\n",
    "predictions = RF_base.predict(test[FEATURES])\n",
    "\n",
    "with open('./models/RFmodel.pkl', 'wb') as model:\n",
    "    pickle.dump(RF_base, model)\n",
    "\n",
    "print(metrics.classification_report(test[TARGET], predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T21:18:15.118372Z",
     "start_time": "2020-02-27T21:18:14.672943Z"
    }
   },
   "outputs": [
    {
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       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
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       "\n",
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       "            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 / mpl.ratio, fig.canvas.height / mpl.ratio);\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\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"pdf\";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 overridden (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",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\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": [
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f2bde6aa0f0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "metrics.plot_confusion_matrix(RF_base, test[FEATURES], test[TARGET], values_format='d')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gradient Boosted Trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T21:18:56.130380Z",
     "start_time": "2020-02-27T21:18:23.917482Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.19      0.62      0.28       906\n",
      "           1       0.93      0.65      0.76      6981\n",
      "\n",
      "    accuracy                           0.64      7887\n",
      "   macro avg       0.56      0.63      0.52      7887\n",
      "weighted avg       0.84      0.64      0.71      7887\n",
      "\n"
     ]
    }
   ],
   "source": [
    "GBT_base = GradientBoostingClassifier()\n",
    "GBT_base.fit(train[FEATURES], train[TARGET])\n",
    "predictions = GBT_base.predict(test[FEATURES])\n",
    "\n",
    "with open('./models/GBTmodel.pkl', 'wb') as model:\n",
    "    pickle.dump(GBT_base, model)\n",
    "\n",
    "print(metrics.classification_report(test[TARGET], predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T21:18:57.881313Z",
     "start_time": "2020-02-27T21:18:57.782619Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
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       "    } 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",
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       "\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",
       "        fig.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 / mpl.ratio, fig.canvas.height / mpl.ratio);\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\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"pdf\";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 overridden (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",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\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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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f2bde8768d0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "metrics.plot_confusion_matrix(GBT_base, test[FEATURES], test[TARGET], values_format='d')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Adaboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T21:21:07.314074Z",
     "start_time": "2020-02-27T21:20:48.823905Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.18      0.62      0.27       906\n",
      "           1       0.93      0.63      0.75      6981\n",
      "\n",
      "    accuracy                           0.62      7887\n",
      "   macro avg       0.55      0.62      0.51      7887\n",
      "weighted avg       0.84      0.62      0.69      7887\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "ada_base=AdaBoostClassifier(n_estimators=100, learning_rate=1)\n",
    "ada_base.fit(train[FEATURES], train[TARGET])\n",
    "predictions = ada_base.predict(test[FEATURES])\n",
    "\n",
    "with open('./models/AdaBmodel.pkl', 'wb') as model:\n",
    "    pickle.dump(ada_base, model)\n",
    "\n",
    "print(metrics.classification_report(test[TARGET], predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T21:21:09.296532Z",
     "start_time": "2020-02-27T21:21:08.790274Z"
    }
   },
   "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",
