CreditFraud_Beta_ADASYN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
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     "data": {
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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",
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       "      <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",
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       "    <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",
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       "      <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",
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       "    <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",
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       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
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       "      <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",
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       "   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": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load in \n",
    "\n",
    "\n",
    "\n",
    "# Imported Libraries\n",
    "\n",
    "import numpy as np \n",
    "import pandas as pd \n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.decomposition import PCA, TruncatedSVD\n",
    "import matplotlib.patches as mpatches\n",
    "import time\n",
    "\n",
    "# Classifier Libraries\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC,LinearSVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "from xgboost import XGBClassifier\n",
    "import collections\n",
    "\n",
    "import keras\n",
    "from keras import backend as K\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Activation\n",
    "from keras.layers.core import Dense\n",
    "from keras.optimizers import Adam\n",
    "from keras.metrics import categorical_crossentropy\n",
    "import itertools\n",
    "\n",
    "# Other Libraries\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from imblearn.pipeline import make_pipeline as imbalanced_make_pipeline\n",
    "from imblearn.over_sampling import SMOTE, ADASYN\n",
    "from imblearn.under_sampling import NearMiss\n",
    "from imblearn.metrics import classification_report_imbalanced\n",
    "from sklearn.metrics import precision_score, recall_score, f1_score, roc_auc_score, accuracy_score, classification_report\n",
    "from collections import Counter\n",
    "from sklearn.model_selection import KFold, StratifiedKFold\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "\n",
    "\n",
    "from sklearn.model_selection import cross_val_predict\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "\n",
    "\n",
    "df = pd.read_csv('creditcardcomplete.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pip install xgboost\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# pip install tensorflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No Frauds 99.83 % of the dataset\n",
      "Frauds 0.17 % of the dataset\n"
     ]
    }
   ],
   "source": [
    "# The classes are heavily skewed we need to solve this issue later.\n",
    "print('No Frauds', round(df['Class'].value_counts()[0]/len(df) * 100,2), '% of the dataset')\n",
    "print('Frauds', round(df['Class'].value_counts()[1]/len(df) * 100,2), '% of the dataset')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Since most of our data has already been scaled we should scale the columns that are left to scale (Amount and Time)\n",
    "from sklearn.preprocessing import StandardScaler, RobustScaler\n",
    "\n",
    "# RobustScaler is less prone to outliers.\n",
    "\n",
    "std_scaler = StandardScaler()\n",
    "rob_scaler = RobustScaler()\n",
    "\n",
    "df['scaled_amount'] = rob_scaler.fit_transform(df['Amount'].values.reshape(-1,1))\n",
    "df['scaled_time'] = rob_scaler.fit_transform(df['Time'].values.reshape(-1,1))\n",
    "\n",
    "df.drop(['Time','Amount'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>scaled_amount</th>\n",
       "      <th>scaled_time</th>\n",
       "      <th>V1</th>\n",
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       "      <th>Class</th>\n",
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       "      <th>0</th>\n",
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       "      <td>-0.072781</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.269825</td>\n",
       "      <td>-0.994983</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>...</td>\n",
       "      <td>-0.069083</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>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.983721</td>\n",
       "      <td>-0.994972</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>...</td>\n",
       "      <td>0.524980</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.418291</td>\n",
       "      <td>-0.994972</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>...</td>\n",
       "      <td>-0.208038</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>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.670579</td>\n",
       "      <td>-0.994960</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>...</td>\n",
       "      <td>0.408542</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>0</td>\n",
