{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project 4: Classification\n",
    "\n",
    "### CMSC320\n",
    "\n",
    "*Last Update*: 2020-04-28"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data\n",
    "\n",
    "We will use Mortgage Affordability data from Zillow to experiment with classification algorithms. The data was downloaded from Zillow Research page: https://www.zillow.com/research/data/\n",
    "\n",
    "It is made available here: http://www.hcbravo.org/IntroDataSci/misc/Affordability_Wide_2017Q4_Public.csv\n",
    "\n",
    "Download the csv file to your project directory.\n",
    "\n",
    "### Preparing data\n",
    "\n",
    "First, we will tidy the data. Please include this piece of code in your submission."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from plotnine import *\n",
    "\n",
    "theme_set(theme_bw())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RegionID</th>\n",
       "      <th>RegionName</th>\n",
       "      <th>time</th>\n",
       "      <th>affordability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>394913</td>\n",
       "      <td>New York, NY</td>\n",
       "      <td>1979-03-01</td>\n",
       "      <td>0.261700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>753899</td>\n",
       "      <td>Los Angeles-Long Beach-Anaheim, CA</td>\n",
       "      <td>1979-03-01</td>\n",
       "      <td>0.357694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>394463</td>\n",
       "      <td>Chicago, IL</td>\n",
       "      <td>1979-03-01</td>\n",
       "      <td>0.261928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>394514</td>\n",
       "      <td>Dallas-Fort Worth, TX</td>\n",
       "      <td>1979-03-01</td>\n",
       "      <td>0.301131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>394974</td>\n",
       "      <td>Philadelphia, PA</td>\n",
       "      <td>1979-03-01</td>\n",
       "      <td>0.204333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RegionID                          RegionName       time  affordability\n",
       "0    394913                        New York, NY 1979-03-01       0.261700\n",
       "1    753899  Los Angeles-Long Beach-Anaheim, CA 1979-03-01       0.357694\n",
       "2    394463                         Chicago, IL 1979-03-01       0.261928\n",
       "3    394514               Dallas-Fort Worth, TX 1979-03-01       0.301131\n",
       "4    394974                    Philadelphia, PA 1979-03-01       0.204333"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read and prepare data\n",
    "\n",
    "df = (pd.read_csv('data/Affordability_Wide_2017Q4_Public.csv')\n",
    "        .query('Index == \"Mortgage Affordability\" and SizeRank != 0')\n",
    "        .drop(columns=['Index', 'HistoricAverage_1985thru1999','SizeRank'])\n",
    "        .dropna()\n",
    "        .melt(id_vars=['RegionID','RegionName'],\n",
    "              var_name='time', value_name='affordability'))\n",
    "df['time'] = pd.to_datetime(df.time, format=\"%Y-%m\")\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is what the data looks like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/hcorrada/opt/miniconda3/envs/cmsc320/lib/python3.6/site-packages/plotnine/utils.py:284: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "  ndistinct = ids.apply(len_unique, axis=0).as_matrix()\n",
      "/Users/hcorrada/opt/miniconda3/envs/cmsc320/lib/python3.6/site-packages/pandas/core/generic.py:5191: FutureWarning: Attribute 'is_copy' is deprecated and will be removed in a future version.\n",
      "  object.__getattribute__(self, name)\n",
      "/Users/hcorrada/opt/miniconda3/envs/cmsc320/lib/python3.6/site-packages/pandas/core/generic.py:5192: FutureWarning: Attribute 'is_copy' is deprecated and will be removed in a future version.\n",
      "  return object.__setattr__(self, name, value)\n"
     ]
    },
    {
     "data": {
      "image/png": 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8vAHRY7NmzRpEIhFs2LABFosFjz32GHJzc2M2T7NarXj22WexbNkynH766Th0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\nrVaL/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8dCEFGa/JJoM/g4h3IqRtbW1rggIx7bysoK1Go1mpqa4HQ60d7ejoGBAebALC0t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\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InSfkAJL7MBU6uVwOJpOJ14LJyUnmf9G1m5+fx09+8hOMj49jaGjoMwrTP3W85Ni8YLzk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\nRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (302488959)>"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# plot data\n",
