3.MODELING and PredictionFinal-Copy2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Here  i would start from the cleaned data as i have merged by data sets for comparison while working on the eda section. I am going to show which model works better as compared to other models and this Jupyter notebook is going to show the detailed steps needed to accomplish the task. Which is which model works good and which model does not and if  the model overfits or underfitts also**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modeling and Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Project 4: Group exercise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kirosgebremariam/anaconda3/envs/dsi/lib/python3.6/site-packages/pandas_profiling/plot.py:15: UserWarning: \n",
      "This call to matplotlib.use() has no effect because the backend has already\n",
      "been chosen; matplotlib.use() must be called *before* pylab, matplotlib.pyplot,\n",
      "or matplotlib.backends is imported for the first time.\n",
      "\n",
      "The backend was *originally* set to 'module://ipykernel.pylab.backend_inline' by the following code:\n",
      "  File \"/Users/kirosgebremariam/anaconda3/envs/dsi/lib/python3.6/runpy.py\", line 193, in _run_module_as_main\n",
      "    \"__main__\", mod_spec)\n",
      "  File \"/Users/kirosgebremariam/anaconda3/envs/dsi/lib/python3.6/runpy.py\", line 85, in _run_code\n",
      "    exec(code, run_globals)\n",
      "  File \"/Users/kirosgebremariam/anaconda3/envs/dsi/lib/python3.6/site-packages/ipykernel/__main__.py\", line 3, in <module>\n",
      "    app.launch_new_instance()\n",
      "  File \"/Users/kirosgebremariam/anaconda3/envs/dsi/lib/python3.6/site-packages/traitlets/config/application.py\", line 658, in launch_instance\n",
      "    app.start()\n",
      "  File \"/Users/kirosgebremariam/anaconda3/envs/dsi/lib/python3.6/site-packages/ipykernel/kernelapp.py\", line 486, in start\n",
      "    self.io_loop.start()\n",
      "  File \"/Users/kirosgebremariam/anaconda3/envs/dsi/lib/python3.6/site-packages/tornado/platform/asyncio.py\", line 127, in start\n",
      "    self.asyncio_loop.run_forever()\n",
      "  File \"/Users/kirosgebremariam/anaconda3/envs/dsi/lib/python3.6/asyncio/base_events.py\", line 422, in run_forever\n",
      "    self._run_once()\n",
      "  File \"/Users/kirosgebremariam/anaconda3/envs/dsi/lib/python3.6/asyncio/base_events.py\", line 1432, in _run_once\n",
      "    handle._run()\n",
      "  File \"/Users/kirosgebremariam/anaconda3/envs/dsi/lib/python3.6/asyncio/events.py\", line 145, in _run\n",
      "    self._callback(*self._args)\n",
      "  File \"/Users/kirosgebremariam/anaconda3/envs/dsi/lib/python3.6/site-packages/tornado/platform/asyncio.py\", line 117, in _handle_events\n",
      "    handler_func(fileobj, events)\n",
      "  File \"/Users/kirosgebremariam/anaconda3/envs/dsi/lib/python3.6/site-packages/tornado/stack_context.py\", line 276, in null_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "  File \"/Users/kirosgebremariam/anaconda3/envs/dsi/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py\", line 450, in _handle_events\n",
      "    self._handle_recv()\n",
      "  File \"/Users/kirosgebremariam/anaconda3/envs/dsi/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py\", line 480, in _handle_recv\n",
      "    self._run_callback(callback, msg)\n",
      "  File \"/Users/kirosgebremariam/anaconda3/envs/dsi/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py\", line 432, in _run_callback\n",
      "    callback(*args, **kwargs)\n",
      "  File \"/Users/kirosgebremariam/anaconda3/envs/dsi/lib/python3.6/site-packages/tornado/stack_context.py\", line 276, in null_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "  File \"/Users/kirosgebremariam/anaconda3/envs/dsi/lib/python3.6/site-packages/ipykernel/kernelbase.py\", line 283, in dispatcher\n",
      "    return self.dispatch_shell(stream, msg)\n",
      "  File \"/Users/kirosgebremariam/anaconda3/envs/dsi/lib/python3.6/site-packages/ipykernel/kernelbase.py\", line 233, in dispatch_shell\n",
      "    handler(stream, idents, msg)\n",
      "  File \"/Users/kirosgebremariam/anaconda3/envs/dsi/lib/python3.6/site-packages/ipykernel/kernelbase.py\", line 399, in execute_request\n",
      "    user_expressions, allow_stdin)\n",
      "  File \"/Users/kirosgebremariam/anaconda3/envs/dsi/lib/python3.6/site-packages/ipykernel/ipkernel.py\", line 208, in do_execute\n",
      "    res = shell.run_cell(code, store_history=store_history, silent=silent)\n",
      "  File \"/Users/kirosgebremariam/anaconda3/envs/dsi/lib/python3.6/site-packages/ipykernel/zmqshell.py\", line 537, in run_cell\n",
      "    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n",
      "  File \"/Users/kirosgebremariam/anaconda3/envs/dsi/lib/python3.6/site-packages/IPython/core/interactiveshell.py\", line 2662, in run_cell\n",
      "    raw_cell, store_history, silent, shell_futures)\n",
      "  File \"/Users/kirosgebremariam/anaconda3/envs/dsi/lib/python3.6/site-packages/IPython/core/interactiveshell.py\", line 2785, in _run_cell\n",
      "    interactivity=interactivity, compiler=compiler, result=result)\n",
      "  File \"/Users/kirosgebremariam/anaconda3/envs/dsi/lib/python3.6/site-packages/IPython/core/interactiveshell.py\", line 2903, in run_ast_nodes\n",
      "    if self.run_code(code, result):\n",
      "  File \"/Users/kirosgebremariam/anaconda3/envs/dsi/lib/python3.6/site-packages/IPython/core/interactiveshell.py\", line 2963, in run_code\n",
      "    exec(code_obj, self.user_global_ns, self.user_ns)\n",
      "  File \"<ipython-input-1-426a49b4237d>\", line 4, in <module>\n",
      "    import matplotlib.pyplot as plt\n",
      "  File \"/Users/kirosgebremariam/anaconda3/envs/dsi/lib/python3.6/site-packages/matplotlib/pyplot.py\", line 71, in <module>\n",
      "    from matplotlib.backends import pylab_setup\n",
      "  File \"/Users/kirosgebremariam/anaconda3/envs/dsi/lib/python3.6/site-packages/matplotlib/backends/__init__.py\", line 16, in <module>\n",
      "    line for line in traceback.format_stack()\n",
      "\n",
      "\n",
      "  matplotlib.use(BACKEND)\n",
      "/Users/kirosgebremariam/anaconda3/envs/dsi/lib/python3.6/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "# importing necessary libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas_profiling\n",
    "np.random.seed(42)\n",
    "import re\n",
    "from sklearn import svm\n",
    "from sklearn.pipeline import Pipeline\n",
    "from imblearn.over_sampling import SMOTE\n",
    "import xgboost as xgb\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import confusion_matrix as cm\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
    "from sklearn.model_selection import train_test_split,GridSearchCV\n",
    "from sklearn.cross_validation import cross_val_score\n",
    "from sklearn import cross_validation,metrics\n",
    "\n",
    "from matplotlib import rcParams\n",
    "rcParams.update({'figure.autolayout':True})\n",
    "\n",
    "# setting visual\n",
    "\n",
    "plt.style.use('fivethirtyeight')\n",
    "sns.set_style('whitegrid')\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "% load_ext giphy_magic\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction_data=pd.read_csv('./assets/prediction_data1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "working_data=pd.read_csv('./assets/working_data1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train, test=train_test_split(working_data, test_size=.20, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((10506, 41), (116293, 39))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#confirming that the number of rows/observations is still the same\n",
    "working_data.shape, prediction_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#assert working_data.isnull().sum().sum() == 0 # Never hurts to check again for null values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Date</th>\n",
       "      <th>Address</th>\n",
       "      <th>Species</th>\n",
       "      <th>Block</th>\n",
       "      <th>Street</th>\n",
       "      <th>Trap</th>\n",
       "      <th>AddressNumberAndStreet</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>...</th>\n",
       "      <th>SnowFall</th>\n",
       "      <th>PrecipTotal</th>\n",
       "      <th>StnPressure</th>\n",
       "      <th>SeaLevel</th>\n",
       "      <th>ResultSpeed</th>\n",
       "      <th>ResultDir</th>\n",
       "      <th>AvgSpeed</th>\n",
       "      <th>DewPointcl</th>\n",
       "      <th>Trange</th>\n",
       "      <th>SatT</th>\n",
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       "  </thead>\n",
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       "      <th>10505</th>\n",
       "      <td>10505</td>\n",
       "      <td>2013-09-26</td>\n",
       "      <td>4200 West 65th Street, Chicago, IL 60621, USA</td>\n",
       "      <td>CULEX PIPIENS/RESTUANS</td>\n",
       "      <td>42</td>\n",
       "      <td>W 65TH ST</td>\n",
       "      <td>T235</td>\n",
       "      <td>4200  W 65TH ST, Chicago, IL</td>\n",
       "      <td>41.776428</td>\n",
       "      <td>-87.627096</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.34</td>\n",
       "      <td>30.04</td>\n",
       "      <td>3.8</td>\n",
       "      <td>8</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>11.0</td>\n",
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       "<p>1 rows × 41 columns</p>\n",
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      ],
      "text/plain": [
       "       Unnamed: 0        Date                                        Address  \\\n",
       "10505       10505  2013-09-26  4200 West 65th Street, Chicago, IL 60621, USA   \n",
       "\n",
       "                      Species  Block      Street  Trap  \\\n",
       "10505  CULEX PIPIENS/RESTUANS     42   W 65TH ST  T235   \n",
       "\n",
       "             AddressNumberAndStreet   Latitude  Longitude  ...   SnowFall  \\\n",
       "10505  4200  W 65TH ST, Chicago, IL  41.776428 -87.627096  ...        0.0   \n",
       "\n",
       "       PrecipTotal  StnPressure  SeaLevel  ResultSpeed  ResultDir  AvgSpeed  \\\n",