       "        fig.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 / mpl.ratio, fig.canvas.height / mpl.ratio);\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\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"pdf\";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 overridden (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",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\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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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f2bdb004128>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "metrics.plot_confusion_matrix(ada_base, test[FEATURES], test[TARGET], values_format='d')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# XGBoost\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T21:22:00.629644Z",
     "start_time": "2020-02-27T21:21:30.958268Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.19      0.62      0.29       906\n",
      "           1       0.93      0.65      0.76      6981\n",
      "\n",
      "    accuracy                           0.65      7887\n",
      "   macro avg       0.56      0.63      0.53      7887\n",
      "weighted avg       0.84      0.65      0.71      7887\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import xgboost as xgb\n",
    "xgb_base = xgb.XGBClassifier()\n",
    "xgb_base.fit(train[FEATURES], train[TARGET])\n",
    "predictions = xgb_base.predict(test[FEATURES])\n",
    "\n",
    "with open('./models/XGBmodel.pkl', 'wb') as model:\n",
    "    pickle.dump(xgb_base, model)\n",
    "\n",
    "print(metrics.classification_report(test[TARGET], predictions))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T21:22:02.242636Z",
     "start_time": "2020-02-27T21:22:02.131184Z"
    }
   },
   "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",
       "        fig.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 / mpl.ratio, fig.canvas.height / mpl.ratio);\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\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"pdf\";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 overridden (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",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\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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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f2bc6d556d8>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.plot_confusion_matrix(xgb_base, test[FEATURES], test[TARGET], values_format='d')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simple Neural Network\n",
    "\n",
    "Using Keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-25T16:40:34.202343Z",
     "start_time": "2020-02-25T16:40:29.021791Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from keras import Sequential, layers, models\n",
    "\n",
    "input_shape = train[FEATURES].shape[1]\n",
    "\n",
    "# Instantiation = Initialising the neural network object,it's a sequential model\n",
    "model = models.Sequential()\n",
    "# First hidden layer\n",
    "model.add(layers.Dense(activation=\"relu\", input_dim=input_shape, units=100, kernel_initializer=\"uniform\"))\n",
    "# Second hidden \n",
    "model.add(layers.Dense(activation=\"relu\", units=50, kernel_initializer=\"uniform\"))\n",
    "# Add the Output layer\n",
    "model.add(layers.Dense(1, activation=\"sigmoid\", kernel_initializer=\"uniform\"))\n",
    "\n",
    "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "\n",
    "NN_base = model.fit(train[FEATURES],train[TARGET], epochs=50, batch_size=256, validation_split=0.2, verbose=3)\n",
    "\n",
    "with open('./models/NNmodel.pkl', 'wb') as model:\n",
    "    pickle.dump(NN_base.model, model)\n",
    "\n",
    "predictions = NN_base.model.predict_classes(test_balanced[FEATURES])\n",
    "print(metrics.classification_report(test_balanced[TARGET], predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Ranking\n",
    "\n",
    "Let's see if we can get some better performance by removing low importance features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T21:22:12.666935Z",
     "start_time": "2020-02-27T21:22:12.576844Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time_since_last\t\t0.12179\n",
      "dsny_heat_score\t\t0.09273\n",
      "dep_heat_score\t\t0.09260\n",
      "dohmh_heat_score\t\t0.09104\n",
      "tmax_3d\t\t0.07587\n",
      "tmax\t\t0.07101\n",
      "past_score\t\t0.06622\n",
