Jenny-Problem4-100900-Final.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jennyyang/anaconda3/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": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "import time \n",
    "import pickle\n",
    "import random\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import precision_recall_fscore_support\n",
    "from itertools import cycle\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "from sklearn import metrics\n",
    "from xgboost import XGBClassifier\n",
    "from xgboost import XGBRegressor\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from math import sqrt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## All Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#train_all = pd.read_csv('train0_one5th.csv')\n",
    "#train_all = pd.read_csv('train5050.csv')\n",
    "#train_all = pd.read_csv('train1040.csv')\n",
    "train_all = pd.read_csv('train100900.csv')\n",
    "#train_all = pd.read_csv('train005995.csv')\n",
    "test_data = pd.read_csv('test_full.csv')\n",
    "val_all = pd.read_csv('val_full.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>click</th>\n",
       "      <th>weekday</th>\n",
       "      <th>hour</th>\n",
       "      <th>adexchange</th>\n",
       "      <th>slotwidth</th>\n",
       "      <th>slotheight</th>\n",
       "      <th>slotprice</th>\n",
       "      <th>advertiser</th>\n",
       "      <th>usertag_0</th>\n",
       "      <th>usertag_10052</th>\n",
       "      <th>...</th>\n",
       "      <th>browser_maxthon</th>\n",
       "      <th>browser_opera</th>\n",
       "      <th>browser_other</th>\n",
       "      <th>browser_safari</th>\n",
       "      <th>browser_sogou</th>\n",
       "      <th>browser_theworld</th>\n",
       "      <th>bidprice</th>\n",
       "      <th>payprice</th>\n",
       "      <th>freq_c</th>\n",
       "      <th>freq_r</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>250</td>\n",
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       "      <td>3386</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>300</td>\n",
       "      <td>14</td>\n",
       "      <td>0.001344</td>\n",
       "      <td>0.033467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>950</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>3427</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>227</td>\n",
       "      <td>74</td>\n",
       "      <td>0.001258</td>\n",
       "      <td>0.029835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>1000</td>\n",
       "      <td>90</td>\n",
       "      <td>70</td>\n",
       "      <td>3476</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>254</td>\n",
       "      <td>73</td>\n",
       "      <td>0.001286</td>\n",
       "      <td>0.044469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>728</td>\n",
       "      <td>90</td>\n",
       "      <td>52</td>\n",
       "      <td>2821</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>277</td>\n",
       "      <td>52</td>\n",
       "      <td>0.001983</td>\n",
       "      <td>0.044469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>300</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>3386</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>300</td>\n",
       "      <td>47</td>\n",
       "      <td>0.001750</td>\n",
       "      <td>0.012885</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 103 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   click  weekday  hour  adexchange  slotwidth  slotheight  slotprice  \\\n",
       "0      0        3    21           2        300         250          5   \n",
       "1      0        5     1           1        950          90          0   \n",
       "2      0        5     7           3       1000          90         70   \n",
       "3      0        1     1           2        728          90         52   \n",
       "4      0        2    17           1        300         250          0   \n",
       "\n",
       "   advertiser  usertag_0  usertag_10052    ...     browser_maxthon  \\\n",
       "0        3386          0              0    ...                   0   \n",
       "1        3427          1              0    ...                   0   \n",
       "2        3476          0              0    ...                   0   \n",