       "10505          0.0        29.34     30.04          3.8          8       4.2   \n",
       "\n",
       "       DewPointcl  Trange  SatT  \n",
       "10505           0    25.0  11.0  \n",
       "\n",
       "[1 rows x 41 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "working_data.tail(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Date</th>\n",
       "      <th>Address</th>\n",
       "      <th>Species</th>\n",
       "      <th>Block</th>\n",
       "      <th>Street</th>\n",
       "      <th>Trap</th>\n",
       "      <th>AddressNumberAndStreet</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>...</th>\n",
       "      <th>SnowFall</th>\n",
       "      <th>PrecipTotal</th>\n",
       "      <th>StnPressure</th>\n",
       "      <th>SeaLevel</th>\n",
       "      <th>ResultSpeed</th>\n",
       "      <th>ResultDir</th>\n",
       "      <th>AvgSpeed</th>\n",
       "      <th>DewPointcl</th>\n",
       "      <th>Trange</th>\n",
       "      <th>SatT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>116292</th>\n",
       "      <td>116292</td>\n",
       "      <td>2014-10-02</td>\n",
       "      <td>2100 North Cannon Drive, Chicago, IL 60614, USA</td>\n",
       "      <td>CULEX ERRATICUS</td>\n",
       "      <td>21</td>\n",
       "      <td>N CANNON DR</td>\n",
       "      <td>T054C</td>\n",
       "      <td>2100  N CANNON DR, Chicago, IL</td>\n",
       "      <td>41.925652</td>\n",
       "      <td>-87.63359</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.32</td>\n",
       "      <td>29.03</td>\n",
       "      <td>29.78</td>\n",
       "      <td>7.2</td>\n",
       "      <td>17</td>\n",
       "      <td>7.9</td>\n",
       "      <td>0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
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       "<p>1 rows × 39 columns</p>\n",
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      ],
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       "        Unnamed: 0        Date  \\\n",
       "116292      116292  2014-10-02   \n",
       "\n",
       "                                                Address          Species  \\\n",
       "116292  2100 North Cannon Drive, Chicago, IL 60614, USA  CULEX ERRATICUS   \n",
       "\n",
       "        Block        Street   Trap          AddressNumberAndStreet   Latitude  \\\n",
       "116292     21   N CANNON DR  T054C  2100  N CANNON DR, Chicago, IL  41.925652   \n",
       "\n",
       "        Longitude  ...   SnowFall  PrecipTotal  StnPressure  SeaLevel  \\\n",
       "116292  -87.63359  ...        0.0         0.32        29.03     29.78   \n",
       "\n",
       "        ResultSpeed  ResultDir  AvgSpeed  DewPointcl  Trange  SatT  \n",
       "116292          7.2         17       7.9           0     9.0   7.0  \n",
       "\n",
       "[1 rows x 39 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_data.tail(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'Date', 'Address', 'Species', 'Block', 'Street', 'Trap',\n",
       "       'AddressNumberAndStreet', 'Latitude', 'Longitude', 'AddressAccuracy',\n",
       "       'NumMosquitos', 'WnvPresent', 'Hour', 'Week', 'Month', 'Year', 'Day',\n",
       "       'Station', 'Tmax', 'Tmin', 'Tavg', 'Depart', 'DewPoint', 'WetBulb',\n",
       "       'Heat', 'Cool', 'Sunrise', 'Sunset', 'CodeSum', 'Depth', 'SnowFall',\n",
       "       'PrecipTotal', 'StnPressure', 'SeaLevel', 'ResultSpeed', 'ResultDir',\n",
       "       'AvgSpeed', 'DewPointcl', 'Trange', 'SatT'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "working_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'Date', 'Address', 'Species', 'Block', 'Street', 'Trap',\n",
       "       'AddressNumberAndStreet', 'Latitude', 'Longitude', 'AddressAccuracy',\n",
       "       'Hour', 'Month', 'Year', 'Day', 'Week', 'Station', 'Tmax', 'Tmin',\n",
       "       'Tavg', 'Depart', 'DewPoint', 'WetBulb', 'Heat', 'Cool', 'Sunrise',\n",
       "       'Sunset', 'CodeSum', 'Depth', 'SnowFall', 'PrecipTotal', 'StnPressure',\n",
       "       'SeaLevel', 'ResultSpeed', 'ResultDir', 'AvgSpeed', 'DewPointcl',\n",
       "       'Trange', 'SatT'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10506, 41) (116293, 39)\n"
     ]
    }
   ],
   "source": [
    "print(working_data.shape, prediction_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.05244622120692932"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# # calculating Baseline\n",
    "## Check for baseline. \n",
    "\n",
    "baseline = len(working_data[working_data['WnvPresent'] == 0]['WnvPresent']) / float(len(working_data))\n",
    "1- baseline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Feature Engineering\n",
    "These features didn't improve predictive scores, and were not used in the final model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #get dummy variables for only for mosquito species that have Wnv \n",
    "prediction_data_species = pd.get_dummies(prediction_data['Species'])[['CULEX PIPIENS/RESTUANS','CULEX PIPIENS','CULEX RESTUANS']]\n",
    "working_data_species = pd.get_dummies(working_data['Species'])[['CULEX PIPIENS/RESTUANS','CULEX PIPIENS','CULEX RESTUANS']]\n",
    "                                                      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'Date', 'Address', 'Species', 'Block', 'Street', 'Trap',\n",
       "       'AddressNumberAndStreet', 'Latitude', 'Longitude', 'AddressAccuracy',\n",
       "       'NumMosquitos', 'WnvPresent', 'Hour', 'Week', 'Month', 'Year', 'Day',\n",
       "       'Station', 'Tmax', 'Tmin', 'Tavg', 'Depart', 'DewPoint', 'WetBulb',\n",
       "       'Heat', 'Cool', 'Sunrise', 'Sunset', 'CodeSum', 'Depth', 'SnowFall',\n",
       "       'PrecipTotal', 'StnPressure', 'SeaLevel', 'ResultSpeed', 'ResultDir',\n",
       "       'AvgSpeed', 'DewPointcl', 'Trange', 'SatT'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "working_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'Date', 'Address', 'Species', 'Block', 'Street', 'Trap',\n",
       "       'AddressNumberAndStreet', 'Latitude', 'Longitude', 'AddressAccuracy',\n",
       "       'Hour', 'Month', 'Year', 'Day', 'Week', 'Station', 'Tmax', 'Tmin',\n",
       "       'Tavg', 'Depart', 'DewPoint', 'WetBulb', 'Heat', 'Cool', 'Sunrise',\n",
       "       'Sunset', 'CodeSum', 'Depth', 'SnowFall', 'PrecipTotal', 'StnPressure',\n",
       "       'SeaLevel', 'ResultSpeed', 'ResultDir', 'AvgSpeed', 'DewPointcl',\n",
       "       'Trange', 'SatT'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#adding to pipiens/restauns, so it can maybe learn from those, since those are the ones that carry wnv?\n",
    "#so it makes it extra sensitive?\n",
    "\n",
    "new_species = []\n",
    "for i in prediction_data['Species']:\n",
    "    if i == 'UNSPECIFIED CULEX':\n",
    "        new_species.append('CULEX PIPIENS/RESTUANS')\n",
    "    else:\n",
    "        new_species.append(i)\n",
    "prediction_data['Species'] = new_species"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CULEX PIPIENS/RESTUANS    29704\n",
       "CULEX RESTUANS            14670\n",
       "CULEX PIPIENS             14521\n",
       "CULEX SALINARIUS          14355\n",
       "CULEX TERRITANS           14351\n",
       "CULEX TARSALIS            14347\n",
       "CULEX ERRATICUS           14345\n",
       "Name: Species, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_data['Species'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CULEX PIPIENS/RESTUANS    4752\n",
       "CULEX RESTUANS            2740\n",
       "CULEX PIPIENS             2699\n",
       "CULEX TERRITANS            222\n",
       "CULEX SALINARIUS            86\n",
       "CULEX TARSALIS               6\n",
       "CULEX ERRATICUS              1\n",
       "Name: Species, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "working_data['Species'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To get  the missing zipcodes, maybe calculate based on lat/long?\n",
    "# i tried it but i did not see its relevance\n",
    "\n",
    "import re\n",
    "zipcodes = []\n",
    "for i in working_data['Address']:\n",
    "    reg = re.compile('\\d{5}')\n",
    "    zipcodes.append(reg.findall(i)) \n",
    "#working_data['Zipcode'] = zipcodes\n",
    "\n",
    "zipcodes2 = []\n",
    "for i in prediction_data['Address']:\n",
    "    reg = re.compile('\\d{5}')\n",
    "    zipcodes2.append(reg.findall(i)) \n",
    "#prediction_data['Zipcode'] = zipcodes2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# working_data.drop(columns=['WetBulb'], axis=1, inplace=True)\n",
    "\n",
    "working_data = pd.get_dummies(working_data, columns=['Species'], drop_first=True)\n",
    "\n",
    "prediction_data.drop(columns=['Unnamed: 0','AddressNumberAndStreet','Date',\n",
    "                        'Latitude', 'Longitude','Address','CodeSum'], axis=1, inplace=True)\n",
    "\n",
    "prediction_data = pd.get_dummies(prediction_data, columns=['Species'], drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'Date', 'Address', 'Block', 'Street', 'Trap',\n",
       "       'AddressNumberAndStreet', 'Latitude', 'Longitude', 'AddressAccuracy',\n",
       "       'NumMosquitos', 'WnvPresent', 'Hour', 'Week', 'Month', 'Year', 'Day',\n",
       "       'Station', 'Tmax', 'Tmin', 'Tavg', 'Depart', 'DewPoint', 'WetBulb',\n",
       "       'Heat', 'Cool', 'Sunrise', 'Sunset', 'CodeSum', 'Depth', 'SnowFall',\n",
       "       'PrecipTotal', 'StnPressure', 'SeaLevel', 'ResultSpeed', 'ResultDir',\n",
       "       'AvgSpeed', 'DewPointcl', 'Trange', 'SatT', 'Species_CULEX PIPIENS',\n",
       "       'Species_CULEX PIPIENS/RESTUANS', 'Species_CULEX RESTUANS',\n",
       "       'Species_CULEX SALINARIUS', 'Species_CULEX TARSALIS',\n",
       "       'Species_CULEX TERRITANS'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "working_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "working_data.drop(columns=['Unnamed: 0','Address','Date', 'Street', 'Trap','AddressNumberAndStreet','CodeSum','WetBulb',\n",
    "                          'Heat', 'Cool', 'Sunrise', 'Sunset', 'CodeSum','Depart', 'DewPoint', 'Depth', 'SnowFall','PrecipTotal', \n",
    "                           'StnPressure', 'SeaLevel', 'ResultSpeed', 'ResultDir','AvgSpeed', 'DewPointcl', 'Trange',\n",