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      "text/plain": [
       "   scaled_amount  scaled_time        V1        V2        V3        V4  \\\n",
       "0       1.783274    -0.994983 -1.359807 -0.072781  2.536347  1.378155   \n",
       "1      -0.269825    -0.994983  1.191857  0.266151  0.166480  0.448154   \n",
       "2       4.983721    -0.994972 -1.358354 -1.340163  1.773209  0.379780   \n",
       "3       1.418291    -0.994972 -0.966272 -0.185226  1.792993 -0.863291   \n",
       "4       0.670579    -0.994960 -1.158233  0.877737  1.548718  0.403034   \n",
       "\n",
       "         V5        V6        V7        V8  ...       V20       V21       V22  \\\n",
       "0 -0.338321  0.462388  0.239599  0.098698  ...  0.251412 -0.018307  0.277838   \n",
       "1  0.060018 -0.082361 -0.078803  0.085102  ... -0.069083 -0.225775 -0.638672   \n",
       "2 -0.503198  1.800499  0.791461  0.247676  ...  0.524980  0.247998  0.771679   \n",
       "3 -0.010309  1.247203  0.237609  0.377436  ... -0.208038 -0.108300  0.005274   \n",
       "4 -0.407193  0.095921  0.592941 -0.270533  ...  0.408542 -0.009431  0.798278   \n",
       "\n",
       "        V23       V24       V25       V26       V27       V28  Class  \n",
       "0 -0.110474  0.066928  0.128539 -0.189115  0.133558 -0.021053      0  \n",
       "1  0.101288 -0.339846  0.167170  0.125895 -0.008983  0.014724      0  \n",
       "2  0.909412 -0.689281 -0.327642 -0.139097 -0.055353 -0.059752      0  \n",
       "3 -0.190321 -1.175575  0.647376 -0.221929  0.062723  0.061458      0  \n",
       "4 -0.137458  0.141267 -0.206010  0.502292  0.219422  0.215153      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_amount = df['scaled_amount']\n",
    "scaled_time = df['scaled_time']\n",
    "\n",
    "df.drop(['scaled_amount', 'scaled_time'], axis=1, inplace=True)\n",
    "df.insert(0, 'scaled_amount', scaled_amount)\n",
    "df.insert(1, 'scaled_time', scaled_time)\n",
    "\n",
    "# Amount and Time are Scaled!\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No Frauds 99.83 % of the dataset\n",
      "Frauds 0.17 % of the dataset\n",
      "Train: [ 30473  30496  31002 ... 284804 284805 284806] Test: [    0     1     2 ... 57017 57018 57019]\n",
      "Train: [     0      1      2 ... 284804 284805 284806] Test: [ 30473  30496  31002 ... 113964 113965 113966]\n",
      "Train: [     0      1      2 ... 284804 284805 284806] Test: [ 81609  82400  83053 ... 170946 170947 170948]\n",
      "Train: [     0      1      2 ... 284804 284805 284806] Test: [150654 150660 150661 ... 227866 227867 227868]\n",
      "Train: [     0      1      2 ... 227866 227867 227868] Test: [212516 212644 213092 ... 284804 284805 284806]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "Label Distributions: \n",
      "\n",
      "[0.99827076 0.00172924]\n",
      "[0.99827952 0.00172048]\n"
     ]
    }
   ],
   "source": [
    "print('No Frauds', round(df['Class'].value_counts()[0]/len(df) * 100,2), '% of the dataset')\n",
    "print('Frauds', round(df['Class'].value_counts()[1]/len(df) * 100,2), '% of the dataset')\n",
    "\n",
    "X = df.drop('Class', axis=1)\n",
    "y = df['Class']\n",
    "\n",
    "sss = StratifiedKFold(n_splits=5, random_state=None, shuffle=False)\n",
    "\n",
    "for train_index, test_index in sss.split(X, y):\n",
    "    print(\"Train:\", train_index, \"Test:\", test_index)\n",
    "    original_Xtrain, original_Xtest = X.iloc[train_index], X.iloc[test_index]\n",
    "    original_ytrain, original_ytest = y.iloc[train_index], y.iloc[test_index]\n",
    "\n",
    "# We already have X_train and y_train for undersample data thats why I am using original to distinguish and to not overwrite these variables.\n",
    "# original_Xtrain, original_Xtest, original_ytrain, original_ytest = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Check the Distribution of the labels\n",
    "\n",
    "\n",
    "# Turn into an array\n",
    "original_Xtrain = original_Xtrain.values\n",
    "original_Xtest = original_Xtest.values\n",
    "original_ytrain = original_ytrain.values\n",
    "original_ytest = original_ytest.values\n",
    "\n",
    "# See if both the train and test label distribution are similarly distributed\n",
    "train_unique_label, train_counts_label = np.unique(original_ytrain, return_counts=True)\n",
    "test_unique_label, test_counts_label = np.unique(original_ytest, return_counts=True)\n",
    "print('-' * 100)\n",
    "\n",
    "print('Label Distributions: \\n')\n",
    "print(train_counts_label/ len(original_ytrain))\n",
    "print(test_counts_label/ len(original_ytest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'plt' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-8354ea46e90e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m                           \u001b[0mnormalize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m                           \u001b[0mtitle\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'Confusion matrix'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m                           cmap=plt.cm.Blues):\n\u001b[0m\u001b[0;32m      5\u001b[0m     \"\"\"\n\u001b[0;32m      6\u001b[0m     \u001b[0mThis\u001b[0m \u001b[0mfunction\u001b[0m \u001b[0mprints\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mplots\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mconfusion\u001b[0m \u001b[0mmatrix\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'plt' is not defined"