       "3        2821          1              0    ...                   0   \n",
       "4        3386          1              0    ...                   0   \n",
       "\n",
       "   browser_opera  browser_other  browser_safari  browser_sogou  \\\n",
       "0              0              0               0              0   \n",
       "1              0              0               0              0   \n",
       "2              0              0               0              0   \n",
       "3              0              0               0              0   \n",
       "4              0              0               1              0   \n",
       "\n",
       "   browser_theworld  bidprice  payprice    freq_c    freq_r  \n",
       "0                 0       300        14  0.001344  0.033467  \n",
       "1                 0       227        74  0.001258  0.029835  \n",
       "2                 0       254        73  0.001286  0.044469  \n",
       "3                 0       277        52  0.001983  0.044469  \n",
       "4                 0       300        47  0.001750  0.012885  \n",
       "\n",
       "[5 rows x 103 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#train_all = train_all.drop(train_all.columns[[0,1]],axis =1)\n",
    "train_all= train_all.drop(['region','city','counts_r','counts_c','Unnamed: 0','index'],axis =1)\n",
    "train_all.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>weekday</th>\n",
       "      <th>hour</th>\n",
       "      <th>adexchange</th>\n",
       "      <th>slotwidth</th>\n",
       "      <th>slotheight</th>\n",
       "      <th>slotprice</th>\n",
       "      <th>advertiser</th>\n",
       "      <th>usertag_10024</th>\n",
       "      <th>usertag_10077</th>\n",
       "      <th>usertag_10075</th>\n",
       "      <th>...</th>\n",
       "      <th>browser_firefox</th>\n",
       "      <th>browser_ie</th>\n",
       "      <th>browser_maxthon</th>\n",
       "      <th>browser_opera</th>\n",
       "      <th>browser_other</th>\n",
       "      <th>browser_safari</th>\n",
       "      <th>browser_sogou</th>\n",
       "      <th>browser_theworld</th>\n",
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       "      <td>280</td>\n",
       "      <td>0</td>\n",
       "      <td>1458</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.001335</td>\n",
       "      <td>0.012635</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>4</td>\n",
       "      <td>960</td>\n",
       "      <td>90</td>\n",
       "      <td>30</td>\n",
       "      <td>2821</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000910</td>\n",
       "      <td>0.023984</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>300</td>\n",
       "      <td>250</td>\n",
       "      <td>50</td>\n",
       "      <td>3386</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000722</td>\n",
       "      <td>0.012635</td>\n",
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       "</table>\n",
       "<p>5 rows × 100 columns</p>\n",
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      ],
      "text/plain": [
       "   weekday  hour  adexchange  slotwidth  slotheight  slotprice  advertiser  \\\n",
       "0        0    12           3        300         250         10        3427   \n",
       "1        3    14           0        320          50          5        2997   \n",
       "2        5    19           1        336         280          0        1458   \n",
       "3        0    21           4        960          90         30        2821   \n",
       "4        2    20           3        300         250         50        3386   \n",
       "\n",
       "   usertag_10024  usertag_10077  usertag_10075    ...     browser_firefox  \\\n",
       "0              1              1              1    ...                   0   \n",
       "1              0              0              0    ...                   0   \n",
       "2              1              0              0    ...                   0   \n",
       "3              0              0              1    ...                   0   \n",
       "4              0              0              0    ...                   0   \n",
       "\n",
       "   browser_ie  browser_maxthon  browser_opera  browser_other  browser_safari  \\\n",
       "0           0                0              0              0               0   \n",