    "                           'SatT'], axis =1, inplace =True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Block', 'Street', 'Trap', 'AddressAccuracy', 'Hour', 'Month', 'Year',\n",
       "       'Day', 'Week', 'Station', 'Tmax', 'Tmin', 'Tavg', 'Depart', 'DewPoint',\n",
       "       'WetBulb', 'Heat', 'Cool', 'Sunrise', 'Sunset', 'Depth', 'SnowFall',\n",
       "       'PrecipTotal', 'StnPressure', 'SeaLevel', 'ResultSpeed', 'ResultDir',\n",
       "       'AvgSpeed', 'DewPointcl', 'Trange', 'SatT', 'Species_CULEX PIPIENS',\n",
       "       'Species_CULEX PIPIENS/RESTUANS', 'Species_CULEX RESTUANS',\n",
       "       'Species_CULEX SALINARIUS', 'Species_CULEX TARSALIS',\n",
       "       'Species_CULEX TERRITANS'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction_data.drop(columns=['Street', 'Trap','WetBulb','Heat', 'Cool', 'Sunrise',\n",
    "                              'Sunset', 'Depart', 'DewPoint','Depth', 'SnowFall','PrecipTotal',\n",
    "                              'StnPressure', 'SeaLevel', 'ResultSpeed', 'ResultDir','AvgSpeed', \n",
    "                              'DewPointcl', 'Trange','SatT'], axis =1, inplace =True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((10506, 21), (116293, 17))"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "working_data.shape, prediction_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. Model Building \n",
    "\n",
    "I was working on Each of these models were gird searched manually to find the best hyper-parameters, then the best scoring output  will be submitted to Kaggle based on David Beron and Harsha decisions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# working_data[columns] = working_data.columns.apply(lambda x: float(x.split()[0].replace(',', '')))\n",
    "# prediction_data[columns] = prediction_data.columns.apply(lambda x: float(x.split()[0].replace(',', '')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #making sure index is in order\n",
    "# prediction_data = prediction_data.sort_values(by=['Id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "target = working_data.WnvPresent\n",
    "features = working_data.drop(columns=['WnvPresent', \"NumMosquitos\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(features,\n",
    "                                                    target,\n",
    "                                                    stratify=target,\n",
    "                                                    random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "ss = StandardScaler()\n",
    "X_train = ss.fit_transform(X_train)\n",
    "X_test = ss.transform(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import scale\n",
    "X_test = scale(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define X and y variables, train test split, convert into dense matrix (preffered xgboost inputs)\n",
    "\n",
    "X = working_data.drop('WnvPresent', 1)\n",
    "y = working_data.WnvPresent\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y)\n",
    "\n",
    "D_xgb_mat_working_data = xgb.DMatrix(X_train, y_train)\n",
    "D_xgb_mat_ = xgb.DMatrix(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Grid Search Max Depth and Min Child Weight\n",
    "\n",
    "cv_params = {'max_depth': [3,5,7], 'min_child_weight': [1,3,5]}\n",
    "ind_params = {'learning_rate': 0.1, 'n_estimators': 1000, 'subsample': 0.8, 'colsample_bytree': 0.8, \n",
    "             'objective': 'binary:logistic'}\n",
    "\n",
    "optimized_GBM = GridSearchCV(xgb.XGBClassifier(**ind_params), \n",
    "                            cv_params, \n",
    "                             scoring = 'roc_auc', cv = 5, n_jobs = -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "       colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_delta_step=0,\n",
       "       max_depth=3, min_child_weight=1, missing=None, n_estimators=1000,\n",
       "       n_jobs=1, nthread=None, objective='binary:logistic', random_state=0,\n",
       "       reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
       "       silent=True, subsample=0.8),\n",
       "       fit_params=None, iid=True, n_jobs=-1,\n",
       "       param_grid={'max_depth': [3, 5, 7], 'min_child_weight': [1, 3, 5]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring='roc_auc', verbose=0)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "optimized_GBM.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kirosgebremariam/anaconda3/envs/dsi/lib/python3.6/site-packages/sklearn/model_selection/_search.py:761: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[mean: 0.83494, std: 0.00994, params: {'max_depth': 3, 'min_child_weight': 1},\n",
       " mean: 0.83598, std: 0.01104, params: {'max_depth': 3, 'min_child_weight': 3},\n",
       " mean: 0.83551, std: 0.01083, params: {'max_depth': 3, 'min_child_weight': 5},\n",
       " mean: 0.82042, std: 0.01589, params: {'max_depth': 5, 'min_child_weight': 1},\n",
       " mean: 0.81968, std: 0.01676, params: {'max_depth': 5, 'min_child_weight': 3},\n",
       " mean: 0.82266, std: 0.01688, params: {'max_depth': 5, 'min_child_weight': 5},\n",
       " mean: 0.81777, std: 0.01346, params: {'max_depth': 7, 'min_child_weight': 1},\n",
       " mean: 0.81584, std: 0.01780, params: {'max_depth': 7, 'min_child_weight': 3},\n",
       " mean: 0.81795, std: 0.01739, params: {'max_depth': 7, 'min_child_weight': 5}]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "optimized_GBM.grid_scores_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 3, 'min_child_weight': 3}"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Best Scores\n",
    "optimized_GBM.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "       colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_delta_step=0,\n",
       "       max_depth=3, min_child_weight=1, missing=None, n_estimators=1000,\n",
       "       n_jobs=1, nthread=None, objective='binary:logistic', random_state=0,\n",
       "       reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
       "       silent=True, subsample=1),\n",
       "       fit_params=None, iid=True, n_jobs=-1,\n",
       "       param_grid={'learning_rate': [0.1, 0.01], 'subsample': [0.7, 0.8, 0.9]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring='roc_auc', verbose=0)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Grid search learning rate and subsample\n",
    "\n",
    "cv_params = {'learning_rate': [0.1, 0.01], 'subsample': [0.7,0.8,0.9]}\n",
    "ind_params = {'n_estimators': 1000, 'colsample_bytree': 0.8, \n",
    "             'objective': 'binary:logistic', 'max_depth': 3, 'min_child_weight': 1}\n",
    "\n",
    "\n",
    "optimized_GBM = GridSearchCV(xgb.XGBClassifier(**ind_params), \n",
    "                            cv_params, \n",
    "                             scoring = 'roc_auc', cv = 5, n_jobs = -1)\n",
    "optimized_GBM.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kirosgebremariam/anaconda3/envs/dsi/lib/python3.6/site-packages/sklearn/model_selection/_search.py:761: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[mean: 0.83476, std: 0.00963, params: {'learning_rate': 0.1, 'subsample': 0.7},\n",
       " mean: 0.83494, std: 0.00994, params: {'learning_rate': 0.1, 'subsample': 0.8},\n",
       " mean: 0.83716, std: 0.01105, params: {'learning_rate': 0.1, 'subsample': 0.9},\n",
       " mean: 0.86738, std: 0.00730, params: {'learning_rate': 0.01, 'subsample': 0.7},\n",
       " mean: 0.86709, std: 0.00810, params: {'learning_rate': 0.01, 'subsample': 0.8},\n",
       " mean: 0.86757, std: 0.00812, params: {'learning_rate': 0.01, 'subsample': 0.9}]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "optimized_GBM.grid_scores_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'learning_rate': 0.01, 'subsample': 0.9}"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Best Grid Search results\n",
    "\n",
    "optimized_GBM.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use parameters from grid search to define our parameters to train our data\n",
    "\n",
    "our_params = {'eta': 0.1, 'silent':0, 'subsample': 0.9, 'colsample_bytree': 0.8, \n",
    "             'objective': 'binary:logistic', 'max_depth':3, 'min_child_weight':1,\n",
    "             'learning_rate':0.1} \n",
    "\n",
    "# Grid Search CV optimized settings\n",
    "\n",
    "cv_xgb = xgb.cv(params = our_params, dtrain = D_xgb_mat_working_data, num_boost_round = 3000, nfold = 5, \n",
    "                metrics = ['auc'], # Make sure you enter metrics inside a list or you may encounter issues!\n",
    "                early_stopping_rounds = 100) # Look for early stopping that minimizes error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>train-auc-mean</th>\n",
       "      <th>train-auc-std</th>\n",
       "      <th>test-auc-mean</th>\n",
       "      <th>test-auc-std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>0.913802</td>\n",
       "      <td>0.001371</td>\n",
       "      <td>0.870194</td>\n",
       "      <td>0.009794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>0.914099</td>\n",
       "      <td>0.001431</td>\n",
       "      <td>0.870390</td>\n",
       "      <td>0.009814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>0.914485</td>\n",
       "      <td>0.001474</td>\n",
       "      <td>0.870434</td>\n",
       "      <td>0.009812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>0.914862</td>\n",
       "      <td>0.001505</td>\n",
       "      <td>0.870419</td>\n",
       "      <td>0.009539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>0.915238</td>\n",
       "      <td>0.001492</td>\n",
       "      <td>0.870458</td>\n",
       "      <td>0.009278</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     train-auc-mean  train-auc-std  test-auc-mean  test-auc-std\n",
       "97         0.913802       0.001371       0.870194      0.009794\n",
       "98         0.914099       0.001431       0.870390      0.009814\n",
       "99         0.914485       0.001474       0.870434      0.009812\n",
       "100        0.914862       0.001505       0.870419      0.009539\n",
       "101        0.915238       0.001492       0.870458      0.009278"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Optimal number of boosting rounds. Does not carry much efficacy, but is supposed to help against overfitting. \n",
