Lee_xgboost_ieee.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#one hot encoding + xgboost (svm took too long to train)\n",
    "\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import OneHotEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['TransactionID', 'isFraud', 'TransactionDT', 'TransactionAmt',\n",
       "       'ProductCD', 'card1', 'card2', 'card3', 'card4', 'card5',\n",
       "       ...\n",
       "       'V330', 'V331', 'V332', 'V333', 'V334', 'V335', 'V336', 'V337', 'V338',\n",
       "       'V339'],\n",
       "      dtype='object', length=394)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('datasets/train_transaction.csv')\n",
    "#df_val = pd.read_csv('datasets/test_transaction.csv')\n",
    "df.shape\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = ['isFraud', 'TransactionDT',\n",
    "            'TransactionAmt','ProductCD', 'P_emaildomain', 'R_emaildomain']\n",
    "\n",
    "#drop R_emaildomain. too many null value\n",
    "\n",
    "df = df[features]\n",
    "\n",
    "target = 'isFraud'\n",
    "y = df[target]\n",
    "cat = ['ProductCD', 'P_emaildomain','R_emaildomain']\n",
    "num = ['TransactionAmt', 'TransactionDT']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#null value strategy\n",
    "#no nulls in ProductCD, TransactionDT, TransactionAmt\n",
    "\n",
    "df['ProductCD'].head()\n",
    "df['ProductCD'].value_counts()\n",
    "\n",
    "df['ProductCD'].isna().sum()\n",
    "\n",
    "df['TransactionDT'].isna().sum()\n",
    "df['TransactionAmt'].isna().sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(590540, 126) (590540, 2)\n",
      "(590540, 128)\n"
     ]
    }
   ],
   "source": [
    "#one hot encoding\n",
    "# Apply one-hot encoder to each column with categorical data\n",
    "OH_encoder = OneHotEncoder(handle_unknown='infrequent_if_exist', sparse_output=False)\n",
    "OH_cols = pd.DataFrame(OH_encoder.fit_transform(df[cat]))\n",
    "\n",
    "#put index back since ohehot encoding ggets rid of index\n",
    "OH_cols.index = df.index\n",
    "  \n",
    "print(OH_cols.shape, df[num].shape)\n",
    "OH_cols.head()\n",
    "\n",
    "# Merge with main\n",
    "# kernel dies here\n",
    "processed_X = pd.concat([OH_cols, df[num]], axis=1)\n",
    "print(processed_X.shape) # getting weird shape. how did we end up with 201643 rows when we should have gotten 126227?\n",
    "# processed_X.head()\n",
    "\n",
    "#make sure every column has string type\n",
    "processed_X.columns = processed_X.columns.astype(str)\n",
    "processed_X.shape\n",
    "\n",
    "# Separate data into training and validation sets\n",
    "X_train, X_valid, y_train, y_valid = train_test_split(processed_X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Collecting xgboost\n",
      "  Using cached xgboost-1.7.5-py3-none-macosx_12_0_arm64.whl (1.6 MB)\n",
      "Requirement already satisfied: scipy in /Users/seohyeonlee/Library/Python/3.9/lib/python/site-packages (from xgboost) (1.10.1)\n",
      "Requirement already satisfied: numpy in /Users/seohyeonlee/Library/Python/3.9/lib/python/site-packages (from xgboost) (1.24.2)\n",
      "Installing collected packages: xgboost\n",
      "Successfully installed xgboost-1.7.5\n",
      "\u001b[33mWARNING: You are using pip version 21.2.4; however, version 23.1.1 is available.\n",
      "You should consider upgrading via the '/Library/Developer/CommandLineTools/usr/bin/python3 -m pip install --upgrade pip' command.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "!{sys.executable} -m pip install xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.05, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=1000, n_jobs=None, num_parallel_tree=None,\n",
       "              predictor=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.05, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=1000, n_jobs=None, num_parallel_tree=None,\n",
       "              predictor=None, random_state=None, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.05, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=1000, n_jobs=None, num_parallel_tree=None,\n",
       "              predictor=None, random_state=None, ...)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from xgboost import XGBClassifier\n",
    "\n",
    "my_model = XGBClassifier(n_estimators=1000, learning_rate=0.05)\n",
    "my_model.fit(X_train, y_train, \n",
    "             early_stopping_rounds=5, \n",
    "             eval_set=[(X_valid, y_valid)], \n",
    "             verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#process validation data the same way\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "predictions = my_model.predict(X_valid)\n",
    "predictions_series = pd.Series(predictions)\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "sns.histplot(data = predictions_series)\n",
    "threshold = 0.36\n",
    "\n",
    "predictions_bin = predictions_series.map(lambda x: 1 if x >threshold else 0)\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9682189182781861\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x2db15dee0>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix\n",
    "\n",
    "import matplotlib as plt\n",
    "\n",
    "confusion_matrix = confusion_matrix(y_valid, predictions_bin)\n",
    "cm_display = ConfusionMatrixDisplay(confusion_matrix = confusion_matrix, display_labels = [False, True])\n",
    "print(accuracy_score(predictions_bin, y_valid))\n",
    "cm_display.plot()\n",
    "#plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (491811380.py, line 3)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  Cell \u001b[0;32mIn[70], line 3\u001b[0;36m\u001b[0m\n\u001b[0;31m    import imbalanced-learn\u001b[0m\n\u001b[0m                     ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "from collections import Counter\n",
    "from sklearn.datasets import make_classification\n",
    "import imbalanced-learn\n",
    "from imblearn.under_sampling import TomekLinks\n",
    "\n",
    "X, y = make_classification(n_classes=2, class_sep=2,\n",
    "weights=[0.1, 0.9], n_informative=3, n_redundant=1, flip_y=0,\n",
    "n_features=20, n_clusters_per_class=1, n_samples=1000, random_state=10)\n",
    "print('Original dataset shape %s' % Counter(y))\n",
    "tl = TomekLinks()\n",
    "\n",
    "X_res, y_res = tl.fit_resample(X, y)\n",
    "print('Resampled dataset shape %s' % Counter(y_res))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.9.6"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}