       "1           0                0              0              0               1   \n",
       "2           1                0              0              0               0   \n",
       "3           1                0              0              0               0   \n",
       "4           1                0              0              0               0   \n",
       "\n",
       "   browser_sogou  browser_theworld    freq_c    freq_r  \n",
       "0              0                 0  0.004638  0.065971  \n",
       "1              0                 0  0.041582  0.041582  \n",
       "2              0                 0  0.001335  0.012635  \n",
       "3              0                 0  0.000910  0.023984  \n",
       "4              0                 0  0.000722  0.012635  \n",
       "\n",
       "[5 rows x 100 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#test_data = test_data.drop(test_data.columns[[0]],axis =1)\n",
    "test_data= test_data.drop(['region','city','counts_r','counts_c','Unnamed: 0'],axis =1)\n",
    "test_data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <th>adexchange</th>\n",
       "      <th>slotwidth</th>\n",
       "      <th>slotheight</th>\n",
       "      <th>slotprice</th>\n",
       "      <th>bidprice</th>\n",
       "      <th>payprice</th>\n",
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       "      <th>...</th>\n",
       "      <th>browser_firefox</th>\n",
       "      <th>browser_ie</th>\n",
       "      <th>browser_maxthon</th>\n",
       "      <th>browser_opera</th>\n",
       "      <th>browser_other</th>\n",
       "      <th>browser_safari</th>\n",
       "      <th>browser_sogou</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.026276</td>\n",
       "      <td>0.026276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>300</td>\n",
       "      <td>250</td>\n",
       "      <td>5</td>\n",
       "      <td>238</td>\n",
       "      <td>65</td>\n",
       "      <td>3358</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.014994</td>\n",
       "      <td>0.014994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>336</td>\n",
       "      <td>280</td>\n",
       "      <td>5</td>\n",
       "      <td>238</td>\n",
       "      <td>6</td>\n",
       "      <td>3358</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.002172</td>\n",
       "      <td>0.027197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>5</td>\n",
       "      <td>249</td>\n",
       "      <td>5</td>\n",
       "      <td>3476</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.005459</td>\n",
       "      <td>0.019347</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 103 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   weekday  hour  adexchange  slotwidth  slotheight  slotprice  bidprice  \\\n",
       "0        4    20           1        160         600          0       300   \n",
       "1        1    21           1        950          90          0       238   \n",
       "2        4     8           2        300         250          5       238   \n",
       "3        5    15           2        336         280          5       238   \n",
       "4        1    18           2        200         200          5       249   \n",
       "\n",
       "   payprice  advertiser  click    ...     browser_firefox  browser_ie  \\\n",
       "0        23        1458      0    ...                   0           1   \n",
       "1        75        3476      0    ...                   0           0   \n",
       "2        65        3358      0    ...                   0           1   \n",
       "3         6        3358      0    ...                   0           0   \n",
       "4         5        3476      0    ...                   0           0   \n",
       "\n",
       "   browser_maxthon  browser_opera  browser_other  browser_safari  \\\n",
       "0                0              0              0               0   \n",
       "1                0              0              0               0   \n",
       "2                0              0              0               0   \n",
       "3                0              0              0               0   \n",
       "4                0              0              0               0   \n",
       "\n",
