Decision_Tree_Model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "da72da60",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Training/testing Decision Tree Model using Scikit-learn and SnapML\n",
    "#Install snapML\n",
    "# !pip install snapml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "99fb194d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing libraries\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline   \n",
    "# above (%matplotlib) works only in jupyter notebook\n",
    "\n",
    "#import modules from scikit-learn\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import normalize, StandardScaler\n",
    "from sklearn.utils.class_weight import compute_sample_weight\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "#other libraries\n",
    "import time \n",
    "import gc,sys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6c746cf8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 284807 observations in the data set\n",
      "There are 31 variables in the dataset.\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>...</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.359807</td>\n",
       "      <td>-0.072781</td>\n",
       "      <td>2.536347</td>\n",
       "      <td>1.378155</td>\n",
       "      <td>-0.338321</td>\n",
       "      <td>0.462388</td>\n",
       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>0.363787</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>149.62</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>...</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>378.66</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>123.50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>0.817739</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009431</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>69.99</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "\n",
       "         V8        V9  ...       V21       V22       V23       V24       V25  \\\n",
       "0  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928  0.128539   \n",
       "1  0.085102 -0.255425  ... -0.225775 -0.638672  0.101288 -0.339846  0.167170   \n",
       "2  0.247676 -1.514654  ...  0.247998  0.771679  0.909412 -0.689281 -0.327642   \n",
       "3  0.377436 -1.387024  ... -0.108300  0.005274 -0.190321 -1.175575  0.647376   \n",
       "4 -0.270533  0.817739  ... -0.009431  0.798278 -0.137458  0.141267 -0.206010   \n",
       "\n",
       "        V26       V27       V28  Amount  Class  \n",
       "0 -0.189115  0.133558 -0.021053  149.62      0  \n",
       "1  0.125895 -0.008983  0.014724    2.69      0  \n",
       "2 -0.139097 -0.055353 -0.059752  378.66      0  \n",
       "3 -0.221929  0.062723  0.061458  123.50      0  \n",
       "4  0.502292  0.219422  0.215153   69.99      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Analyse Data set (taken from Kaggle)\n",
    "#Read and visualize the data set (use pandas)\n",
    "\n",
    "raw_data=pd.read_csv('creditcard.csv')\n",
    "print(\"There are \" +str(len(raw_data))+ \" observations in the data set\")\n",
    "print(\"There are \"+str(len(raw_data.columns))+ \" variables in the dataset.\")\n",
    "\n",
    "raw_data.head()  #display first rows "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e756d682",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 2848070 observations in the inflated dataset\n",
      "There are 31 variables in the dataset\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
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       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>...</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.359807</td>\n",
       "      <td>-0.072781</td>\n",
       "      <td>2.536347</td>\n",
       "      <td>1.378155</td>\n",
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       "      <td>...</td>\n",
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       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>149.62</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.359807</td>\n",
       "      <td>-0.072781</td>\n",
       "      <td>2.536347</td>\n",
       "      <td>1.378155</td>\n",
       "      <td>-0.338321</td>\n",
       "      <td>0.462388</td>\n",
       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>0.363787</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>149.62</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.359807</td>\n",
       "      <td>-0.072781</td>\n",
       "      <td>2.536347</td>\n",
       "      <td>1.378155</td>\n",
       "      <td>-0.338321</td>\n",
       "      <td>0.462388</td>\n",
       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>0.363787</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>149.62</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.359807</td>\n",
       "      <td>-0.072781</td>\n",
       "      <td>2.536347</td>\n",
       "      <td>1.378155</td>\n",
       "      <td>-0.338321</td>\n",
       "      <td>0.462388</td>\n",
       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>0.363787</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>149.62</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.359807</td>\n",
       "      <td>-0.072781</td>\n",
       "      <td>2.536347</td>\n",
       "      <td>1.378155</td>\n",
       "      <td>-0.338321</td>\n",
       "      <td>0.462388</td>\n",
       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>0.363787</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>149.62</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
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      ],
      "text/plain": [
       "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "1   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "2   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "3   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "4   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "\n",
       "         V8        V9  ...       V21       V22       V23       V24       V25  \\\n",
       "0  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928  0.128539   \n",
       "1  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928  0.128539   \n",
       "2  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928  0.128539   \n",
       "3  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928  0.128539   \n",
       "4  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928  0.128539   \n",
       "\n",
       "        V26       V27       V28  Amount  Class  \n",
       "0 -0.189115  0.133558 -0.021053  149.62    0.0  \n",
       "1 -0.189115  0.133558 -0.021053  149.62    0.0  \n",