      "is_chain\t\t0.02000\n",
      "Wednesday\t\t0.01138\n",
      "Thursday\t\t0.01098\n",
      "Tuesday\t\t0.01096\n",
      "Manhattan\t\t0.01095\n",
      "Monday\t\t0.01013\n",
      "Brooklyn\t\t0.00971\n",
      "past_critical\t\t0.00953\n",
      "Queens\t\t0.00929\n",
      "Friday\t\t0.00784\n",
      "Bronx\t\t0.00696\n",
      "B\t\t0.00533\n",
      "10019.0\t\t0.00487\n",
      "10003.0\t\t0.00469\n",
      "10036.0\t\t0.00450\n",
      "C\t\t0.00427\n",
      "10013.0\t\t0.00422\n",
      "A\t\t0.00417\n",
      "Saturday\t\t0.00414\n",
      "Staten Island\t\t0.00390\n",
      "10001.0\t\t0.00386\n",
      "10022.0\t\t0.00375\n",
      "10014.0\t\t0.00355\n",
      "10017.0\t\t0.00347\n",
      "10016.0\t\t0.00345\n",
      "10011.0\t\t0.00340\n",
      "10012.0\t\t0.00324\n",
      "10018.0\t\t0.00323\n",
      "11201.0\t\t0.00321\n",
      "11217.0\t\t0.00319\n",
      "11215.0\t\t0.00316\n",
      "10002.0\t\t0.00311\n",
      "11220.0\t\t0.00309\n",
      "11372.0\t\t0.00298\n",
      "11101.0\t\t0.00260\n",
      "11354.0\t\t0.00258\n",
      "11368.0\t\t0.00257\n",
      "11385.0\t\t0.00256\n",
      "init_inspec\t\t0.00251\n",
      "10010.0\t\t0.00244\n",
      "10009.0\t\t0.00243\n",
      "10023.0\t\t0.00236\n",
      "11373.0\t\t0.00233\n",
      "11226.0\t\t0.00231\n",
      "11432.0\t\t0.00230\n",
      "10025.0\t\t0.00227\n",
      "11238.0\t\t0.00226\n",
      "11103.0\t\t0.00225\n",
      "11211.0\t\t0.00224\n",
      "11209.0\t\t0.00216\n",
      "11377.0\t\t0.00208\n",
      "11237.0\t\t0.00208\n",
      "10029.0\t\t0.00206\n",
      "11222.0\t\t0.00200\n",
      "10121.0\t\t0.00200\n",
      "10028.0\t\t0.00197\n",
      "10458.0\t\t0.00194\n",
      "10065.0\t\t0.00192\n",
      "10027.0\t\t0.00192\n",
      "11355.0\t\t0.00191\n",
      "10038.0\t\t0.00191\n",
      "11206.0\t\t0.00191\n",
      "11216.0\t\t0.00190\n",
      "11430.0\t\t0.00187\n",
      "11375.0\t\t0.00181\n",
      "11235.0\t\t0.00177\n",
      "10461.0\t\t0.00175\n",
      "10004.0\t\t0.00173\n",
      "11214.0\t\t0.00173\n",
      "10451.0\t\t0.00173\n",
      "11223.0\t\t0.00171\n",
      "11105.0\t\t0.00167\n",
      "10128.0\t\t0.00165\n",
      "11225.0\t\t0.00158\n",
      "10467.0\t\t0.00157\n",
      "10024.0\t\t0.00156\n",
      "11204.0\t\t0.00156\n",
      "10314.0\t\t0.00155\n",
      "10007.0\t\t0.00154\n",
      "10021.0\t\t0.00154\n",
      "11207.0\t\t0.00152\n",
      "11231.0\t\t0.00148\n",
      "11218.0\t\t0.00147\n",
      "10033.0\t\t0.00146\n",
      "10468.0\t\t0.00146\n",
      "11234.0\t\t0.00145\n",
      "11106.0\t\t0.00144\n",
      "11205.0\t\t0.00143\n",
      "11358.0\t\t0.00142\n",
      "10462.0\t\t0.00141\n",
      "11249.0\t\t0.00138\n",
      "10463.0\t\t0.00138\n",
      "11361.0\t\t0.00137\n",
      "11369.0\t\t0.00135\n",
      "11219.0\t\t0.00129\n",
      "11374.0\t\t0.00125\n",
      "11102.0\t\t0.00125\n",
      "11435.0\t\t0.00124\n",
      "10032.0\t\t0.00122\n",
      "11419.0\t\t0.00119\n",
      "11232.0\t\t0.00117\n",
      "10031.0\t\t0.00116\n",
      "10466.0\t\t0.00114\n",
      "10453.0\t\t0.00112\n",
      "11212.0\t\t0.00108\n",
      "10455.0\t\t0.00108\n",
      "11208.0\t\t0.00107\n",
      "11236.0\t\t0.00106\n",
      "10452.0\t\t0.00106\n",
      "10005.0\t\t0.00105\n",
      "11203.0\t\t0.00105\n",
      "11229.0\t\t0.00105\n",
      "10456.0\t\t0.00103\n",
      "11378.0\t\t0.00103\n",
      "11221.0\t\t0.00100\n"
     ]
    }
   ],
   "source": [
    "idx = np.argsort(RF_base.feature_importances_)[::-1]\n",
    "for f, i in zip(np.asarray(FEATURES)[idx], RF_base.feature_importances_[idx]):\n",
    "    if i > 0.001:\n",
    "        print(f'{f}\\t\\t{i:.5f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So according to the RF, the first 26 features are \"important\" and then it's all the zip codes... Let's use the first 26"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T21:22:14.188540Z",
     "start_time": "2020-02-27T21:22:14.180392Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['time_since_last', 'dsny_heat_score', 'dep_heat_score', 'dohmh_heat_score', 'tmax_3d', 'tmax', 'past_score', 'is_chain', 'Wednesday', 'Thursday', 'Tuesday', 'Manhattan', 'Monday', 'Brooklyn', 'past_critical', 'Queens', 'Friday', 'Bronx', 'B', '10019.0', '10003.0', '10036.0', 'C', '10013.0', 'A', 'Saturday', 'Staten Island']\n"
     ]
    }
   ],
   "source": [
    "new_FEATURES = np.asarray(FEATURES)[idx][:27].tolist()\n",
    "print(new_FEATURES)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T21:22:15.805739Z",
