model_decision_tree.ipynb

{
 "cells": [
  {
   "cell_type": "code",
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   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.tree import DecisionTreeClassifier as DTC\n",
    "from joblib import dump\n",
    "from config_store import targetDataSet, randomSeed\n",
    "from utility_functions import dataSetSplit, doGridSearch, score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Website</th>\n",
       "      <th>Packets</th>\n",
       "      <th>Bytes</th>\n",
       "      <th>Packets A → B</th>\n",
       "      <th>Bytes A → B</th>\n",
       "      <th>Packets B → A</th>\n",
       "      <th>Bytes B → A</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Bits/s A → B</th>\n",
       "      <th>Bits/s B → A</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>712</td>\n",
       "      <td>1</td>\n",
       "      <td>712</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16.763719</td>\n",
       "      <td>339</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
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       "      <td>718</td>\n",
       "      <td>1</td>\n",
       "      <td>718</td>\n",
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       "      <td>10.784664</td>\n",
       "      <td>532</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>889</td>\n",
       "      <td>1</td>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.927569</td>\n",
       "      <td>1443</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>712</td>\n",
       "      <td>1</td>\n",
       "      <td>712</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.479684</td>\n",
       "      <td>422</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>718</td>\n",
       "      <td>1</td>\n",
       "      <td>718</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12.706807</td>\n",
       "      <td>452</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Website  Packets  Bytes  Packets A → B  Bytes A → B  Packets B → A  \\\n",
       "0        0        1    712              1          712              0   \n",
       "1        0        1    718              1          718              0   \n",
       "2        0        1    889              1          889              0   \n",
       "3        0        1    712              1          712              0   \n",
       "4        0        1    718              1          718              0   \n",
       "\n",
       "   Bytes B → A   Duration  Bits/s A → B  Bits/s B → A  \n",
       "0            0  16.763719           339             0  \n",
       "1            0  10.784664           532             0  \n",
       "2            0   4.927569          1443             0  \n",
       "3            0  13.479684           422             0  \n",
       "4            0  12.706807           452             0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import and preview data set\n",
    "data = pd.read_csv(targetDataSet)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separate data set using test/train split\n",
    "XTrain, XTest, yTrain, yTest = dataSetSplit(data.drop(columns = [\"Website\"]).values, data[\"Website\"].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Perform hyperparameter optimization\n",
    "# Define hyperparameters to check\n",
    "hyperparameters = dict(min_samples_split = np.array(range(2, 23)), min_samples_leaf = np.array(range(1, 21)))\n",
    "\n",
    "# Set up and perform grid search\n",
    "grid = doGridSearch(DTC(random_state = randomSeed), hyperparameters, XTrain, yTrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>param_min_samples_split</th>\n",
       "      <th>param_min_samples_leaf</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.826524</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.801707</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.801768</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.799268</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.804268</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>18</td>\n",
       "      <td>20</td>\n",
       "      <td>0.653293</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>19</td>\n",
       "      <td>20</td>\n",
       "      <td>0.653293</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>0.653293</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418</th>\n",
       "      <td>21</td>\n",
       "      <td>20</td>\n",
       "      <td>0.653293</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419</th>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>0.653293</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>420 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    param_min_samples_split param_min_samples_leaf  mean_test_score  \\\n",
       "0                         2                      1         0.826524   \n",
       "1                         3                      1         0.801707   \n",
       "2                         4                      1         0.801768   \n",
       "3                         5                      1         0.799268   \n",
       "4                         6                      1         0.804268   \n",
       "..                      ...                    ...              ...   \n",
       "415                      18                     20         0.653293   \n",
       "416                      19                     20         0.653293   \n",
       "417                      20                     20         0.653293   \n",
       "418                      21                     20         0.653293   \n",
       "419                      22                     20         0.653293   \n",
       "\n",
       "     rank_test_score  \n",
       "0                  1  \n",
       "1                 56  \n",
       "2                 54  \n",
       "3                 83  \n",
       "4                 36  \n",
       "..               ...  \n",
       "415              400  \n",
       "416              400  \n",
       "417              400  \n",
       "418              400  \n",
       "419              400  \n",
       "\n",
       "[420 rows x 4 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check grid search results\n",
    "gridDataframe = pd.DataFrame(grid.cv_results_)[[\"param_min_samples_split\", \"param_min_samples_leaf\", \"mean_test_score\", \"rank_test_score\"]]\n",
    "gridDataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>param_min_samples_split</th>\n",
       "      <th>param_min_samples_leaf</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.826524</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  param_min_samples_split param_min_samples_leaf  mean_test_score  \\\n",
       "0                       2                      1         0.826524   \n",
       "\n",
       "   rank_test_score  \n",
       "0                1  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show best hyperparameter set\n",
    "bestHyperSet = gridDataframe[gridDataframe[\"rank_test_score\"] <= 1]\n",
    "bestHyperSet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "DecisionTreeClassifier(random_state=201)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Final decision tree model\n",
    "dtc = DTC(random_state = randomSeed, min_samples_split = bestHyperSet[\"param_min_samples_split\"].iloc(0)[0], min_samples_leaf = bestHyperSet[\"param_min_samples_leaf\"].iloc(0)[0])\n",
    "dtc.fit(XTrain, yTrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training score: 1.0\n",
      "Testing score: 0.7549019607843137\n"
     ]
    }
   ],
   "source": [
    "# Score model\n",
    "score(dtc, XTrain, yTrain, XTest, yTest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['models/dtc.joblib']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Save model\n",
    "dump(dtc, \"models/dtc.joblib\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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