    "\n",
    "(ggplot(df, aes(x='time', y='affordability', group='factor(RegionID)')) +\n",
    "  geom_line(color=\"GRAY\", alpha=3/4, size=1/2) +\n",
    "  theme(axis_text_x=element_text(angle=45,hjust=1)) +\n",
    "  labs(title=\"County-Level Mortgage Affordability over Time\",\n",
    "          x=\"Date\", y=\"Mortgage Affordability\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The prediction task\n",
    "\n",
    "The prediction task we are going to answer is:\n",
    "\n",
    "> Can we predict if mortgage affordability will increase or decrease a year from now\"\n",
    "\n",
    "Specifically, we will do this for quarter 4 (Q4) of 2017. To create the outcome we will predict we will compare affordability for Q4 of 2017 to Q4 of 2016 and label it as `up` or `down` depending on the sign of the this difference. Let's create the outcome we want to predict (again, copy this bit of code to your submission):\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>time</th>\n",
       "      <th>RegionID</th>\n",
       "      <th>Direction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>394304</td>\n",
       "      <td>Down</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>394312</td>\n",
       "      <td>Down</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>394318</td>\n",
       "      <td>Down</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>394347</td>\n",
       "      <td>Up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>394355</td>\n",
       "      <td>Up</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "time  RegionID Direction\n",
       "0       394304      Down\n",
       "1       394312      Down\n",
       "2       394318      Down\n",
       "3       394347        Up\n",
       "4       394355        Up"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outcome_dates = ['2016-12-01', '2017-12-01']\n",
    "outcome_df = (df.query('time in @outcome_dates')\n",
    "   .pivot(index='RegionID', columns='time', values='affordability'))\n",
    "\n",
    "outcome_df['diff'] = outcome_df['2017-12-01'] - outcome_df['2016-12-01']\n",
    "outcome_df['Direction'] = 'Down'\n",
    "outcome_df['Direction'] = outcome_df['Direction'].where(outcome_df['diff'] < 0, 'Up') # this replaces where conditions is False\n",
    "outcome_df = outcome_df.reset_index()[['RegionID','Direction']]\n",
    "outcome_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, you have a dataframe with outcomes (labels) for each county in the dataset.\n",
    "\n",
    "The goal is then given predictors $X_i$ for county $i$, build a classifier for outcome $G_i \\in \\{\\mathtt{up},\\mathtt{down}\\}$.\n",
    "\n",
    "To train your classifiers you should use data up to 2016. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictor_df = df.query('time < 20170101')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Your project\n",
    "\n",
    "Your goal for this project is to do an experiment to address a (one, single) technical question about our ability to make this prediction. Here is a list of possible questions you may address below. Each of them asks to compare two specific choices in the classification workflow (e.g., two classification algorithms, two feature representations, etc.). You will implement each of the two choices and use 10-fold cross validation (across RegionID's) to compare their relative performance. You will also create an AUROC curve to compare them.\n",
    "\n",
    "### Possible Questions\n",
    "\n",
    "#### Feature representation and preprocessing\n",
    "\n",
    "- Does standardizing affordability for each region affect  prediction performance? Compare standardized to non-standardized affordability.\n",
    "- Is using quarter to quarter change (continuous or discrete) improve prediction performance? Compare quarter to quarter change in affordability as predictors to affordability as predictor?\n",
    "- Should we use the full time series for each region, or should we use only the last few years? Compare full time series to a subset of the time series?\n",
    "- Should we expand the training set to multiple time series per region? For example, create a similar outcome for each time point in the dataset (change relative to affordability one year ago) and use data from the last couple of years as predictors. Train on the extended dataset and test on the 2017 data above?\n",
    "- Should we do dimensionality reduction (PCA) and use the embedded data to do prediction?\n",
    "- Create your own question!\n",
    "\n",
    "#### Classification Algorithm\n",