       "   browser_sogou  browser_theworld    freq_c    freq_r  \n",
       "0              0                 0  0.026276  0.026276  \n",
       "1              0                 0  0.026276  0.026276  \n",
       "2              0                 0  0.014994  0.014994  \n",
       "3              0                 0  0.002172  0.027197  \n",
       "4              0                 0  0.005459  0.019347  \n",
       "\n",
       "[5 rows x 103 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#val_all = val_all.drop(val_all.columns[[0,1]],axis =1)\n",
    "val_all= val_all.drop(['region','city','counts_r','counts_c','Unnamed: 0','index'],axis =1)\n",
    "val_all.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jennyyang/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: FutureWarning: '.reindex_axis' is deprecated and will be removed in a future version. Use '.reindex' instead.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(717200, 103)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_all_1 = train_all.reindex_axis(sorted(train_all.columns), axis=1)\n",
    "#list(train_all_1)\n",
    "train_all_1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jennyyang/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: FutureWarning: '.reindex_axis' is deprecated and will be removed in a future version. Use '.reindex' instead.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(303375, 100)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data_1 = test_data.reindex_axis(sorted(test_data.columns), axis=1)\n",
    "#list(test_data_1)\n",
    "test_data_1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jennyyang/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: FutureWarning: '.reindex_axis' is deprecated and will be removed in a future version. Use '.reindex' instead.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(303925, 103)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val_all_1 = val_all.reindex_axis(sorted(val_all.columns), axis=1)\n",
    "#list(val_all_1)\n",
    "val_all_1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Split data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(717200,)\n",
      "(717200,)\n",
      "(717200, 100)\n",
      "(303925,)\n",
      "(303925,)\n",
      "(303925, 100)\n"
     ]
    }
   ],
   "source": [
    "#100 features\n",
    "train_y_ctr = train_all_1['click']\n",
    "train_y_wp = train_all_1['payprice']\n",
    "train_x = train_all_1.drop(['click','payprice','bidprice'],axis =1)\n",
    "test_y_ctr = val_all_1['click']\n",
    "test_y_wp = val_all_1['payprice']\n",
    "test_x = val_all_1.drop(['click','payprice','bidprice'],axis =1)\n",
    "\n",
    "print(train_y_ctr.shape)\n",
    "print(train_y_wp.shape)\n",
    "print(train_x.shape)\n",
    "print(test_y_ctr.shape)\n",
    "print(test_y_wp.shape)\n",
    "print(test_x.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predict pCTR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MODEL 1 - Xgboost Classifier - pCTR\n",
      "score = 0.995255408407\n",
      "auc = 0.695846769316\n",
      "Running time = 218.16753101348877\n",
      "pCTR: \n",
      "[ 0.01810302  0.013336    0.01698269 ...,  0.05522097  0.03828786\n",
      "  0.04730557]\n"
     ]
    }
   ],
   "source": [
    "#\"MODEL 1 - Xgboost Classifier - clf_pCTR \"\n",
    "\n",
    "start = time.time()\n",
    "clf = XGBClassifier(learning_rate= 0.1, max_depth= 5, \n",
    "                    n_estimators = 100,\n",
    "                    gamma = 0,\n",
    "                      min_child_weight =7,\n",
    "                      colsample_bytree=0.6,\n",
    "                      subsample=0.95, reg_alpha = 0.03)\n",
    "clf_pCTR = clf.fit(train_x, train_y_ctr)\n",
    "prediction1 = np.array(clf_pCTR.predict(test_x))\n",
    "pCTR_m1 = clf_pCTR.predict_proba(test_x)  \n",
    "end = time.time()\n",
    "\n",
    "# AUC accuracy\n",
    "fpr, tpr, thresholds = metrics.roc_curve(test_y_ctr, prediction1)\n",
    "auc = metrics.auc(fpr, tpr)\n",
    "\n",
    "score = accuracy_score(test_y_ctr, prediction1)\n",
    "\n",
    "    \n",
    "print(\"MODEL 1 - Xgboost Classifier - pCTR\")\n",
    "print(\"score =\", score)\n",
    "print(\"auc =\", auc)\n",
    "#print (\"error_model_1 = \", 1-score)\n",
    "print(\"Running time =\", end - start)\n",
    "print(\"pCTR: \")\n",
