intro_to_python_data_science_on_aws.ipynb

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  {
   "cell_type": "code",
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   "outputs": [
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     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pandas in /usr/local/lib/python2.7/dist-packages\n",
      "Requirement already satisfied: pytz>=2011k in /usr/local/lib/python2.7/dist-packages (from pandas)\n",
      "Requirement already satisfied: python-dateutil in /usr/local/lib/python2.7/dist-packages (from pandas)\n",
      "Requirement already satisfied: numpy>=1.7.0 in /usr/local/lib/python2.7/dist-packages (from pandas)\n",
      "Requirement already satisfied: seaborn in /usr/local/lib/python2.7/dist-packages\n",
      "Requirement already satisfied: sklearn in /usr/local/lib/python2.7/dist-packages\n",
      "Requirement already satisfied: scikit-learn in /usr/local/lib/python2.7/dist-packages (from sklearn)\n",
      "Requirement already satisfied: numpy in /usr/local/lib/python2.7/dist-packages\n",
      "shape of the data frame(150, 5)\n",
      "species\n",
      "setosa        50\n",
      "versicolor    50\n",
      "virginica     50\n",
      "dtype: int64\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5.7</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>5.2</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>5.5</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>5.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>4.4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>4.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>4.4</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>5.3</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    sepal_length  sepal_width  petal_length  petal_width species\n",
       "0            5.1          3.5           1.4          0.2  setosa\n",
       "1            4.9          3.0           1.4          0.2  setosa\n",
       "2            4.7          3.2           1.3          0.2  setosa\n",
       "3            4.6          3.1           1.5          0.2  setosa\n",
       "4            5.0          3.6           1.4          0.2  setosa\n",
       "5            5.4          3.9           1.7          0.4  setosa\n",
       "6            4.6          3.4           1.4          0.3  setosa\n",
       "7            5.0          3.4           1.5          0.2  setosa\n",
       "8            4.4          2.9           1.4          0.2  setosa\n",
       "9            4.9          3.1           1.5          0.1  setosa\n",
       "10           5.4          3.7           1.5          0.2  setosa\n",
       "11           4.8          3.4           1.6          0.2  setosa\n",
       "12           4.8          3.0           1.4          0.1  setosa\n",
       "13           4.3          3.0           1.1          0.1  setosa\n",
       "14           5.8          4.0           1.2          0.2  setosa\n",
       "15           5.7          4.4           1.5          0.4  setosa\n",
       "16           5.4          3.9           1.3          0.4  setosa\n",
       "17           5.1          3.5           1.4          0.3  setosa\n",
       "18           5.7          3.8           1.7          0.3  setosa\n",
       "19           5.1          3.8           1.5          0.3  setosa\n",
       "20           5.4          3.4           1.7          0.2  setosa\n",
       "21           5.1          3.7           1.5          0.4  setosa\n",
       "22           4.6          3.6           1.0          0.2  setosa\n",
       "23           5.1          3.3           1.7          0.5  setosa\n",
       "24           4.8          3.4           1.9          0.2  setosa\n",
       "25           5.0          3.0           1.6          0.2  setosa\n",
       "26           5.0          3.4           1.6          0.4  setosa\n",
       "27           5.2          3.5           1.5          0.2  setosa\n",
       "28           5.2          3.4           1.4          0.2  setosa\n",