    "\n",
    "- Is a decision tree better than logistic regression?\n",
    "- Is a random forest better than a decision tree?\n",
    "- Is K-nearest neighbors bettern than a random forest?\n",
    "- Create your own question!\n",
    "\n",
    "Note that you still have to make some choices regardless of the question you choose. For example, to do the feature preprocessing and representation experiments you have to choose a classifier (random forest for example), and decide what to do about hyper-parameters if appropriate.\n",
    "\n",
    "### Submission\n",
    "\n",
    "Prepare a Jupyter notebook that which includes:\n",
    "\n",
    "1) Code to prepare data (copied from chunks above), plus any additional data prep for your experiment    \n",
    "2) Discussion of the question you have chosen to address including discussion of other choices you have made (e.g., feature representation, classification algorithm) to carry out your experiment.  \n",
    "3) Code to carry out your cross-validation experiment.  \n",
    "4) Table (result of hypothesis testing difference between algorithms) and plot comparing AUROCs  \n",
    "5) ROC curves for both experimental settings.  \n",
    "6) Interpretation and discussion of your experiment results.  \n",
    "\n",
    "Save to pdf and submit on ELMS.\n",
    "\n",
    "## An example experiment\n",
    "\n",
    "Question: Does the number of trees used in a random forest classifier affect\n",
    "performance (AUROC measured with 5-fold CV)?\n",
    "\n",
    "Other decisions: We are transforming input data to use quarterly differences after data standardization for years 2014-2016. \n",
    "\n",
    "### Data preparation\n",
    "\n",
    "First, filter to the years of interest and standardize affordability for each region."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RegionName</th>\n",
       "      <th>time</th>\n",
       "      <th>affordability</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>std_affordability</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RegionID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>394304</th>\n",
       "      <td>Akron, OH</td>\n",
       "      <td>2014-03-01</td>\n",
       "      <td>0.110923</td>\n",
       "      <td>0.108193</td>\n",
       "      <td>0.003617</td>\n",
       "      <td>0.754555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394304</th>\n",
       "      <td>Akron, OH</td>\n",
       "      <td>2014-06-01</td>\n",
       "      <td>0.109178</td>\n",
       "      <td>0.108193</td>\n",
       "      <td>0.003617</td>\n",
       "      <td>0.272298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394304</th>\n",
       "      <td>Akron, OH</td>\n",
       "      <td>2014-09-01</td>\n",
       "      <td>0.108367</td>\n",
       "      <td>0.108193</td>\n",
       "      <td>0.003617</td>\n",
       "      <td>0.047965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394304</th>\n",
       "      <td>Akron, OH</td>\n",
       "      <td>2014-12-01</td>\n",
       "      <td>0.104267</td>\n",
       "      <td>0.108193</td>\n",
       "      <td>0.003617</td>\n",
       "      <td>-1.085563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394304</th>\n",
       "      <td>Akron, OH</td>\n",
       "      <td>2015-03-01</td>\n",
       "      <td>0.103859</td>\n",
       "      <td>0.108193</td>\n",
       "      <td>0.003617</td>\n",
       "      <td>-1.198250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         RegionName       time  affordability      mean       std  \\\n",
       "RegionID                                                            \n",
       "394304    Akron, OH 2014-03-01       0.110923  0.108193  0.003617   \n",
       "394304    Akron, OH 2014-06-01       0.109178  0.108193  0.003617   \n",
       "394304    Akron, OH 2014-09-01       0.108367  0.108193  0.003617   \n",
       "394304    Akron, OH 2014-12-01       0.104267  0.108193  0.003617   \n",
       "394304    Akron, OH 2015-03-01       0.103859  0.108193  0.003617   \n",
       "\n",
       "          std_affordability  \n",
       "RegionID                     \n",
       "394304             0.754555  \n",
       "394304             0.272298  \n",
       "394304             0.047965  \n",
       "394304            -1.085563  \n",
       "394304            -1.198250  "
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# subset data and compute mean and standard deviation per region\n",
    "tmp_df = predictor_df.query('time > 20131201 & time < 20170101')\n",