     "start_time": "2020-02-27T21:22:15.802196Z"
    }
   },
   "outputs": [],
   "source": [
    "# There is some issue with just using them... AH!\n",
    "# I remember... the column headings are floats. So we have to convert them from strings..\n",
    "# seems wonky, but it works... \n",
    "nf = []\n",
    "for f in new_FEATURES:\n",
    "    try:\n",
    "        nf.append(float(f))\n",
    "    except ValueError:\n",
    "        nf.append(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T21:22:17.400521Z",
     "start_time": "2020-02-27T21:22:17.396330Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['time_since_last',\n",
       " 'dsny_heat_score',\n",
       " 'dep_heat_score',\n",
       " 'dohmh_heat_score',\n",
       " 'tmax_3d',\n",
       " 'tmax',\n",
       " 'past_score',\n",
       " 'is_chain',\n",
       " 'Wednesday',\n",
       " 'Thursday',\n",
       " 'Tuesday',\n",
       " 'Manhattan',\n",
       " 'Monday',\n",
       " 'Brooklyn',\n",
       " 'past_critical',\n",
       " 'Queens',\n",
       " 'Friday',\n",
       " 'Bronx',\n",
       " 'B',\n",
       " 10019.0,\n",
       " 10003.0,\n",
       " 10036.0,\n",
       " 'C',\n",
       " 10013.0,\n",
       " 'A',\n",
       " 'Saturday',\n",
       " 'Staten Island']"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T21:22:29.551203Z",
     "start_time": "2020-02-27T21:22:18.973466Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.38      0.08      0.13       906\n",
      "           1       0.89      0.98      0.94      6981\n",
      "\n",
      "    accuracy                           0.88      7887\n",
      "   macro avg       0.63      0.53      0.53      7887\n",
      "weighted avg       0.83      0.88      0.84      7887\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Look at the RF again... \n",
    "\n",
    "RF_base.fit(train[nf], train[TARGET])\n",
    "predictions = RF_base.predict(test[nf])\n",
    "\n",
    "print(metrics.classification_report(test[TARGET], predictions))\n",
    "\n",
    "\n",
    "### So removing the extra features doesn't acutally help it... Let's look at another one... "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T21:22:37.839645Z",
     "start_time": "2020-02-27T21:22:32.464537Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.18      0.64      0.29       906\n",
      "           1       0.93      0.63      0.75      6981\n",
      "\n",
      "    accuracy                           0.63      7887\n",
      "   macro avg       0.56      0.63      0.52      7887\n",
      "weighted avg       0.84      0.63      0.70      7887\n",
      "\n"
     ]
    }
   ],
   "source": [
    "xgb_base.fit(train[nf], train[TARGET])\n",
    "predictions = xgb_base.predict(test[nf])\n",
    "\n",
    "print(metrics.classification_report(test[TARGET], predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hyperparameter Tuning\n",
    "\n",
    "Gonna use a search!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T21:27:09.405022Z",
     "start_time": "2020-02-27T21:22:52.533068Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  30 out of  30 | elapsed:  4.2min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=3, error_score=nan,\n",
       "                   estimator=RandomForestClassifier(bootstrap=True,\n",
       "                                                    ccp_alpha=0.0,\n",
       "                                                    class_weight=None,\n",
       "                                                    criterion='gini',\n",
       "                                                    max_depth=None,\n",
       "                                                    max_features='auto',\n",
       "                                                    max_leaf_nodes=None,\n",
       "                                                    max_samples=None,\n",
       "                                                    min_impurity_decrease=0.0,\n",
       "                                                    min_impurity_split=None,\n",
       "                                                    min_samples_leaf=1,\n",
       "                                                    min_samples_split=2,\n",
       "                                                    min_weight_fraction_leaf=0.0,\n",
       "                                                    n_estimators=100,\n",
       "                                                    n_jobs...\n",