    "# Did not use because it did not seem to help my score\n",
    "\n",
    "cv_xgb.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train our data\n",
    "\n",
    "final_gb = xgb.train(our_params, D_xgb_mat_working_data, num_boost_round = 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Predict training set\n",
    "X_test_predprob = final_gb.predict(D_xgb_mat_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC Score (Test): 0.863849\n"
     ]
    }
   ],
   "source": [
    "print (\"AUC Score (Test): %f\" % metrics.roc_auc_score(y_test, X_test_predprob))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a0fa11be0>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kirosgebremariam/anaconda3/envs/dsi/lib/python3.6/site-packages/matplotlib/figure.py:2267: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  warnings.warn(\"This figure includes Axes that are not compatible \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 568,
       "width": 712
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rcParams['figure.figsize'] = 10, 8\n",
    "xgb.plot_importance(final_gb)\n",
    "\n",
    "# Plot feature importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'NumMosquitos': 32,\n",
       " 'Year': 10,\n",
       " 'Species_CULEX PIPIENS': 1,\n",
       " 'Species_CULEX RESTUANS': 1,\n",
       " 'Week': 27,\n",
       " 'Month': 23,\n",
       " 'Tmax': 8,\n",
       " 'Latitude': 8,\n",
       " 'Longitude': 9,\n",
       " 'Day': 2,\n",
       " 'Block': 1,\n",
       " 'Tavg': 1,\n",
       " 'Tmin': 1}"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "importances = final_gb.get_fscore()\n",
    "importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a107f8588>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kirosgebremariam/anaconda3/envs/dsi/lib/python3.6/site-packages/matplotlib/figure.py:2267: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  warnings.warn(\"This figure includes Axes that are not compatible \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 568,
       "width": 568
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot features in a different style\n",
    "\n",
    "importance_frame = pd.DataFrame({'Importance': list(importances.values()), 'Feature': list(importances.keys())})\n",
    "importance_frame.sort_values(by = 'Importance', inplace = True)\n",
    "importance_frame.plot(kind = 'barh', x = 'Feature', figsize = (8,8), color = 'orange')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "D_xgb_test = xgb.DMatrix(X_test)\n",
    "D_xgb_untest = xgb.DMatrix(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.06828458 0.25459436 0.07989744 ... 0.174178   0.14226216 0.06828458]\n"
     ]
    }
   ],
   "source": [
    "#Predict training set:\n",
    "X_test_predprob = final_gb.predict(D_xgb_test)\n",
    "print(X_test_predprob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Export predictions to CSV\n",
    "\n",
    "predictions = final_gb.predict(D_xgb_test)\n",
    "\n",
    "sample = pd.DataFrame(predictions, columns=['WnvPresent'])\n",
    "sample['Id'] = (sample.index + 1)\n",
    "\n",
    "sample.to_csv('AUC_Grid_num_beat_the_benchmark_unbalanced_XGB.csv', index=False)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>WnvPresent</th>\n",
       "      <th>Id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.068285</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.254594</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.079897</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.068285</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.068285</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.076173</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.089058</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.068912</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.101587</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.075696</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.083787</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.070582</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.068285</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.075696</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.068285</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.078687</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.104662</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.197017</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.095404</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.091652</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.083787</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.068285</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.147133</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.086375</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.077116</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.091652</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.181616</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.181616</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.083787</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.086375</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2597</th>\n",
       "      <td>0.068285</td>\n",
       "      <td>2598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2598</th>\n",
       "      <td>0.165303</td>\n",
       "      <td>2599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2599</th>\n",
       "      <td>0.113864</td>\n",
       "      <td>2600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2600</th>\n",
       "      <td>0.098410</td>\n",
       "      <td>2601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2601</th>\n",
       "      <td>0.181616</td>\n",
       "      <td>2602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2602</th>\n",
       "      <td>0.076173</td>\n",
       "      <td>2603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2603</th>\n",
       "      <td>0.078687</td>\n",
       "      <td>2604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2604</th>\n",
       "      <td>0.090635</td>\n",
       "      <td>2605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2605</th>\n",
       "      <td>0.092550</td>\n",
       "      <td>2606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2606</th>\n",
       "      <td>0.068285</td>\n",
       "      <td>2607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2607</th>\n",
       "      <td>0.091652</td>\n",
       "      <td>2608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2608</th>\n",
       "      <td>0.091652</td>\n",
       "      <td>2609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2609</th>\n",
       "      <td>0.093688</td>\n",
       "      <td>2610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2610</th>\n",
       "      <td>0.150698</td>\n",
       "      <td>2611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2611</th>\n",
       "      <td>0.181616</td>\n",
       "      <td>2612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2612</th>\n",
       "      <td>0.083787</td>\n",
       "      <td>2613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2613</th>\n",
       "      <td>0.076310</td>\n",
       "      <td>2614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2614</th>\n",
       "      <td>0.070582</td>\n",
       "      <td>2615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2615</th>\n",
       "      <td>0.086375</td>\n",
       "      <td>2616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2616</th>\n",
       "      <td>0.081392</td>\n",
       "      <td>2617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2617</th>\n",
       "      <td>0.068285</td>\n",
       "      <td>2618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2618</th>\n",
       "      <td>0.306134</td>\n",
       "      <td>2619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2619</th>\n",
       "      <td>0.076310</td>\n",
       "      <td>2620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2620</th>\n",
       "      <td>0.077928</td>\n",
       "      <td>2621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2621</th>\n",
       "      <td>0.076173</td>\n",
       "      <td>2622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2622</th>\n",
       "      <td>0.068285</td>\n",
       "      <td>2623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2623</th>\n",
       "      <td>0.068285</td>\n",
       "      <td>2624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2624</th>\n",
       "      <td>0.174178</td>\n",
       "      <td>2625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2625</th>\n",
       "      <td>0.142262</td>\n",
       "      <td>2626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2626</th>\n",
       "      <td>0.068285</td>\n",
       "      <td>2627</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2627 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      WnvPresent    Id\n",
       "0       0.068285     1\n",
       "1       0.254594     2\n",
       "2       0.079897     3\n",
       "3       0.068285     4\n",
       "4       0.068285     5\n",
       "5       0.076173     6\n",
       "6       0.089058     7\n",
       "7       0.068912     8\n",
       "8       0.101587     9\n",
       "9       0.075696    10\n",
       "10      0.083787    11\n",
       "11      0.070582    12\n",
       "12      0.068285    13\n",
       "13      0.075696    14\n",
       "14      0.068285    15\n",
       "15      0.078687    16\n",
       "16      0.104662    17\n",
       "17      0.197017    18\n",
       "18      0.095404    19\n",
       "19      0.091652    20\n",
       "20      0.083787    21\n",
       "21      0.068285    22\n",
       "22      0.147133    23\n",
       "23      0.086375    24\n",
       "24      0.077116    25\n",
       "25      0.091652    26\n",
       "26      0.181616    27\n",
       "27      0.181616    28\n",
       "28      0.083787    29\n",
       "29      0.086375    30\n",
       "...          ...   ...\n",
       "2597    0.068285  2598\n",
       "2598    0.165303  2599\n",
       "2599    0.113864  2600\n",
