test-andrew.ipynb

{
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13-final"
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  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3 (ipykernel)"
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 },
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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics\n",
    "from datetime import datetime\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import accuracy_score \n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from imblearn.combine import SMOTETomek\n",
    "from imblearn.under_sampling import RandomUnderSampler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This turns all the axes white in all the matplotlib plots. Comment this out if you dont want that\n",
    "COLOR = 'black'\n",
    "matplotlib.rcParams['text.color'] = COLOR\n",
    "matplotlib.rcParams['axes.labelcolor'] = COLOR\n",
    "matplotlib.rcParams['xtick.color'] = COLOR\n",
    "matplotlib.rcParams['ytick.color'] = COLOR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   user_id    signup_time purchase_time  purchase_value      device_id source  \\\n",
       "0    22058  2/24/15 22:55  4/18/15 2:47              34  QVPSPJUOCKZAR    SEO   \n",
       "1   333320   6/7/15 20:39   6/8/15 1:38              16  EOGFQPIZPYXFZ    Ads   \n",
       "2     1359   1/1/15 18:52  1/1/15 18:52              15  YSSKYOSJHPPLJ    SEO   \n",
       "3   150084  4/28/15 21:13  5/4/15 13:54              44  ATGTXKYKUDUQN    SEO   \n",
       "4   221365   7/21/15 7:09  9/9/15 18:40              39  NAUITBZFJKHWW    Ads   \n",
       "\n",
       "  browser sex  age    ip_address  class  \n",
       "0  Chrome   M   39  7.327584e+08      0  \n",
       "1  Chrome   F   53  3.503114e+08      0  \n",
       "2   Opera   M   53  2.621474e+09      1  \n",
       "3  Safari   M   41  3.840542e+09      0  \n",
       "4  Safari   M   45  4.155831e+08      0  "
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>user_id</th>\n      <th>signup_time</th>\n      <th>purchase_time</th>\n      <th>purchase_value</th>\n      <th>device_id</th>\n      <th>source</th>\n      <th>browser</th>\n      <th>sex</th>\n      <th>age</th>\n      <th>ip_address</th>\n      <th>class</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>22058</td>\n      <td>2/24/15 22:55</td>\n      <td>4/18/15 2:47</td>\n      <td>34</td>\n      <td>QVPSPJUOCKZAR</td>\n      <td>SEO</td>\n      <td>Chrome</td>\n      <td>M</td>\n      <td>39</td>\n      <td>7.327584e+08</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>333320</td>\n      <td>6/7/15 20:39</td>\n      <td>6/8/15 1:38</td>\n      <td>16</td>\n      <td>EOGFQPIZPYXFZ</td>\n      <td>Ads</td>\n      <td>Chrome</td>\n      <td>F</td>\n      <td>53</td>\n      <td>3.503114e+08</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1359</td>\n      <td>1/1/15 18:52</td>\n      <td>1/1/15 18:52</td>\n      <td>15</td>\n      <td>YSSKYOSJHPPLJ</td>\n      <td>SEO</td>\n      <td>Opera</td>\n      <td>M</td>\n      <td>53</td>\n      <td>2.621474e+09</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>150084</td>\n      <td>4/28/15 21:13</td>\n      <td>5/4/15 13:54</td>\n      <td>44</td>\n      <td>ATGTXKYKUDUQN</td>\n      <td>SEO</td>\n      <td>Safari</td>\n      <td>M</td>\n      <td>41</td>\n      <td>3.840542e+09</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>221365</td>\n      <td>7/21/15 7:09</td>\n      <td>9/9/15 18:40</td>\n      <td>39</td>\n      <td>NAUITBZFJKHWW</td>\n      <td>Ads</td>\n      <td>Safari</td>\n      <td>M</td>\n      <td>45</td>\n      <td>4.155831e+08</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 21
    }
   ],
   "source": [
    "df_ecommerce = pd.read_csv('datasets/ecommerce/Fraud_Data.csv')\n",
    "df_ecommerce.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "906672"
      ]
     },
     "metadata": {},
     "execution_count": 22
    }
   ],
   "source": [
    "# Features that are used, isFraud is the target\n",
    "features = ['purchase_value', 'source', 'browser', 'sex', 'age', 'class']\n",
    "df = df_ecommerce[features]\n",
    "df.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sort features into categorical and numerical types\n",
    "target = 'class'\n",
    "# Categorical features\n",
    "cat = ['source', 'browser', 'sex'] \n",
    "num = ['purchase_value', 'age']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   purchase_value source browser sex  age  class\n",
       "0              34    SEO  Chrome   M   39      0\n",
       "1              16    Ads  Chrome   F   53      0\n",
       "2              15    SEO   Opera   M   53      1\n",
       "3              44    SEO  Safari   M   41      0\n",
       "4              39    Ads  Safari   M   45      0"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>purchase_value</th>\n      <th>source</th>\n      <th>browser</th>\n      <th>sex</th>\n      <th>age</th>\n      <th>class</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>34</td>\n      <td>SEO</td>\n      <td>Chrome</td>\n      <td>M</td>\n      <td>39</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>16</td>\n      <td>Ads</td>\n      <td>Chrome</td>\n      <td>F</td>\n      <td>53</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>15</td>\n      <td>SEO</td>\n      <td>Opera</td>\n      <td>M</td>\n      <td>53</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>44</td>\n      <td>SEO</td>\n      <td>Safari</td>\n      <td>M</td>\n      <td>41</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>39</td>\n      <td>Ads</td>\n      <td>Safari</td>\n      <td>M</td>\n      <td>45</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 24
