{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "The data set in this example represents 1059 songs from various countries obtained \n",
    "from the UCI Machine Learning library. Various features of the audio tracks have been \n",
    "extracted, and each track has been tagged with the latitude and longitude of the capital\n",
    "city of its country of origin. \n",
    "\n",
    "We'll treat this as a classification problem, and attempt to train a model to predict \n",
    "the country of origin of each model. \n",
    "\n",
    "Data source did not specifify what the audio features specifically are, just\n",
    "    \"In the 'default_features_1059_tracks.txt' file, the first 68 columns are audio \n",
    "    features of the track, and the last two columns are the origin of the music, \n",
    "    represented by latitude and longitude. \n",
    "\n",
    "    In the 'default_plus_chromatic_features_1059_tracks.txt' file, the first 116 \n",
    "    columns are audio features of the track, and the last two columns are the \n",
    "    origin of the music.\"\n",
    "\"\"\"\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sklearn\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.utils.multiclass import unique_labels\n",
    "import sys\n",
    "\n",
    "#First get the data. The UCI ML Library distributes it as a zipped file;\n",
    "#download the data and extract the two provided files to the 'data' folder before continuing\n",
    "music_df = pd.read_csv('data\\default_plus_chromatic_features_1059_tracks.txt', header=None)\n",
    "music = music_df.as_matrix()\n",
    "\n",
    "\n",
    "#Our features are all but the last two columns\n",
    "X = music[:,0:-2]\n",
    "\n",
    "#Since feature names were not given, we'll just assign strings with an incrementing integer\n",
    "names = np.linspace(start=1, stop=116, num=116, dtype='int').tolist()\n",
    "for idx, name in enumerate(names):\n",
    "    names[idx] = \"Feature \" + str(name)\n",
    "\n",
    "\n",
    "#The source data said that each song as tied to the capital city of it's origin country via a lat/lon pair. \n",
    "#Let's treat this as a multi-class classification problem. \n",
    "#Rather than reverse-geocoding, we'll just make a string out of the unique lat/lon pairs\n",
    "lats = [\"%.2f\" % lat for lat in music_df[116]]\n",
    "lons = [\"%.2f\" % lon for lon in music_df[117]]\n",
    "song_latlons = []\n",
    "for index, value in enumerate(lats):\n",
    "    city_id = lats[index] + \",\" + lons[index]\n",
    "    song_latlons.append(city_id)\n",
    "\n",
    "unique_latlons = unique_labels(song_latlons)\n",
    "city_options = ['A','B','C','D','E','F','G','H','I','J','K','L','M','N','O','P','Q','R','S','T','U','V','W','X','Y','Z','AA','AB','AC','AD','AE','AF','AG']\n",
    "city_name_map = {}\n",
    "for idx,latlon in enumerate(unique_latlons):\n",
    "    city_name_map[latlon] = city_options[idx]\n",
    "\n",
    "ylist = []\n",
    "for latlon in song_latlons:\n",
    "    ylist.append(city_name_map[latlon])\n",
    "y = np.array(ylist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#We want yellowbrick to import from this repository, and assume this notebook is in repofolder/examples/subfolder/\n",
    "sys.path.append(\"../../\")\n",
    "import yellowbrick as yb\n",
    "from yellowbrick.features.rankd import Rank2D \n",
    "from yellowbrick.features.radviz import RadViz \n",
    "from yellowbrick.features.pcoords import ParallelCoordinates "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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byEuv/EkVR2IYxrZSQcXWT5ECC973xg0i0gJcAZyhqoNMEWVTbKo6ICJnAv+D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\nvTF5/pKInAxcAXyunM5kyrA5juOMN9fOOba4fcO7Zd/GcQy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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2691c8f8748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#See how well correlated the features are\n",
    "visualizer = Rank2D(features = names, algorithm = 'pearson')\n",
    "visualizer.fit(X, y)\n",
    "visualizer.transform(X)\n",
    "visualizer.poof()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn import metrics\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from yellowbrick.classifier import ClassificationReport\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def train_and_classification_report(model):\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X,y, test_size =0.2, random_state=11)\n",
    "    \n",
    "    model.fit(X_train, y_train)\n",
    "    y_predict = model.predict(X_test)\n",
    "\n",
    "    print(\"prec: {}\".format(metrics.precision_score(y_true = y_test, y_pred = y_predict, average=\"weighted\")))\n",
    "    print(\"rec: {}\".format(metrics.recall_score(y_true= y_test, y_pred = y_predict, average = \"weighted\")))\n",
    "\n",
    "    cr_viz = ClassificationReport(model) #,classes=city_options\n",
    "    cr_viz.fit(X_train, y_train)\n",
    "    cr_viz.score(X_test, y_test)\n",
    "    cr_viz.poof()\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prec: 0.430726301383904\n",
      "rec: 0.4056603773584906\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\humph\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1115: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n",