       "29           4.7          3.2           1.6          0.2  setosa\n",
       "30           4.8          3.1           1.6          0.2  setosa\n",
       "31           5.4          3.4           1.5          0.4  setosa\n",
       "32           5.2          4.1           1.5          0.1  setosa\n",
       "33           5.5          4.2           1.4          0.2  setosa\n",
       "34           4.9          3.1           1.5          0.2  setosa\n",
       "35           5.0          3.2           1.2          0.2  setosa\n",
       "36           5.5          3.5           1.3          0.2  setosa\n",
       "37           4.9          3.6           1.4          0.1  setosa\n",
       "38           4.4          3.0           1.3          0.2  setosa\n",
       "39           5.1          3.4           1.5          0.2  setosa\n",
       "40           5.0          3.5           1.3          0.3  setosa\n",
       "41           4.5          2.3           1.3          0.3  setosa\n",
       "42           4.4          3.2           1.3          0.2  setosa\n",
       "43           5.0          3.5           1.6          0.6  setosa\n",
       "44           5.1          3.8           1.9          0.4  setosa\n",
       "45           4.8          3.0           1.4          0.3  setosa\n",
       "46           5.1          3.8           1.6          0.2  setosa\n",
       "47           4.6          3.2           1.4          0.2  setosa\n",
       "48           5.3          3.7           1.5          0.2  setosa\n",
       "49           5.0          3.3           1.4          0.2  setosa"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#this code introduces you to machine learning training and inference (predictions) in Python on AWS\n",
    "\n",
    "#start a line with '!' to write to the command line instead. \n",
    "#This allows you to install packages to the EC2 instance from within jupyter\n",
    "!pip install pandas\n",
    "!pip install seaborn\n",
    "!pip install sklearn\n",
    "!pip install numpy\n",
    "\n",
    "#import the packages that you installed\n",
    "import pandas as pd\n",
    "import seaborn.apionly as sns\n",
    "import sklearn\n",
    "from sklearn.model_selection import train_test_split\n",
    "import numpy as np\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import confusion_matrix, roc_curve, auc\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import randint as sp_randint\n",
    "import time\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "#load a default seaborn dataset, display simple stats about data size, and then print the data's head\n",
    "df = pd.DataFrame(sns.load_dataset('iris'))\n",
    "print 'shape of the data frame'+str(df.shape)\n",
    "print df.groupby(['species']).size()\n",
    "df.head(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7ff50fab96d0>"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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n3jyGbfct9CuiQgxzvEl7SrMkS9sl9SpZypPzgSNLjwLcR5+c+basQdSAnD4C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\nNj744INpv/3tb/UFBQVnNm/eXH7zzTeP1el0yMrKU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U2ffiB7lSG2o0c7cI0g3GU46Vh/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\niqIoiqIoSUWNDkVRFEVRFEVRksr/B4m4RCriMiVXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff50fab9450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#showoff some of what seaborn can do\n",
    "sns.pairplot(df, hue=\"species\", diag_kind=\"kde\",markers=['o','x','+'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y\n",
      "0    100\n",
      "1     50\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#let's group setosa and verginica together for the sake of this machine learning exercise\n",
    "df['y']= np.where(df['species']=='versicolor', 1,0)\n",
    "print df.groupby(['y']).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the number in training set: 112\n",
      "the number in test set: 38\n"
     ]
    }
   ],
   "source": [