    "stat_df = tmp_df.groupby('RegionID').agg({'affordability': ['mean', 'std']})\n",
    "stat_df.columns = stat_df.columns.get_level_values(1)\n",
    "stat_df.reset_index(col_level=1).set_index('RegionID')\n",
    "\n",
    "# standardize affordability for each region\n",
    "std_df = tmp_df.set_index('RegionID').join(stat_df)\n",
    "std_df['std_affordability'] = (std_df['affordability'] - std_df['mean'])/std_df['std']\n",
    "std_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To train our model we need a table with one row per region, and attributes corresponding to differences in quarterly affordability. We will do this in stages, first we turn the tidy dataset into a wide dataset using `pd.pivot` then create a dataframe containing the differences we use as features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "# switch to a 'wide' data frame\n",
    "std_df['RegionID'] = std_df.index\n",
    "std_df = (std_df[['RegionID','time','std_affordability']]\n",
    "  .pivot(index='RegionID', columns='time', values='std_affordability'))\n",
    "\n",
    "# construct matrix of quarterly differences\n",
    "mat1 = std_df.iloc[:,1:].to_numpy()\n",
    "mat2 = std_df.iloc[:,:-1].to_numpy()\n",
    "X = mat1 - mat2\n",
    "\n",
    "# get the outcome from the dataframe we created\n",
    "outcome_df['y'] = 0\n",
    "outcome_df['y'] = outcome_df['y'].where(outcome_df['Direction'] == \"Down\", 1)\n",
    "y = outcome_df['y'].to_numpy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run the experiment \n",
    "\n",
    "We will use but 5-fold cross-validation to compare a random forest with 500 trees, with a random forest with 10 trees. Since this is a smallish dataset, I am using 5-fold cross validation to make the validation sets have more examples and therefore more reliable performance estimates.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn.ensemble\n",
    "import sklearn.model_selection\n",
    "import sklearn.metrics\n",
    "import matplotlib.pylab as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "small_rf = sklearn.ensemble.RandomForestClassifier(n_estimators=10)\n",
    "big_rf = sklearn.ensemble.RandomForestClassifier(n_estimators=500)\n",
    "\n",
    "parameters = {'max_features': [2,6,11]} # we do this to match what the R version did\n",
    "small_cv = sklearn.model_selection.GridSearchCV(small_rf, parameters, cv=5)\n",
    "big_cv = sklearn.model_selection.GridSearchCV(big_rf, parameters, cv=5)\n",
    "\n",
    "# following sklearn tutorial\n",
    "# https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc_crossval.html#sphx-glr-auto-examples-model-selection-plot-roc-crossval-py\n",
    "cv_obj = sklearn.model_selection.StratifiedKFold(n_splits=5)\n",
    "\n",
    "\n",
    "def get_roc_data(model, cv_obj):\n",
    "    curve_df = None\n",
    "    aucs = []\n",
    "    mean_fpr = np.linspace(0, 1, 100)\n",
    "    \n",
    "    for i, (train, test) in enumerate(cv_obj.split(X, y)):\n",
    "        model.fit(X[train], y[train])\n",
    "        scores = model.predict_proba(X[test])[:,1]\n",
    "        fpr, tpr, _ = sklearn.metrics.roc_curve(y[test],scores)\n",
    "        \n",
    "        interp_tpr = np.interp(mean_fpr, fpr, tpr)\n",
    "        interp_tpr[0] = 0.0\n",
    "        tmp = pd.DataFrame({'fold':i, 'fpr': mean_fpr, 'tpr': interp_tpr})\n",
    "        curve_df = tmp if curve_df is None else pd.concat([curve_df, tmp])\n",
    "        \n",
    "        aucs.append(sklearn.metrics.auc(fpr, tpr))\n",
    "        \n",
    "    curve_df = curve_df.groupby('fpr').agg({'tpr': 'mean'}).reset_index()\n",
    "    curve_df.iloc[-1,1] = 1.0\n",
    "    \n",
    "    auc_df = pd.DataFrame({'fold': np.arange(len(aucs)), 'auc': aucs})\n",
    "    return curve_df, auc_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get roc curve data for small model\n",
    "small_curve_df, small_auc_df = get_roc_data(small_cv, cv_obj)\n",
    "small_curve_df['model'] = 'small'\n",
    "small_auc_df['model'] = 'small'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get roc curve data for big model\n",
    "big_curve_df, big_auc_df = get_roc_data(big_cv, cv_obj)\n",
    "big_curve_df['model'] = 'big'\n",
    "big_auc_df['model'] = 'big'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fold</th>\n",
       "      <th>auc</th>\n",
       "      <th>model</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.658333</td>\n",
       "      <td>small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.781818</td>\n",