       "2600    0.098410  2601\n",
       "2601    0.181616  2602\n",
       "2602    0.076173  2603\n",
       "2603    0.078687  2604\n",
       "2604    0.090635  2605\n",
       "2605    0.092550  2606\n",
       "2606    0.068285  2607\n",
       "2607    0.091652  2608\n",
       "2608    0.091652  2609\n",
       "2609    0.093688  2610\n",
       "2610    0.150698  2611\n",
       "2611    0.181616  2612\n",
       "2612    0.083787  2613\n",
       "2613    0.076310  2614\n",
       "2614    0.070582  2615\n",
       "2615    0.086375  2616\n",
       "2616    0.081392  2617\n",
       "2617    0.068285  2618\n",
       "2618    0.306134  2619\n",
       "2619    0.076310  2620\n",
       "2620    0.077928  2621\n",
       "2621    0.076173  2622\n",
       "2622    0.068285  2623\n",
       "2623    0.068285  2624\n",
       "2624    0.174178  2625\n",
       "2625    0.142262  2626\n",
       "2626    0.068285  2627\n",
       "\n",
       "[2627 rows x 2 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "wnv = working_data[working_data['WnvPresent'] == 0]\n",
    "w1nv = working_data[working_data['WnvPresent'] == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total 10506\n",
      "No wnv 9955\n",
      "wnv 551\n"
     ]
    }
   ],
   "source": [
    "print ('total', len(working_data))\n",
    "print ('No wnv', len(wnv))\n",
    "print ('wnv', len(w1nv))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bootstrap_sample(sample, n=100, replicate = False, size=100):\n",
    "    ''' Generates bootstrap samples and bootstrap replicates. \n",
    "    Bootstrap sample size defaults to 100. \n",
    "    Returns bootstrap samples unless replicate function supplied.'''\n",
    "        \n",
    "    bootstraps = [list(np.random.choice(sample, size = size)) for i in range(n)]\n",
    "    \n",
    "    if replicate == False:\n",
    "        return(bootstraps)\n",
    "    else:\n",
    "        return(list(map(replicate, bootstraps)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Repeating the above process after checking the existance of Data gap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "target = working_data.WnvPresent\n",
    "features = working_data.drop(columns=['WnvPresent', \"NumMosquitos\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(features,\n",
    "                                                    target,\n",
    "                                                    stratify=target,\n",
    "                                                    random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.947554\n",
       "1    0.052446\n",
       "Name: WnvPresent, dtype: float64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "working_data.WnvPresent.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys([1, 0])\n",
      "dict_values([7466, 7466])\n"
     ]
    }
   ],
   "source": [
    "# using SMOTE to achieve a better ratio of wnv. SMOTE is a synthetic data bootstrapping.\n",
    "# ratio 0.5 will set the data to be a 66% original data to 34% new data.\n",
    "sm = SMOTE(random_state=42, ratio='auto')\n",
    "\n",
    "X_train_res, y_train_res = sm.fit_sample(X_train, y_train)\n",
    "\n",
    "from collections import Counter\n",
    "\n",
    "print(Counter(y_train_res).keys()) # 0 = Wnv not present, 1 = Wnv present.\n",
    "print(Counter(y_train_res).values()) # The values represent the keys respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ustandardization and applying all models at once\n",
    "ss  = StandardScaler()\n",
    "\n",
    "logreg = LogisticRegression()\n",
    "rfc = RandomForestClassifier(n_estimators=14, random_state=42)\n",
    "adb = AdaBoostClassifier(n_estimators=100, learning_rate=0.1, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# making pipeline for choosing best model\n",
    "pipe_logreg = Pipeline([\n",
    "    ('ss', ss),\n",
    "    ('logreg', logreg)\n",
    "])\n",
    "pipe_rfc = Pipeline([\n",
    "    ('ss', ss),\n",
    "    ('rfc', rfc),\n",
    "])\n",
    "pipe_adb = Pipeline([\n",
    "    ('ss', ss),\n",
    "    ('adb', adb)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # what are the best parameters that would predict wnv\n",
    "\n",
    "# grid_logreg = GridSearchCV(estimator=pipe_logreg,\n",
    "#                         param_grid=params_logreg,\n",
    "#                         scoring='roc_auc',\n",
    "#                         cv=5)\n",
    "# grid_rfc = GridSearchCV(estimator=pipe_rfc,\n",
    "#                         param_grid=params_rfc,\n",
    "#                         scoring='roc_auc',\n",
    "#                         cv=5)\n",
    "# grid_adb = GridSearchCV(estimator=pipe_adb,\n",
    "#                         param_grid=params_adb,\n",
    "#                         scoring='roc_auc',\n",
    "#                         cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Score Train/Test: Logistic Regression\n",
      "0.6625206244447265\n",
      "0.6688237533307956\n",
      "\n",
      "Score Train/Test: Random Forest\n",
      "0.9729661124508187\n",
      "0.9219642177388656\n",
      "\n",
      "Score Train/Test: Adaboost\n",
      "0.7678639421246352\n",
      "0.7742672249714503\n",
      "CPU times: user 2.38 s, sys: 37.5 ms, total: 2.42 s\n",
      "Wall time: 2.69 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "pipes    = [ \n",
    "    pipe_logreg, \n",
    "    pipe_rfc, \n",
    "    pipe_adb,\n",
    "]\n",
    "pipe_idx = {0: 'Logistic Regression', \n",
    "            1: 'Random Forest', \n",
    "            2: 'Adaboost'\n",
    "           }\n",
    "\n",
    "for idx, pipe in enumerate(pipes):\n",
    "    pipe.fit(X_train_res, y_train_res)\n",
    "    print('\\nScore Train/Test: %s' % pipe_idx[idx])\n",
    "    print(pipe.score(X_train, y_train))\n",
    "    print(pipe.score(X_test, y_test))\n",
    "#     print('Best params: %s' % pipe.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.1439739681792844, 'Week'),\n",
       " (0.13469505558181408, 'Month'),\n",
       " (0.10594375250841767, 'Species_CULEX PIPIENS'),\n",
       " (0.07834948186662886, 'Longitude'),\n",
       " (0.07209189536213723, 'Block'),\n",
       " (0.07170000394834501, 'AddressAccuracy'),\n",
       " (0.062337820000151756, 'Species_CULEX PIPIENS/RESTUANS'),\n",
       " (0.05984270055456977, 'Latitude'),\n",
       " (0.055052959986298555, 'Year'),\n",
       " (0.04664851479300487, 'Tmax'),\n",
       " (0.04563059057718505, 'Tmin'),\n",
       " (0.04496273511580777, 'Species_CULEX RESTUANS'),\n",
       " (0.03861015037999303, 'Day'),\n",
       " (0.037790413434139344, 'Tavg'),\n",
       " (0.0019836644409385157, 'Species_CULEX TERRITANS'),\n",
       " (0.0003534344291004285, 'Species_CULEX SALINARIUS'),\n",
       " (3.285884218373334e-05, 'Species_CULEX TARSALIS'),\n",
       " (0.0, 'Station'),\n",
       " (0.0, 'Hour')]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat = pipe_rfc.named_steps['rfc']\n",
    "\n",
    "importance = feat.feature_importances_\n",
    "\n",
    "# Daylight and months correlate to being the most important features.  Clearly based on time of year\n",
    "# In addition with latitude and longitude to target the specific\n",
    "sorted(list(zip(importance, features.keys())), reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction_data.to_csv('Prediction_data_transformed.csv', index=False)\n",
    "working_data.to_csv('working_data_transformed.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# predictions = pipe_rfc.predict(prediction_data )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2627"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sample_submission = pd.read_csv('./assets/sampleSubmission.csv')\n",
    "# sample_submission['WnvPresent'] = predictions\n",
    "# # sample_submission.to_csv('submission_randomforest6.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(Counter(predictions).keys()) # 0 = Wnv not present, 1 = Wnv present.\n",
    "# print(Counter(predictions).values()) # The values represent the keys respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    " #add additional package\n",
    "import os\n",
    "from scipy import stats\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.linear_model import LogisticRegressionCV\n",
    "from sklearn.metrics import roc_auc_score, confusion_matrix\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = working_data.WnvPresent\n",
    "X = working_data.drop(columns=['WnvPresent', \"NumMosquitos\"])\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,\n",
    "                                                    y,\n",
    "                                                    stratify=y,\n",
    "                                                    random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6303, 19)\n",
      "(2101, 19)\n",
      "(2102, 19)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=.4) # Using an initial split of 60/40\n",
    "X_val, X_test, y_val, y_test     = train_test_split(X_test, y_test, test_size=.5) # Splitting the test set\n",
    "\n",
    "print(X_train.shape)\n",
    "print(X_val.shape)\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5849595430747263"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logreg = LogisticRegressionCV(class_weight = 'balanced', penalty='l1', solver='liblinear', scoring = 'roc_auc')\n",
    "logreg.fit(X_train,y_train)\n",
    "logreg.score(X_val, y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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>preds</th>\n",
       "      <th>probability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.524218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.531343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>0.589183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "      <td>0.501635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1</td>\n",
       "      <td>0.824965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1</td>\n",
       "      <td>0.643224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1</td>\n",
       "      <td>0.577002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1</td>\n",
       "      <td>0.639590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1</td>\n",