    "#split into train and test\n",
    "X=df.drop('species',1).drop('y',1)\n",
    "y=df['y']\n",
    "RANDOM_STATE=0\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=RANDOM_STATE)\n",
    "print ('the number in training set: '+str(len(X_train)))\n",
    "print ('the number in test set: '+str(len(X_test)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomizedSearchCV examined 30 candidate parameter settings.\n",
      "Model with rank: 1\n",
      "Mean OOB score: 0.973 (std: 0.022)\n",
      "Parameters: {'bootstrap': False, 'min_samples_leaf': 2, 'min_samples_split': 3, 'criterion': 'gini', 'max_features': 3, 'max_depth': None}\n",
      "\n",
      "Model with rank: 2\n",
      "Mean OOB score: 0.964 (std: 0.025)\n",
      "Parameters: {'bootstrap': False, 'min_samples_leaf': 2, 'min_samples_split': 2, 'criterion': 'entropy', 'max_features': 2, 'max_depth': None}\n",
      "\n",
      "Model with rank: 2\n",
      "Mean OOB score: 0.964 (std: 0.025)\n",
      "Parameters: {'bootstrap': False, 'min_samples_leaf': 1, 'min_samples_split': 3, 'criterion': 'entropy', 'max_features': 3, 'max_depth': 3}\n",
      "\n",
      "Model with rank: 4\n",
      "Mean OOB score: 0.955 (std: 0.033)\n",
      "Parameters: {'bootstrap': True, 'min_samples_leaf': 1, 'min_samples_split': 2, 'criterion': 'gini', 'max_features': 3, 'max_depth': 3}\n",
      "\n",
      "Model with rank: 4\n",
      "Mean OOB score: 0.955 (std: 0.033)\n",
      "Parameters: {'bootstrap': False, 'min_samples_leaf': 3, 'min_samples_split': 4, 'criterion': 'gini', 'max_features': 2, 'max_depth': None}\n",
      "\n",
      "Model with rank: 4\n",
      "Mean OOB score: 0.955 (std: 0.033)\n",
      "Parameters: {'bootstrap': True, 'min_samples_leaf': 2, 'min_samples_split': 4, 'criterion': 'entropy', 'max_features': 2, 'max_depth': 3}\n",
      "\n",
      "Model with rank: 4\n",
      "Mean OOB score: 0.955 (std: 0.033)\n",
      "Parameters: {'bootstrap': True, 'min_samples_leaf': 3, 'min_samples_split': 2, 'criterion': 'gini', 'max_features': 2, 'max_depth': None}\n",
      "\n",
      "Model with rank: 4\n",
      "Mean OOB score: 0.955 (std: 0.033)\n",
      "Parameters: {'bootstrap': True, 'min_samples_leaf': 2, 'min_samples_split': 2, 'criterion': 'gini', 'max_features': 2, 'max_depth': None}\n",
      "\n",
      "Model with rank: 4\n",
      "Mean OOB score: 0.955 (std: 0.033)\n",
      "Parameters: {'bootstrap': True, 'min_samples_leaf': 3, 'min_samples_split': 3, 'criterion': 'gini', 'max_features': 3, 'max_depth': None}\n",
      "\n",
      "Model with rank: 4\n",
      "Mean OOB score: 0.955 (std: 0.033)\n",
      "Parameters: {'bootstrap': True, 'min_samples_leaf': 2, 'min_samples_split': 4, 'criterion': 'entropy', 'max_features': 3, 'max_depth': 3}\n",
      "\n",
      "Model with rank: 4\n",
      "Mean OOB score: 0.955 (std: 0.033)\n",
      "Parameters: {'bootstrap': True, 'min_samples_leaf': 4, 'min_samples_split': 3, 'criterion': 'entropy', 'max_features': 3, 'max_depth': 3}\n",
      "\n",
      "Model with rank: 4\n",
      "Mean OOB score: 0.955 (std: 0.033)\n",
      "Parameters: {'bootstrap': True, 'min_samples_leaf': 4, 'min_samples_split': 3, 'criterion': 'entropy', 'max_features': 3, 'max_depth': 3}\n",
      "\n",
      "Model with rank: 4\n",
      "Mean OOB score: 0.955 (std: 0.033)\n",
      "Parameters: {'bootstrap': True, 'min_samples_leaf': 2, 'min_samples_split': 4, 'criterion': 'entropy', 'max_features': 2, 'max_depth': 3}\n",
      "\n",
      "Model with rank: 4\n",
      "Mean OOB score: 0.955 (std: 0.033)\n",
      "Parameters: {'bootstrap': True, 'min_samples_leaf': 1, 'min_samples_split': 3, 'criterion': 'entropy', 'max_features': 3, 'max_depth': 3}\n",
      "\n",
      "Model with rank: 4\n",
      "Mean OOB score: 0.955 (std: 0.033)\n",
      "Parameters: {'bootstrap': True, 'min_samples_leaf': 4, 'min_samples_split': 3, 'criterion': 'entropy', 'max_features': 3, 'max_depth': None}\n",
      "\n",
      "Model with rank: 4\n",
      "Mean OOB score: 0.955 (std: 0.033)\n",