       "2 -0.189115  0.133558 -0.021053  149.62    0.0  \n",
       "3 -0.189115  0.133558 -0.021053  149.62    0.0  \n",
       "4 -0.189115  0.133558 -0.021053  149.62    0.0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#In real time, datasets are huge\n",
    "#Inflating original data set 10 times \n",
    "\n",
    "replica=10\n",
    "\n",
    "big_raw_data=pd.DataFrame(np.repeat(raw_data.values,replica,axis=0), columns=raw_data.columns)\n",
    "\n",
    "print(\"There are \"+ str(len(big_raw_data))+ \" observations in the inflated dataset\")\n",
    "print(\"There are \"+ str(len(big_raw_data.columns))+ \" variables in the dataset\")\n",
    "\n",
    "big_raw_data.head() #display the first rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "990f2e27",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Class variable in the displayed rows above is the target variable\n",
    "#train the model to use other variables and predic the value of class variable\n",
    "#Class values :  1(fraud) 0(otherwise)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0979014b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#retreive basic statistics of class variable \n",
    "\n",
    "# distinct classes\n",
    "labels = big_raw_data.Class.unique()\n",
    "\n",
    "# count\n",
    "sizes = big_raw_data.Class.value_counts().values\n",
    "\n",
    "# plotting the values\n",
    "fig, ax = plt.subplots()\n",
    "ax.pie(sizes, labels=labels, autopct='%1.3f%%')\n",
    "ax.set_title('Class Variable Value Counts')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "769b37f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X.shape= (2848070, 29) Y.shape= (2848070,)\n"
     ]
    }
   ],
   "source": [
    "#Preprocessing the dataset \n",
    "\n",
    "big_raw_data.iloc[:, 1:30] = StandardScaler().fit_transform(big_raw_data.iloc[:, 1:30])\n",
    "data_matrix = big_raw_data.values\n",
    "\n",
    "# Exclude the Time variable from the dataset for analysis\n",
    "X = data_matrix[:, 1:30]\n",
    "\n",
    "# labels vector\n",
    "Y = data_matrix[:, 30]\n",
    "\n",
    "# normalising data\n",
    "X = normalize(X, norm=\"l1\")\n",
    "\n",
    "print('X.shape=', X.shape, 'Y.shape=', Y.shape)  #shape of features and labels matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bd4176e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# using gc (garbage collector) to delete the data sets (not needed anymore and can occupy more space)\n",
    "\n",
    "del raw_data\n",
    "del big_raw_data\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f9eb274d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train.shape= (1993649, 29) Y_train.shape= (1993649,)\n",
      "X_test.shape= (854421, 29) Y_test.shape= (854421,)\n"
     ]
    }
   ],
   "source": [
    "#Splitting the datasets for traning and testing purposes\n",
    "\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.3, random_state=42, stratify=Y)       \n",
    "print('X_train.shape=', X_train.shape, 'Y_train.shape=', Y_train.shape)\n",
    "print('X_test.shape=', X_test.shape, 'Y_test.shape=', Y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "39e6bb12",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Building the Decision Tree Classifier (Scikit-learn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d40c2d29",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scikit-Learn --> Training time (s):  123.36663\n"
     ]
    }
   ],
   "source": [
    "w_train = compute_sample_weight('balanced', Y_train)\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "sklearn_dt = DecisionTreeClassifier(max_depth=4, random_state=35)\n",
    "\n",
    "#Training the tree classifier using scikit-learn\n",
    "\n",
    "t0 = time.time()\n",
    "sklearn_dt.fit(X_train, Y_train, sample_weight=w_train)\n",
    "sklearn_time = time.time()-t0\n",
    "print(\"Scikit-Learn --> Training time (s):  {0:.5f}\".format(sklearn_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "25f90e0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Building the Decision Tree Classifier (snapML)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a2926d83",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Snap ML--> Training time (s):  7.52148\n"
     ]
    }
   ],
   "source": [
    "#use the same w_train from above (scikit-learn)\n",
    "from snapml import DecisionTreeClassifier\n",
    "\n",
    "#feature of snapml --> increases computational ease (gpu)\n",
    "#use_gpu=True\n",
    "#snapml_dt = DecisionTreeClassifier(max_depth=4, random_state=45, use_gpu=True)\n",
    "snapml_dt = DecisionTreeClassifier(max_depth=4, random_state=45, n_jobs=4)\n",
    "\n",
    "# Training the tree classifier using snapMl\n",
    "\n",
    "t0 = time.time()\n",
    "snapml_dt.fit(X_train, Y_train, sample_weight=w_train)\n",
    "snapml_time = time.time()-t0\n",
    "print(\"Snap ML--> Training time (s):  {0:.5f}\".format(snapml_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ea96bf4d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree Classifier --> Snap ML vs. Scikit-Learn speed : 16.40x \n"
     ]
    }
   ],
   "source": [
    "#Comparing Scikit-learn and SnapML training speeds\n",
    "\n",
    "training_speed = sklearn_time/snapml_time\n",
    "print('Decision Tree Classifier --> Snap ML vs. Scikit-Learn speed : {0:.2f}x '.format(training_speed))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f77e8198",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Computing probabilties of the test samples\n",
    "\n",
    "sklearn_pred = sklearn_dt.predict_proba(X_test)[:,1]\n",
    "snapml_pred = snapml_dt.predict_proba(X_test)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "37138856",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scikit-Learn --> ROC-AUC score : 0.966\n",
      "Snap ML --> ROC-AUC score : 0.966\n"
     ]
    }
   ],
   "source": [
    "# Calculating the ROC-AUC score of the predictions of both classifiers\n",
    "\n",
    "sklearn_roc_auc = roc_auc_score(Y_test, sklearn_pred)\n",
    "print('Scikit-Learn --> ROC-AUC score : {0:.3f}'.format(sklearn_roc_auc))\n",
    "\n",
    "snapml_roc_auc = roc_auc_score(Y_test, snapml_pred)   \n",
    "print('Snap ML --> ROC-AUC score : {0:.3f}'.format(snapml_roc_auc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6ce26500",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Note: Score is same on the test dataset ; but SnapML is faster than Scikit-learn (for decision tree model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2c623042",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
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 },
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