    "print(pCTR_m1[:,1])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf_pCTR = pickle.dumps(clf_pCTR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf_pCTR = pickle.loads(clf_pCTR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.01810302,  0.013336  ,  0.01698269, ...,  0.05522097,\n",
       "        0.03828786,  0.04730557], dtype=float32)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_pCTR = pCTR_m1[:,1]\n",
    "results_pCTR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>pCTR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.018103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.013336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.016983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.019592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.009988</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       pCTR\n",
       "0  0.018103\n",
       "1  0.013336\n",
       "2  0.016983\n",
       "3  0.019592\n",
       "4  0.009988"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_pCTR=pd.DataFrame(data =results_pCTR, columns=['pCTR'])\n",
    "r_pCTR.head(5)\n",
    "#r.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gen_pCTR (df):\n",
    "    pCTR_m1 = np.array(clf_pCTR.predict_proba(df))\n",
    "    results_pCTR = pCTR_m1[:,1]\n",
    "    r_pCTR=pd.DataFrame(data =results_pCTR, columns=['pCTR'])\n",
    "    return r_pCTR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#r_pCTR = gen_pCTR (test_data_1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predict pWP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MODEL 2 - LR Classifier - clf2_pWP\n",
      "Running time = 212.0104010105133\n",
      "pWP: \n",
      "[  65.6975708    91.52745819   77.35237122 ...,  108.89304352   57.02135849\n",
      "   59.96221542]\n"
     ]
    }
   ],
   "source": [
    "#\"MODEL 2 - XGBRegressor() - clf2_pWP\"\n",
    "\n",
    "# Train and Predict\n",
    "start = time.time()\n",
    "#clf2_pWP = XGBClassifier()\n",
    "clf2_pWP = XGBRegressor()\n",
    "clf2_pWP.fit(train_x, train_y_wp)\n",
    "prediction2_wp = np.array(clf2_pWP.predict(test_x))\n",
    "rms = sqrt(mean_squared_error(test_y_wp, prediction2_wp))\n",
    "pWP_m6 = clf.predict_proba(test_x)  \n",
    "end = time.time()\n",
    "\n",
    "print(\"MODEL 2 - LR Classifier - clf2_pWP\")\n",
    "print(\"Running time =\", end - start)\n",
    "print(\"pWP: \")\n",
    "print(prediction2_wp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf2_pWP = pickle.dumps(clf2_pWP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf2_pWP = pickle.loads(clf2_pWP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>pWP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>65.697571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>91.527458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>77.352371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>59.052113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57.523701</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         pWP\n",
       "0  65.697571\n",
       "1  91.527458\n",
       "2  77.352371\n",
       "3  59.052113\n",
       "4  57.523701"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_pWP = pd.DataFrame(data = prediction2_wp, columns=['pWP'])\n",
    "r_pWP.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gen_pWP (df):\n",
    "    prediction2_wp = np.array(clf2_pWP.predict(df))\n",
    "    r_pWP = pd.DataFrame(data = prediction2_wp, columns=['pWP'])\n",
    "    return r_pWP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#r_pWP = gen_pWP(test_data_1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merge results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def comb (df, r_pCTR, r_pWP):\n",
    "    data_comb = df.reset_index()\n",
    "    data_comb = data_comb.drop(data_comb.columns[[0]], axis=1)\n",
    "    print('test_comb: ', data_comb.shape)\n",
    "    print('r_pCTR:', r_pCTR.shape)\n",
    "    print('r_pWP:' ,r_pWP.shape)\n",
    "    data_comb= pd.concat([data_comb, r_pWP], axis = 1)\n",
    "    data_comb= pd.concat([data_comb, r_pCTR], axis = 1)\n",
    "    print('new test_comb: ',data_comb.shape)\n",
    "    return data_comb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test_comb:  (303925, 103)\n",
      "r_pCTR: (303925, 1)\n",