     ]
    }
   ],
   "source": [
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "\n",
    "    print(cm)\n",
    "\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title, fontsize=14)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt),\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_confusion_matrx(original_ytest,fraud_predictions, name = \" \"):\n",
    "    oversample_smote = confusion_matrix(original_ytest, fraud_predictions)\n",
    "    actual_cm = confusion_matrix(original_ytest, original_ytest)\n",
    "    labels = ['No Fraud', 'Fraud']\n",
    "\n",
    "    fig = plt.figure(figsize=(16,8))\n",
    "\n",
    "    fig.add_subplot(221)\n",
    "    plot_confusion_matrix(oversample_smote, labels, title=\"ADASYN \"+name+\"\\n Confusion Matrix\", cmap=plt.cm.Oranges)\n",
    "\n",
    "    fig.add_subplot(222)\n",
    "    plot_confusion_matrix(actual_cm, labels, title=\"Confusion Matrix \\n (Ideal Case)\", cmap=plt.cm.Greens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tune_the_estimator(estimator, params,Xsm_train,ysm_train, X_test, y_test):\n",
    "\n",
    "    grid_search = GridSearchCV(estimator = estimator, param_grid = params, \n",
    "                              cv = 2, n_jobs = -1, verbose = 2)\n",
    "\n",
    "    grid_search.fit(Xsm_train,ysm_train)\n",
    "    bestXgbClassifier = grid_search.best_estimator_\n",
    "    print(bestXgbClassifier)\n",
    "    fraud_predictions = bestXgbClassifier.predict(X_test)\n",
    "    print(classification_report(y_test, fraud_predictions))\n",
    "    print(bestXgbClassifier)\n",
    "    print('-' * 45 + \"Test Performance\"+\"-\"*45)\n",
    "    print('Recall Score: {:.2f} '    .format(recall_score   (y_test, fraud_predictions)))\n",
    "    print('Precision Score: {:.2f}'  .format(precision_score(y_test, fraud_predictions)))\n",
    "    print('F1 Score: {:.2f}'         .format(f1_score       (y_test, fraud_predictions)))\n",
    "    print('Accuracy Score: {:.2f} \\n'.format(accuracy_score (y_test, fraud_predictions)))\n",
    "#     print('---' * 45)\n",
    "    return fraud_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_params = {\n",
    "        'n_estimators' : [200,300],\n",
    "        'learning_rate' : [0.001,0.01],\n",
    "        'min_child_weight': [2,4],\n",
    "        'gamma': [0.1],\n",
    "        'subsample': [0.5],\n",
    "        'colsample_bytree': [0.4],\n",
    "        'max_depth': [4,8]\n",
    "        }\n",
    "# knears_params = {\"n_neighbors\": list(range(2,10,2)), 'algorithm': ['auto', 'ball_tree', 'kd_tree', 'brute']}\n",
    "\n",
    "knears_params = {\"n_neighbors\": [3], 'algorithm': ['auto']}\n",
    "\n",
    "\n",
    "log_reg_params = {\"penalty\": ['l2'], 'C': [ 0.01, 0.1, 1, 10]}\n",
    "\n",
    "\n",
    "random_forest_parameters = {\n",
    "    'criterion'         : [ 'gini'],     \n",
    "    'n_estimators'      : [100, 200],\n",
    "    'max_depth'         : [15],\n",
    "    \n",
    "    'min_samples_split' : [2],\n",
    "    'min_samples_leaf' :  [2] \n",
    "   \n",
    "}\n",
    "\n",
    "tree_params = {\"criterion\": [\"gini\", \"entropy\"], \"max_depth\": list(range(2,4,1)), \n",
    "              \"min_samples_leaf\": list(range(5,7,1))}\n",
    "\n",
    "svc_params = {'C': [0.5, 0.7, 0.9, 1]}\n",
    "\n",
    "\n",
    "\n",
    "classifiers_for_tune = {\n",
    "    \"LogisiticRegression\": LogisticRegression(solver = \"lbfgs\"),\n",
    "    \"KNearest\": KNeighborsClassifier(),\n",
    "    \"SVC\": LinearSVC(),\n",
    "    \"DecisionTreeClassifier\": DecisionTreeClassifier(),\n",
    "    \"Random Forest\":RandomForestClassifier(),\n",
    "    \"xGBClassifier\":XGBClassifier()\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SMOTE Technique (OverSampling) After splitting and Cross Validating\n",
    "sm = ADASYN(ratio='minority', random_state=42)\n",
    "# Xsm_train, ysm_train = sm.fit_sample(X_train, y_train)\n",
    "\n",
    "\n",
    "# This will be the data were we are going to \n",
    "Xsm_train, ysm_train = sm.fit_sample(original_Xtrain, original_ytrain)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 2 folds for each of 4 candidates, totalling 8 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   3 out of   8 | elapsed:   40.6s remaining:  1.1min\n",
      "[Parallel(n_jobs=-1)]: Done   8 out of   8 | elapsed:   43.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   8 out of   8 | elapsed:   43.1s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression(C=0.01, class_weight=None, dual=False, fit_intercept=True,\n",
      "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
      "          n_jobs=None, penalty='l2', random_state=None, solver='lbfgs',\n",
      "          tol=0.0001, verbose=0, warm_start=False)\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.96      0.98     56863\n",
      "           1       0.04      0.90      0.08        98\n",
      "\n",
      "   micro avg       0.96      0.96      0.96     56961\n",
      "   macro avg       0.52      0.93      0.53     56961\n",
      "weighted avg       1.00      0.96      0.98     56961\n",