      "Parameters: {'bootstrap': True, 'min_samples_leaf': 2, 'min_samples_split': 3, 'criterion': 'gini', 'max_features': 3, 'max_depth': 3}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Randomized search for model selection\n",
    "clf = RandomForestClassifier(n_estimators=50)\n",
    "# Utility function to report best scores\n",
    "def report(results, n_top=5):\n",
    "    for i in range(1, n_top + 1):\n",
    "        candidates = np.flatnonzero(results['rank_test_score'] == i)\n",
    "        for candidate in candidates:\n",
    "            print(\"Model with rank: {0}\".format(i))\n",
    "            print(\"Mean OOB score: {0:.3f} (std: {1:.3f})\".format(\n",
    "                  results['mean_test_score'][candidate],\n",
    "                  results['std_test_score'][candidate]))\n",
    "            print(\"Parameters: {0}\".format(results['params'][candidate]))\n",
    "            print(\"\")\n",
    "\n",
    "\n",
    "# specify parameters and distributions to sample from\n",
    "param_dist = {\"max_depth\": [3, None],\n",
    "              \"max_features\": sp_randint(1, 4),\n",
    "              \"min_samples_split\": sp_randint(2, 5),\n",
    "              \"min_samples_leaf\": sp_randint(1, 5),\n",
    "              \"bootstrap\": [True, False],\n",
    "              \"criterion\": [\"gini\", \"entropy\"]}\n",
    "\n",
    "# run randomized search\n",
    "n_iter_search = 30\n",
    "random_search = RandomizedSearchCV(clf, param_distributions=param_dist,\n",
    "                                   n_iter=n_iter_search)\n",
    "\n",
    "random_search.fit(X_train, y_train)\n",
    "#this might take a minute to run\n",
    "print(\"RandomizedSearchCV examined %d candidate parameter settings.\" % (n_iter_search))\n",
    "report(random_search.cv_results_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "feature importance \n",
      "0.526 petal_length\n",
      "0.377 petal_width\n",
      "0.094 sepal_width\n",
      "0.002 sepal_length\n",
      "\n",
      "confusion matrix\n",
      "Predicted   0   1  All\n",
      "True                  \n",
      "0          22   0   22\n",
      "1           1  15   16\n",
      "All        23  15   38\n",
      "\n",
      "auc score 0.988636363636\n"
     ]
    }
   ],
   "source": [
    "#2.5 Train the random forest according to the best paramater setting given above. Show model outputs.\n",
    "\n",
    "#YOUR HOMEWORK IS TO SWITCH RANDOM FOREST CLASSIFIED WITH ANOTHER CLASSIFIER FROM THE SCIKIT LEARN API\n",
    "#http://scikit-learn.org/stable/\n",
    "clf = RandomForestClassifier(n_estimators=50, oob_score=True, max_features=3, criterion='gini',\n",
    "                             bootstrap=True,min_samples_split=2,min_samples_leaf=4,max_depth=3)\n",
    "\n",
    "clf = clf.fit(X_train, y_train)\n",
    "\n",
    "importances = clf.feature_importances_\n",
    "names = X.columns\n",
    "items=sorted(zip(map(lambda r: round(r, 3), clf.feature_importances_), names), reverse=True)\n",
    "\n",
    "print ('\\nfeature importance ')\n",
    "for r in items:\n",
    "    print(str(round(r[0],4))+' '+r[1])\n",
    "    \n",
    "y_pred=clf.predict(X_test)\n",
    "y_scores=clf.predict_proba(X_test)\n",
    "print ('\\nconfusion matrix')\n",
    "print(pd.crosstab(y_test, y_pred, rownames=['True'], colnames=['Predicted'], margins=True))\n",
    "\n",
    "false_positive_rate, true_positive_rate, thresholds = roc_curve(y_test, y_scores[:,1])\n",
    "print('\\nauc score '+str(auc(false_positive_rate, true_positive_rate)))\n",
    "\n",
    "#show a tradeoff curve for precision vs recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tt3odkrmIgBKFiLQGXgdyA2VEpDrwvKreFczgjDFZS3x8PAA5c+akXr16hISEMGjQIBvA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\nBKMWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff50fab9d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#2.7 ROC Curve\n",
    "fpr, tpr , threshold = roc_curve(y_test,clf.predict_proba(X_test)[:,1])\n",
    "roc_auc=auc(fpr, tpr)\n",
    "plt.figure()\n",