      "r_pWP: (303925, 1)\n",
      "new test_comb:  (303925, 105)\n"
     ]
    }
   ],
   "source": [
    "val_comb = comb (val_all_1, r_pCTR, r_pWP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test_comb:  (303375, 100)\n",
      "r_pCTR: (303375, 1)\n",
      "r_pWP: (303375, 1)\n",
      "new test_comb:  (303375, 102)\n"
     ]
    }
   ],
   "source": [
    "test_comb = comb (test_data_1, r_pCTR, r_pWP)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate Rho"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(303925, 106)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val_comb['Rho']= val_comb['pCTR']/val_comb['pWP']\n",
    "val_comb.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(303375, 103)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_comb['Rho']= test_comb['pCTR']/test_comb['pWP']\n",
    "test_comb.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.721414\n",
       "1    3.111351\n",
       "2    0.187286\n",
       "3    1.687714\n",
       "4    0.837441\n",
       "Name: Rho, dtype: float32"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_comb['Rho'] = test_comb['Rho'] * 1000\n",
    "test_comb['Rho'].head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.275551\n",
       "1    0.145705\n",
       "2    0.219550\n",
       "3    0.331771\n",
       "4    0.173629\n",
       "Name: Rho, dtype: float32"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val_comb['Rho'] = val_comb['Rho'] * 1000\n",
    "val_comb['Rho'].head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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s7RhqiYwwzJqs1aDhJiU+iqgIox66iMDyXdVsr2hg5sRskuKinC5HeshjDOlDYthf1+x0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\nXUTEJRToIiIuoUAXEXEJBbqIiEv8PyTW090N3ZrcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1396cfeb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "q_75 = val_comb['Rho'].quantile(0.75)\n",
    "q_25 = val_comb['Rho'].quantile(0.25)\n",
    "less_25 = val_comb['Rho'][val_comb['Rho'] <= q_25]\n",
    "sns.distplot(less_25, bins = 500)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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w0AfOfWt9D7o/XNrIk3cttRvRRijYH+iBsGLWNN4+Vkl9aydJMe5ghzNqlhCM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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e987bb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(val_comb['bidprice']-val_comb['payprice'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Function: check clicks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "#B_curr=6250000\n",
    "\n",
    "def test_clicks (df, E_Rho, B_curr, min_inc):\n",
    "    i = 0\n",
    "    c = 0\n",
    "    s = 0\n",
    "    b = 0\n",
    "    while (B_curr > 0) and (i < len(df)):\n",
    "        if (df['Rho'].iloc[i] < E_Rho): \n",
    "            i += 1\n",
    "        elif (df['payprice'].iloc[i] > B_curr):\n",
    "            i += 1\n",
    "        elif (df['pWP'].iloc[i] < df['slotprice'].iloc[i]):\n",
    "            i += 1\n",
    "        elif ((df['pWP'].iloc[i] + min_inc) < df['bidprice'].iloc[i]):\n",
    "            i += 1  \n",
    "        else: \n",
    "            B_curr -= df['payprice'].iloc[i]\n",
    "            c += df['click'].iloc[i]\n",
    "            b += 1\n",
    "            s += df['payprice'].iloc[i]\n",
    "            i += 1\n",
    "            #print(i, c, s, B_curr)\n",
    "    return c, s, b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Find optimal Rho"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(202, 106)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val_comb_1=val_comb[val_comb['click']==1]\n",
    "val_comb_1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rho_avg:  0.7293069958686829\n",
      "Rho_med:  0.47073841094970703\n",
      "Rho1_avg:  5.463656425476074\n",
      "Rho1_med:  3.305744171142578\n",
      "Rho1_min:  0.213741\n",
      "Rho1_max:  23.8784\n",
      "Rho1_q25:  1.12327441573143\n"
     ]
    }
   ],
   "source": [
    "Rho = val_comb['Rho']\n",
    "Rho_avg = Rho.mean()\n",
    "print('Rho_avg: ', Rho_avg)\n",
    "Rho_med = Rho.median()\n",
    "print('Rho_med: ',Rho_med)\n",
    "\n",
    "Rho1 = val_comb_1['Rho']\n",
    "Rho1_avg = Rho1.mean()\n",
    "print('Rho1_avg: ',Rho1_avg)\n",
    "Rho1_med = Rho1.median()\n",