      "\n",
      "LogisticRegression(C=0.01, class_weight=None, dual=False, fit_intercept=True,\n",
      "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
      "          n_jobs=None, penalty='l2', random_state=None, solver='lbfgs',\n",
      "          tol=0.0001, verbose=0, warm_start=False)\n",
      "---------------------------------------------Test Performance---------------------------------------------\n",
      "Recall Score: 0.90 \n",
      "Precision Score: 0.04\n",
      "F1 Score: 0.08\n",
      "Accuracy Score: 0.96 \n",
      "\n",
      "Confusion matrix, without normalization\n",
      "Confusion matrix, without normalization\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fraud_predictions = tune_the_estimator(estimator=classifiers_for_tune[\"LogisiticRegression\"], params=log_reg_params,Xsm_train = Xsm_train,ysm_train = ysm_train, \n",
    "                      X_test = original_Xtest, y_test = original_ytest)\n",
    "\n",
    "plot_confusion_matrx(original_ytest,fraud_predictions, name=\"LogisiticRegressor\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_inputs = Xsm_train.shape[1]\n",
    "\n",
    "oversample_model = Sequential([\n",
    "    Dense(n_inputs, input_shape=(n_inputs, ), activation='relu'),\n",
    "    Dense(32, activation='relu'),\n",
    "    Dense(2, activation='softmax')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "oversample_model.compile(Adam(lr=0.001), loss='sparse_categorical_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 363962 samples, validate on 90991 samples\n",
      "Epoch 1/20\n",
      " - 4s - loss: 0.0724 - accuracy: 0.9756 - val_loss: 2.7207 - val_accuracy: 0.7149\n",
      "Epoch 2/20\n",
      " - 4s - loss: 0.0102 - accuracy: 0.9982 - val_loss: 3.3583 - val_accuracy: 0.7072\n",
      "Epoch 3/20\n",
      " - 3s - loss: 0.0065 - accuracy: 0.9989 - val_loss: 3.2277 - val_accuracy: 0.7142\n",
      "Epoch 4/20\n",
      " - 4s - loss: 0.0049 - accuracy: 0.9992 - val_loss: 2.6077 - val_accuracy: 0.7483\n",
      "Epoch 5/20\n",
      " - 4s - loss: 0.0039 - accuracy: 0.9993 - val_loss: 2.4562 - val_accuracy: 0.7382\n",
      "Epoch 6/20\n",
      " - 4s - loss: 0.0033 - accuracy: 0.9994 - val_loss: 2.5705 - val_accuracy: 0.7140\n",
      "Epoch 7/20\n",
      " - 4s - loss: 0.0027 - accuracy: 0.9995 - val_loss: 1.6735 - val_accuracy: 0.7649\n",
      "Epoch 8/20\n",
      " - 4s - loss: 0.0025 - accuracy: 0.9996 - val_loss: 1.4172 - val_accuracy: 0.7543\n",
      "Epoch 9/20\n",
      " - 4s - loss: 0.0022 - accuracy: 0.9996 - val_loss: 1.0957 - val_accuracy: 0.8230\n",
      "Epoch 10/20\n",
      " - 3s - loss: 0.0019 - accuracy: 0.9997 - val_loss: 1.1604 - val_accuracy: 0.8056\n",
      "Epoch 11/20\n",
      " - 3s - loss: 0.0018 - accuracy: 0.9997 - val_loss: 1.0692 - val_accuracy: 0.8166\n",
      "Epoch 12/20\n",
      " - 4s - loss: 0.0016 - accuracy: 0.9997 - val_loss: 0.9103 - val_accuracy: 0.8247\n",
      "Epoch 13/20\n",
      " - 3s - loss: 0.0014 - accuracy: 0.9997 - val_loss: 0.7802 - val_accuracy: 0.8167\n",
      "Epoch 14/20\n",
      " - 4s - loss: 0.0013 - accuracy: 0.9998 - val_loss: 0.8365 - val_accuracy: 0.8143\n",
      "Epoch 15/20\n",
      " - 3s - loss: 0.0012 - accuracy: 0.9998 - val_loss: 0.6625 - val_accuracy: 0.8303\n",
      "Epoch 16/20\n",
      " - 3s - loss: 0.0011 - accuracy: 0.9998 - val_loss: 0.8827 - val_accuracy: 0.8041\n",
      "Epoch 17/20\n",
      " - 3s - loss: 0.0010 - accuracy: 0.9998 - val_loss: 0.6252 - val_accuracy: 0.8014\n",
      "Epoch 18/20\n",
      " - 4s - loss: 0.0011 - accuracy: 0.9998 - val_loss: 0.4690 - val_accuracy: 0.8884\n",
      "Epoch 19/20\n",
      " - 3s - loss: 0.0011 - accuracy: 0.9997 - val_loss: 0.6696 - val_accuracy: 0.8212\n",
      "Epoch 20/20\n",
      " - 3s - loss: 9.2841e-04 - accuracy: 0.9998 - val_loss: 0.3852 - val_accuracy: 0.8728\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.callbacks.History at 0x18e44c7e248>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oversample_model.fit(Xsm_train, ysm_train, validation_split=0.2, batch_size=300, epochs=20, shuffle=True, verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "oversample_predictions = oversample_model.predict(original_Xtest, batch_size=200, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "oversample_fraud_predictions = oversample_model.predict_classes(original_Xtest, batch_size=200, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "Confusion matrix, without normalization\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrx(original_ytest,oversample_fraud_predictions, name = \"Neural Networks\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(56961, 30)\n",
      "(454953, 30)\n",
      "1    227501\n",
      "0    227452\n",
      "Name: 0, dtype: int64\n",
      "0    56863\n",
      "1       98\n",
      "Name: 0, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(original_Xtest.shape)\n",
    "# type(Xsm_train)\n",
    "print(Xsm_train.shape)\n",
    "print(pd.DataFrame(ysm_train)[0].value_counts())\n",
    "\n",
    "print(pd.DataFrame(original_ytest)[0].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 2 folds for each of 2 candidates, totalling 4 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   4 out of   4 | elapsed: 12.2min remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   4 out of   4 | elapsed: 12.2min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