       "      <td>0.701548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1</td>\n",
       "      <td>0.562743</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    preds  probability\n",
       "1       1     0.524218\n",
       "2       1     0.531343\n",
       "9       1     0.589183\n",
       "13      1     0.501635\n",
       "14      1     0.824965\n",
       "17      1     0.643224\n",
       "18      1     0.577002\n",
       "19      1     0.639590\n",
       "21      1     0.701548\n",
       "22      1     0.562743"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = pd.DataFrame({'preds':logreg.predict(X_val), 'probability':[x[1] for x in logreg.predict_proba(X_val)]})\n",
    "predictions[predictions['preds'] == True].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6936936936936936"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_val, y_score = predictions['probability'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1154  844]\n",
      " [  28   75]]\n"
     ]
    }
   ],
   "source": [
    "CM = confusion_matrix(y_val, y_pred = logreg.predict(X_val))\n",
    "print(CM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "TN = CM[0][0]\n",
    "TP = CM[1][1]\n",
    "FN = CM[1][0]\n",
    "FP = CM[0][1]\n",
    "\n",
    "# Sensitivity, or true positive rate\n",
    "_ = TP / (TP + FN)\n",
    "\n",
    "# False positives per true positive\n",
    "_ = FP / TP\n",
    "\n",
    "# # Precision, or positive predictive value\n",
    "_ = TP / (TP + FP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_with_threshold(probas, threshold):\n",
    "    '''Returns predictions of the positive class using a given classification\n",
    "    threshold. Assumes the positive class is the second (index 1).'''\n",
    "    prob_positive = [x[1] for x in probas]\n",
    "    preds         = [1 if x > threshold else 0 for x in prob_positive]\n",
    "    return(preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1792  206]\n",
      " [  85   18]]\n"
     ]
    }
   ],
   "source": [
    "CM = confusion_matrix(y_val, y_pred = predict_with_threshold(logreg.predict_proba(X_val), .7))\n",
    "print(CM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "TN = CM[0][0]\n",
    "TP = CM[1][1]\n",
    "FN = CM[1][0]\n",
    "FP = CM[0][1]\n",
    "\n",
    "# Sensitivity, or true positive rate\n",
    "_ = TP / (TP + FN)\n",
    "\n",
    "# False positives per true positive\n",
    "_ = FP / TP\n",
    "\n",
    "# # Precision, or positive predictive value\n",
    "_ = TP / (TP + FP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "logreg_coefficients = pd.DataFrame({'Variable':X_train.columns,'Coefficients':logreg.coef_[0]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "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>Variable</th>\n",
       "      <th>Coefficients</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Month</td>\n",
       "      <td>2.734254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Latitude</td>\n",
       "      <td>0.268518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Tavg</td>\n",
       "      <td>0.119805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AddressAccuracy</td>\n",
       "      <td>0.097889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Day</td>\n",
       "      <td>0.089391</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Variable  Coefficients\n",
       "6             Month      2.734254\n",
       "1          Latitude      0.268518\n",
       "12             Tavg      0.119805\n",
       "3   AddressAccuracy      0.097889\n",
       "8               Day      0.089391"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logreg_coefficients.sort_values(by='Coefficients', ascending=False).head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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>Variable</th>\n",
       "      <th>Coefficients</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Station</td>\n",
       "      <td>-20.015796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Species_CULEX TERRITANS</td>\n",
       "      <td>-12.275491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Species_CULEX SALINARIUS</td>\n",
       "      <td>-11.017101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Species_CULEX TARSALIS</td>\n",
       "      <td>-8.513104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Species_CULEX RESTUANS</td>\n",
       "      <td>-1.363158</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Variable  Coefficients\n",
       "9                    Station    -20.015796\n",
       "18   Species_CULEX TERRITANS    -12.275491\n",
       "16  Species_CULEX SALINARIUS    -11.017101\n",
       "17    Species_CULEX TARSALIS     -8.513104\n",
       "15    Species_CULEX RESTUANS     -1.363158"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logreg_coefficients.sort_values(by='Coefficients', ascending=True).head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9471680152308425"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest = RandomForestClassifier()\n",
    "forest.fit(X_train, y_train)\n",
    "forest.score(X_val, y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7271300426640233"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = pd.DataFrame({'preds':forest.predict(X_val), 'probability':[x[1] for x in forest.predict_proba(X_val)]})\n",
    "\n",
    "roc_auc_score(y_val, y_score = predictions['probability'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Re-initialize the model with a few hyperparameters set.\n",
    "forest = RandomForestClassifier(max_depth=150, max_features='auto', class_weight='balanced_subsample')\n",
    "\n",
    "# Then create a grid of parameter values to try:\n",
    "param_grid = {'n_estimators':[300,500]} #,'max_features':['auto',None], 'max_depth':[150, 200]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=None, error_score='raise',\n",
       "       estimator=RandomForestClassifier(bootstrap=True, class_weight='balanced_subsample',\n",
       "            criterion='gini', max_depth=150, max_features='auto',\n",
       "            max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "            min_impurity_split=None, min_samples_leaf=1,\n",
       "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "            n_estimators=10, n_jobs=1, oob_score=False, random_state=None,\n",
       "            verbose=0, warm_start=False),\n",
       "       fit_params=None, iid=True, n_jobs=-1,\n",
       "       param_grid={'n_estimators': [300, 500]}, pre_dispatch='2*n_jobs',\n",
       "       refit=True, return_train_score='warn', scoring='roc_auc', verbose=0)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest_grid = GridSearchCV(forest, param_grid=param_grid, scoring='roc_auc', n_jobs = -1)\n",
    "forest_grid.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight='balanced_subsample',\n",
       "            criterion='gini', max_depth=150, max_features='auto',\n",
       "            max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "            min_impurity_split=None, min_samples_leaf=1,\n",
       "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "            n_estimators=500, n_jobs=1, oob_score=False, random_state=None,\n",
       "            verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the hyperparameter values we already established by grid searching incrementally.\n",
    "forest_grid.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9262256068538791"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest = forest_grid.best_estimator_\n",
    "forest.fit(X_train, y_train)\n",
    "forest.score(X_val, y_val)\n",
    "\n",
    "# When we train and evaluate the accuracy of our model using the best\n",
    "# hyperparameters, we observe an accuracy score "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "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>preds</th>\n",
       "      <th>probability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>0.505885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1</td>\n",
       "      <td>0.596716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>1</td>\n",
       "      <td>0.560944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>1</td>\n",
       "      <td>0.691328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>1</td>\n",
       "      <td>0.513263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>1</td>\n",
       "      <td>0.611893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>1</td>\n",
       "      <td>0.812407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>1</td>\n",
       "      <td>0.591940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>1</td>\n",
       "      <td>0.846645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>1</td>\n",
       "      <td>0.782578</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     preds  probability\n",
       "7        1     0.505885\n",
       "23       1     0.596716\n",
       "48       1     0.560944\n",
       "49       1     0.691328\n",
       "53       1     0.513263\n",
       "61       1     0.611893\n",
       "100      1     0.812407\n",
       "103      1     0.591940\n",
       "115      1     0.846645\n",
       "128      1     0.782578"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = pd.DataFrame({'preds':forest.predict(X_val), 'probability':[x[1] for x in forest.predict_proba(X_val)]})\n",
    "predictions[predictions['preds'] == True].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7676147020807215"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# And the AUC-ROC score Just based on performance, the optimized random forest model \n",
    "roc_auc_score(y_val, y_score = predictions['probability'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1923   75]\n",
      " [  80   23]]\n"
     ]
    }
   ],
   "source": [
    "# We can do the same analysis for random forests that we did for the logistic regression,\n",
    "# which is what we'll do in order to help decide which model is best:\n",
    "\n",
    "CM = confusion_matrix(y_val, y_pred = forest.predict(X_val))\n",
    "print(CM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.22330097087378642 3.260869565217391 0.23469387755102042\n"
     ]