    "print('Rho1_med: ',Rho1_med)\n",
    "Rho1_min = Rho1.min()\n",
    "print('Rho1_min: ',Rho1_min)\n",
    "Rho1_max = Rho1.max()\n",
    "print('Rho1_max: ',Rho1_max)\n",
    "Rho1_q25 = Rho1.quantile(0.25)\n",
    "print('Rho1_q25: ',Rho1_q25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MdvevR7VZEbQpCcaLgjbljZ7rVuDWYHQUsCYYzqENvgjaUGerF1Rze1HN7aMr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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1374bda58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "r1_q25 = val_comb_1['Rho'][val_comb_1['Rho'] <= Rho1_q25]\n",
    "sns.distplot(r1_q25, bins = 50)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  63.   75.   93.  122.  146.  162.  160.  160.  151.  139.] [ 6250000.  6250000.  6250000.  6250000.  6250000.  5536640.  4600442.\n",
      "  3953017.  3168223.  2559648.] [ 6250000.  6250000.  6250000.  6250000.  6250000.  5536640.  4600442.\n",
      "  3953017.  3168223.  2559648.    33982.]\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "\n",
    "df = val_comb\n",
    "\n",
    "#j = 0\n",
    "clicks =[]\n",
    "spends=[]\n",
    "bids = []\n",
    "J=[]\n",
    "j=0.25\n",
    "\n",
    "while j < 1.2: \n",
    "    min_inc = random.sample(range(235,275), 1)\n",
    "    c, s, b = test_clicks(df, j, 6250000,min_inc)\n",
    "    clicks = np.append(clicks,c)\n",
    "    spends = np.append(spends,s)\n",
    "    bids = np.append(spends,b)\n",
    "    J = np.append(J,j)\n",
    "    j +=0.1\n",
    "\n",
    "print(clicks, spends, bids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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>Rho_threshold</th>\n",
       "      <th>Clicks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.25</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.35</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.45</td>\n",
       "      <td>93.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.55</td>\n",
       "      <td>122.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.65</td>\n",
       "      <td>146.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.75</td>\n",
       "      <td>162.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.85</td>\n",
       "      <td>160.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.95</td>\n",
       "      <td>160.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.05</td>\n",
       "      <td>151.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.15</td>\n",
       "      <td>139.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rho_threshold  Clicks\n",
       "0           0.25    63.0\n",
       "1           0.35    75.0\n",
       "2           0.45    93.0\n",
       "3           0.55   122.0\n",
       "4           0.65   146.0\n",
       "5           0.75   162.0\n",
       "6           0.85   160.0\n",
       "7           0.95   160.0\n",
       "8           1.05   151.0\n",
       "9           1.15   139.0"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_C = pd.DataFrame(data =clicks,columns=['Clicks'] )\n",
    "df_J = pd.DataFrame(data =J,columns=['Rho_threshold'] )\n",
    "df_T = pd.concat([df_J,df_C],axis = 1)\n",
    "df_T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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DHxGJLwqIOPD8x1m89Lka/ohIfFFAhOyVL5fwt/Fq+CMi8UdTgYakoMB56fPF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\nUqT/B53UBveiH2m8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e988c080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df_T ['Rho_threshold'],  df_T ['Clicks']) \n",
    "plt.xlabel(\"Rho Benchmark\")\n",
    "plt.ylabel(\"Clicks\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jennyyang/anaconda3/lib/python3.6/site-packages/numpy/core/fromnumeric.py:57: FutureWarning: 'argmax' is deprecated. Use 'idxmax' instead. The behavior of 'argmax' will be corrected to return the positional maximum in the future. Use 'series.values.argmax' to get the position of the maximum now.\n",
      "  return getattr(obj, method)(*args, **kwds)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.75"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Rho_min = np.argmax(df_T ['Clicks'])*0.1+0.25\n",
    "Rho_min "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Find optimal bidding increment "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 160.  160.  160.  161.  161.  162.  162.  162.  162.  162.  162.  165.\n",