      "            max_depth=15, max_features='auto', max_leaf_nodes=None,\n",
      "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "            min_samples_leaf=2, min_samples_split=2,\n",
      "            min_weight_fraction_leaf=0.0, n_estimators=200, n_jobs=None,\n",
      "            oob_score=False, random_state=None, verbose=0,\n",
      "            warm_start=False)\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     56863\n",
      "           1       1.00      0.61      0.76        98\n",
      "\n",
      "   micro avg       1.00      1.00      1.00     56961\n",
      "   macro avg       1.00      0.81      0.88     56961\n",
      "weighted avg       1.00      1.00      1.00     56961\n",
      "\n",
      "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
      "            max_depth=15, max_features='auto', max_leaf_nodes=None,\n",
      "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "            min_samples_leaf=2, min_samples_split=2,\n",
      "            min_weight_fraction_leaf=0.0, n_estimators=200, n_jobs=None,\n",
      "            oob_score=False, random_state=None, verbose=0,\n",
      "            warm_start=False)\n",
      "---------------------------------------------Test Performance---------------------------------------------\n",
      "Recall Score: 0.61 \n",
      "Precision Score: 1.00\n",
      "F1 Score: 0.76\n",
      "Accuracy Score: 1.00 \n",
      "\n",
      "Confusion matrix, without normalization\n",
      "Confusion matrix, without normalization\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fraud_predictions = tune_the_estimator(estimator=classifiers_for_tune[\"Random Forest\"], params=random_forest_parameters,Xsm_train = Xsm_train,ysm_train = ysm_train, \n",
    "                      X_test = original_Xtest, y_test = original_ytest)\n",
    "\n",
    "plot_confusion_matrx(original_ytest,fraud_predictions, name = \"Random Forest\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## XGBoost Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 2 folds for each of 16 candidates, totalling 32 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-17-3672053c99cf>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m fraud_predictions = tune_the_estimator(estimator=classifiers_for_tune[\"xGBClassifier\"], params=xgb_params,Xsm_train = Xsm_train,ysm_train = ysm_train, \n\u001b[1;32m----> 2\u001b[1;33m                       X_test = original_Xtest, y_test = original_ytest)\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mplot_confusion_matrx\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moriginal_ytest\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mfraud_predictions\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"XGBoost\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-10-e5d2a5345026>\u001b[0m in \u001b[0;36mtune_the_estimator\u001b[1;34m(estimator, params, Xsm_train, ysm_train, X_test, y_test)\u001b[0m\n\u001b[0;32m      4\u001b[0m                               cv = 2, n_jobs = -1, verbose = 2)\n\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m     \u001b[0mgrid_search\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mXsm_train\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mysm_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m     \u001b[0mbestXgbClassifier\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgrid_search\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbest_estimator_\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbestXgbClassifier\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, groups, **fit_params)\u001b[0m\n\u001b[0;32m    720\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mresults_container\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    721\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 722\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_run_search\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mevaluate_candidates\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    723\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    724\u001b[0m         \u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mresults_container\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py\u001b[0m in \u001b[0;36m_run_search\u001b[1;34m(self, evaluate_candidates)\u001b[0m\n\u001b[0;32m   1189\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_run_search\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mevaluate_candidates\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1190\u001b[0m         \u001b[1;34m\"\"\"Search all candidates in param_grid\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1191\u001b[1;33m         \u001b[0mevaluate_candidates\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mParameterGrid\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparam_grid\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1192\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1193\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py\u001b[0m in \u001b[0;36mevaluate_candidates\u001b[1;34m(candidate_params)\u001b[0m\n\u001b[0;32m    709\u001b[0m                                \u001b[1;32mfor\u001b[0m \u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    710\u001b[0m                                in product(candidate_params,\n\u001b[1;32m--> 711\u001b[1;33m                                           cv.split(X, y, groups)))\n\u001b[0m\u001b[0;32m    712\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    713\u001b[0m                 \u001b[0mall_candidate_params\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcandidate_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\externals\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m    928\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    929\u001b[0m             \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mretrieval_context\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 930\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mretrieve\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    931\u001b[0m             \u001b[1;31m# Make sure that we get a last message telling us we are done\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    932\u001b[0m             \u001b[0melapsed_time\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_start_time\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\externals\\joblib\\parallel.py\u001b[0m in \u001b[0;36mretrieve\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    831\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    832\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'supports_timeout'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 833\u001b[1;33m                     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    834\u001b[0m                 \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    835\u001b[0m                     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\externals\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36mwrap_future_result\u001b[1;34m(future, timeout)\u001b[0m\n\u001b[0;32m    519\u001b[0m         AsyncResults.get from multiprocessing.\"\"\"\n\u001b[0;32m    520\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 521\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfuture\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    522\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mLokyTimeoutError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    523\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mTimeoutError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\concurrent\\futures\\_base.py\u001b[0m in \u001b[0;36mresult\u001b[1;34m(self, timeout)\u001b[0m\n\u001b[0;32m    428\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__get_result\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    429\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 430\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_condition\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    431\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    432\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_state\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mCANCELLED\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mCANCELLED_AND_NOTIFIED\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\threading.py\u001b[0m in \u001b[0;36mwait\u001b[1;34m(self, timeout)\u001b[0m\n\u001b[0;32m    294\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m    \u001b[1;31m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    295\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 296\u001b[1;33m                 \u001b[0mwaiter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    297\u001b[0m                 \u001b[0mgotit\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    298\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "fraud_predictions = tune_the_estimator(estimator=classifiers_for_tune[\"xGBClassifier\"], params=xgb_params,Xsm_train = Xsm_train,ysm_train = ysm_train, \n",
    "                      X_test = original_Xtest, y_test = original_ytest)\n",
    "\n",
    "\n",
    "plot_confusion_matrx(original_ytest,fraud_predictions, name = \"XGBoost\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Linear SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 2 folds for each of 4 candidates, totalling 8 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   3 out of   8 | elapsed:  1.4min remaining:  2.4min\n",
      "[Parallel(n_jobs=-1)]: Done   8 out of   8 | elapsed:  1.7min remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   8 out of   8 | elapsed:  1.7min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearSVC(C=1, class_weight=None, dual=True, fit_intercept=True,\n",
      "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
      "     multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n",
      "     verbose=0)\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.96      0.98     56863\n",
      "           1       0.04      0.90      0.08        98\n",
      "\n",
      "   micro avg       0.96      0.96      0.96     56961\n",
      "   macro avg       0.52      0.93      0.53     56961\n",
      "weighted avg       1.00      0.96      0.98     56961\n",
      "\n",
      "LinearSVC(C=1, class_weight=None, dual=True, fit_intercept=True,\n",
      "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
      "     multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n",