       "      <td>small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.433333</td>\n",
       "      <td>big</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.650000</td>\n",
       "      <td>big</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.658333</td>\n",
       "      <td>big</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.516667</td>\n",
       "      <td>big</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.572727</td>\n",
       "      <td>big</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fold       auc  model\n",
       "0     0  0.416667  small\n",
       "1     1  0.466667  small\n",
       "2     2  0.658333  small\n",
       "3     3  0.366667  small\n",
       "4     4  0.781818  small\n",
       "0     0  0.433333    big\n",
       "1     1  0.650000    big\n",
       "2     2  0.658333    big\n",
       "3     3  0.516667    big\n",
       "4     4  0.572727    big"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# combine the roc curve data\n",
    "curve_df = pd.concat([small_curve_df, big_curve_df])\n",
    "auc_df = pd.concat([small_auc_df, big_auc_df])\n",
    "\n",
    "auc_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/hcorrada/opt/miniconda3/envs/cmsc320/lib/python3.6/site-packages/plotnine/utils.py:284: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "  ndistinct = ids.apply(len_unique, axis=0).as_matrix()\n",
      "/Users/hcorrada/opt/miniconda3/envs/cmsc320/lib/python3.6/site-packages/pandas/core/generic.py:5191: FutureWarning: Attribute 'is_copy' is deprecated and will be removed in a future version.\n",
      "  object.__getattribute__(self, name)\n",
      "/Users/hcorrada/opt/miniconda3/envs/cmsc320/lib/python3.6/site-packages/pandas/core/generic.py:5192: FutureWarning: Attribute 'is_copy' is deprecated and will be removed in a future version.\n",
      "  return object.__setattr__(self, name, value)\n"
     ]
    },
    {
     "data": {
      "image/png": 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9qBMAACBbZPpSfkkKDw/Xm2++qcjISA0dOlSRkZFauHBhVtUGAACQ7TJ9KX9K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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (305798327)>"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# plot the distribution of auc estimates\n",
    "(ggplot(auc_df, aes(x='model', y='auc')) + \n",
    "     geom_jitter(position=position_jitter(0.1)) +\n",
    "     coord_flip() +\n",
    "     labs(title = \"AUC comparison\",\n",
    "          x=\"Model\",\n",
    "          y=\"Area under ROC curve\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We test for differences using linear regression."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [],
   "source": [
    "# use a two-sided test (based on linear regression) to see if there is a \n",
    "# statistically significant difference in auc estimates\n",
    "import statsmodels.formula.api as smf\n",
    "lm_fit = smf.ols('auc~model', data=auc_df).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/hcorrada/opt/miniconda3/envs/cmsc320/lib/python3.6/site-packages/scipy/stats/stats.py:1535: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=10\n",
      "  \"anyway, n=%i\" % int(n))\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>auc</td>       <th>  R-squared:         </th> <td>   0.012</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>  -0.111</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>  0.1001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 28 Apr 2020</td> <th>  Prob (F-statistic):</th>  <td> 0.760</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>09:18:28</td>     <th>  Log-Likelihood:    </th> <td>  6.5256</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    10</td>      <th>  AIC:               </th> <td>  -9.051</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>     8</td>      <th>  BIC:               </th> <td>  -8.446</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "         <td></td>           <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>      <td>    0.5662</td> <td>    0.063</td> <td>    8.988</td> <td> 0.000</td> <td>    0.421</td> <td>    0.711</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>model[T.small]</th> <td>   -0.0282</td> <td>    0.089</td> <td>   -0.316</td> <td> 0.760</td> <td>   -0.234</td> <td>    0.177</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 0.629</td> <th>  Durbin-Watson:     </th> <td>   3.205</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.730</td> <th>  Jarque-Bera (JB):  </th> <td>   0.603</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.397</td> <th>  Prob(JB):          </th> <td>   0.740</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.097</td> <th>  Cond. No.          </th> <td>    2.62</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                    auc   R-squared:                       0.012\n",