      "C:\\Users\\humph\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1115: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "list index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-34-10397df83f0c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0myellowbrick\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclassifier\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mClassificationReport\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[1;31m#This produces an IndexError: list index out of range.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mtrain_and_classification_report\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mLogisticRegression\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-21-cd9cb9f45f93>\u001b[0m in \u001b[0;36mtrain_and_classification_report\u001b[0;34m(model)\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0mcr_viz\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mClassificationReport\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m#,classes=city_options\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m     \u001b[0mcr_viz\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m     \u001b[0mcr_viz\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     13\u001b[0m     \u001b[0mcr_viz\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpoof\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Users\\humph\\Documents\\Github\\yellowbrick\\yellowbrick\\classifier.py\u001b[0m in \u001b[0;36mscore\u001b[0;34m(self, X, y, **kwargs)\u001b[0m\n\u001b[1;32m    133\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscores\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mdict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclasses_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscores\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    134\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscores\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkeys\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscores\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 135\u001b[0;31m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    136\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    137\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Users\\humph\\Documents\\Github\\yellowbrick\\yellowbrick\\classifier.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, y, y_pred)\u001b[0m\n\u001b[1;32m    158\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mcolumn\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmatrix\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    159\u001b[0m             \u001b[1;32mfor\u001b[0m \u001b[0mrow\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclasses_\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 160\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtext\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mrow\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmatrix\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mrow\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mva\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'center'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mha\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'center'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    161\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    162\u001b[0m         \u001b[0mfig\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmatrix\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minterpolation\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'nearest'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcmap\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvmin\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2691e0eb8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Adding the reloading functionality so we can edit the source code and see results here. \n",
    "import importlib\n",
    "importlib.reload(yb.classifier)\n",
    "from yellowbrick.classifier import ClassificationReport\n",
    "\n",
    "#This produces an IndexError: list index out of range. \n",
    "train_and_classification_report(LogisticRegression())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#This demonstrates a version of the Seaborn confusion matrix heatmap we could replicate (and improve on). \n",
    "def train_and_confusion_matrix(model):\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X,y, test_size =0.2, random_state=11)\n",
    "    \n",
    "    model.fit(X_train, y_train)\n",
    "    y_predict = model.predict(X_test)\n",
    "\n",
    "    print(\"prec: {}\".format(metrics.precision_score(y_true = y_test, y_pred = y_predict, average=\"weighted\")))\n",
    "    print(\"rec: {}\".format(metrics.recall_score(y_true= y_test, y_pred = y_predict, average = \"weighted\")))\n",
    "\n",
    "    c_matrix = confusion_matrix(y_true = y_test, y_pred = y_predict)\n",
    "    \n",
    "    sns.heatmap(c_matrix, square=True, annot=True, cbar=False, xticklabels=city_options, yticklabels = city_options)\n",
    "    plt.xlabel('predicted value')\n",
    "    plt.ylabel('true value')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prec: 0.430726301383904\n",
      "rec: 0.4056603773584906\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\humph\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1115: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n"
     ]
    },
    {
     "data": {
      "image/png": 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IcLieGZ5uxfvSI6jqD3xt/rT6ti9A3759GThwIAMHDmTYsGE88cQTLF26lOjo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BAAAg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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2691e1ae940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_and_confusion_matrix(LogisticRegression())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def train_and_class_balance(model):\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X,y, test_size =0.2, random_state=11)\n",
    "    \n",
    "    class_balance = yb.classifier.ClassBalance(model, classes=city_options)\n",
    "    class_balance.fit(X_train, y_train)\n",
    "    class_balance.score(X_test, y_test)\n",
    "    class_balance.poof()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\humph\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1115: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n"
     ]
    },
    {
     "data": {
      "image/png": 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oqCht27ZNX3zxhR5//HGnaVatWqVLly4pKirK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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2691e6c7588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_and_class_balance(LogisticRegression())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}