      "     verbose=0)\n",
      "---------------------------------------------Test Performance---------------------------------------------\n",
      "Recall Score: 0.90 \n",
      "Precision Score: 0.04\n",
      "F1 Score: 0.08\n",
      "Accuracy Score: 0.96 \n",
      "\n",
      "Confusion matrix, without normalization\n",
      "Confusion matrix, without normalization\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fraud_predictions = tune_the_estimator(estimator=classifiers_for_tune[\"SVC\"], params=svc_params,Xsm_train = Xsm_train,ysm_train = ysm_train, \n",
    "                      X_test = original_Xtest, y_test = original_ytest)\n",
    "\n",
    "plot_confusion_matrx(original_ytest,fraud_predictions, name = \"SVC\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decsion tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 2 folds for each of 8 candidates, totalling 16 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  10 out of  16 | elapsed:    8.9s remaining:    5.3s\n",
      "[Parallel(n_jobs=-1)]: Done  16 out of  16 | elapsed:   11.7s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=2,\n",
      "            max_features=None, max_leaf_nodes=None,\n",
      "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "            min_samples_leaf=5, min_samples_split=2,\n",
      "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
      "            splitter='best')\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.83      0.91     56863\n",
      "           1       0.01      0.94      0.02        98\n",
      "\n",
      "   micro avg       0.83      0.83      0.83     56961\n",
      "   macro avg       0.50      0.89      0.46     56961\n",
      "weighted avg       1.00      0.83      0.91     56961\n",
      "\n",
      "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=2,\n",
      "            max_features=None, max_leaf_nodes=None,\n",
      "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "            min_samples_leaf=5, min_samples_split=2,\n",
      "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
      "            splitter='best')\n",
      "---------------------------------------------Test Performance---------------------------------------------\n",
      "Recall Score: 0.94 \n",
      "Precision Score: 0.01\n",
      "F1 Score: 0.02\n",
      "Accuracy Score: 0.83 \n",
      "\n",
      "Confusion matrix, without normalization\n",
      "Confusion matrix, without normalization\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fraud_predictions = tune_the_estimator(estimator=classifiers_for_tune[\"DecisionTreeClassifier\"], params=tree_params,Xsm_train = Xsm_train,ysm_train = ysm_train, \n",
    "                      X_test = original_Xtest, y_test = original_ytest)\n",
    "\n",
    "\n",
    "plot_confusion_matrx(original_ytest,fraud_predictions, name = \"DecisionTreeClassifier\")\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 2 folds for each of 1 candidates, totalling 2 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-19-bf93965358fd>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m fraud_predictions = tune_the_estimator(estimator=classifiers_for_tune[\"KNearest\"], params=knears_params,Xsm_train = Xsm_train,ysm_train = ysm_train, \n\u001b[1;32m----> 2\u001b[1;33m                       X_test = original_Xtest, y_test = original_ytest)\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mplot_confusion_matrx\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moriginal_ytest\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mfraud_predictions\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"KNN\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-11-e5d2a5345026>\u001b[0m in \u001b[0;36mtune_the_estimator\u001b[1;34m(estimator, params, Xsm_train, ysm_train, X_test, y_test)\u001b[0m\n\u001b[0;32m      4\u001b[0m                               cv = 2, n_jobs = -1, verbose = 2)\n\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m     \u001b[0mgrid_search\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mXsm_train\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mysm_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m     \u001b[0mbestXgbClassifier\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgrid_search\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbest_estimator_\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbestXgbClassifier\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, groups, **fit_params)\u001b[0m\n\u001b[0;32m    720\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mresults_container\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    721\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 722\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_run_search\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mevaluate_candidates\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    723\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    724\u001b[0m         \u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mresults_container\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py\u001b[0m in \u001b[0;36m_run_search\u001b[1;34m(self, evaluate_candidates)\u001b[0m\n\u001b[0;32m   