       "Model:                            OLS   Adj. R-squared:                 -0.111\n",
       "Method:                 Least Squares   F-statistic:                    0.1001\n",
       "Date:                Tue, 28 Apr 2020   Prob (F-statistic):              0.760\n",
       "Time:                        09:18:28   Log-Likelihood:                 6.5256\n",
       "No. Observations:                  10   AIC:                            -9.051\n",
       "Df Residuals:                       8   BIC:                            -8.446\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==================================================================================\n",
       "                     coef    std err          t      P>|t|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------\n",
       "Intercept          0.5662      0.063      8.988      0.000       0.421       0.711\n",
       "model[T.small]    -0.0282      0.089     -0.316      0.760      -0.234       0.177\n",
       "==============================================================================\n",
       "Omnibus:                        0.629   Durbin-Watson:                   3.205\n",
       "Prob(Omnibus):                  0.730   Jarque-Bera (JB):                0.603\n",
       "Skew:                           0.397   Prob(JB):                        0.740\n",
       "Kurtosis:                       2.097   Cond. No.                         2.62\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm_fit.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a small decrease (2.8%) in average AUROC for the small model but it is not a statistically significant difference.\n",
    "\n",
    "Finally, here are ROC curves of both models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/hcorrada/opt/miniconda3/envs/cmsc320/lib/python3.6/site-packages/plotnine/utils.py:284: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "  ndistinct = ids.apply(len_unique, axis=0).as_matrix()\n",
      "/Users/hcorrada/opt/miniconda3/envs/cmsc320/lib/python3.6/site-packages/pandas/core/generic.py:5191: FutureWarning: Attribute 'is_copy' is deprecated and will be removed in a future version.\n",
      "  object.__getattribute__(self, name)\n",
      "/Users/hcorrada/opt/miniconda3/envs/cmsc320/lib/python3.6/site-packages/pandas/core/generic.py:5192: FutureWarning: Attribute 'is_copy' is deprecated and will be removed in a future version.\n",
      "  return object.__setattr__(self, name, value)\n"
     ]
    },
    {
     "data": {
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UREREwq7CHyDV3xDpMqQNUwgVERGRsKvAQrphRLoMacMUQkVERCTsqmJiSY/RpXg5NIVQ\nERERCSvTNKmKt5MeHx/pUqQNUwgVERGRsKoNGPhjYsh0JES6FGnDFEJFREQkrMrqGx9IykhyRrgS\nacsUQkVERCSsytwe7N46kjRQvRyGQqiIiIiEVVltLSm11cQqhMphKISKiIhIWJV76kh21WJ16nK8\nHJpCqIiIiIRVeX0DqfVeLFbFDDk0fXeIiIhIWFUYBmkBzZYkh6cQKiIiImFVabGSHukipM1TCBUR\nEZGwMU2Tqth40mJjIl2KtHEKoSIiIhI2tQGDhpgYMu22SJcibZxCqIiIiIRNRYMfgMzExAhXIm2d\nQqiIiIiETXmDH7vPizMlJdKlSBunECoiIiJhU1ZXR3JtNTEaqF5+hkKoiIiIhE2Zy713tqTUSJci\nbZxCqIiIiIRNuddLcm0NMcm6HC+HpxAqIiIiYVPeECC1oV6zJcnP0neIiIiIhE0lkG4EIl2GtAMK\noSIiIhIWpmlSaY0l3WqJdCnSDiiEioiISFi4AgYNVivp8bGRLkXaAYVQERERCYt9A9VnOBIiXIm0\nBwqhIiIiEhZlDX5s9T6SkpIjXYq0AwqhIiIiEhYVfj8prhpi0zRQvfw8hVARERE5ag2GSb67juTq\nKmJSFELl5+nOYSAnJ4fY2FhM0wzrcU3TDB433MduC/adUzSeG0R/+4HasL2L9vYDtWFbZJomFf4A\nP3h95HvrKfDVk+/1scfXgAGcVbSbmOFDm5xbS7RhbKwiTHunFgSuvPJKAPx+f9iPnZaWhmEYGIYR\n9mO3FYFA9I4H1xHaD9SG7V00tx+oDSPJaxj86Gsg31dPga+BAl9j6PQYJjaLha62OLrZ4hmV4qSb\nLZ6s3fm4NryDecn0Jr9TW6IN03TJv91TCAWeeOIJJk6cSFZWVliPaxgG5eXlZGRkYI3CmSNM0yQQ\nCBATE4PFEn1jwkV7+4HasL2L9vYDtWFrMUyT4voG8r315PvqG//3+ihp8GMBOsXH0d0ezwCng3Mz\nU+lut5EVF4v1JzW7vqmhLimJOLv9P8duoTYsLS0N27EkMhRCgaKiIvx+f9jfACwWS/C40foLAoja\n8+so7Qdqw/Yums9PbRh+tf4A+V5fk7BZ4K2n3jRxxljpbrfR3R7PSUkOutttdLXHYz/C8BjYez/o\n/ufSUm3YElcvpXUphIqIiEQhv2Gyu/4/QXPf/5X+ADFAF1s83e3xnJLsZFJ2PHl2G2mxofXImqaJ\nWe/DcLsJuN34du0kNjW15U5KoopCqIiISDu270Gh/YNmgbee3b56AkBabAx5e3s3f5GaRHd7PJ3j\n44ndb2pNs6GBgNtFg8dNwO3CcLsIuN3/+d/jxnC5CHjcewOnq/F/jxv275G0WEibMLH1vwjSLimE\nioiItBNew6DAW0/B3sD5w97L6u6A0figUFwMXS3wC8NPZ6OOzp5aHG5Xk9BouFwU/SRMmvW+Jq9j\nTXBgTUwkJjERa6ITq6Px4/i09OCyxv8Tidm73proxGq3Y4nCe3elZSiEioiItBGmYWDU1eF3uyhy\nucn31JHfEOBHw+RHayxlsfGYFguZdW5yqyvoUl7KkOLdZO8pILW4EOt+QyBZbDZ8iYk0OJz/CYuJ\nicR