    "plt.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % roc_auc)\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "#Specificity: How many negatives were identified as negative.\n",
    "plt.xlabel('False Positive Rate (Specificity)')\n",
    "#Recall: How many positives were identified as positive.\n",
    "plt.ylabel('True Positive Rate (Recall)')\n",
    "plt.title('ROC Curve')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data-science-ipython-notebooks\t\t   kaggle-titanic   src\r\n",
      "finalized_model.sav\t\t\t   mxnet-notebooks\r\n",
      "Intro to Python Data Science on AWS.ipynb  seaborn-data\r\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\"\\n\\nopen('hello.txt').write('Hello, world!')\\n\\n# Upload the file to S3\\ns3_client.upload_file('hello.txt', 'MyBucket', 'hello-remote.txt')\\n\\n# Download the file from S3\\ns3_client.download_file('MyBucket', 'hello-remote.txt', 'hello2.txt')\\nprint(open('hello2.txt').read())\\n\""
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#code to save your model to disk and then to s3\n",
    "import pickle\n",
    "local_path = \"/home/ubuntu\" # temp path to export your model\n",
    "bucket_name = \"pickledemo\" # s3 key to save your network to\n",
    "\n",
    "# save the model to disk\n",
    "filename = 'finalized_model.sav'\n",
    "pickle.dump(clf, open(filename, 'wb'))\n",
    "#you should now see your finalized_model.sav in the file path\n",
    "#the ls command prints the contents of this notebook's root folder\n",
    "!ls\n",
    " \n",
    "# Upload to S3\n",
    "#TO GET THIS WORKING, YOU MUST ASSIGN AN ADMIN ROLE TO YOUR EC2 INSTANCE\n",
    "import boto3\n",
    "s3 = boto3.resource('s3')\n",
    "s3.Bucket('pickledemo').put_object(Key='finalized_model.sav', Body=open('finalized_model.sav'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.973684210526\n"
     ]
    }
   ],
   "source": [
    "# some time later...\n",
    " \n",
    "# load the model from s3 and make new predictions\n",
    "s3.Bucket(bucket_name).download_file(filename, 'my_local_pickle.sav')\n",
    "loaded_model = pickle.load(open('my_local_pickle.sav', 'rb'))\n",
    "result = loaded_model.score(X_test, y_test)\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#note that you can now call this model saved in S3 from ANY internet connected location\n",
    "\n",
    "#now push your notebook to your github page and submit the link as your homework\n",
    "#instructions: https://www.howtoforge.com/tutorial/install-git-and-github-on-ubuntu-14.04/\n",
    "#identify your username\n",
    "!git config --global user.name \"kljohnson721\"\n",
    "#make a local git repository\n",
    "!git init myrepo\n",
    "#navigate to the repo\n",
    "cd myrepo/\n",
    "#create a README\n",
    "!echo \"this is my README file\" >>README\n",
    "#move your notebook into the git folder (you will need to re-enter your notebook interface after moving the file)\n",
    "!mv /home/ubuntu/intro_to_python_data_science_on_aws.ipynb /home/ubuntu/myrepo/\n",
    "#make a new repo for yourself within your github.com account\n",
    "#add the files that you just made to tracking\n",
    "!git add intro_to_python_data_science_on_aws.ipynb\n",
    "!git add README\n",
    "#then remote add the github repo that you just made\n",
    "!git remote add origin https://github.com/kljohnson721/ADS_Class.git\n",
    "#then push your files to the git account\n",
    "!git push origin master\n",
    "#you will be prompted to enter your security credentials\n",
    "\n",
    "\n",
    "#if this isn't working for you, another option is to download the file to your local machine using the following:\n",
    "#sudo scp -r -i 'yourkeypair.pem' ubuntu@publicDNS:/home/ubuntu/intro_to_python_data_science_on_aws.ipynb localpath\n",
    "#windows people should use something like FileZilla to transfer files from a linux EC2 server to a local windows machine\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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