       "                   param_distributions={'bootstrap': [True, False],\n",
       "                                        'max_depth': [10, 59, 108, 157, 206,\n",
       "                                                      255, 304, 353, 402, 451,\n",
       "                                                      500, None],\n",
       "                                        'max_features': ['auto', 'sqrt'],\n",
       "                                        'min_samples_leaf': [1, 2, 4],\n",
       "                                        'min_samples_split': [2, 5, 10],\n",
       "                                        'n_estimators': [10, 20, 30, 40, 50, 60,\n",
       "                                                         70, 80, 90, 100, 110,\n",
       "                                                         120, 130, 140, 150,\n",
       "                                                         160, 170, 180, 190,\n",
       "                                                         200]},\n",
       "                   pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
       "                   return_train_score=False, scoring=None, verbose=2)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV, RandomizedSearchCV\n",
    "\n",
    "\n",
    "### Here's a bunch of code I copied from the net ###\n",
    "\n",
    "# Number of trees in random forest\n",
    "n_estimators = [int(x) for x in np.linspace(start=10, stop=200, num=20)]\n",
    "# Number of features to consider at every split\n",
    "max_features = ['auto', 'sqrt']\n",
    "# Maximum number of levels in tree\n",
    "max_depth = [int(x) for x in np.linspace(10, 500, num = 11)]\n",
    "max_depth.append(None)\n",
    "# Minimum number of samples required to split a node\n",
    "min_samples_split = [2, 5, 10]\n",
    "# Minimum number of samples required at each leaf node\n",
    "min_samples_leaf = [1, 2, 4]\n",
    "# Method of selecting samples for training each tree\n",
    "bootstrap = [True, False]# Create the random grid\n",
    "\n",
    "random_grid = {'n_estimators': n_estimators,\n",
    "               'max_features': max_features,\n",
    "               'max_depth': max_depth,\n",
    "               'min_samples_split': min_samples_split,\n",
    "               'min_samples_leaf': min_samples_leaf,\n",
    "               'bootstrap': bootstrap}\n",
    "\n",
    "\n",
    "RF_tuned = RandomizedSearchCV(estimator=RF_base, \n",
    "                               param_distributions=random_grid, \n",
    "                               n_iter=10, \n",
    "                               cv=3, \n",
    "                               verbose=2, \n",
    "                               n_jobs =-1)\n",
    "\n",
    "RF_tuned.fit(train[FEATURES], train[TARGET])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T21:27:11.005340Z",
     "start_time": "2020-02-27T21:27:11.000258Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 30,\n",
       " 'min_samples_split': 5,\n",
       " 'min_samples_leaf': 1,\n",
       " 'max_features': 'sqrt',\n",
       " 'max_depth': 108,\n",
       " 'bootstrap': True}"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RF_tuned.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T21:27:12.574570Z",
     "start_time": "2020-02-27T21:27:12.566427Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bootstrap': True,\n",
       " 'ccp_alpha': 0.0,\n",
       " 'class_weight': None,\n",
       " 'criterion': 'gini',\n",
       " 'max_depth': 108,\n",
       " 'max_features': 'sqrt',\n",
       " 'max_leaf_nodes': None,\n",
       " 'max_samples': None,\n",
       " 'min_impurity_decrease': 0.0,\n",
       " 'min_impurity_split': None,\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_samples_split': 5,\n",
       " 'min_weight_fraction_leaf': 0.0,\n",
       " 'n_estimators': 30,\n",
       " 'n_jobs': None,\n",
       " 'oob_score': False,\n",
       " 'random_state': None,\n",
       " 'verbose': 0,\n",
       " 'warm_start': False}"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RF_tuned.best_estimator_.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T21:27:58.940005Z",
     "start_time": "2020-02-27T21:27:58.795460Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.38      0.10      0.16       906\n",
      "           1       0.89      0.98      0.93      6981\n",
      "\n",
      "    accuracy                           0.88      7887\n",
      "   macro avg       0.64      0.54      0.55      7887\n",