1189\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_run_search\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mevaluate_candidates\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1190\u001b[0m         \u001b[1;34m\"\"\"Search all candidates in param_grid\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1191\u001b[1;33m         \u001b[0mevaluate_candidates\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mParameterGrid\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparam_grid\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1192\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1193\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py\u001b[0m in \u001b[0;36mevaluate_candidates\u001b[1;34m(candidate_params)\u001b[0m\n\u001b[0;32m    709\u001b[0m                                \u001b[1;32mfor\u001b[0m \u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    710\u001b[0m                                in product(candidate_params,\n\u001b[1;32m--> 711\u001b[1;33m                                           cv.split(X, y, groups)))\n\u001b[0m\u001b[0;32m    712\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    713\u001b[0m                 \u001b[0mall_candidate_params\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcandidate_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\externals\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m    928\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    929\u001b[0m             \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mretrieval_context\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 930\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mretrieve\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    931\u001b[0m             \u001b[1;31m# Make sure that we get a last message telling us we are done\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    932\u001b[0m             \u001b[0melapsed_time\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_start_time\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\externals\\joblib\\parallel.py\u001b[0m in \u001b[0;36mretrieve\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    831\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    832\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'supports_timeout'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 833\u001b[1;33m                     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    834\u001b[0m                 \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    835\u001b[0m                     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\externals\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36mwrap_future_result\u001b[1;34m(future, timeout)\u001b[0m\n\u001b[0;32m    519\u001b[0m         AsyncResults.get from multiprocessing.\"\"\"\n\u001b[0;32m    520\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 521\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfuture\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    522\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mLokyTimeoutError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    523\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mTimeoutError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\concurrent\\futures\\_base.py\u001b[0m in \u001b[0;36mresult\u001b[1;34m(self, timeout)\u001b[0m\n\u001b[0;32m    428\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__get_result\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    429\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 430\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_condition\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    431\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    432\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_state\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mCANCELLED\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mCANCELLED_AND_NOTIFIED\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\threading.py\u001b[0m in \u001b[0;36mwait\u001b[1;34m(self, timeout)\u001b[0m\n\u001b[0;32m    294\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m    \u001b[1;31m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    295\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 296\u001b[1;33m                 \u001b[0mwaiter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    297\u001b[0m                 \u001b[0mgotit\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    298\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "fraud_predictions = tune_the_estimator(estimator=classifiers_for_tune[\"KNearest\"], params=knears_params,Xsm_train = Xsm_train,ysm_train = ysm_train, \n",
    "                      X_test = original_Xtest, y_test = original_ytest)\n",
    "\n",
    "plot_confusion_matrx(original_ytest,fraud_predictions,name=\"KNN\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "precision: 0.8441558441558441\n",
      "Recall: 0.6632653061224489\n",
      "F1:  0.7428571428571428\n"
     ]
    }
   ],
   "source": [
    "a = 12\n",
    "b = 65\n",
    "c = 33\n",
    "p=b/(a+b)\n",
    "r=b/(c+b)\n",
    "\n",
    "print(\"precision:\",p)\n",
    "print(\"Recall:\",r)\n",
    "print(\"F1: \",2*((p*r)/(p+r)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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