    }
   ],
   "source": [
    "TN = CM[0][0]\n",
    "TP = CM[1][1]\n",
    "FN = CM[1][0]\n",
    "FP = CM[0][1]\n",
    "\n",
    "# Sensitivity, or true positive rate\n",
    "sens = TP / (TP + FN)\n",
    "\n",
    "# False positives per true positive\n",
    "fp_tp = FP / TP\n",
    "\n",
    "# Precision, or positive predictive value\n",
    "prec = TP / (TP + FP)\n",
    "\n",
    "print(sens, fp_tp, prec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1836  162]\n",
      " [  63   40]]\n"
     ]
    }
   ],
   "source": [
    "# With a lower classification threshold, the model performs more \n",
    "# closely to the ogistic regression's\n",
    "\n",
    "CM = confusion_matrix(y_val, y_pred = predict_with_threshold(forest.predict_proba(X_val), .25))\n",
    "print(CM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.3883495145631068 4.05 0.19801980198019803\n"
     ]
    }
   ],
   "source": [
    "TN = CM[0][0]\n",
    "TP = CM[1][1]\n",
    "FN = CM[1][0]\n",
    "FP = CM[0][1]\n",
    "\n",
    "# Sensitivity, or true positive rate\n",
    "sens = TP / (TP + FN)\n",
    "\n",
    "# False positives per true positive\n",
    "fp_tp = FP / TP\n",
    "\n",
    "# Precision, or positive predictive value\n",
    "prec = TP / (TP + FP)\n",
    "\n",
    "print(sens, fp_tp, prec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_predictions = pd.DataFrame({'y_true':y_test,\n",
    "                                 'LR_prob':[x[1] for x in logreg.predict_proba(X_test)],\n",
    "                                 'Forest_prob':[x[1] for x in forest.predict_proba(X_test)]})\n",
    "# Visually inspecting the results of each model we can see that the random forest model\n",
    "# is more likely to classify non wnv."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression Score on Test Set:0.7407842785355349\n",
      "Random Forest Score on Test Set:0.8095163316582914\n"
     ]
    }
   ],
   "source": [
    "print('Logistic Regression Score on Test Set:' + str(roc_auc_score(y_test, y_score = test_predictions['LR_prob'])))\n",
    "print('Random Forest Score on Test Set:' + str(roc_auc_score(y_test, y_score = test_predictions['Forest_prob'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>Variables</th>\n",
       "      <th>Importances</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Longitude</td>\n",
       "      <td>0.145159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Week</td>\n",
       "      <td>0.134970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Latitude</td>\n",
       "      <td>0.127043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Block</td>\n",
       "      <td>0.105998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Month</td>\n",
       "      <td>0.078383</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Variables  Importances\n",
       "2  Longitude     0.145159\n",
       "5       Week     0.134970\n",
       "1   Latitude     0.127043\n",
       "0      Block     0.105998\n",
       "6      Month     0.078383"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To understand the effect of different features in a random forest model, we have to \n",
    "# compute feature importances:\n",
    "Forest_Importances = pd.DataFrame({'Variables':X_train.columns,'Importances':forest.feature_importances_})\n",
    "Forest_Importances.sort_values(by='Importances', ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>Variables</th>\n",
       "      <th>Importances</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Station</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Hour</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Species_CULEX TARSALIS</td>\n",
       "      <td>0.000023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Species_CULEX SALINARIUS</td>\n",
       "      <td>0.000957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Species_CULEX TERRITANS</td>\n",
       "      <td>0.004000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Variables  Importances\n",
       "9                    Station     0.000000\n",
       "4                       Hour     0.000000\n",
       "17    Species_CULEX TARSALIS     0.000023\n",
       "16  Species_CULEX SALINARIUS     0.000957\n",
       "18   Species_CULEX TERRITANS     0.004000"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Forest_Importances.sort_values(by='Importances', ascending=True).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "# summary summarize model performance:\n",
    "\n",
    "Final_Model_Performance = pd.DataFrame(columns = ['Threshold','True Positive Rate',\n",
    "                                        'False Positives Per True Positive'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "for threshold in range(10,95,5):\n",
    "    CM = confusion_matrix(y_test, y_pred = predict_with_threshold(forest.predict_proba(X_test), threshold/100))\n",
    "\n",
    "    TP = CM[1][1]\n",
    "    FN = CM[1][0]\n",
    "    FP = CM[0][1]\n",
    "\n",
    "    sens = TP / (TP + FN)\n",
    "    try:\n",
    "        fp_tp = FP / TP\n",
    "    except:\n",
    "        fp_tp = 0\n",
    "    Final_Model_Performance.loc[Final_Model_Performance.shape[0]] = [threshold, sens, fp_tp] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "Final_Model_Performance = Final_Model_Performance.set_index('Threshold')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kirosgebremariam/anaconda3/envs/dsi/lib/python3.6/site-packages/matplotlib/figure.py:2267: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  warnings.warn(\"This figure includes Axes that are not compatible \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 568,
       "width": 712
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = Final_Model_Performance.plot()\n",
    "_ = plt.title('Model Performance :\\nHow Many False Positives Are You Comfortable  to have wnv?')\n",
    "_ = plt.xlabel('Classification Threshold')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9466920514040933\n",
      "0.9467174119885823\n"
     ]
    }
   ],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.linear_model import LogisticRegression\n",
    " \n",
    "\n",
    "logreg = LogisticRegression()\n",
    "\n",
    "log_pipe = Pipeline([ \n",
    "    ('log',logreg), \n",
    "])\n",
    "\n",
    "log_pipe.fit(X_train, y_train)\n",
    "print(log_pipe.score(X_train,y_train))\n",
    "print(log_pipe.score(X_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tn: 1990, fp: 0, fn: 112, tp: 0\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "y_pred = log_pipe.predict(X_test)\n",
    "tn, fp, fn, tp = confusion_matrix(y_test, y_pred).ravel()\n",
    "print(\"tn: {}, fp: {}, fn: {}, tp: {}\".format (tn, fp, fn, tp))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "# knn takes forever"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9429115128449096"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler \n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "\n",
    "knn = KNeighborsClassifier()\n",
    "\n",
    "knn_c_pipe = Pipeline([ \n",
    "    ('knn',knn), \n",
    "])\n",
    "knn_c_pipe.fit(X_train, y_train)\n",
    "knn_c_pipe.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['memory', 'steps', 'knn', 'knn__algorithm', 'knn__leaf_size', 'knn__metric', 'knn__metric_params', 'knn__n_jobs', 'knn__n_neighbors', 'knn__p', 'knn__weights'])"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_c_pipe.get_params().keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Decision Tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9466920514040933\n",
      "{'max_depth': 1, 'max_features': 1}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "dt_c = DecisionTreeClassifier(max_depth=5)\n",
    "\n",
    "\n",
    "params = {\n",
    "    'max_depth':[1, 2, 3, 5, 7, 10],\n",
    "    'max_features':[1, 2,  3, 5]\n",
    "}\n",
    "\n",
    "\n",
    "gs_dt = GridSearchCV(estimator = dt_c, param_grid = params)\n",
    "gs_dt.fit(X_train, y_train)\n",
    "print(gs_dt.best_score_)\n",
    "print(gs_dt.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['class_weight', 'criterion', 'max_depth', 'max_features', 'max_leaf_nodes', 'min_impurity_decrease', 'min_impurity_split', 'min_samples_leaf', 'min_samples_split', 'min_weight_fraction_leaf', 'presort', 'random_state', 'splitter'])"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_c.get_params().keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tn: 1990, fp: 0, fn: 112, tp: 0\n"
     ]
    }
   ],
   "source": [
    "y_pred = gs_dt.predict(X_test)\n",
    "tn, fp, fn, tp = confusion_matrix(y_test, y_pred).ravel()\n",
    "print(\"tn: {}, fp: {}, fn: {}, tp: {}\".format (tn, fp, fn, tp))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9466920514040933\n",
      "{'max_depth': 1, 'max_features': 3, 'n_estimators': 3}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rf_c = RandomForestClassifier()\n",
    "\n",
    "\n",
    "params = {\n",
    "    'n_estimators': [3,5,7,10], \n",
    "    'max_features': [3, 5, 7],\n",
    "    'max_depth': [1, 2 , 3, 4]\n",
    "}\n",
    "gs_rf = GridSearchCV(rf_c, param_grid=params)\n",
    "gs_rf.fit(X_train, y_train)\n",
    "print(gs_rf.best_score_)\n",
    "print(gs_rf.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['bootstrap', 'class_weight', 'criterion', 'max_depth', 'max_features', 'max_leaf_nodes', 'min_impurity_decrease', 'min_impurity_split', 'min_samples_leaf', 'min_samples_split', 'min_weight_fraction_leaf', 'n_estimators', 'n_jobs', 'oob_score', 'random_state', 'verbose', 'warm_start'])"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_c.get_params().keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tn: 1990, fp: 0, fn: 112, tp: 0\n"
     ]
    }
   ],
   "source": [
    "y_pred = gs_rf.predict(X_test)\n",
    "tn, fp, fn, tp = confusion_matrix(y_test, y_pred).ravel()\n",
    "print(\"tn: {}, fp: {}, fn: {}, tp: {}\".format (tn, fp, fn, tp))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sample_submission = pd.read_csv('./assets/sampleSubmission.csv')\n",
    "# sample_submission['WnvPresent'] = predictions\n",
    "# sample_submission.to_csv('submission_randomforest.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### AdaBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9466920514040933\n",