      "weighted avg       0.83      0.88      0.85      7887\n",
      "\n"
     ]
    }
   ],
   "source": [
    "predictions = RF_tuned.best_estimator_.predict(test[FEATURES])\n",
    "\n",
    "print(metrics.classification_report(test[TARGET], predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T21:28:02.793820Z",
     "start_time": "2020-02-27T21:28:02.620869Z"
    }
   },
   "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",
       "        fig.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 / mpl.ratio, fig.canvas.height / mpl.ratio);\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\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"pdf\";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 overridden (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",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\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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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f2bc6d45cf8>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.plot_confusion_matrix(RF_tuned.best_estimator_, test[FEATURES], test[TARGET], values_format='d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-25T17:16:12.928574Z",
     "start_time": "2020-02-25T16:55:51.120524Z"
    }
   },
   "outputs": [],
   "source": [
    "# adaboost doesn't have that many parameters\n",
    "\n",
    "param_grid_ada = {'n_estimators': [30, 50, 70,100, 150],\n",
    "                'learning_rate': [1.0, 0.5, 0.1]}\n",
    "\n",
    "grid_ada_res = GridSearchCV(estimator=ada_base,\n",
    "                     param_grid=param_grid_ada,\n",
    "                     cv=3,\n",
    "                     verbose=2,\n",
    "                         n_jobs=-1)\n",
    "grid_ada_res.fit(train[FEATURES], train[TARGET])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-25T17:21:01.667695Z",
     "start_time": "2020-02-25T17:21:01.662844Z"
    }
   },
   "outputs": [],
   "source": [
    "grid_ada_res.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-25T17:21:04.770757Z",
     "start_time": "2020-02-25T17:21:04.764343Z"
    }
   },
   "outputs": [],
   "source": [
    "grid_ada_res.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-25T17:21:09.323546Z",
     "start_time": "2020-02-25T17:21:05.927740Z"
    }
   },
   "outputs": [],
   "source": [
    "predictions = grid_ada_res.best_estimator_.predict(test[FEATURES])\n",
    "\n",
    "print(metrics.classification_report(test[TARGET], predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-25T17:29:09.635134Z",
     "start_time": "2020-02-25T17:29:06.337576Z"
    }
   },
   "outputs": [],
   "source": [
    "metrics.plot_confusion_matrix(grid_ada_res.best_estimator_, test[FEATURES], test[TARGET], values_format='d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ada_base=AdaBoostClassifier(learning_rate=grid_ada_res.best_params_['learning_rate'],\n",
    "                            n_estimators=grid_ada_res.best_params_['n_estimators'])\n",
    "ada_base.fit(train[FEATURES], train[TARGET])\n",
    "predictions = ada_base.predict(test2[FEATURES])\n",
    "\n",
    "print(metrics.classification_report(test2[TARGET], predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T21:28:29.657027Z",
     "start_time": "2020-02-27T21:28:29.642001Z"
    }
   },
   "outputs": [],
   "source": [
    "import xgboost as xgb\n",
    "\n",
    "# Number of trees in random forest\n",
    "n_estimators = [int(x) for x in np.linspace(start=10, stop=200, num=20)]\n",
    "# Maximum number of levels in tree\n",
    "max_depth = [x for x in np.arange(1, 11)]\n",
    "max_depth.append(None)\n",
    "# Minimum number of samples required to split a node\n",
    "min_child_weight = [0, 1, 2, 3]\n",
    "# Minimum number of samples required at each leaf node\n",
    "learning_rate = [x for x in np.linspace(0.1, 1, num=5)]\n",
    "# Method of selecting samples for training each tree\n",
    "bootstrap = [True, False]# Create the random grid\n",
    "\n",
    "param_grid_xgb= {\n",
    "    \"learning_rate\": [0.3,0.5,0.7],\n",
    "    'max_depth': [5,6,7],\n",
    "    'min_child_weight': [0,1,3],\n",
    "    'n_estimators': [10,100],\n",
    "}\n",
    "\n",
    "\n",
    "random_grid = {'n_estimators': n_estimators,\n",
    "               'max_depth': max_depth,\n",
    "               'min_child_weight': min_child_weight,\n",
    "               'learning_rate': learning_rate,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T21:28:31.142342Z",