      "  165.  166.  166.  166.  166.  166.  167.  168.  169.  169.  170.  170.\n",
      "  170.  170.  170.  170.  170.  170.  170.  170.  170.  170.  170.  170.\n",
      "  170.  170.  170.  170.  170.] [ 5301544.  5330348.  5354376.  5396573.  5439530.  5478489.  5497175.\n",
      "  5517600.  5536640.  5555105.  5580454.  5611095.  5646625.  5673093.\n",
      "  5687083.  5693915.  5701726.  5711605.  5728343.  5749689.  5774413.\n",
      "  5819462.  5850797.  5886606.  5907232.  5921919.  5929217.  5931876.\n",
      "  5933866.  5935446.  5937911.  5939231.  5939431.  5939691.  5939751.\n",
      "  5939751.  5939751.  5939751.  5939751.  5939751.  5939751.] [ 5301544.  5330348.  5354376.  5396573.  5439530.  5478489.  5497175.\n",
      "  5517600.  5536640.  5555105.  5580454.  5611095.  5646625.  5673093.\n",
      "  5687083.  5693915.  5701726.  5711605.  5728343.  5749689.  5774413.\n",
      "  5819462.  5850797.  5886606.  5907232.  5921919.  5929217.  5931876.\n",
      "  5933866.  5935446.  5937911.  5939231.  5939431.  5939691.  5939751.\n",
      "  5939751.  5939751.  5939751.  5939751.  5939751.  5939751.    84842.]\n"
     ]
    }
   ],
   "source": [
    "#j = 0\n",
    "clicks =[]\n",
    "spends=[]\n",
    "bids=[]\n",
    "M=[]\n",
    "m=240\n",
    "\n",
    "while m <= 280: \n",
    "    c, s, b = test_clicks(val_comb, 0.75, 6250000,m)\n",
    "    clicks = np.append(clicks,c)\n",
    "    spends = np.append(spends,s)\n",
    "    bids = np.append(spends,b)\n",
    "    M = np.append(M,m)\n",
    "    m +=1\n",
    "\n",
    "print(clicks, spends, bids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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>Min_increment</th>\n",
       "      <th>Clicks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>240.0</td>\n",
       "      <td>160.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>241.0</td>\n",
       "      <td>160.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>242.0</td>\n",
       "      <td>160.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>243.0</td>\n",
       "      <td>161.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>244.0</td>\n",
       "      <td>161.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Min_increment  Clicks\n",
       "0          240.0   160.0\n",
       "1          241.0   160.0\n",
       "2          242.0   160.0\n",
       "3          243.0   161.0\n",
       "4          244.0   161.0"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_C = pd.DataFrame(data =clicks,columns=['Clicks'] )\n",
    "df_M = pd.DataFrame(data =M,columns=['Min_increment'] )\n",
    "df_T = pd.concat([df_M,df_C],axis = 1)\n",
    "df_T.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1oOXvAH4XEb+JQruD743uX9dseN6DMBskIh5J9hZmU+h/M5tCz5uepNvupIj4\ntQq3dbwE+BtJPwfuBloi4twRfFaPpPuAdwN3jPbfxexIeA/CbBBJ7wCOAl6mcNeu3ckX+UoKh4SQ\nNA/ojIjvAV8B3kmhqdxsSecm64zXMDd4SvZSzqfQZG2gbcBCSSclr68Zlb+c2Qh4D8KsoP8cBBQO\nK10XEb2Svg/8SNIzQDOFL24ozDz6iqQ+oAe4PiL2JyeY/0HSdAq/X9+g0P1zsP5zEOMpdOz8zsCF\nEdEtaTXwY0mdwK8o3JvYrGzczdXMzIryISYzMyvKAWFmZkU5IMzMrCgHhJmZFeWAMDOzohwQZmZW\nlAPCzMyKckCYmVlR/x/Z5mumN6ox0wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16d97a240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df_T ['Min_increment'],  df_T ['Clicks']) \n",
    "plt.xlabel(\"Base Bid\")\n",
    "plt.ylabel(\"Clicks\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jennyyang/anaconda3/lib/python3.6/site-packages/numpy/core/fromnumeric.py:57: FutureWarning: 'argmax' is deprecated. Use 'idxmax' instead. The behavior of 'argmax' will be corrected to return the positional maximum in the future. Use 'series.values.argmax' to get the position of the maximum now.\n",
      "  return getattr(obj, method)(*args, **kwds)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "262"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Inc_max = np.argmax(df_T ['Clicks'])+240\n",
    "Inc_max "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(170, 5850797, 81099)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_clicks(val_comb, 0.75, 6250000,262)"
   ]
  },
  {
   "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": []
  }
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