16oTN4fxPwNwvbMYkJmJNcGCJiYngV0E6CoVQERGRMNp3n2TA5Wq8jL3/5et9l7j3LXe5qK2v\np9DuYLcjicKUNIozO1Gc1YmGuHjsXoPcynJya6o4y+Oic4OXrkaAhAR7Y6hMT8LadQgxZ57VJGjG\nJDqxaBxNaeP0HSoiItJM5f/7D3w7v8fw7LuHsjFwsv+4nxYLVqcTEpMoz8mlKDuXoqyuFPZKY4/D\nSVVcPFbTJMc06BZj4bT4OLo7E8hLSiLTkRCVQ1OJgEKoiIhIs5iGQeU//5ek084g4djjgvdNWhyJ\n1DoS2R1v50dLLAUBg3zf3geFTEiNjaGbLZ6eCTZG2OLpHBtDd0cC8TEKm9KxKISKiIg0Q6C6igar\nlapx57M7IYkC33+eTq91G8R56uhmj6e7LZ4Rqcl0t8fT3W4jOfY/91uaponf72/ypLpIR6EQKiIi\n0gz+inL+MuP3VFbWke1qoLvdRl+HnTHpyeTZbeTExx0wo5CI/IdCqIiISDPUVlRQmZrBnT270sdh\n//kdRKQJ3YAiIiLSDMU1tQB0scVFuBKR9kkhVEREpBmKvF4cDfU4NKamSLMohIqIiDRDqd8g8ycz\nDYnIkVMIFRERaYZSrGRiRLoMkXZLIVRERKQZyuJtZMfqUrxIcymEioiIhMgMBKh0OOmkp+JFmk0h\nVEREJET1lRVUpqTSKSk50qWItFsKoSIiIiEqr6jAHxtH57TUSJci0m4phIqIiISoqKYGi2GQ6UiI\ndCki7ZZmTBIREQlRsbuO1EAssZqWU6TZFEJFRERCVNLgJ8PqjXQZIu2aLseLiIiEqBQLmUYg0mWI\ntGsKoSIiIiEqj40jO0a/QkWOhi7Hi4iIhKg8IZHsGM2WJHI0FEJFRERC4KuvpyYxiZxYf6RLEWnX\ndC1BREQkBEUVFQDkpKVFuBKR9k09oSIiIiEoqqomriFAenp6pEsRadcUQkVEREJQ7HaT7vZhjYuL\ndCki7Zoux4uIiISg2NdAhrcu0mWItHsKoSIiIiEoNUwyjYZIlyHS7imEioiIhKAsJo4sTdcpctQU\nQkVERI6QaZpU2BPoFK/7QUWOlkKoiIjIEXIFDLxx8WQ7EyNdiki7pxAqIiJyhIo9jQ8k5aSkRLgS\nkfZPIVREROQIFVVX43TXkpiREelSRNq9NjFOqMvlYsmSJWzbto2EhAQmT57MuHHjDthu3bp1PPro\no8HPTdPE5/Mxd+5chg8fzmeffcbtt9+OzWYLbjNp0iQmT57cKuchIiLRrajWRVp1JbGpgyJdiki7\n1yZC6NKlSwkEAjz55JMUFhYyf/58unbtyqBBTX/IR4wYwYgRI4Kff/jhhyxevJghQ4YEl6WkpPDM\nM8+0VukiItKBFHt9ZHjcWGJiIl2KSLsX8cvxXq+X9evXc8kll+BwOOjVqxejRo1izZo1P7vv6tWr\nOeOMM5r0fIqIiLSU0oBBhr8+0mWIRIWI94Tu3r0bgO7duweX9ezZk1deeeWw+9XW1vLBBx9w9913\nH7D80ksvJS4ujpNOOolLL72UpKSkJtsUFhZSWFgIQGlpKV6vFwDDMI76fPa373jhPm5bYZomhmFg\nGAaWKBwzL9rbD9SG7V20tx+0vTYstcZwAmbY6lEbSkcW8RDq9XpJSEhosiwxMZG6usNPibZu3Tpy\ncnI49thjg8u6du3Kww8/TNeuXamoqODRRx/loYce4k9/+lOTfZcuXcodd9wR/PzGG28EoKio6GhP\n56BKSkpa5LjSOtR+7Z/asP1rC20YME0q42wkGf4W+30RzdpCG0rbEvEQarfbDwicbrf7gGD6U2vW\nrGHMmDFNlqWlpZGWlgZAZmYmV199Nddccw0+n6/JJfuZM2cyYcIEoLEndO3atQDk5OQc9fnszzAM\nSkpKyM7OxmqN+J0PYWeaJn6/n9jY2Kj8Cz7a2w/Uhu1dtLcftK02LK1vwKisIy8tNWy/L9SGzac/\nBNq/iIfQLl26AFBQUEC3bt0A2LlzJ3l5eYfc5/vvvyc/P5+RI0ce9thWqxXTNDFNs8ny3NxccnNz\nAdizZw8bN24Mbt8SrFZrxN88W4JpmsFzi9Y3T4je9gO1YXvXUdoP2kYblvoDxAQCZKemhq0WtaF0\nZBH/brDb7Zx++uksX74cj8fDzp07eeeddxg9evQh91mzZg1DhgwJ9nru8+mnn1JcXIxpmlRWVrJs\n2TJOOOEE7HZ7S5+GiIhEMcM0+dblIbWmkvh0jREqEg4R7wmFxsvjf/3rX5kxYwYOh4Pp06czePBg\nACZPnsyCBQs4/vjjAWhoaODdd99l9uzZBxzn+++/56GHHqK2tpbExEROOukkLrvsslY9FxERiQ5e\nw+Bzl4dttR4+qnVT6Q9wxrdfETvg/EiXJhIV2kQIdTqdzJ0796DrVq5c2eTzuLg4li9fftBtL7jg\nAi644IKw1yciIh1DWX0D22o9bKt184W7DqsFBjkdTO6UwbGFBbjfX0OMOjdEwqJNhFAREZFI2lrj\nZmVJOfneerLiYhmSlMivMlLon+ggztp4r2bNl+X4UtOw6L5GkbBQCBURkQ6t2u/n0YJChtZW8Zvy\nQjpVlmO4XRhuN8UeFwGXG8PjJuB2Ye/dN9LlikQNhVAREenQnvuxmNSyEs5Z9Q/iMzIxHIlYE53E\npmcQk9j4cYwjEWtiIvFduka6XJGooRAqIiId1lcuD+/Xerjuw/fJW3Qv1vj4SJck0mEohIqISIfk\nN03+9t0PDPnyU4ZdNEUBVKSV6e5qERHpkN7YVUBVfQNTs1KxdTv0BCki0jIUQkVEpMMpr/PycpWb\n8d9/SZfRv4x0OSIdki7Hi4gIAIbPBz+Z5jjSDMPA9PkwvF4I41SZT3z0Gdl1XsaPHRv102WKtFUK\noSIigvvjbRQ+dF+kyzikXWE4Rkl6Fp8dO5DP+g2kMjWNeU4b8alpP7+jiLQIhVAREaF+dwHx3fLo\n9NtrI11KE4ZpUF5WTkZmBlZL6D2hJYbJ5gaTD/wG+QbkWWF0nJVTkxzk5uS0QMUicqSaHUJdLhdf\nffUVBQUFjB49mpSUFEzT1GUNEZF2yF9RTnxOLra8HpEupQnDMLDG27Hl5GA9wsvxFQ1+Nla72FBd\ny3d1PrrY4hiemcqcFCedbXoCXqStaFYIXbRoEQ888AC1tbVYLBa2bNnCSSedxDnnnMMvfvELbr/9\n9nDXKSIiLchfUU5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lS3SHUIvFwrJly5gzZw5r1qyhrKyM9PR0xowZw4ABA1qiRhGRo+Kv\nrqb69f/FqK8PLvvRkcSmrM5szcwBYEh5EX8o2UM3Ty0Arr3/WpP7ow9JO/d8HAMGt/Iri8iR2j8k\nQuPzMIda7vV6qaysBCAnJ6fJ+n2fV1RUAI2X+Q92S+P+93lWVlYSCAS44YYbuOGGGw7YtqCgINTT\niaiQQ6jH48HhcNC/f3/69+/fEjWJiISNaZqUPbWMhpJijH79+TAjh03ZnfkxMZk+1RVM2vUNgypK\niDeNSJdKysgxpF8wKdJliEgYpac3XtUoLi5usryoqKjJ+tzcXEpKSg7Yf/9lqampWCwWbrvttuAt\nkfvLzMwMW92tIeQQmpWVxfnnn8+0adM455xziIuL7PzHIiKH07BtC9/Uuvnm8tlsaTBwxlg5Ky2Z\nP6QmkWPTg5Qi0rL69etHVlYWK1euZOLEicHlL7zwAhaLhTPOOAOAYcOG8dhjj1FdXU1KSgoAb7/9\nNjU1NcF9EhMTOe200/jqq6+46667WvdEWkDIIXThwoW88MILnH/++aSlpTFx4kQuvvhiRowYoSFC\nRKTNKPD6WFdYzHtZ3XFfeDwn2ezMyUnmhCQHMXqvEpFWEhMTw/z585k9ezZZWVmcd955bNu2jQUL\nFnD55ZdzzDHHADBnzhyWLFnCr371K+bOnUtlZSULFiwI9pTus3jxYkaNGsWUKVOYOnUqaWlp/Pjj\nj6xevZrLL7+cESNGROAsmyfkRy9vvvlmtm7dyvbt27n++uvZsGEDo0ePpmvXrtxwww188MEHLVGn\niMjPKqlv4NXSSuZ+m8/N3xbw+Y97GLHjc5b07c5NebkMSU5UABWRVjdr1iwef/xxVq9ezfjx41my\nZAk333wzS5cuDW6Tm5vLqlWrqKur46KLLuLee+9lyZIl5ObmNjnW8OHDef/993G5XFx++eWMGzeO\nRYsW4XA42t0wmRbTNM2jPcinn37K888/z9NPP01JSQl+vz8ctbWKPXv2sGzZMq6++mo6d+4c1mMb\nhkFRURE5OTmNM5pEmWgfKDva2w+iow0rGvxsqnaxsbqWHXU+OtviGJ6SxKCvPiHm709hm/0HOvc/\nPirbMBra7+dE+8+h2rD5WvL3t7SOkC/H/1RlZSUffPABmzdvpqSkBIfDEY66REQOqcYf4IMaFxuq\nXXzlriMzLpbhKU5+2zmb7vZ4/KUl5P/9KTKmXIInPSPS5YqIyEE0K4S63W5eeeUVVqxYwerVq7FY\nLJxzzjk8//zznHfeeeGuUUT2U/HqS1SvXRPpMlpdXbyNL/N680nPfnzXJQ9nnZuB33/Dtd9/Q9fS\nIiyAAewCDK8Xe59+JJ01Cs9PnkgVEZG2IeQQOmXKFF5//XXq6+sZMWIEjz32GBMnTgw+ySUiLcvz\n5eck9DuOxCFDj/5gpkkgYBATY4U2eCnQB3wSY2NLjI3PY+KxYzLE7+OC+hp6Wxqw9sqDXnkH7GfB\nQsKAQVF7eVNEWl8Y7l48pI76XhVyCM3Pz+eee+5h8uTJdOrUqSVqEpHD8FeUk3zGWSQNO+2oj9UW\n70erNww+cXnYUO1iW42bWIuFocmJ3JySxABnQkgPFhlG5Mf+FJHo8PHHH7fIe8rgwYODM1B2NCGf\n9caNG1uiDhE5AqZp4q+siLopHf2myWcuDxurXWypcWNgcnJSIr/vlsMgp4M4a9sIyCIiEj5HFEK/\n/PJLevXqhc1m48svv/zZ7TWTkkjLCNTWgN9PbBQ8bGOYJl+569hQ7eKDGhdew+SEJAczu2RzYpID\nWxQ+CS0i7d++ueLDYfv27WE7Vnt0RCF0wIABbNq0iWHDhjFgwIBDXrYzTROLxUIgEAhrkSLSyF9R\nDtBuQ6hhmuyo87Kx2sWmahe1/gCDnA5+k5PJyclOHDEKniIiHcURhdBVq1Zx3HHHAfDmm2+2mXvH\nRDoaf0UFVkciVrs90qUcMdM02eX1saHaxcZqF+UNfo5PTGBydjpDk50kxcZEukQREYmAIwqhY8eO\nDX58zjnntFgxInJ4/oqydtMLWrBf8Cyqb6Cfw874zFROTXaSGtcxb8IXETkSPXr04PHHHz8gc+Xn\n59O/f3/Ky8ux2WwRqi58Qv5N0L9/f1544QUGDhx4wLovv/ySSZMmHdF9oyISOn9FObHpbfehpCJf\nPRurGweRL/DV0zPBxpj0ZE5NdpIZHxfp8kRE2rXu3bvjcrkiXUbYhBxCv/76a+rq6g66zu12s2PH\njqMuSkQOzl9R0eZ6QsvqG9i0d/ai7+t8dLPFMzzFyWkpTnJs8ZEuT0RE2qgjCqF+v5/6+vrgQK1e\nrxePx9NkG6/XyxtvvEFubm74qxQRoLEn1DFgUKTLoMrvZ/PeHs9vPF5y4uMYnuLk2i7ZdLO3/0tE\nIiKR9tFHH3HzzTeTn5/PmDFjeOKJJ6iqquKYY46hrq4Ou91Ofn4+M2bMYMuWLQwePJgzzzyTDRs2\nsG7dukiXf0SOKIT++c9/ZtGiRUDjqP4jR4485La33XZbeCoTkQNEcoxQ19752jdWu/jcXUf63vna\nZ+Rm0sNu0wOLIiJh9NRTT7Fq1SqysrKYNm0a119/PXfccUeTbaZNm8aJJ57Im2++yddff82vfvUr\n+vXrF6GKQ3dEIXT8+PHk5ORgmibXXXcdt9xyC8ccc0yTbeLj4znuuOM49dRTW6RQkY7ONIzGe0Iz\nWu9yvCdg8GGtmw3VtXxa68EZG8NpyU4mZafTx2HHquApIh2E87EHsdTXUxzGY6YAxiPLICn5gHWz\nZs2iZ8+eQGNn4LBhw1iwYEFwfX5+Phs3bmTVqlXY7XZOOOEEpk+fztatW8NYYcs6ohA6ZMgQhgwZ\nAjT2hE6cOJGsrKywFeFyuViyZAnbtm0jISGByZMnM27cuINuO2HCBGy2//S69O/fn4ULFwbXv/76\n67z44ovU1dUxZMgQZs2ahcPhCFutIpESqKmBQKDF7wn1GQYf1XrYUF3LR7UebFYLw5Kd3NqjM/0T\nExQ8RURaQffu3YMf5+XlUV9fT2lpaXDZnj17SElJITn5PwG2W7du0RdC9zdz5sywF7F06VICgQBP\nPvkkhYWFzJ8/n65duzJo0MHvfXvwwQfp2rXrAcs/+ugjVqxYwaJFi8jJyeHBBx9k6dKl3HDDDWGv\nWaS1+Sv3DlSfFv4Q2mCYfOZuDJ5ba91YgaHJTm7snsNAp4NYBU8RkVaVn5/f5OO4uDgyMzODyzp3\n7kx1dTW1tbUkJSUBUFBQ0Op1Ho0jCqHDhg3jqaeeon///gwbNuyw21osFjZv3nzEBXi9XtavX89D\nDz2Ew+GgV69ejBo1ijVr1hwyhB7Kv/71L0aPHh3svp4+fTo33XQT1113XVSMpyUdm7+iHGuiE2uY\nvpcDpsnnLg/rq2r4sLaOBtPkpKREfte1Eyc4HcRr2kwRkSDXtTdgGEbYp+20Jhz8au2jjz7K+PHj\nyczM5Pbbb2fKlCnExPxnco/u3btz6qmnMm/ePBYvXsw333zD3//+97DW19KOKIT26tUL+94ZWnr2\n7BnWBxB2794NNO127tmzJ6+88soh97n99tsJBAL06dOHGTNmBPf94YcfgrcNQGP3tWEY7Nmzp8k9\nrIWFhRQWFgJQWlqK1+sFwDCMsJ3X/scL93HbCtM0MQwDwzCi8qGUttZ+DWWNA9UfTT2GafKNx8vG\nGjcf1LjwGAaDHAlcnpPBScmJ2PcLnm3lvI9GW2vDcIv2n0FQG0aDaG/DlnLppZcyYcIE8vPzGTVq\nFA8//DA1NTVNtvn73//OjBkzyMzMZNCgQUyfPp1PP/00QhWH7ohC6PPPPx/8eMWKFWEtwOv1kpCQ\n0GRZYmLiIccivfvuu+nXrx8NDQ28/PLLzJ8/n0cffRSHw4HX6yUxMTG4rcViweFwHHCspUuXNnnC\n7MYbbwSgqKgoXKfVRElJSYscV1pHW2m/+h8LMByJIX+fmqZJfsDgY1+Aj+oDuEyTPnFWxtljGRgX\nQ4LVAl43VV53C1UeeW2lDaX51Ibtn9rwyO3atQuAW2+9tcny9PT04HCZ0Diz0v7DMV1//fV069at\nNUoMi7DNnWeaZrP+irPb7QeERLfbfUAw3WfAgAEAxMXFcckll7B27Vq++uorhgwZgt1uP2D8Uo/H\nc8CxZs6cyYQJE4DGntC1a9cCkJOTE3L9h2MYBiUlJWRnZ2ONwkubpmni9/uJjY2Nyr/g21r7Ffu8\nWHNzyTqC71PTNCnw1bOh2s3GWhdlDX6Oddi5KCeVoUmJJO+dr11t2L5Fe/uB2jAatFQbtlTHUXvy\n4YcfkpycTO/evVm/fj1PP/102DsLW1LIIXTFihVUVVVxzTXXAI0zKF100UVs376dkSNHsnz5cjJC\nGEKmS5cuQOPNtPvS+86dO8nLyzui/ff/oc3Ly2Pnzp2cddZZQOPleavVSufOnZvsk5ubGxxUf8+e\nPWzcuBGgxd7grFZr1L557ju3aH3zhLbTfoHKCmzduh+2lj2++r3ztdey29dAnwQbv8pI5dQUJ+kH\nma9dbdi+dZT2A7VhNIjWNoyk4uJiJk6cSGlpKbm5uSxcuPCA+ebbspC/G+655x4aGhqCn8+ePZv6\n+nr+/Oc/s2PHDubNmxfS8ex2O6effjrLly/H4/Gwc+dO3nnnHUaPHn3Atvn5+Xz33XcEAgF8Ph9/\n//vfqa+vDw7MOmrUKN555x127tyJx+Nh+fLlnHHGGXooSaJC47zxB/6BV1LfwKullcz9Np8bd+Sz\ntcbFmanJ/KVvHnf26sa4zNSDBlAREWnfxo0bxw8//IDH4+G7775jzpw5kS4pJCH/Ztq5cyfHH388\nABUVFaxbt45XXnmFc889ly5duvDHP/4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L6IyJgSYqutOT0de63NhntaGgXyoGmfx3Hd5Q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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (306595911)>"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_curve_df = curve_df.groupby(['model','fpr']).agg({'tpr': 'mean'}).reset_index()\n",
    "(ggplot(mean_curve_df, aes(x='fpr', y='tpr', color='model')) +\n",
    "    geom_line() +\n",
    "    labs(title = \"ROC curves\",\n",
    "         x = \"False positive rate\",\n",
    "         y = \"True positive rate\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a small difference in the low false positive rate between the models, but essentially these models perform very similarly. Neither model is very good at this task."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}