     "start_time": "2020-02-27T21:28:31.135488Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': [10,\n",
       "  20,\n",
       "  30,\n",
       "  40,\n",
       "  50,\n",
       "  60,\n",
       "  70,\n",
       "  80,\n",
       "  90,\n",
       "  100,\n",
       "  110,\n",
       "  120,\n",
       "  130,\n",
       "  140,\n",
       "  150,\n",
       "  160,\n",
       "  170,\n",
       "  180,\n",
       "  190,\n",
       "  200],\n",
       " 'max_depth': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, None],\n",
       " 'min_child_weight': [0, 1, 2, 3],\n",
       " 'learning_rate': [0.1, 0.325, 0.55, 0.775, 1.0]}"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T21:38:36.796503Z",
     "start_time": "2020-02-27T21:28:32.587458Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  30 out of  30 | elapsed:  8.3min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=3, error_score=nan,\n",
       "                   estimator=XGBClassifier(base_score=0.5, booster='gbtree',\n",
       "                                           colsample_bylevel=1,\n",
       "                                           colsample_bynode=1,\n",
       "                                           colsample_bytree=1, gamma=0,\n",
       "                                           learning_rate=0.1, max_delta_step=0,\n",
       "                                           max_depth=3, min_child_weight=1,\n",
       "                                           missing=None, n_estimators=100,\n",
       "                                           n_jobs=1, nthread=None,\n",
       "                                           objective='binary:logistic',\n",
       "                                           random_state=0, reg_alpha=0,\n",
       "                                           reg_lambda=1, sc...\n",
       "                   iid='deprecated', n_iter=10, n_jobs=-1,\n",
       "                   param_distributions={'learning_rate': [0.1, 0.325, 0.55,\n",
       "                                                          0.775, 1.0],\n",
       "                                        'max_depth': [1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
       "                                                      10, None],\n",
       "                                        'min_child_weight': [0, 1, 2, 3],\n",
       "                                        'n_estimators': [10, 20, 30, 40, 50, 60,\n",
       "                                                         70, 80, 90, 100, 110,\n",
       "                                                         120, 130, 140, 150,\n",
       "                                                         160, 170, 180, 190,\n",
       "                                                         200]},\n",
       "                   pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
       "                   return_train_score=False, scoring=None, verbose=2)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_xgb = RandomizedSearchCV(estimator=xgb.XGBClassifier(),\n",
    "                               param_distributions=random_grid, \n",
    "                               n_iter=10, \n",
    "                               cv=3, \n",
    "                               verbose=2, \n",
    "                               n_jobs =-1)\n",
    "\n",
    "grid_xgb.fit(train[FEATURES], train[TARGET])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T21:38:38.245081Z",
     "start_time": "2020-02-27T21:38:38.239575Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 130,\n",
       " 'min_child_weight': 2,\n",
       " 'max_depth': 10,\n",
       " 'learning_rate': 0.325}"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_xgb.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T21:38:39.664803Z",
     "start_time": "2020-02-27T21:38:39.659372Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8998331205932324"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_xgb.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T21:38:41.234477Z",
     "start_time": "2020-02-27T21:38:41.093158Z"
    }
   },
   "outputs": [],
   "source": [
    "predictions = grid_xgb.best_estimator_.predict(test[FEATURES])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T21:38:42.668443Z",
     "start_time": "2020-02-27T21:38:42.648112Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.22      0.24      0.23       906\n",
      "           1       0.90      0.89      0.89      6981\n",