    }
   ],
   "source": [
    "# Drop rows with missing features\n",
    "df = df.dropna()\n",
    "y = df[target].values\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_cat = df.filter(items = cat).values\n",
    "x_num = df.filter(items = num).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "labelencoder_X = LabelEncoder()\n",
    "# Label encode every categorical column\n",
    "for i in range(len(cat)): \n",
    "    x_cat[:, i] = labelencoder_X.fit_transform(x_cat[:, i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build input vector X, the training data\n",
    "X = np.concatenate((x_cat, x_num), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(105778, 45334, 105778, 45334)"
      ]
     },
     "metadata": {},
     "execution_count": 38
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 0)\n",
    "len(X_train), len(X_test),len(y_train), len(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(81950, 81950)"
      ]
     },
     "metadata": {},
     "execution_count": 47
    }
   ],
   "source": [
    "# resampling using SMOTETomek \n",
    "resamp = SMOTETomek(random_state=0)\n",
    "X_test_resample, y_test_resample = resamp.fit_resample(X_test, y_test)\n",
    "len(X_test_resample),len(y_test_resample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(19988, 8314, 19988, 8314)"
      ]
     },
     "metadata": {},
     "execution_count": 49
    }
   ],
   "source": [
    "# random undersampling\n",
    "undersample = RandomUnderSampler(random_state=0)\n",
    "\n",
    "X_train_usample, y_train_usample = undersample.fit_resample(X_train, y_train)\n",
    "X_test_usample, y_test_usample = undersample.fit_resample(X_test, y_test)\n",
    "\n",
    "len(X_train_usample), len(X_test_usample), len(y_train_usample), len(y_test_usample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(19988, 45334)"
      ]
     },
     "metadata": {},
     "execution_count": 50
    }
   ],
   "source": [
    "# feature scaling - prepares training and test data for ML by standardizing features to have similar scales\n",
    "sc = StandardScaler()\n",
    "sc.fit(X_train_usample)\n",
    "X_train_std = sc.transform(X_train_usample)\n",
    "X_test_std = sc.transform(X_test)\n",
    "#Y_train_std = sc.transform(y_train)\n",
    "#Y_test_std = sc.transform(y_test)\n",
    "\n",
    "len(X_train_std), len(X_test_std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "SVC(C=1, kernel='linear')"
      ]
     },
     "metadata": {},
     "execution_count": 51
    }
   ],
   "source": [
    "model_linear = SVC(C=1,kernel='linear')\n",
    "model_linear.fit(X_train_std, y_train_usample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "0.43285392861869676\n"
     ]
    }
   ],
   "source": [
    "pred = model_linear.predict(X_test_std)\n",
    "print(accuracy_score(y_test,pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "confusion_matrix = metrics.confusion_matrix(y_test, pred)\n",
    "cm_display = metrics.ConfusionMatrixDisplay(confusion_matrix = confusion_matrix, display_labels = [False, True])\n",
    "cm_display.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### Make a function to do all of this\n",
    "# inputs: lower bound, upper bound, step for C values, kernel type (linear, poly, rbf) \n",
    "\n",
    "def tuner(range, kernel):\n",
    "    ''' \n",
    "    range (list of floats): lower, upper, and step for values of c \n",
    "    kernel (str): 'linear', 'poly' 'rbf' (could also include sigmoid)\n",
    "    prints the accuracy of model of specified type with all c values in given range\n",
    "    '''\n",
    "    results = {}\n",
    "    for c in np.arange(range[0],range[1],range[2]):\n",
    "        c = round(c,3)\n",
    "        model = SVC(C=c, kernel=kernel)\n",
    "        model.fit(X_train_std, y_train)\n",
    "        pred = model.predict(X_test_std)\n",
    "        results[c] = accuracy_score(y_test,pred)\n",
    "\n",
    "    return(results)\n",
    "\n",
    "def plot_tuning(results):\n",
    "    '''\n",
    "    results (dict): dictionary of results of hyperparameter tuning\n",
    "    displays a graph of the regularization paramater value against model accuracy\n",
    "    '''\n",
    "    c_vals = list(results.keys())\n",
    "    accuracy_vals = list(results.values())\n",
    "\n",
    "    for r in results:\n",
    "        print(\"model with regularization parameter \" + str(round(r,3)) + \" had accuracy \" + str(round(results[r],3)))\n",
    "\n",
    "    increment = list(results.keys())[1] - list(results.keys())[0]\n",
    "    plt.plot(c_vals, accuracy_vals, 'ro')\n",
    "    plt.xlim(list(results.keys())[0] - increment, list(results.keys())[-1] + increment)\n",
    "    plt.ylim(0,1.1)\n",
    "    plt.xticks(np.arange(list(results.keys())[0] - increment, list(results.keys())[-1] + increment, increment))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "res = tuner([0.01,1,.2], 'linear')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
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   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "model with regularization parameter 0.01 had accuracy 0.908\nmodel with regularization parameter 0.21 had accuracy 0.908\nmodel with regularization parameter 0.41 had accuracy 0.908\nmodel with regularization parameter 0.61 had accuracy 0.908\nmodel with regularization parameter 0.81 had accuracy 0.908\n"
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "plot_tuning(res)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notes: recommended to scale your data before using an SVM, can be done using a pipeline, could try different kernel functions, look more into run-time of SVM"
   ]
  }
 ]
}