      "{'n_estimators': 1}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "ada_r = AdaBoostClassifier()\n",
    "\n",
    "params = {\n",
    "    'n_estimators':range(1,7)\n",
    "}\n",
    "gs_ada = GridSearchCV(ada_r, param_grid = params)\n",
    "gs_ada.fit(X_train, y_train)\n",
    "print(gs_ada.best_score_)\n",
    "print(gs_ada.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['cv', 'error_score', 'estimator__algorithm', 'estimator__base_estimator', 'estimator__learning_rate', 'estimator__n_estimators', 'estimator__random_state', 'estimator', 'fit_params', 'iid', 'n_jobs', 'param_grid', 'pre_dispatch', 'refit', 'return_train_score', 'scoring', 'verbose'])"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs_ada.get_params().keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tn: 1990, fp: 0, fn: 112, tp: 0\n"
     ]
    }
   ],
   "source": [
    "y_pred = gs_ada.predict(X_test)\n",
    "tn, fp, fn, tp = confusion_matrix(y_test, y_pred).ravel()\n",
    "print(\"tn: {}, fp: {}, fn: {}, tp: {}\".format (tn, fp, fn, tp))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Support Vector Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9467174119885823\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "clf_c = SVC(kernel='linear')\n",
    "\n",
    "clf_c_pipe = Pipeline([ \n",
    "    ('clf',clf_c), \n",
    "])\n",
    "\n",
    "clf_c_pipe.fit(X_train, y_train)\n",
    "print(clf_c_pipe.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['memory', 'steps', 'clf', 'clf__C', 'clf__cache_size', 'clf__class_weight', 'clf__coef0', 'clf__decision_function_shape', 'clf__degree', 'clf__gamma', 'clf__kernel', 'clf__max_iter', 'clf__probability', 'clf__random_state', 'clf__shrinking', 'clf__tol', 'clf__verbose'])"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_c_pipe.get_params().keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tn: 1990, fp: 0, fn: 112, tp: 0\n"
     ]
    }
   ],
   "source": [
    "y_pred = clf_c_pipe.predict(X_test)\n",
    "tn, fp, fn, tp = confusion_matrix(y_test, y_pred).ravel()\n",
    "print(\"tn: {}, fp: {}, fn: {}, tp: {}\".format (tn, fp, fn, tp))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### CNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kirosgebremariam/anaconda3/envs/dsi/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "ss = StandardScaler()\n",
    "X_train = ss.fit_transform(X_train)\n",
    "X_test = ss.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 6303 samples, validate on 2102 samples\n",
      "Epoch 1/30\n",
      "6303/6303 [==============================] - 1s 142us/step - loss: 0.4609 - acc: 0.8068 - val_loss: 0.2609 - val_acc: 0.9467\n",
      "Epoch 2/30\n",
      "6303/6303 [==============================] - 1s 96us/step - loss: 0.2176 - acc: 0.9467 - val_loss: 0.2002 - val_acc: 0.9467\n",
      "Epoch 3/30\n",
      "6303/6303 [==============================] - 1s 98us/step - loss: 0.1893 - acc: 0.9467 - val_loss: 0.1878 - val_acc: 0.9467\n",
      "Epoch 4/30\n",
      "6303/6303 [==============================] - 1s 94us/step - loss: 0.1819 - acc: 0.9467 - val_loss: 0.1825 - val_acc: 0.9467\n",
      "Epoch 5/30\n",
      "6303/6303 [==============================] - 1s 97us/step - loss: 0.1782 - acc: 0.9467 - val_loss: 0.1790 - val_acc: 0.9467\n",
      "Epoch 6/30\n",
      "6303/6303 [==============================] - 1s 91us/step - loss: 0.1757 - acc: 0.9469 - val_loss: 0.1770 - val_acc: 0.9467\n",
      "Epoch 7/30\n",
      "6303/6303 [==============================] - 1s 93us/step - loss: 0.1742 - acc: 0.9472 - val_loss: 0.1750 - val_acc: 0.9467\n",
      "Epoch 8/30\n",
      "6303/6303 [==============================] - 1s 92us/step - loss: 0.1727 - acc: 0.9470 - val_loss: 0.1739 - val_acc: 0.9467\n",
      "Epoch 9/30\n",
      "6303/6303 [==============================] - 1s 89us/step - loss: 0.1713 - acc: 0.9467 - val_loss: 0.1735 - val_acc: 0.9467\n",
      "Epoch 10/30\n",
      "6303/6303 [==============================] - 1s 86us/step - loss: 0.1704 - acc: 0.9472 - val_loss: 0.1718 - val_acc: 0.9467\n",
      "Epoch 11/30\n",
      "6303/6303 [==============================] - 1s 86us/step - loss: 0.1694 - acc: 0.9472 - val_loss: 0.1707 - val_acc: 0.9467\n",
      "Epoch 12/30\n",
      "6303/6303 [==============================] - 1s 88us/step - loss: 0.1686 - acc: 0.9472 - val_loss: 0.1702 - val_acc: 0.9467\n",
      "Epoch 13/30\n",
      "6303/6303 [==============================] - 1s 86us/step - loss: 0.1680 - acc: 0.9472 - val_loss: 0.1695 - val_acc: 0.9467\n",
      "Epoch 14/30\n",
      "6303/6303 [==============================] - 1s 90us/step - loss: 0.1673 - acc: 0.9469 - val_loss: 0.1688 - val_acc: 0.9467\n",
      "Epoch 15/30\n",
      "6303/6303 [==============================] - 1s 85us/step - loss: 0.1667 - acc: 0.9465 - val_loss: 0.1682 - val_acc: 0.9467\n",
      "Epoch 16/30\n",
      "6303/6303 [==============================] - 1s 86us/step - loss: 0.1661 - acc: 0.9465 - val_loss: 0.1675 - val_acc: 0.9467\n",
      "Epoch 17/30\n",
      "6303/6303 [==============================] - 1s 87us/step - loss: 0.1656 - acc: 0.9467 - val_loss: 0.1671 - val_acc: 0.9467\n",
      "Epoch 18/30\n",
      "6303/6303 [==============================] - 1s 88us/step - loss: 0.1650 - acc: 0.9473 - val_loss: 0.1664 - val_acc: 0.9467\n",
      "Epoch 19/30\n",
      "6303/6303 [==============================] - 1s 87us/step - loss: 0.1646 - acc: 0.9469 - val_loss: 0.1665 - val_acc: 0.9467\n",
      "Epoch 20/30\n",
      "6303/6303 [==============================] - 1s 85us/step - loss: 0.1641 - acc: 0.9470 - val_loss: 0.1660 - val_acc: 0.9467\n",
      "Epoch 21/30\n",
      "6303/6303 [==============================] - 1s 93us/step - loss: 0.1638 - acc: 0.9469 - val_loss: 0.1660 - val_acc: 0.9467\n",
      "Epoch 22/30\n",
      "6303/6303 [==============================] - 1s 81us/step - loss: 0.1634 - acc: 0.9470 - val_loss: 0.1659 - val_acc: 0.9467\n",
      "Epoch 23/30\n",
      "6303/6303 [==============================] - 1s 89us/step - loss: 0.1630 - acc: 0.9475 - val_loss: 0.1660 - val_acc: 0.9467\n",
      "Epoch 24/30\n",
      "6303/6303 [==============================] - 1s 89us/step - loss: 0.1625 - acc: 0.9465 - val_loss: 0.1651 - val_acc: 0.9467\n",
      "Epoch 25/30\n",
      "6303/6303 [==============================] - 1s 86us/step - loss: 0.1623 - acc: 0.9470 - val_loss: 0.1658 - val_acc: 0.9448\n",
      "Epoch 26/30\n",
      "6303/6303 [==============================] - 1s 90us/step - loss: 0.1622 - acc: 0.9467 - val_loss: 0.1646 - val_acc: 0.9467\n",
      "Epoch 27/30\n",
      "6303/6303 [==============================] - 1s 84us/step - loss: 0.1616 - acc: 0.9470 - val_loss: 0.1645 - val_acc: 0.9467\n",
      "Epoch 28/30\n",
      "6303/6303 [==============================] - 1s 85us/step - loss: 0.1615 - acc: 0.9465 - val_loss: 0.1646 - val_acc: 0.9458\n",
      "Epoch 29/30\n",
      "6303/6303 [==============================] - 1s 85us/step - loss: 0.1613 - acc: 0.9467 - val_loss: 0.1637 - val_acc: 0.9467\n",
      "Epoch 30/30\n",
      "6303/6303 [==============================] - 1s 86us/step - loss: 0.1610 - acc: 0.9469 - val_loss: 0.1636 - val_acc: 0.9467\n"
     ]
    }
   ],
   "source": [
    "from keras import regularizers\n",
    "import numpy as np\n",
    "from keras.layers import Dropout\n",
    "from keras.callbacks import EarlyStopping\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "model= Sequential()\n",
    "\n",
    "n_input = X_train.shape[1]\n",
    "n_hidden = n_input\n",
    "n_output = 1\n",
    "\n",
    "model.add(Dense(n_hidden, input_dim=n_input, activation='relu'))\n",
    "model.add(Dense(n_output, activation='sigmoid'))\n",
    "\n",
    "model.compile(loss='binary_crossentropy', optimizer='adam', \n",
    "                metrics=['acc'])\n",
    "\n",
    "#early_stop = EarlyStopping(monitor='val_loss', min_delta=0) \n",
    "\n",
    "\n",
    "history = model.fit(X_train, y_train, validation_data=(X_test, y_test),\n",
    "                      epochs=30, batch_size=None)\n",
    "                     # callbacks=[early_stop])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a2ee77fd0>"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kirosgebremariam/anaconda3/envs/dsi/lib/python3.6/site-packages/matplotlib/figure.py:2267: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  warnings.warn(\"This figure includes Axes that are not compatible \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 568,
       "width": 712
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_loss_2 = history.history['loss']\n",
    "test_loss_2 = history.history['val_loss']\n",
    "plt.plot(train_loss_2, label='Training loss')\n",
    "plt.plot(test_loss_2, label='Testing loss')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### MODEL EVALUATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "def evaluate (X_test, y_test, model):\n",
    "    y_pred = model.predict(X_test)\n",
    "    tn, fp, fn, tp = confusion_matrix(y_test, y_pred).ravel()\n",
    "    return(\"tn: {}, fp: {}, fn: {}, tp: {}\".format (tn, fp, fn, tp))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tn: 738, fp: 1252, fn: 9, tp: 103\n",
      "tn: 1990, fp: 0, fn: 112, tp: 0\n",
      "tn: 1990, fp: 0, fn: 112, tp: 0\n",
      "tn: 1990, fp: 0, fn: 112, tp: 0\n",
      "tn: 1990, fp: 0, fn: 112, tp: 0\n"
     ]
    }
   ],
   "source": [
    "models = [logreg, gs_dt, gs_rf, gs_ada, clf_c_pipe]\n",
    "for model in models:\n",
    "    print (evaluate (X_test, y_test, model))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  Final words "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "The training scores and Kaggle submission scores correlated fairly well, although the Kaggle submission score usually about a tenth of a point lower. This is an indication that model results probably have some validity. Possible ways of improving the model: include features for cumulative weather conditions; these could include average temperature over the past week, average precipitation over the past week, and number of days without preciptiation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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