      "\n",
      "    accuracy                           0.81      7887\n",
      "   macro avg       0.56      0.56      0.56      7887\n",
      "weighted avg       0.82      0.81      0.82      7887\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(metrics.classification_report(test[TARGET], predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T21:38:44.272671Z",
     "start_time": "2020-02-27T21:38:44.089831Z"
    }
   },
   "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",
       "        fig.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 / mpl.ratio, fig.canvas.height / mpl.ratio);\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\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"pdf\";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 overridden (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",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\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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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f2bce61b710>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.plot_confusion_matrix(grid_xgb.best_estimator_, test[FEATURES], test[TARGET], values_format='d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = grid_xgb.best_estimator_\n",
    "\n",
    "idx = np.argsort(clf.feature_importances_)[::-1]\n",
    "\n",
    "for f, i in zip(np.asarray(FEATURES)[idx], clf.feature_importances_[idx]):\n",
    "        print(f, i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.feature_selection import RFE\n",
    "\n",
    "# clf = GradientBoostingClassifier(n_estimators=100)\n",
    "# rfe = RFE(estimator=clf, n_features_to_select=None, step=1)\n",
    "# rfe.fit(train[FEATURES], train[TARGET])\n",
    "# print(rfe.support_)\n",
    "# print(rfe.ranking_)\n",
    "\n",
    "# new_FEATURES = np.array(FEATURES)[rfe.support_]\n",
    "# new_FEATURES = new_FEATURES.tolist()\n",
    "\n",
    "# clf = GradientBoostingClassifier(n_estimators=100)\n",
    "# clf = clf.fit(train[nf], train[TARGET])\n",
    "# predictions = clf.predict(test[nf])\n",
    "\n",
    "# print(metrics.classification_report(test[TARGET], predictions))\n",
    "\n",
    "# nf = []\n",
    "# for f in new_FEATURES:\n",
    "#     try:\n",
    "#         nf.append(float(f))\n",
    "#     except ValueError:\n",
    "#         nf.append(f)\n",
    "\n",
    "# # Create the RFE object and rank each pixel\n",
    "# svc = SVC(kernel=\"linear\", C=1)\n",
    "# rfe = RFE(estimator=svc, n_features_to_select=1, step=1)\n",
    "# rfe.fit(train[FEATURES], train[TARGET])\n",
    "\n",
    "# print(rfe.support_)\n",
    "# print(rfe.ranking_)\n",
    "\n",
    "# FEATURES[idx]\n",
    "\n",
    "# from tpot import TPOTClassifier\n",
    "\n",
    "# tpot = TPOTClassifier(verbosity=2, max_time_mins=5, max_eval_time_mins=0.04, population_size=10)\n",
    "\n",
    "# tpot.fit(train[FEATURES], train[TARGET])\n",
    "# tpot.score(test[FEATURES], test[TARGET])\n",
    "# predictions = tpot.predict(test[FEATURES])\n",
    "# print(metrics.classification_report(test[TARGET], predictions))\n",
    "\n",
    "# y_pred_proba = tpot.predict_proba(test[FEATURES])[::,1]\n",
    "# fpr, tpr, _ = metrics.roc_curve(test[TARGET],  y_pred_proba)\n",
    "# auc = metrics.roc_auc_score(test[TARGET], y_pred_proba)\n",
    "# plt.plot(fpr,tpr,label=\"data 1, auc=\"+str(auc))\n",
    "# plt.legend(loc=4)\n",
    "\n",
    "# def plt_hist(history):\n",
    "#     acc = history.history['accuracy']\n",
    "#     val_acc = history.history['val_accuracy']\n",
    "#     loss = history.history['loss']\n",
    "#     val_loss = history.history['val_loss']\n",
    "#     epochs = range(len(acc))\n",
    "#     plt.plot(epochs, acc, 'bo', label='Training acc')\n",
    "#     plt.plot(epochs, val_acc, 'b', label='Validation acc')\n",
    "#     plt.title('Training and validation accuracy')\n",
    "#     plt.legend()\n",
    "#     plt.figure()\n",
    "#     plt.plot(epochs, loss, 'bo', label='Training loss')\n",
    "#     plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
    "#     plt.title('Training and validation loss')\n",
    "#     plt.legend()\n",
    "\n",
    "# plt_hist(NN_base)\n",
    "\n",
    "# predictions = model.predict_classes(test2[FEATURES])\n",
    "# print(metrics.classification_report(test2[TARGET], predictions))\n",
    "\n",
    "# model.summary()"
   ]
  }
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